HPMCD: the database of human microbial communities from metagenomic datasets and microbial reference genomes.

PubMed

Forster, Samuel C; Browne, Hilary P; Kumar, Nitin; Hunt, Martin; Denise, Hubert; Mitchell, Alex; Finn, Robert D; Lawley, Trevor D

2016-01-04

The Human Pan-Microbe Communities (HPMC) database (http://www.hpmcd.org/) provides a manually curated, searchable, metagenomic resource to facilitate investigation of human gastrointestinal microbiota. Over the past decade, the application of metagenome sequencing to elucidate the microbial composition and functional capacity present in the human microbiome has revolutionized many concepts in our basic biology. When sufficient high quality reference genomes are available, whole genome metagenomic sequencing can provide direct biological insights and high-resolution classification. The HPMC database provides species level, standardized phylogenetic classification of over 1800 human gastrointestinal metagenomic samples. This is achieved by combining a manually curated list of bacterial genomes from human faecal samples with over 21000 additional reference genomes representing bacteria, viruses, archaea and fungi with manually curated species classification and enhanced sample metadata annotation. A user-friendly, web-based interface provides the ability to search for (i) microbial groups associated with health or disease state, (ii) health or disease states and community structure associated with a microbial group, (iii) the enrichment of a microbial gene or sequence and (iv) enrichment of a functional annotation. The HPMC database enables detailed analysis of human microbial communities and supports research from basic microbiology and immunology to therapeutic development in human health and disease. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

MGDB: a comprehensive database of genes involved in melanoma.

PubMed

Zhang, Di; Zhu, Rongrong; Zhang, Hanqian; Zheng, Chun-Hou; Xia, Junfeng

2015-01-01

The Melanoma Gene Database (MGDB) is a manually curated catalog of molecular genetic data relating to genes involved in melanoma. The main purpose of this database is to establish a network of melanoma related genes and to facilitate the mechanistic study of melanoma tumorigenesis. The entries describing the relationships between melanoma and genes in the current release were manually extracted from PubMed abstracts, which contains cumulative to date 527 human melanoma genes (422 protein-coding and 105 non-coding genes). Each melanoma gene was annotated in seven different aspects (General Information, Expression, Methylation, Mutation, Interaction, Pathway and Drug). In addition, manually curated literature references have also been provided to support the inclusion of the gene in MGDB and establish its association with melanoma. MGDB has a user-friendly web interface with multiple browse and search functions. We hoped MGDB will enrich our knowledge about melanoma genetics and serve as a useful complement to the existing public resources. Database URL: http://bioinfo.ahu.edu.cn:8080/Melanoma/index.jsp. © The Author(s) 2015. Published by Oxford University Press.

Automatic vs. manual curation of a multi-source chemical dictionary: the impact on text mining.

PubMed

Hettne, Kristina M; Williams, Antony J; van Mulligen, Erik M; Kleinjans, Jos; Tkachenko, Valery; Kors, Jan A

2010-03-23

Previously, we developed a combined dictionary dubbed Chemlist for the identification of small molecules and drugs in text based on a number of publicly available databases and tested it on an annotated corpus. To achieve an acceptable recall and precision we used a number of automatic and semi-automatic processing steps together with disambiguation rules. However, it remained to be investigated which impact an extensive manual curation of a multi-source chemical dictionary would have on chemical term identification in text. ChemSpider is a chemical database that has undergone extensive manual curation aimed at establishing valid chemical name-to-structure relationships. We acquired the component of ChemSpider containing only manually curated names and synonyms. Rule-based term filtering, semi-automatic manual curation, and disambiguation rules were applied. We tested the dictionary from ChemSpider on an annotated corpus and compared the results with those for the Chemlist dictionary. The ChemSpider dictionary of ca. 80 k names was only a 1/3 to a 1/4 the size of Chemlist at around 300 k. The ChemSpider dictionary had a precision of 0.43 and a recall of 0.19 before the application of filtering and disambiguation and a precision of 0.87 and a recall of 0.19 after filtering and disambiguation. The Chemlist dictionary had a precision of 0.20 and a recall of 0.47 before the application of filtering and disambiguation and a precision of 0.67 and a recall of 0.40 after filtering and disambiguation. We conclude the following: (1) The ChemSpider dictionary achieved the best precision but the Chemlist dictionary had a higher recall and the best F-score; (2) Rule-based filtering and disambiguation is necessary to achieve a high precision for both the automatically generated and the manually curated dictionary. ChemSpider is available as a web service at http://www.chemspider.com/ and the Chemlist dictionary is freely available as an XML file in Simple Knowledge Organization System format on the web at http://www.biosemantics.org/chemlist.

Automatic vs. manual curation of a multi-source chemical dictionary: the impact on text mining

PubMed Central

2010-01-01

Background Previously, we developed a combined dictionary dubbed Chemlist for the identification of small molecules and drugs in text based on a number of publicly available databases and tested it on an annotated corpus. To achieve an acceptable recall and precision we used a number of automatic and semi-automatic processing steps together with disambiguation rules. However, it remained to be investigated which impact an extensive manual curation of a multi-source chemical dictionary would have on chemical term identification in text. ChemSpider is a chemical database that has undergone extensive manual curation aimed at establishing valid chemical name-to-structure relationships. Results We acquired the component of ChemSpider containing only manually curated names and synonyms. Rule-based term filtering, semi-automatic manual curation, and disambiguation rules were applied. We tested the dictionary from ChemSpider on an annotated corpus and compared the results with those for the Chemlist dictionary. The ChemSpider dictionary of ca. 80 k names was only a 1/3 to a 1/4 the size of Chemlist at around 300 k. The ChemSpider dictionary had a precision of 0.43 and a recall of 0.19 before the application of filtering and disambiguation and a precision of 0.87 and a recall of 0.19 after filtering and disambiguation. The Chemlist dictionary had a precision of 0.20 and a recall of 0.47 before the application of filtering and disambiguation and a precision of 0.67 and a recall of 0.40 after filtering and disambiguation. Conclusions We conclude the following: (1) The ChemSpider dictionary achieved the best precision but the Chemlist dictionary had a higher recall and the best F-score; (2) Rule-based filtering and disambiguation is necessary to achieve a high precision for both the automatically generated and the manually curated dictionary. ChemSpider is available as a web service at http://www.chemspider.com/ and the Chemlist dictionary is freely available as an XML file in Simple Knowledge Organization System format on the web at http://www.biosemantics.org/chemlist. PMID:20331846

SABIO-RK: an updated resource for manually curated biochemical reaction kinetics

PubMed Central

Rey, Maja; Weidemann, Andreas; Kania, Renate; Müller, Wolfgang

2018-01-01

Abstract SABIO-RK (http://sabiork.h-its.org/) is a manually curated database containing data about biochemical reactions and their reaction kinetics. The data are primarily extracted from scientific literature and stored in a relational database. The content comprises both naturally occurring and alternatively measured biochemical reactions and is not restricted to any organism class. The data are made available to the public by a web-based search interface and by web services for programmatic access. In this update we describe major improvements and extensions of SABIO-RK since our last publication in the database issue of Nucleic Acid Research (2012). (i) The website has been completely revised and (ii) allows now also free text search for kinetics data. (iii) Additional interlinkages with other databases in our field have been established; this enables users to gain directly comprehensive knowledge about the properties of enzymes and kinetics beyond SABIO-RK. (iv) Vice versa, direct access to SABIO-RK data has been implemented in several systems biology tools and workflows. (v) On request of our experimental users, the data can be exported now additionally in spreadsheet formats. (vi) The newly established SABIO-RK Curation Service allows to respond to specific data requirements. PMID:29092055

NSDNA: a manually curated database of experimentally supported ncRNAs associated with nervous system diseases

PubMed Central

Wang, Jianjian; Cao, Yuze; Zhang, Huixue; Wang, Tianfeng; Tian, Qinghua; Lu, Xiaoyu; Lu, Xiaoyan; Kong, Xiaotong; Liu, Zhaojun; Wang, Ning; Zhang, Shuai; Ma, Heping; Ning, Shangwei; Wang, Lihua

2017-01-01

The Nervous System Disease NcRNAome Atlas (NSDNA) (http://www.bio-bigdata.net/nsdna/) is a manually curated database that provides comprehensive experimentally supported associations about nervous system diseases (NSDs) and noncoding RNAs (ncRNAs). NSDs represent a common group of disorders, some of which are characterized by high morbidity and disabilities. The pathogenesis of NSDs at the molecular level remains poorly understood. ncRNAs are a large family of functionally important RNA molecules. Increasing evidence shows that diverse ncRNAs play a critical role in various NSDs. Mining and summarizing NSD–ncRNA association data can help researchers discover useful information. Hence, we developed an NSDNA database that documents 24 713 associations between 142 NSDs and 8593 ncRNAs in 11 species, curated from more than 1300 articles. This database provides a user-friendly interface for browsing and searching and allows for data downloading flexibility. In addition, NSDNA offers a submission page for researchers to submit novel NSD–ncRNA associations. It represents an extremely useful and valuable resource for researchers who seek to understand the functions and molecular mechanisms of ncRNA involved in NSDs. PMID:27899613

BC4GO: a full-text corpus for the BioCreative IV GO Task

USDA-ARS?s Scientific Manuscript database

Gene function curation via Gene Ontology (GO) annotation is a common task among Model Organism Database (MOD) groups. Due to its manual nature, this task is time-consuming and labor-intensive, and thus considered one of the bottlenecks in literature curation. There have been many previous attempts a...

The porcine translational research database: A manually curated, genomics and proteomics-based research resource

USDA-ARS?s Scientific Manuscript database

The use of swine in biomedical research has increased dramatically in the last decade. Diverse genomic- and proteomic databases have been developed to facilitate research using human and rodent models. Current porcine gene databases, however, lack the robust annotation to study pig models that are...

CCDB: a curated database of genes involved in cervix cancer.

PubMed

Agarwal, Subhash M; Raghav, Dhwani; Singh, Harinder; Raghava, G P S

2011-01-01

The Cervical Cancer gene DataBase (CCDB, http://crdd.osdd.net/raghava/ccdb) is a manually curated catalog of experimentally validated genes that are thought, or are known to be involved in the different stages of cervical carcinogenesis. In spite of the large women population that is presently affected from this malignancy still at present, no database exists that catalogs information on genes associated with cervical cancer. Therefore, we have compiled 537 genes in CCDB that are linked with cervical cancer causation processes such as methylation, gene amplification, mutation, polymorphism and change in expression level, as evident from published literature. Each record contains details related to gene like architecture (exon-intron structure), location, function, sequences (mRNA/CDS/protein), ontology, interacting partners, homology to other eukaryotic genomes, structure and links to other public databases, thus augmenting CCDB with external data. Also, manually curated literature references have been provided to support the inclusion of the gene in the database and establish its association with cervix cancer. In addition, CCDB provides information on microRNA altered in cervical cancer as well as search facility for querying, several browse options and an online tool for sequence similarity search, thereby providing researchers with easy access to the latest information on genes involved in cervix cancer.

Using distant supervised learning to identify protein subcellular localizations from full-text scientific articles.

PubMed

Zheng, Wu; Blake, Catherine

2015-10-01

Databases of curated biomedical knowledge, such as the protein-locations reflected in the UniProtKB database, provide an accurate and useful resource to researchers and decision makers. Our goal is to augment the manual efforts currently used to curate knowledge bases with automated approaches that leverage the increased availability of full-text scientific articles. This paper describes experiments that use distant supervised learning to identify protein subcellular localizations, which are important to understand protein function and to identify candidate drug targets. Experiments consider Swiss-Prot, the manually annotated subset of the UniProtKB protein knowledge base, and 43,000 full-text articles from the Journal of Biological Chemistry that contain just under 11.5 million sentences. The system achieves 0.81 precision and 0.49 recall at sentence level and an accuracy of 57% on held-out instances in a test set. Moreover, the approach identifies 8210 instances that are not in the UniProtKB knowledge base. Manual inspection of the 50 most likely relations showed that 41 (82%) were valid. These results have immediate benefit to researchers interested in protein function, and suggest that distant supervision should be explored to complement other manual data curation efforts. Copyright © 2015 Elsevier Inc. All rights reserved.

A curated gluten protein sequence database to support development of proteomics methods for determination of gluten in gluten-free foods.

PubMed

Bromilow, Sophie; Gethings, Lee A; Buckley, Mike; Bromley, Mike; Shewry, Peter R; Langridge, James I; Clare Mills, E N

2017-06-23

The unique physiochemical properties of wheat gluten enable a diverse range of food products to be manufactured. However, gluten triggers coeliac disease, a condition which is treated using a gluten-free diet. Analytical methods are required to confirm if foods are gluten-free, but current immunoassay-based methods can unreliable and proteomic methods offer an alternative but require comprehensive and well annotated sequence databases which are lacking for gluten. A manually a curated database (GluPro V1.0) of gluten proteins, comprising 630 discrete unique full length protein sequences has been compiled. It is representative of the different types of gliadin and glutenin components found in gluten. An in silico comparison of their coeliac toxicity was undertaken by analysing the distribution of coeliac toxic motifs. This demonstrated that whilst the α-gliadin proteins contained more toxic motifs, these were distributed across all gluten protein sub-types. Comparison of annotations observed using a discovery proteomics dataset acquired using ion mobility MS/MS showed that more reliable identifications were obtained using the GluPro V1.0 database compared to the complete reviewed Viridiplantae database. This highlights the value of a curated sequence database specifically designed to support the proteomic workflows and the development of methods to detect and quantify gluten. We have constructed the first manually curated open-source wheat gluten protein sequence database (GluPro V1.0) in a FASTA format to support the application of proteomic methods for gluten protein detection and quantification. We have also analysed the manually verified sequences to give the first comprehensive overview of the distribution of sequences able to elicit a reaction in coeliac disease, the prevalent form of gluten intolerance. Provision of this database will improve the reliability of gluten protein identification by proteomic analysis, and aid the development of targeted mass spectrometry methods in line with Codex Alimentarius Commission requirements for foods designed to meet the needs of gluten intolerant individuals. Copyright © 2017. Published by Elsevier B.V.

HypoxiaDB: a database of hypoxia-regulated proteins

PubMed Central

Khurana, Pankaj; Sugadev, Ragumani; Jain, Jaspreet; Singh, Shashi Bala

2013-01-01

There has been intense interest in the cellular response to hypoxia, and a large number of differentially expressed proteins have been identified through various high-throughput experiments. These valuable data are scattered, and there have been no systematic attempts to document the various proteins regulated by hypoxia. Compilation, curation and annotation of these data are important in deciphering their role in hypoxia and hypoxia-related disorders. Therefore, we have compiled HypoxiaDB, a database of hypoxia-regulated proteins. It is a comprehensive, manually-curated, non-redundant catalog of proteins whose expressions are shown experimentally to be altered at different levels and durations of hypoxia. The database currently contains 72 000 manually curated entries taken on 3500 proteins extracted from 73 peer-reviewed publications selected from PubMed. HypoxiaDB is distinctive from other generalized databases: (i) it compiles tissue-specific protein expression changes under different levels and duration of hypoxia. Also, it provides manually curated literature references to support the inclusion of the protein in the database and establish its association with hypoxia. (ii) For each protein, HypoxiaDB integrates data on gene ontology, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway, protein–protein interactions, protein family (Pfam), OMIM (Online Mendelian Inheritance in Man), PDB (Protein Data Bank) structures and homology to other sequenced genomes. (iii) It also provides pre-compiled information on hypoxia-proteins, which otherwise requires tedious computational analysis. This includes information like chromosomal location, identifiers like Entrez, HGNC, Unigene, Uniprot, Ensembl, Vega, GI numbers and Genbank accession numbers associated with the protein. These are further cross-linked to respective public databases augmenting HypoxiaDB to the external repositories. (iv) In addition, HypoxiaDB provides an online sequence-similarity search tool for users to compare their protein sequences with HypoxiaDB protein database. We hope that HypoxiaDB will enrich our knowledge about hypoxia-related biology and eventually will lead to the development of novel hypothesis and advancements in diagnostic and therapeutic activities. HypoxiaDB is freely accessible for academic and non-profit users via http://www.hypoxiadb.com. Database URL: http://www.hypoxiadb.com PMID:24178989

Curation of food-relevant chemicals in ToxCast.

PubMed

Karmaus, Agnes L; Trautman, Thomas D; Krishan, Mansi; Filer, Dayne L; Fix, Laurel A

2017-05-01

High-throughput in vitro assays and exposure prediction efforts are paving the way for modeling chemical risk; however, the utility of such extensive datasets can be limited or misleading when annotation fails to capture current chemical usage. To address this data gap and provide context for food-use in the United States (US), manual curation of food-relevant chemicals in ToxCast was conducted. Chemicals were categorized into three food-use categories: (1) direct food additives, (2) indirect food additives, or (3) pesticide residues. Manual curation resulted in 30% of chemicals having new annotation as well as the removal of 319 chemicals, most due to cancellation or only foreign usage. These results highlight that manual curation of chemical use information provided significant insight affecting the overall inventory and chemical categorization. In total, 1211 chemicals were confirmed as current day food-use in the US by manual curation; 1154 of these chemicals were also identified as food-related in the globally sourced chemical use information from Chemical/Product Categories database (CPCat). The refined list of food-use chemicals and the sources highlighted for compiling annotated information required to confirm food-use are valuable resources for providing needed context when evaluating large-scale inventories such as ToxCast. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Targeted journal curation as a method to improve data currency at the Comparative Toxicogenomics Database

PubMed Central

Davis, Allan Peter; Johnson, Robin J.; Lennon-Hopkins, Kelley; Sciaky, Daniela; Rosenstein, Michael C.; Wiegers, Thomas C.; Mattingly, Carolyn J.

2012-01-01

The Comparative Toxicogenomics Database (CTD) is a public resource that promotes understanding about the effects of environmental chemicals on human health. CTD biocurators read the scientific literature and manually curate a triad of chemical–gene, chemical–disease and gene–disease interactions. Typically, articles for CTD are selected using a chemical-centric approach by querying PubMed to retrieve a corpus containing the chemical of interest. Although this technique ensures adequate coverage of knowledge about the chemical (i.e. data completeness), it does not necessarily reflect the most current state of all toxicological research in the community at large (i.e. data currency). Keeping databases current with the most recent scientific results, as well as providing a rich historical background from legacy articles, is a challenging process. To address this issue of data currency, CTD designed and tested a journal-centric approach of curation to complement our chemical-centric method. We first identified priority journals based on defined criteria. Next, over 7 weeks, three biocurators reviewed 2425 articles from three consecutive years (2009–2011) of three targeted journals. From this corpus, 1252 articles contained relevant data for CTD and 52 752 interactions were manually curated. Here, we describe our journal selection process, two methods of document delivery for the biocurators and the analysis of the resulting curation metrics, including data currency, and both intra-journal and inter-journal comparisons of research topics. Based on our results, we expect that curation by select journals can (i) be easily incorporated into the curation pipeline to complement our chemical-centric approach; (ii) build content more evenly for chemicals, genes and diseases in CTD (rather than biasing data by chemicals-of-interest); (iii) reflect developing areas in environmental health and (iv) improve overall data currency for chemicals, genes and diseases. Database URL: http://ctdbase.org/ PMID:23221299

Argo: an integrative, interactive, text mining-based workbench supporting curation

PubMed Central

Rak, Rafal; Rowley, Andrew; Black, William; Ananiadou, Sophia

2012-01-01

Curation of biomedical literature is often supported by the automatic analysis of textual content that generally involves a sequence of individual processing components. Text mining (TM) has been used to enhance the process of manual biocuration, but has been focused on specific databases and tasks rather than an environment integrating TM tools into the curation pipeline, catering for a variety of tasks, types of information and applications. Processing components usually come from different sources and often lack interoperability. The well established Unstructured Information Management Architecture is a framework that addresses interoperability by defining common data structures and interfaces. However, most of the efforts are targeted towards software developers and are not suitable for curators, or are otherwise inconvenient to use on a higher level of abstraction. To overcome these issues we introduce Argo, an interoperable, integrative, interactive and collaborative system for text analysis with a convenient graphic user interface to ease the development of processing workflows and boost productivity in labour-intensive manual curation. Robust, scalable text analytics follow a modular approach, adopting component modules for distinct levels of text analysis. The user interface is available entirely through a web browser that saves the user from going through often complicated and platform-dependent installation procedures. Argo comes with a predefined set of processing components commonly used in text analysis, while giving the users the ability to deposit their own components. The system accommodates various areas and levels of user expertise, from TM and computational linguistics to ontology-based curation. One of the key functionalities of Argo is its ability to seamlessly incorporate user-interactive components, such as manual annotation editors, into otherwise completely automatic pipelines. As a use case, we demonstrate the functionality of an in-built manual annotation editor that is well suited for in-text corpus annotation tasks. Database URL: http://www.nactem.ac.uk/Argo PMID:22434844

A hybrid human and machine resource curation pipeline for the Neuroscience Information Framework.

PubMed

Bandrowski, A E; Cachat, J; Li, Y; Müller, H M; Sternberg, P W; Ciccarese, P; Clark, T; Marenco, L; Wang, R; Astakhov, V; Grethe, J S; Martone, M E

2012-01-01

The breadth of information resources available to researchers on the Internet continues to expand, particularly in light of recently implemented data-sharing policies required by funding agencies. However, the nature of dense, multifaceted neuroscience data and the design of contemporary search engine systems makes efficient, reliable and relevant discovery of such information a significant challenge. This challenge is specifically pertinent for online databases, whose dynamic content is 'hidden' from search engines. The Neuroscience Information Framework (NIF; http://www.neuinfo.org) was funded by the NIH Blueprint for Neuroscience Research to address the problem of finding and utilizing neuroscience-relevant resources such as software tools, data sets, experimental animals and antibodies across the Internet. From the outset, NIF sought to provide an accounting of available resources, whereas developing technical solutions to finding, accessing and utilizing them. The curators therefore, are tasked with identifying and registering resources, examining data, writing configuration files to index and display data and keeping the contents current. In the initial phases of the project, all aspects of the registration and curation processes were manual. However, as the number of resources grew, manual curation became impractical. This report describes our experiences and successes with developing automated resource discovery and semiautomated type characterization with text-mining scripts that facilitate curation team efforts to discover, integrate and display new content. We also describe the DISCO framework, a suite of automated web services that significantly reduce manual curation efforts to periodically check for resource updates. Lastly, we discuss DOMEO, a semi-automated annotation tool that improves the discovery and curation of resources that are not necessarily website-based (i.e. reagents, software tools). Although the ultimate goal of automation was to reduce the workload of the curators, it has resulted in valuable analytic by-products that address accessibility, use and citation of resources that can now be shared with resource owners and the larger scientific community. DATABASE URL: http://neuinfo.org.

A hybrid human and machine resource curation pipeline for the Neuroscience Information Framework

PubMed Central

Bandrowski, A. E.; Cachat, J.; Li, Y.; Müller, H. M.; Sternberg, P. W.; Ciccarese, P.; Clark, T.; Marenco, L.; Wang, R.; Astakhov, V.; Grethe, J. S.; Martone, M. E.

2012-01-01

The breadth of information resources available to researchers on the Internet continues to expand, particularly in light of recently implemented data-sharing policies required by funding agencies. However, the nature of dense, multifaceted neuroscience data and the design of contemporary search engine systems makes efficient, reliable and relevant discovery of such information a significant challenge. This challenge is specifically pertinent for online databases, whose dynamic content is ‘hidden’ from search engines. The Neuroscience Information Framework (NIF; http://www.neuinfo.org) was funded by the NIH Blueprint for Neuroscience Research to address the problem of finding and utilizing neuroscience-relevant resources such as software tools, data sets, experimental animals and antibodies across the Internet. From the outset, NIF sought to provide an accounting of available resources, whereas developing technical solutions to finding, accessing and utilizing them. The curators therefore, are tasked with identifying and registering resources, examining data, writing configuration files to index and display data and keeping the contents current. In the initial phases of the project, all aspects of the registration and curation processes were manual. However, as the number of resources grew, manual curation became impractical. This report describes our experiences and successes with developing automated resource discovery and semiautomated type characterization with text-mining scripts that facilitate curation team efforts to discover, integrate and display new content. We also describe the DISCO framework, a suite of automated web services that significantly reduce manual curation efforts to periodically check for resource updates. Lastly, we discuss DOMEO, a semi-automated annotation tool that improves the discovery and curation of resources that are not necessarily website-based (i.e. reagents, software tools). Although the ultimate goal of automation was to reduce the workload of the curators, it has resulted in valuable analytic by-products that address accessibility, use and citation of resources that can now be shared with resource owners and the larger scientific community. Database URL: http://neuinfo.org PMID:22434839

Building an efficient curation workflow for the Arabidopsis literature corpus

PubMed Central

Li, Donghui; Berardini, Tanya Z.; Muller, Robert J.; Huala, Eva

2012-01-01

TAIR (The Arabidopsis Information Resource) is the model organism database (MOD) for Arabidopsis thaliana, a model plant with a literature corpus of about 39 000 articles in PubMed, with over 4300 new articles added in 2011. We have developed a literature curation workflow incorporating both automated and manual elements to cope with this flood of new research articles. The current workflow can be divided into two phases: article selection and curation. Structured controlled vocabularies, such as the Gene Ontology and Plant Ontology are used to capture free text information in the literature as succinct ontology-based annotations suitable for the application of computational analysis methods. We also describe our curation platform and the use of text mining tools in our workflow. Database URL: www.arabidopsis.org PMID:23221298

Improving links between literature and biological data with text mining: a case study with GEO, PDB and MEDLINE.

PubMed

Névéol, Aurélie; Wilbur, W John; Lu, Zhiyong

2012-01-01

High-throughput experiments and bioinformatics techniques are creating an exploding volume of data that are becoming overwhelming to keep track of for biologists and researchers who need to access, analyze and process existing data. Much of the available data are being deposited in specialized databases, such as the Gene Expression Omnibus (GEO) for microarrays or the Protein Data Bank (PDB) for protein structures and coordinates. Data sets are also being described by their authors in publications archived in literature databases such as MEDLINE and PubMed Central. Currently, the curation of links between biological databases and the literature mainly relies on manual labour, which makes it a time-consuming and daunting task. Herein, we analysed the current state of link curation between GEO, PDB and MEDLINE. We found that the link curation is heterogeneous depending on the sources and databases involved, and that overlap between sources is low, <50% for PDB and GEO. Furthermore, we showed that text-mining tools can automatically provide valuable evidence to help curators broaden the scope of articles and database entries that they review. As a result, we made recommendations to improve the coverage of curated links, as well as the consistency of information available from different databases while maintaining high-quality curation. Database URLs: http://www.ncbi.nlm.nih.gov/PubMed, http://www.ncbi.nlm.nih.gov/geo/, http://www.rcsb.org/pdb/

Improving links between literature and biological data with text mining: a case study with GEO, PDB and MEDLINE

PubMed Central

Névéol, Aurélie; Wilbur, W. John; Lu, Zhiyong

2012-01-01

High-throughput experiments and bioinformatics techniques are creating an exploding volume of data that are becoming overwhelming to keep track of for biologists and researchers who need to access, analyze and process existing data. Much of the available data are being deposited in specialized databases, such as the Gene Expression Omnibus (GEO) for microarrays or the Protein Data Bank (PDB) for protein structures and coordinates. Data sets are also being described by their authors in publications archived in literature databases such as MEDLINE and PubMed Central. Currently, the curation of links between biological databases and the literature mainly relies on manual labour, which makes it a time-consuming and daunting task. Herein, we analysed the current state of link curation between GEO, PDB and MEDLINE. We found that the link curation is heterogeneous depending on the sources and databases involved, and that overlap between sources is low, <50% for PDB and GEO. Furthermore, we showed that text-mining tools can automatically provide valuable evidence to help curators broaden the scope of articles and database entries that they review. As a result, we made recommendations to improve the coverage of curated links, as well as the consistency of information available from different databases while maintaining high-quality curation. Database URLs: http://www.ncbi.nlm.nih.gov/PubMed, http://www.ncbi.nlm.nih.gov/geo/, http://www.rcsb.org/pdb/ PMID:22685160

Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation.

PubMed

O'Leary, Nuala A; Wright, Mathew W; Brister, J Rodney; Ciufo, Stacy; Haddad, Diana; McVeigh, Rich; Rajput, Bhanu; Robbertse, Barbara; Smith-White, Brian; Ako-Adjei, Danso; Astashyn, Alexander; Badretdin, Azat; Bao, Yiming; Blinkova, Olga; Brover, Vyacheslav; Chetvernin, Vyacheslav; Choi, Jinna; Cox, Eric; Ermolaeva, Olga; Farrell, Catherine M; Goldfarb, Tamara; Gupta, Tripti; Haft, Daniel; Hatcher, Eneida; Hlavina, Wratko; Joardar, Vinita S; Kodali, Vamsi K; Li, Wenjun; Maglott, Donna; Masterson, Patrick; McGarvey, Kelly M; Murphy, Michael R; O'Neill, Kathleen; Pujar, Shashikant; Rangwala, Sanjida H; Rausch, Daniel; Riddick, Lillian D; Schoch, Conrad; Shkeda, Andrei; Storz, Susan S; Sun, Hanzhen; Thibaud-Nissen, Francoise; Tolstoy, Igor; Tully, Raymond E; Vatsan, Anjana R; Wallin, Craig; Webb, David; Wu, Wendy; Landrum, Melissa J; Kimchi, Avi; Tatusova, Tatiana; DiCuccio, Michael; Kitts, Paul; Murphy, Terence D; Pruitt, Kim D

2016-01-04

The RefSeq project at the National Center for Biotechnology Information (NCBI) maintains and curates a publicly available database of annotated genomic, transcript, and protein sequence records (http://www.ncbi.nlm.nih.gov/refseq/). The RefSeq project leverages the data submitted to the International Nucleotide Sequence Database Collaboration (INSDC) against a combination of computation, manual curation, and collaboration to produce a standard set of stable, non-redundant reference sequences. The RefSeq project augments these reference sequences with current knowledge including publications, functional features and informative nomenclature. The database currently represents sequences from more than 55,000 organisms (>4800 viruses, >40,000 prokaryotes and >10,000 eukaryotes; RefSeq release 71), ranging from a single record to complete genomes. This paper summarizes the current status of the viral, prokaryotic, and eukaryotic branches of the RefSeq project, reports on improvements to data access and details efforts to further expand the taxonomic representation of the collection. We also highlight diverse functional curation initiatives that support multiple uses of RefSeq data including taxonomic validation, genome annotation, comparative genomics, and clinical testing. We summarize our approach to utilizing available RNA-Seq and other data types in our manual curation process for vertebrate, plant, and other species, and describe a new direction for prokaryotic genomes and protein name management. Published by Oxford University Press on behalf of Nucleic Acids Research 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.

NeuroTransDB: highly curated and structured transcriptomic metadata for neurodegenerative diseases.

PubMed

Bagewadi, Shweta; Adhikari, Subash; Dhrangadhariya, Anjani; Irin, Afroza Khanam; Ebeling, Christian; Namasivayam, Aishwarya Alex; Page, Matthew; Hofmann-Apitius, Martin; Senger, Philipp

2015-01-01

Neurodegenerative diseases are chronic debilitating conditions, characterized by progressive loss of neurons that represent a significant health care burden as the global elderly population continues to grow. Over the past decade, high-throughput technologies such as the Affymetrix GeneChip microarrays have provided new perspectives into the pathomechanisms underlying neurodegeneration. Public transcriptomic data repositories, namely Gene Expression Omnibus and curated ArrayExpress, enable researchers to conduct integrative meta-analysis; increasing the power to detect differentially regulated genes in disease and explore patterns of gene dysregulation across biologically related studies. The reliability of retrospective, large-scale integrative analyses depends on an appropriate combination of related datasets, in turn requiring detailed meta-annotations capturing the experimental setup. In most cases, we observe huge variation in compliance to defined standards for submitted metadata in public databases. Much of the information to complete, or refine meta-annotations are distributed in the associated publications. For example, tissue preparation or comorbidity information is frequently described in an article's supplementary tables. Several value-added databases have employed additional manual efforts to overcome this limitation. However, none of these databases explicate annotations that distinguish human and animal models in neurodegeneration context. Therefore, adopting a more specific disease focus, in combination with dedicated disease ontologies, will better empower the selection of comparable studies with refined annotations to address the research question at hand. In this article, we describe the detailed development of NeuroTransDB, a manually curated database containing metadata annotations for neurodegenerative studies. The database contains more than 20 dimensions of metadata annotations within 31 mouse, 5 rat and 45 human studies, defined in collaboration with domain disease experts. We elucidate the step-by-step guidelines used to critically prioritize studies from public archives and their metadata curation and discuss the key challenges encountered. Curated metadata for Alzheimer's disease gene expression studies are available for download. Database URL: www.scai.fraunhofer.de/NeuroTransDB.html. © The Author(s) 2015. Published by Oxford University Press.

NeuroTransDB: highly curated and structured transcriptomic metadata for neurodegenerative diseases

PubMed Central

Bagewadi, Shweta; Adhikari, Subash; Dhrangadhariya, Anjani; Irin, Afroza Khanam; Ebeling, Christian; Namasivayam, Aishwarya Alex; Page, Matthew; Hofmann-Apitius, Martin

2015-01-01

Neurodegenerative diseases are chronic debilitating conditions, characterized by progressive loss of neurons that represent a significant health care burden as the global elderly population continues to grow. Over the past decade, high-throughput technologies such as the Affymetrix GeneChip microarrays have provided new perspectives into the pathomechanisms underlying neurodegeneration. Public transcriptomic data repositories, namely Gene Expression Omnibus and curated ArrayExpress, enable researchers to conduct integrative meta-analysis; increasing the power to detect differentially regulated genes in disease and explore patterns of gene dysregulation across biologically related studies. The reliability of retrospective, large-scale integrative analyses depends on an appropriate combination of related datasets, in turn requiring detailed meta-annotations capturing the experimental setup. In most cases, we observe huge variation in compliance to defined standards for submitted metadata in public databases. Much of the information to complete, or refine meta-annotations are distributed in the associated publications. For example, tissue preparation or comorbidity information is frequently described in an article’s supplementary tables. Several value-added databases have employed additional manual efforts to overcome this limitation. However, none of these databases explicate annotations that distinguish human and animal models in neurodegeneration context. Therefore, adopting a more specific disease focus, in combination with dedicated disease ontologies, will better empower the selection of comparable studies with refined annotations to address the research question at hand. In this article, we describe the detailed development of NeuroTransDB, a manually curated database containing metadata annotations for neurodegenerative studies. The database contains more than 20 dimensions of metadata annotations within 31 mouse, 5 rat and 45 human studies, defined in collaboration with domain disease experts. We elucidate the step-by-step guidelines used to critically prioritize studies from public archives and their metadata curation and discuss the key challenges encountered. Curated metadata for Alzheimer’s disease gene expression studies are available for download. Database URL: www.scai.fraunhofer.de/NeuroTransDB.html PMID:26475471

NSDNA: a manually curated database of experimentally supported ncRNAs associated with nervous system diseases.

PubMed

Wang, Jianjian; Cao, Yuze; Zhang, Huixue; Wang, Tianfeng; Tian, Qinghua; Lu, Xiaoyu; Lu, Xiaoyan; Kong, Xiaotong; Liu, Zhaojun; Wang, Ning; Zhang, Shuai; Ma, Heping; Ning, Shangwei; Wang, Lihua

2017-01-04

The Nervous System Disease NcRNAome Atlas (NSDNA) (http://www.bio-bigdata.net/nsdna/) is a manually curated database that provides comprehensive experimentally supported associations about nervous system diseases (NSDs) and noncoding RNAs (ncRNAs). NSDs represent a common group of disorders, some of which are characterized by high morbidity and disabilities. The pathogenesis of NSDs at the molecular level remains poorly understood. ncRNAs are a large family of functionally important RNA molecules. Increasing evidence shows that diverse ncRNAs play a critical role in various NSDs. Mining and summarizing NSD-ncRNA association data can help researchers discover useful information. Hence, we developed an NSDNA database that documents 24 713 associations between 142 NSDs and 8593 ncRNAs in 11 species, curated from more than 1300 articles. This database provides a user-friendly interface for browsing and searching and allows for data downloading flexibility. In addition, NSDNA offers a submission page for researchers to submit novel NSD-ncRNA associations. It represents an extremely useful and valuable resource for researchers who seek to understand the functions and molecular mechanisms of ncRNA involved in NSDs. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Cazymes Analysis Toolkit (CAT): Webservice for searching and analyzing carbohydrateactive enzymes in a newly sequenced organism using CAZy database

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karpinets, Tatiana V; Park, Byung; Syed, Mustafa H

2010-01-01

The Carbohydrate-Active Enzyme (CAZy) database provides a rich set of manually annotated enzymes that degrade, modify, or create glycosidic bonds. Despite rich and invaluable information stored in the database, software tools utilizing this information for annotation of newly sequenced genomes by CAZy families are limited. We have employed two annotation approaches to fill the gap between manually curated high-quality protein sequences collected in the CAZy database and the growing number of other protein sequences produced by genome or metagenome sequencing projects. The first approach is based on a similarity search against the entire non-redundant sequences of the CAZy database. Themore » second approach performs annotation using links or correspondences between the CAZy families and protein family domains. The links were discovered using the association rule learning algorithm applied to sequences from the CAZy database. The approaches complement each other and in combination achieved high specificity and sensitivity when cross-evaluated with the manually curated genomes of Clostridium thermocellum ATCC 27405 and Saccharophagus degradans 2-40. The capability of the proposed framework to predict the function of unknown protein domains (DUF) and of hypothetical proteins in the genome of Neurospora crassa is demonstrated. The framework is implemented as a Web service, the CAZymes Analysis Toolkit (CAT), and is available at http://cricket.ornl.gov/cgi-bin/cat.cgi.« less

CAZymes Analysis Toolkit (CAT): web service for searching and analyzing carbohydrate-active enzymes in a newly sequenced organism using CAZy database.

PubMed

Park, Byung H; Karpinets, Tatiana V; Syed, Mustafa H; Leuze, Michael R; Uberbacher, Edward C

2010-12-01

The Carbohydrate-Active Enzyme (CAZy) database provides a rich set of manually annotated enzymes that degrade, modify, or create glycosidic bonds. Despite rich and invaluable information stored in the database, software tools utilizing this information for annotation of newly sequenced genomes by CAZy families are limited. We have employed two annotation approaches to fill the gap between manually curated high-quality protein sequences collected in the CAZy database and the growing number of other protein sequences produced by genome or metagenome sequencing projects. The first approach is based on a similarity search against the entire nonredundant sequences of the CAZy database. The second approach performs annotation using links or correspondences between the CAZy families and protein family domains. The links were discovered using the association rule learning algorithm applied to sequences from the CAZy database. The approaches complement each other and in combination achieved high specificity and sensitivity when cross-evaluated with the manually curated genomes of Clostridium thermocellum ATCC 27405 and Saccharophagus degradans 2-40. The capability of the proposed framework to predict the function of unknown protein domains and of hypothetical proteins in the genome of Neurospora crassa is demonstrated. The framework is implemented as a Web service, the CAZymes Analysis Toolkit, and is available at http://cricket.ornl.gov/cgi-bin/cat.cgi.

Directly e-mailing authors of newly published papers encourages community curation

PubMed Central

Bunt, Stephanie M.; Grumbling, Gary B.; Field, Helen I.; Marygold, Steven J.; Brown, Nicholas H.; Millburn, Gillian H.

2012-01-01

Much of the data within Model Organism Databases (MODs) comes from manual curation of the primary research literature. Given limited funding and an increasing density of published material, a significant challenge facing all MODs is how to efficiently and effectively prioritize the most relevant research papers for detailed curation. Here, we report recent improvements to the triaging process used by FlyBase. We describe an automated method to directly e-mail corresponding authors of new papers, requesting that they list the genes studied and indicate (‘flag’) the types of data described in the paper using an online tool. Based on the author-assigned flags, papers are then prioritized for detailed curation and channelled to appropriate curator teams for full data extraction. The overall response rate has been 44% and the flagging of data types by authors is sufficiently accurate for effective prioritization of papers. In summary, we have established a sustainable community curation program, with the result that FlyBase curators now spend less time triaging and can devote more effort to the specialized task of detailed data extraction. Database URL: http://flybase.org/ PMID:22554788

Plant Reactome: a resource for plant pathways and comparative analysis

PubMed Central

Naithani, Sushma; Preece, Justin; D'Eustachio, Peter; Gupta, Parul; Amarasinghe, Vindhya; Dharmawardhana, Palitha D.; Wu, Guanming; Fabregat, Antonio; Elser, Justin L.; Weiser, Joel; Keays, Maria; Fuentes, Alfonso Munoz-Pomer; Petryszak, Robert; Stein, Lincoln D.; Ware, Doreen; Jaiswal, Pankaj

2017-01-01

Plant Reactome (http://plantreactome.gramene.org/) is a free, open-source, curated plant pathway database portal, provided as part of the Gramene project. The database provides intuitive bioinformatics tools for the visualization, analysis and interpretation of pathway knowledge to support genome annotation, genome analysis, modeling, systems biology, basic research and education. Plant Reactome employs the structural framework of a plant cell to show metabolic, transport, genetic, developmental and signaling pathways. We manually curate molecular details of pathways in these domains for reference species Oryza sativa (rice) supported by published literature and annotation of well-characterized genes. Two hundred twenty-two rice pathways, 1025 reactions associated with 1173 proteins, 907 small molecules and 256 literature references have been curated to date. These reference annotations were used to project pathways for 62 model, crop and evolutionarily significant plant species based on gene homology. Database users can search and browse various components of the database, visualize curated baseline expression of pathway-associated genes provided by the Expression Atlas and upload and analyze their Omics datasets. The database also offers data access via Application Programming Interfaces (APIs) and in various standardized pathway formats, such as SBML and BioPAX. PMID:27799469

Prioritizing PubMed articles for the Comparative Toxicogenomic Database utilizing semantic information

PubMed Central

Wilbur, W. John

2012-01-01

The Comparative Toxicogenomics Database (CTD) contains manually curated literature that describes chemical–gene interactions, chemical–disease relationships and gene–disease relationships. Finding articles containing this information is the first and an important step to assist manual curation efficiency. However, the complex nature of named entities and their relationships make it challenging to choose relevant articles. In this article, we introduce a machine learning framework for prioritizing CTD-relevant articles based on our prior system for the protein–protein interaction article classification task in BioCreative III. To address new challenges in the CTD task, we explore a new entity identification method for genes, chemicals and diseases. In addition, latent topics are analyzed and used as a feature type to overcome the small size of the training set. Applied to the BioCreative 2012 Triage dataset, our method achieved 0.8030 mean average precision (MAP) in the official runs, resulting in the top MAP system among participants. Integrated with PubTator, a Web interface for annotating biomedical literature, the proposed system also received a positive review from the CTD curation team. PMID:23160415

Prioritizing PubMed articles for the Comparative Toxicogenomic Database utilizing semantic information.

PubMed

Kim, Sun; Kim, Won; Wei, Chih-Hsuan; Lu, Zhiyong; Wilbur, W John

2012-01-01

The Comparative Toxicogenomics Database (CTD) contains manually curated literature that describes chemical-gene interactions, chemical-disease relationships and gene-disease relationships. Finding articles containing this information is the first and an important step to assist manual curation efficiency. However, the complex nature of named entities and their relationships make it challenging to choose relevant articles. In this article, we introduce a machine learning framework for prioritizing CTD-relevant articles based on our prior system for the protein-protein interaction article classification task in BioCreative III. To address new challenges in the CTD task, we explore a new entity identification method for genes, chemicals and diseases. In addition, latent topics are analyzed and used as a feature type to overcome the small size of the training set. Applied to the BioCreative 2012 Triage dataset, our method achieved 0.8030 mean average precision (MAP) in the official runs, resulting in the top MAP system among participants. Integrated with PubTator, a Web interface for annotating biomedical literature, the proposed system also received a positive review from the CTD curation team.

Saccharomyces genome database informs human biology

PubMed Central

Skrzypek, Marek S; Nash, Robert S; Wong, Edith D; MacPherson, Kevin A; Karra, Kalpana; Binkley, Gail; Simison, Matt; Miyasato, Stuart R

2018-01-01

Abstract The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org) is an expertly curated database of literature-derived functional information for the model organism budding yeast, Saccharomyces cerevisiae. SGD constantly strives to synergize new types of experimental data and bioinformatics predictions with existing data, and to organize them into a comprehensive and up-to-date information resource. The primary mission of SGD is to facilitate research into the biology of yeast and to provide this wealth of information to advance, in many ways, research on other organisms, even those as evolutionarily distant as humans. To build such a bridge between biological kingdoms, SGD is curating data regarding yeast-human complementation, in which a human gene can successfully replace the function of a yeast gene, and/or vice versa. These data are manually curated from published literature, made available for download, and incorporated into a variety of analysis tools provided by SGD. PMID:29140510

DEXTER: Disease-Expression Relation Extraction from Text.

PubMed

Gupta, Samir; Dingerdissen, Hayley; Ross, Karen E; Hu, Yu; Wu, Cathy H; Mazumder, Raja; Vijay-Shanker, K

2018-01-01

Gene expression levels affect biological processes and play a key role in many diseases. Characterizing expression profiles is useful for clinical research, and diagnostics and prognostics of diseases. There are currently several high-quality databases that capture gene expression information, obtained mostly from large-scale studies, such as microarray and next-generation sequencing technologies, in the context of disease. The scientific literature is another rich source of information on gene expression-disease relationships that not only have been captured from large-scale studies but have also been observed in thousands of small-scale studies. Expression information obtained from literature through manual curation can extend expression databases. While many of the existing databases include information from literature, they are limited by the time-consuming nature of manual curation and have difficulty keeping up with the explosion of publications in the biomedical field. In this work, we describe an automated text-mining tool, Disease-Expression Relation Extraction from Text (DEXTER) to extract information from literature on gene and microRNA expression in the context of disease. One of the motivations in developing DEXTER was to extend the BioXpress database, a cancer-focused gene expression database that includes data derived from large-scale experiments and manual curation of publications. The literature-based portion of BioXpress lags behind significantly compared to expression information obtained from large-scale studies and can benefit from our text-mined results. We have conducted two different evaluations to measure the accuracy of our text-mining tool and achieved average F-scores of 88.51 and 81.81% for the two evaluations, respectively. Also, to demonstrate the ability to extract rich expression information in different disease-related scenarios, we used DEXTER to extract information on differential expression information for 2024 genes in lung cancer, 115 glycosyltransferases in 62 cancers and 826 microRNA in 171 cancers. All extractions using DEXTER are integrated in the literature-based portion of BioXpress.Database URL: http://biotm.cis.udel.edu/DEXTER.

GEOMetaCuration: a web-based application for accurate manual curation of Gene Expression Omnibus metadata

PubMed Central

Li, Zhao; Li, Jin; Yu, Peng

2018-01-01

Abstract Metadata curation has become increasingly important for biological discovery and biomedical research because a large amount of heterogeneous biological data is currently freely available. To facilitate efficient metadata curation, we developed an easy-to-use web-based curation application, GEOMetaCuration, for curating the metadata of Gene Expression Omnibus datasets. It can eliminate mechanical operations that consume precious curation time and can help coordinate curation efforts among multiple curators. It improves the curation process by introducing various features that are critical to metadata curation, such as a back-end curation management system and a curator-friendly front-end. The application is based on a commonly used web development framework of Python/Django and is open-sourced under the GNU General Public License V3. GEOMetaCuration is expected to benefit the biocuration community and to contribute to computational generation of biological insights using large-scale biological data. An example use case can be found at the demo website: http://geometacuration.yubiolab.org. Database URL: https://bitbucket.com/yubiolab/GEOMetaCuration PMID:29688376

Cataloging the biomedical world of pain through semi-automated curation of molecular interactions

PubMed Central

Jamieson, Daniel G.; Roberts, Phoebe M.; Robertson, David L.; Sidders, Ben; Nenadic, Goran

2013-01-01

The vast collection of biomedical literature and its continued expansion has presented a number of challenges to researchers who require structured findings to stay abreast of and analyze molecular mechanisms relevant to their domain of interest. By structuring literature content into topic-specific machine-readable databases, the aggregate data from multiple articles can be used to infer trends that can be compared and contrasted with similar findings from topic-independent resources. Our study presents a generalized procedure for semi-automatically creating a custom topic-specific molecular interaction database through the use of text mining to assist manual curation. We apply the procedure to capture molecular events that underlie ‘pain’, a complex phenomenon with a large societal burden and unmet medical need. We describe how existing text mining solutions are used to build a pain-specific corpus, extract molecular events from it, add context to the extracted events and assess their relevance. The pain-specific corpus contains 765 692 documents from Medline and PubMed Central, from which we extracted 356 499 unique normalized molecular events, with 261 438 single protein events and 93 271 molecular interactions supplied by BioContext. Event chains are annotated with negation, speculation, anatomy, Gene Ontology terms, mutations, pain and disease relevance, which collectively provide detailed insight into how that event chain is associated with pain. The extracted relations are visualized in a wiki platform (wiki-pain.org) that enables efficient manual curation and exploration of the molecular mechanisms that underlie pain. Curation of 1500 grouped event chains ranked by pain relevance revealed 613 accurately extracted unique molecular interactions that in the future can be used to study the underlying mechanisms involved in pain. Our approach demonstrates that combining existing text mining tools with domain-specific terms and wiki-based visualization can facilitate rapid curation of molecular interactions to create a custom database. Database URL: ••• PMID:23707966

Cataloging the biomedical world of pain through semi-automated curation of molecular interactions.

PubMed

Jamieson, Daniel G; Roberts, Phoebe M; Robertson, David L; Sidders, Ben; Nenadic, Goran

2013-01-01

The vast collection of biomedical literature and its continued expansion has presented a number of challenges to researchers who require structured findings to stay abreast of and analyze molecular mechanisms relevant to their domain of interest. By structuring literature content into topic-specific machine-readable databases, the aggregate data from multiple articles can be used to infer trends that can be compared and contrasted with similar findings from topic-independent resources. Our study presents a generalized procedure for semi-automatically creating a custom topic-specific molecular interaction database through the use of text mining to assist manual curation. We apply the procedure to capture molecular events that underlie 'pain', a complex phenomenon with a large societal burden and unmet medical need. We describe how existing text mining solutions are used to build a pain-specific corpus, extract molecular events from it, add context to the extracted events and assess their relevance. The pain-specific corpus contains 765 692 documents from Medline and PubMed Central, from which we extracted 356 499 unique normalized molecular events, with 261 438 single protein events and 93 271 molecular interactions supplied by BioContext. Event chains are annotated with negation, speculation, anatomy, Gene Ontology terms, mutations, pain and disease relevance, which collectively provide detailed insight into how that event chain is associated with pain. The extracted relations are visualized in a wiki platform (wiki-pain.org) that enables efficient manual curation and exploration of the molecular mechanisms that underlie pain. Curation of 1500 grouped event chains ranked by pain relevance revealed 613 accurately extracted unique molecular interactions that in the future can be used to study the underlying mechanisms involved in pain. Our approach demonstrates that combining existing text mining tools with domain-specific terms and wiki-based visualization can facilitate rapid curation of molecular interactions to create a custom database. Database URL: •••

Plant Reactome: a resource for plant pathways and comparative analysis.

PubMed

Naithani, Sushma; Preece, Justin; D'Eustachio, Peter; Gupta, Parul; Amarasinghe, Vindhya; Dharmawardhana, Palitha D; Wu, Guanming; Fabregat, Antonio; Elser, Justin L; Weiser, Joel; Keays, Maria; Fuentes, Alfonso Munoz-Pomer; Petryszak, Robert; Stein, Lincoln D; Ware, Doreen; Jaiswal, Pankaj

2017-01-04

Plant Reactome (http://plantreactome.gramene.org/) is a free, open-source, curated plant pathway database portal, provided as part of the Gramene project. The database provides intuitive bioinformatics tools for the visualization, analysis and interpretation of pathway knowledge to support genome annotation, genome analysis, modeling, systems biology, basic research and education. Plant Reactome employs the structural framework of a plant cell to show metabolic, transport, genetic, developmental and signaling pathways. We manually curate molecular details of pathways in these domains for reference species Oryza sativa (rice) supported by published literature and annotation of well-characterized genes. Two hundred twenty-two rice pathways, 1025 reactions associated with 1173 proteins, 907 small molecules and 256 literature references have been curated to date. These reference annotations were used to project pathways for 62 model, crop and evolutionarily significant plant species based on gene homology. Database users can search and browse various components of the database, visualize curated baseline expression of pathway-associated genes provided by the Expression Atlas and upload and analyze their Omics datasets. The database also offers data access via Application Programming Interfaces (APIs) and in various standardized pathway formats, such as SBML and BioPAX. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

OntoMate: a text-mining tool aiding curation at the Rat Genome Database

PubMed Central

Liu, Weisong; Laulederkind, Stanley J. F.; Hayman, G. Thomas; Wang, Shur-Jen; Nigam, Rajni; Smith, Jennifer R.; De Pons, Jeff; Dwinell, Melinda R.; Shimoyama, Mary

2015-01-01

The Rat Genome Database (RGD) is the premier repository of rat genomic, genetic and physiologic data. Converting data from free text in the scientific literature to a structured format is one of the main tasks of all model organism databases. RGD spends considerable effort manually curating gene, Quantitative Trait Locus (QTL) and strain information. The rapidly growing volume of biomedical literature and the active research in the biological natural language processing (bioNLP) community have given RGD the impetus to adopt text-mining tools to improve curation efficiency. Recently, RGD has initiated a project to use OntoMate, an ontology-driven, concept-based literature search engine developed at RGD, as a replacement for the PubMed (http://www.ncbi.nlm.nih.gov/pubmed) search engine in the gene curation workflow. OntoMate tags abstracts with gene names, gene mutations, organism name and most of the 16 ontologies/vocabularies used at RGD. All terms/ entities tagged to an abstract are listed with the abstract in the search results. All listed terms are linked both to data entry boxes and a term browser in the curation tool. OntoMate also provides user-activated filters for species, date and other parameters relevant to the literature search. Using the system for literature search and import has streamlined the process compared to using PubMed. The system was built with a scalable and open architecture, including features specifically designed to accelerate the RGD gene curation process. With the use of bioNLP tools, RGD has added more automation to its curation workflow. Database URL: http://rgd.mcw.edu PMID:25619558

A CTD–Pfizer collaboration: manual curation of 88 000 scientific articles text mined for drug–disease and drug–phenotype interactions

PubMed Central

Davis, Allan Peter; Wiegers, Thomas C.; Roberts, Phoebe M.; King, Benjamin L.; Lay, Jean M.; Lennon-Hopkins, Kelley; Sciaky, Daniela; Johnson, Robin; Keating, Heather; Greene, Nigel; Hernandez, Robert; McConnell, Kevin J.; Enayetallah, Ahmed E.; Mattingly, Carolyn J.

2013-01-01

Improving the prediction of chemical toxicity is a goal common to both environmental health research and pharmaceutical drug development. To improve safety detection assays, it is critical to have a reference set of molecules with well-defined toxicity annotations for training and validation purposes. Here, we describe a collaboration between safety researchers at Pfizer and the research team at the Comparative Toxicogenomics Database (CTD) to text mine and manually review a collection of 88 629 articles relating over 1 200 pharmaceutical drugs to their potential involvement in cardiovascular, neurological, renal and hepatic toxicity. In 1 year, CTD biocurators curated 2 54 173 toxicogenomic interactions (1 52 173 chemical–disease, 58 572 chemical–gene, 5 345 gene–disease and 38 083 phenotype interactions). All chemical–gene–disease interactions are fully integrated with public CTD, and phenotype interactions can be downloaded. We describe Pfizer’s text-mining process to collate the articles, and CTD’s curation strategy, performance metrics, enhanced data content and new module to curate phenotype information. As well, we show how data integration can connect phenotypes to diseases. This curation can be leveraged for information about toxic endpoints important to drug safety and help develop testable hypotheses for drug–disease events. The availability of these detailed, contextualized, high-quality annotations curated from seven decades’ worth of the scientific literature should help facilitate new mechanistic screening assays for pharmaceutical compound survival. This unique partnership demonstrates the importance of resource sharing and collaboration between public and private entities and underscores the complementary needs of the environmental health science and pharmaceutical communities. Database URL: http://ctdbase.org/ PMID:24288140

A CTD-Pfizer collaboration: manual curation of 88,000 scientific articles text mined for drug-disease and drug-phenotype interactions.

PubMed

Davis, Allan Peter; Wiegers, Thomas C; Roberts, Phoebe M; King, Benjamin L; Lay, Jean M; Lennon-Hopkins, Kelley; Sciaky, Daniela; Johnson, Robin; Keating, Heather; Greene, Nigel; Hernandez, Robert; McConnell, Kevin J; Enayetallah, Ahmed E; Mattingly, Carolyn J

2013-01-01

Improving the prediction of chemical toxicity is a goal common to both environmental health research and pharmaceutical drug development. To improve safety detection assays, it is critical to have a reference set of molecules with well-defined toxicity annotations for training and validation purposes. Here, we describe a collaboration between safety researchers at Pfizer and the research team at the Comparative Toxicogenomics Database (CTD) to text mine and manually review a collection of 88,629 articles relating over 1,200 pharmaceutical drugs to their potential involvement in cardiovascular, neurological, renal and hepatic toxicity. In 1 year, CTD biocurators curated 254,173 toxicogenomic interactions (152,173 chemical-disease, 58,572 chemical-gene, 5,345 gene-disease and 38,083 phenotype interactions). All chemical-gene-disease interactions are fully integrated with public CTD, and phenotype interactions can be downloaded. We describe Pfizer's text-mining process to collate the articles, and CTD's curation strategy, performance metrics, enhanced data content and new module to curate phenotype information. As well, we show how data integration can connect phenotypes to diseases. This curation can be leveraged for information about toxic endpoints important to drug safety and help develop testable hypotheses for drug-disease events. The availability of these detailed, contextualized, high-quality annotations curated from seven decades' worth of the scientific literature should help facilitate new mechanistic screening assays for pharmaceutical compound survival. This unique partnership demonstrates the importance of resource sharing and collaboration between public and private entities and underscores the complementary needs of the environmental health science and pharmaceutical communities. Database URL: http://ctdbase.org/

Saccharomyces genome database informs human biology.

PubMed

Skrzypek, Marek S; Nash, Robert S; Wong, Edith D; MacPherson, Kevin A; Hellerstedt, Sage T; Engel, Stacia R; Karra, Kalpana; Weng, Shuai; Sheppard, Travis K; Binkley, Gail; Simison, Matt; Miyasato, Stuart R; Cherry, J Michael

2018-01-04

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org) is an expertly curated database of literature-derived functional information for the model organism budding yeast, Saccharomyces cerevisiae. SGD constantly strives to synergize new types of experimental data and bioinformatics predictions with existing data, and to organize them into a comprehensive and up-to-date information resource. The primary mission of SGD is to facilitate research into the biology of yeast and to provide this wealth of information to advance, in many ways, research on other organisms, even those as evolutionarily distant as humans. To build such a bridge between biological kingdoms, SGD is curating data regarding yeast-human complementation, in which a human gene can successfully replace the function of a yeast gene, and/or vice versa. These data are manually curated from published literature, made available for download, and incorporated into a variety of analysis tools provided by SGD. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Southern African Treatment Resistance Network (SATuRN) RegaDB HIV drug resistance and clinical management database: supporting patient management, surveillance and research in southern Africa

PubMed Central

Manasa, Justen; Lessells, Richard; Rossouw, Theresa; Naidu, Kevindra; Van Vuuren, Cloete; Goedhals, Dominique; van Zyl, Gert; Bester, Armand; Skingsley, Andrew; Stott, Katharine; Danaviah, Siva; Chetty, Terusha; Singh, Lavanya; Moodley, Pravi; Iwuji, Collins; McGrath, Nuala; Seebregts, Christopher J.; de Oliveira, Tulio

2014-01-01

Abstract Substantial amounts of data have been generated from patient management and academic exercises designed to better understand the human immunodeficiency virus (HIV) epidemic and design interventions to control it. A number of specialized databases have been designed to manage huge data sets from HIV cohort, vaccine, host genomic and drug resistance studies. Besides databases from cohort studies, most of the online databases contain limited curated data and are thus sequence repositories. HIV drug resistance has been shown to have a great potential to derail the progress made thus far through antiretroviral therapy. Thus, a lot of resources have been invested in generating drug resistance data for patient management and surveillance purposes. Unfortunately, most of the data currently available relate to subtype B even though >60% of the epidemic is caused by HIV-1 subtype C. A consortium of clinicians, scientists, public health experts and policy markers working in southern Africa came together and formed a network, the Southern African Treatment and Resistance Network (SATuRN), with the aim of increasing curated HIV-1 subtype C and tuberculosis drug resistance data. This article describes the HIV-1 data curation process using the SATuRN Rega database. The data curation is a manual and time-consuming process done by clinical, laboratory and data curation specialists. Access to the highly curated data sets is through applications that are reviewed by the SATuRN executive committee. Examples of research outputs from the analysis of the curated data include trends in the level of transmitted drug resistance in South Africa, analysis of the levels of acquired resistance among patients failing therapy and factors associated with the absence of genotypic evidence of drug resistance among patients failing therapy. All these studies have been important for informing first- and second-line therapy. This database is a free password-protected open source database available on www.bioafrica.net. Database URL: http://www.bioafrica.net/regadb/ PMID:24504151

Managing expectations: assessment of chemistry databases generated by automated extraction of chemical structures from patents.

PubMed

Senger, Stefan; Bartek, Luca; Papadatos, George; Gaulton, Anna

2015-12-01

First public disclosure of new chemical entities often takes place in patents, which makes them an important source of information. However, with an ever increasing number of patent applications, manual processing and curation on such a large scale becomes even more challenging. An alternative approach better suited for this large corpus of documents is the automated extraction of chemical structures. A number of patent chemistry databases generated by using the latter approach are now available but little is known that can help to manage expectations when using them. This study aims to address this by comparing two such freely available sources, SureChEMBL and IBM SIIP (IBM Strategic Intellectual Property Insight Platform), with manually curated commercial databases. When looking at the percentage of chemical structures successfully extracted from a set of patents, using SciFinder as our reference, 59 and 51 % were also found in our comparison in SureChEMBL and IBM SIIP, respectively. When performing this comparison with compounds as starting point, i.e. establishing if for a list of compounds the databases provide the links between chemical structures and patents they appear in, we obtained similar results. SureChEMBL and IBM SIIP found 62 and 59 %, respectively, of the compound-patent pairs obtained from Reaxys. In our comparison of automatically generated vs. manually curated patent chemistry databases, the former successfully provided approximately 60 % of links between chemical structure and patents. It needs to be stressed that only a very limited number of patents and compound-patent pairs were used for our comparison. Nevertheless, our results will hopefully help to manage expectations of users of patent chemistry databases of this type and provide a useful framework for more studies like ours as well as guide future developments of the workflows used for the automated extraction of chemical structures from patents. The challenges we have encountered whilst performing this study highlight that more needs to be done to make such assessments easier. Above all, more adequate, preferably open access to relevant 'gold standards' is required.

LitVar: a semantic search engine for linking genomic variant data in PubMed and PMC.

PubMed

Allot, Alexis; Peng, Yifan; Wei, Chih-Hsuan; Lee, Kyubum; Phan, Lon; Lu, Zhiyong

2018-05-14

The identification and interpretation of genomic variants play a key role in the diagnosis of genetic diseases and related research. These tasks increasingly rely on accessing relevant manually curated information from domain databases (e.g. SwissProt or ClinVar). However, due to the sheer volume of medical literature and high cost of expert curation, curated variant information in existing databases are often incomplete and out-of-date. In addition, the same genetic variant can be mentioned in publications with various names (e.g. 'A146T' versus 'c.436G>A' versus 'rs121913527'). A search in PubMed using only one name usually cannot retrieve all relevant articles for the variant of interest. Hence, to help scientists, healthcare professionals, and database curators find the most up-to-date published variant research, we have developed LitVar for the search and retrieval of standardized variant information. In addition, LitVar uses advanced text mining techniques to compute and extract relationships between variants and other associated entities such as diseases and chemicals/drugs. LitVar is publicly available at https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/LitVar.

The curation of genetic variants: difficulties and possible solutions.

PubMed

Pandey, Kapil Raj; Maden, Narendra; Poudel, Barsha; Pradhananga, Sailendra; Sharma, Amit Kumar

2012-12-01

The curation of genetic variants from biomedical articles is required for various clinical and research purposes. Nowadays, establishment of variant databases that include overall information about variants is becoming quite popular. These databases have immense utility, serving as a user-friendly information storehouse of variants for information seekers. While manual curation is the gold standard method for curation of variants, it can turn out to be time-consuming on a large scale thus necessitating the need for automation. Curation of variants described in biomedical literature may not be straightforward mainly due to various nomenclature and expression issues. Though current trends in paper writing on variants is inclined to the standard nomenclature such that variants can easily be retrieved, we have a massive store of variants in the literature that are present as non-standard names and the online search engines that are predominantly used may not be capable of finding them. For effective curation of variants, knowledge about the overall process of curation, nature and types of difficulties in curation, and ways to tackle the difficulties during the task are crucial. Only by effective curation, can variants be correctly interpreted. This paper presents the process and difficulties of curation of genetic variants with possible solutions and suggestions from our work experience in the field including literature support. The paper also highlights aspects of interpretation of genetic variants and the importance of writing papers on variants following standard and retrievable methods. Copyright © 2012. Published by Elsevier Ltd.

The Curation of Genetic Variants: Difficulties and Possible Solutions

PubMed Central

Pandey, Kapil Raj; Maden, Narendra; Poudel, Barsha; Pradhananga, Sailendra; Sharma, Amit Kumar

2012-01-01

The curation of genetic variants from biomedical articles is required for various clinical and research purposes. Nowadays, establishment of variant databases that include overall information about variants is becoming quite popular. These databases have immense utility, serving as a user-friendly information storehouse of variants for information seekers. While manual curation is the gold standard method for curation of variants, it can turn out to be time-consuming on a large scale thus necessitating the need for automation. Curation of variants described in biomedical literature may not be straightforward mainly due to various nomenclature and expression issues. Though current trends in paper writing on variants is inclined to the standard nomenclature such that variants can easily be retrieved, we have a massive store of variants in the literature that are present as non-standard names and the online search engines that are predominantly used may not be capable of finding them. For effective curation of variants, knowledge about the overall process of curation, nature and types of difficulties in curation, and ways to tackle the difficulties during the task are crucial. Only by effective curation, can variants be correctly interpreted. This paper presents the process and difficulties of curation of genetic variants with possible solutions and suggestions from our work experience in the field including literature support. The paper also highlights aspects of interpretation of genetic variants and the importance of writing papers on variants following standard and retrievable methods. PMID:23317699

MSDD: a manually curated database of experimentally supported associations among miRNAs, SNPs and human diseases

PubMed Central

Yue, Ming; Zhou, Dianshuang; Zhi, Hui; Wang, Peng; Zhang, Yan; Gao, Yue; Guo, Maoni; Li, Xin; Wang, Yanxia

2018-01-01

Abstract The MiRNA SNP Disease Database (MSDD, http://www.bio-bigdata.com/msdd/) is a manually curated database that provides comprehensive experimentally supported associations among microRNAs (miRNAs), single nucleotide polymorphisms (SNPs) and human diseases. SNPs in miRNA-related functional regions such as mature miRNAs, promoter regions, pri-miRNAs, pre-miRNAs and target gene 3′-UTRs, collectively called ‘miRSNPs’, represent a novel category of functional molecules. miRSNPs can lead to miRNA and its target gene dysregulation, and resulting in susceptibility to or onset of human diseases. A curated collection and summary of miRSNP-associated diseases is essential for a thorough understanding of the mechanisms and functions of miRSNPs. Here, we describe MSDD, which currently documents 525 associations among 182 human miRNAs, 197 SNPs, 153 genes and 164 human diseases through a review of more than 2000 published papers. Each association incorporates information on the miRNAs, SNPs, miRNA target genes and disease names, SNP locations and alleles, the miRNA dysfunctional pattern, experimental techniques, a brief functional description, the original reference and additional annotation. MSDD provides a user-friendly interface to conveniently browse, retrieve, download and submit novel data. MSDD will significantly improve our understanding of miRNA dysfunction in disease, and thus, MSDD has the potential to serve as a timely and valuable resource. PMID:29106642

MSDD: a manually curated database of experimentally supported associations among miRNAs, SNPs and human diseases.

PubMed

Yue, Ming; Zhou, Dianshuang; Zhi, Hui; Wang, Peng; Zhang, Yan; Gao, Yue; Guo, Maoni; Li, Xin; Wang, Yanxia; Zhang, Yunpeng; Ning, Shangwei; Li, Xia

2018-01-04

The MiRNA SNP Disease Database (MSDD, http://www.bio-bigdata.com/msdd/) is a manually curated database that provides comprehensive experimentally supported associations among microRNAs (miRNAs), single nucleotide polymorphisms (SNPs) and human diseases. SNPs in miRNA-related functional regions such as mature miRNAs, promoter regions, pri-miRNAs, pre-miRNAs and target gene 3'-UTRs, collectively called 'miRSNPs', represent a novel category of functional molecules. miRSNPs can lead to miRNA and its target gene dysregulation, and resulting in susceptibility to or onset of human diseases. A curated collection and summary of miRSNP-associated diseases is essential for a thorough understanding of the mechanisms and functions of miRSNPs. Here, we describe MSDD, which currently documents 525 associations among 182 human miRNAs, 197 SNPs, 153 genes and 164 human diseases through a review of more than 2000 published papers. Each association incorporates information on the miRNAs, SNPs, miRNA target genes and disease names, SNP locations and alleles, the miRNA dysfunctional pattern, experimental techniques, a brief functional description, the original reference and additional annotation. MSDD provides a user-friendly interface to conveniently browse, retrieve, download and submit novel data. MSDD will significantly improve our understanding of miRNA dysfunction in disease, and thus, MSDD has the potential to serve as a timely and valuable resource. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

EXTRACT: interactive extraction of environment metadata and term suggestion for metagenomic sample annotation.

PubMed

Pafilis, Evangelos; Buttigieg, Pier Luigi; Ferrell, Barbra; Pereira, Emiliano; Schnetzer, Julia; Arvanitidis, Christos; Jensen, Lars Juhl

2016-01-01

The microbial and molecular ecology research communities have made substantial progress on developing standards for annotating samples with environment metadata. However, sample manual annotation is a highly labor intensive process and requires familiarity with the terminologies used. We have therefore developed an interactive annotation tool, EXTRACT, which helps curators identify and extract standard-compliant terms for annotation of metagenomic records and other samples. Behind its web-based user interface, the system combines published methods for named entity recognition of environment, organism, tissue and disease terms. The evaluators in the BioCreative V Interactive Annotation Task found the system to be intuitive, useful, well documented and sufficiently accurate to be helpful in spotting relevant text passages and extracting organism and environment terms. Comparison of fully manual and text-mining-assisted curation revealed that EXTRACT speeds up annotation by 15-25% and helps curators to detect terms that would otherwise have been missed. Database URL: https://extract.hcmr.gr/. © The Author(s) 2016. Published by Oxford University Press.

MIPS: analysis and annotation of genome information in 2007

PubMed Central

Mewes, H. W.; Dietmann, S.; Frishman, D.; Gregory, R.; Mannhaupt, G.; Mayer, K. F. X.; Münsterkötter, M.; Ruepp, A.; Spannagl, M.; Stümpflen, V.; Rattei, T.

2008-01-01

The Munich Information Center for Protein Sequences (MIPS-GSF, Neuherberg, Germany) combines automatic processing of large amounts of sequences with manual annotation of selected model genomes. Due to the massive growth of the available data, the depth of annotation varies widely between independent databases. Also, the criteria for the transfer of information from known to orthologous sequences are diverse. To cope with the task of global in-depth genome annotation has become unfeasible. Therefore, our efforts are dedicated to three levels of annotation: (i) the curation of selected genomes, in particular from fungal and plant taxa (e.g. CYGD, MNCDB, MatDB), (ii) the comprehensive, consistent, automatic annotation employing exhaustive methods for the computation of sequence similarities and sequence-related attributes as well as the classification of individual sequences (SIMAP, PEDANT and FunCat) and (iii) the compilation of manually curated databases for protein interactions based on scrutinized information from the literature to serve as an accepted set of reliable annotated interaction data (MPACT, MPPI, CORUM). All databases and tools described as well as the detailed descriptions of our projects can be accessed through the MIPS web server (http://mips.gsf.de). PMID:18158298

MIPS: analysis and annotation of genome information in 2007.

PubMed

Mewes, H W; Dietmann, S; Frishman, D; Gregory, R; Mannhaupt, G; Mayer, K F X; Münsterkötter, M; Ruepp, A; Spannagl, M; Stümpflen, V; Rattei, T

2008-01-01

The Munich Information Center for Protein Sequences (MIPS-GSF, Neuherberg, Germany) combines automatic processing of large amounts of sequences with manual annotation of selected model genomes. Due to the massive growth of the available data, the depth of annotation varies widely between independent databases. Also, the criteria for the transfer of information from known to orthologous sequences are diverse. To cope with the task of global in-depth genome annotation has become unfeasible. Therefore, our efforts are dedicated to three levels of annotation: (i) the curation of selected genomes, in particular from fungal and plant taxa (e.g. CYGD, MNCDB, MatDB), (ii) the comprehensive, consistent, automatic annotation employing exhaustive methods for the computation of sequence similarities and sequence-related attributes as well as the classification of individual sequences (SIMAP, PEDANT and FunCat) and (iii) the compilation of manually curated databases for protein interactions based on scrutinized information from the literature to serve as an accepted set of reliable annotated interaction data (MPACT, MPPI, CORUM). All databases and tools described as well as the detailed descriptions of our projects can be accessed through the MIPS web server (http://mips.gsf.de).

An automated procedure to identify biomedical articles that contain cancer-associated gene variants.

PubMed

McDonald, Ryan; Scott Winters, R; Ankuda, Claire K; Murphy, Joan A; Rogers, Amy E; Pereira, Fernando; Greenblatt, Marc S; White, Peter S

2006-09-01

The proliferation of biomedical literature makes it increasingly difficult for researchers to find and manage relevant information. However, identifying research articles containing mutation data, a requisite first step in integrating large and complex mutation data sets, is currently tedious, time-consuming and imprecise. More effective mechanisms for identifying articles containing mutation information would be beneficial both for the curation of mutation databases and for individual researchers. We developed an automated method that uses information extraction, classifier, and relevance ranking techniques to determine the likelihood of MEDLINE abstracts containing information regarding genomic variation data suitable for inclusion in mutation databases. We targeted the CDKN2A (p16) gene and the procedure for document identification currently used by CDKN2A Database curators as a measure of feasibility. A set of abstracts was manually identified from a MEDLINE search as potentially containing specific CDKN2A mutation events. A subset of these abstracts was used as a training set for a maximum entropy classifier to identify text features distinguishing "relevant" from "not relevant" abstracts. Each document was represented as a set of indicative word, word pair, and entity tagger-derived genomic variation features. When applied to a test set of 200 candidate abstracts, the classifier predicted 88 articles as being relevant; of these, 29 of 32 manuscripts in which manual curation found CDKN2A sequence variants were positively predicted. Thus, the set of potentially useful articles that a manual curator would have to review was reduced by 56%, maintaining 91% recall (sensitivity) and more than doubling precision (positive predictive value). Subsequent expansion of the training set to 494 articles yielded similar precision and recall rates, and comparison of the original and expanded trials demonstrated that the average precision improved with the larger data set. Our results show that automated systems can effectively identify article subsets relevant to a given task and may prove to be powerful tools for the broader research community. This procedure can be readily adapted to any or all genes, organisms, or sets of documents. Published 2006 Wiley-Liss, Inc.

The Genomes OnLine Database (GOLD) v.5: a metadata management system based on a four level (meta)genome project classification

PubMed Central

Reddy, T.B.K.; Thomas, Alex D.; Stamatis, Dimitri; Bertsch, Jon; Isbandi, Michelle; Jansson, Jakob; Mallajosyula, Jyothi; Pagani, Ioanna; Lobos, Elizabeth A.; Kyrpides, Nikos C.

2015-01-01

The Genomes OnLine Database (GOLD; http://www.genomesonline.org) is a comprehensive online resource to catalog and monitor genetic studies worldwide. GOLD provides up-to-date status on complete and ongoing sequencing projects along with a broad array of curated metadata. Here we report version 5 (v.5) of the database. The newly designed database schema and web user interface supports several new features including the implementation of a four level (meta)genome project classification system and a simplified intuitive web interface to access reports and launch search tools. The database currently hosts information for about 19 200 studies, 56 000 Biosamples, 56 000 sequencing projects and 39 400 analysis projects. More than just a catalog of worldwide genome projects, GOLD is a manually curated, quality-controlled metadata warehouse. The problems encountered in integrating disparate and varying quality data into GOLD are briefly highlighted. GOLD fully supports and follows the Genomic Standards Consortium (GSC) Minimum Information standards. PMID:25348402

The Zebrafish Model Organism Database: new support for human disease models, mutation details, gene expression phenotypes and searching

PubMed Central

Howe, Douglas G.; Bradford, Yvonne M.; Eagle, Anne; Fashena, David; Frazer, Ken; Kalita, Patrick; Mani, Prita; Martin, Ryan; Moxon, Sierra Taylor; Paddock, Holly; Pich, Christian; Ramachandran, Sridhar; Ruzicka, Leyla; Schaper, Kevin; Shao, Xiang; Singer, Amy; Toro, Sabrina; Van Slyke, Ceri; Westerfield, Monte

2017-01-01

The Zebrafish Model Organism Database (ZFIN; http://zfin.org) is the central resource for zebrafish (Danio rerio) genetic, genomic, phenotypic and developmental data. ZFIN curators provide expert manual curation and integration of comprehensive data involving zebrafish genes, mutants, transgenic constructs and lines, phenotypes, genotypes, gene expressions, morpholinos, TALENs, CRISPRs, antibodies, anatomical structures, models of human disease and publications. We integrate curated, directly submitted, and collaboratively generated data, making these available to zebrafish research community. Among the vertebrate model organisms, zebrafish are superbly suited for rapid generation of sequence-targeted mutant lines, characterization of phenotypes including gene expression patterns, and generation of human disease models. The recent rapid adoption of zebrafish as human disease models is making management of these data particularly important to both the research and clinical communities. Here, we describe recent enhancements to ZFIN including use of the zebrafish experimental conditions ontology, ‘Fish’ records in the ZFIN database, support for gene expression phenotypes, models of human disease, mutation details at the DNA, RNA and protein levels, and updates to the ZFIN single box search. PMID:27899582

Automated PDF highlighting to support faster curation of literature for Parkinson’s and Alzheimer’s disease

PubMed Central

Oellrich, Anika; Girges, Christine; de Bono, Bernard; Hubbard, Tim J.P.; Dobson, Richard J.B.

2017-01-01

Abstract Neurodegenerative disorders such as Parkinson’s and Alzheimer’s disease are devastating and costly illnesses, a source of major global burden. In order to provide successful interventions for patients and reduce costs, both causes and pathological processes need to be understood. The ApiNATOMY project aims to contribute to our understanding of neurodegenerative disorders by manually curating and abstracting data from the vast body of literature amassed on these illnesses. As curation is labour-intensive, we aimed to speed up the process by automatically highlighting those parts of the PDF document of primary importance to the curator. Using techniques similar to those of summarisation, we developed an algorithm that relies on linguistic, semantic and spatial features. Employing this algorithm on a test set manually corrected for tool imprecision, we achieved a macro F1-measure of 0.51, which is an increase of 132% compared to the best bag-of-words baseline model. A user based evaluation was also conducted to assess the usefulness of the methodology on 40 unseen publications, which reveals that in 85% of cases all highlighted sentences are relevant to the curation task and in about 65% of the cases, the highlights are sufficient to support the knowledge curation task without needing to consult the full text. In conclusion, we believe that these are promising results for a step in automating the recognition of curation-relevant sentences. Refining our approach to pre-digest papers will lead to faster processing and cost reduction in the curation process. Database URL: https://github.com/KHP-Informatics/NapEasy PMID:28365743

BioSharing: curated and crowd-sourced metadata standards, databases and data policies in the life sciences.

PubMed

McQuilton, Peter; Gonzalez-Beltran, Alejandra; Rocca-Serra, Philippe; Thurston, Milo; Lister, Allyson; Maguire, Eamonn; Sansone, Susanna-Assunta

2016-01-01

BioSharing (http://www.biosharing.org) is a manually curated, searchable portal of three linked registries. These resources cover standards (terminologies, formats and models, and reporting guidelines), databases, and data policies in the life sciences, broadly encompassing the biological, environmental and biomedical sciences. Launched in 2011 and built by the same core team as the successful MIBBI portal, BioSharing harnesses community curation to collate and cross-reference resources across the life sciences from around the world. BioSharing makes these resources findable and accessible (the core of the FAIR principle). Every record is designed to be interlinked, providing a detailed description not only on the resource itself, but also on its relations with other life science infrastructures. Serving a variety of stakeholders, BioSharing cultivates a growing community, to which it offers diverse benefits. It is a resource for funding bodies and journal publishers to navigate the metadata landscape of the biological sciences; an educational resource for librarians and information advisors; a publicising platform for standard and database developers/curators; and a research tool for bench and computer scientists to plan their work. BioSharing is working with an increasing number of journals and other registries, for example linking standards and databases to training material and tools. Driven by an international Advisory Board, the BioSharing user-base has grown by over 40% (by unique IP address), in the last year thanks to successful engagement with researchers, publishers, librarians, developers and other stakeholders via several routes, including a joint RDA/Force11 working group and a collaboration with the International Society for Biocuration. In this article, we describe BioSharing, with a particular focus on community-led curation.Database URL: https://www.biosharing.org. © The Author(s) 2016. Published by Oxford University Press.

The BioGRID interaction database: 2013 update.

PubMed

Chatr-Aryamontri, Andrew; Breitkreutz, Bobby-Joe; Heinicke, Sven; Boucher, Lorrie; Winter, Andrew; Stark, Chris; Nixon, Julie; Ramage, Lindsay; Kolas, Nadine; O'Donnell, Lara; Reguly, Teresa; Breitkreutz, Ashton; Sellam, Adnane; Chen, Daici; Chang, Christie; Rust, Jennifer; Livstone, Michael; Oughtred, Rose; Dolinski, Kara; Tyers, Mike

2013-01-01

The Biological General Repository for Interaction Datasets (BioGRID: http//thebiogrid.org) is an open access archive of genetic and protein interactions that are curated from the primary biomedical literature for all major model organism species. As of September 2012, BioGRID houses more than 500 000 manually annotated interactions from more than 30 model organisms. BioGRID maintains complete curation coverage of the literature for the budding yeast Saccharomyces cerevisiae, the fission yeast Schizosaccharomyces pombe and the model plant Arabidopsis thaliana. A number of themed curation projects in areas of biomedical importance are also supported. BioGRID has established collaborations and/or shares data records for the annotation of interactions and phenotypes with most major model organism databases, including Saccharomyces Genome Database, PomBase, WormBase, FlyBase and The Arabidopsis Information Resource. BioGRID also actively engages with the text-mining community to benchmark and deploy automated tools to expedite curation workflows. BioGRID data are freely accessible through both a user-defined interactive interface and in batch downloads in a wide variety of formats, including PSI-MI2.5 and tab-delimited files. BioGRID records can also be interrogated and analyzed with a series of new bioinformatics tools, which include a post-translational modification viewer, a graphical viewer, a REST service and a Cytoscape plugin.

Manual Gene Ontology annotation workflow at the Mouse Genome Informatics Database

PubMed Central

Drabkin, Harold J.; Blake, Judith A.

2012-01-01

The Mouse Genome Database, the Gene Expression Database and the Mouse Tumor Biology database are integrated components of the Mouse Genome Informatics (MGI) resource (http://www.informatics.jax.org). The MGI system presents both a consensus view and an experimental view of the knowledge concerning the genetics and genomics of the laboratory mouse. From genotype to phenotype, this information resource integrates information about genes, sequences, maps, expression analyses, alleles, strains and mutant phenotypes. Comparative mammalian data are also presented particularly in regards to the use of the mouse as a model for the investigation of molecular and genetic components of human diseases. These data are collected from literature curation as well as downloads of large datasets (SwissProt, LocusLink, etc.). MGI is one of the founding members of the Gene Ontology (GO) and uses the GO for functional annotation of genes. Here, we discuss the workflow associated with manual GO annotation at MGI, from literature collection to display of the annotations. Peer-reviewed literature is collected mostly from a set of journals available electronically. Selected articles are entered into a master bibliography and indexed to one of eight areas of interest such as ‘GO’ or ‘homology’ or ‘phenotype’. Each article is then either indexed to a gene already contained in the database or funneled through a separate nomenclature database to add genes. The master bibliography and associated indexing provide information for various curator-reports such as ‘papers selected for GO that refer to genes with NO GO annotation’. Once indexed, curators who have expertise in appropriate disciplines enter pertinent information. MGI makes use of several controlled vocabularies that ensure uniform data encoding, enable robust analysis and support the construction of complex queries. These vocabularies range from pick-lists to structured vocabularies such as the GO. All data associations are supported with statements of evidence as well as access to source publications. PMID:23110975

Manual Gene Ontology annotation workflow at the Mouse Genome Informatics Database.

PubMed

Drabkin, Harold J; Blake, Judith A

2012-01-01

The Mouse Genome Database, the Gene Expression Database and the Mouse Tumor Biology database are integrated components of the Mouse Genome Informatics (MGI) resource (http://www.informatics.jax.org). The MGI system presents both a consensus view and an experimental view of the knowledge concerning the genetics and genomics of the laboratory mouse. From genotype to phenotype, this information resource integrates information about genes, sequences, maps, expression analyses, alleles, strains and mutant phenotypes. Comparative mammalian data are also presented particularly in regards to the use of the mouse as a model for the investigation of molecular and genetic components of human diseases. These data are collected from literature curation as well as downloads of large datasets (SwissProt, LocusLink, etc.). MGI is one of the founding members of the Gene Ontology (GO) and uses the GO for functional annotation of genes. Here, we discuss the workflow associated with manual GO annotation at MGI, from literature collection to display of the annotations. Peer-reviewed literature is collected mostly from a set of journals available electronically. Selected articles are entered into a master bibliography and indexed to one of eight areas of interest such as 'GO' or 'homology' or 'phenotype'. Each article is then either indexed to a gene already contained in the database or funneled through a separate nomenclature database to add genes. The master bibliography and associated indexing provide information for various curator-reports such as 'papers selected for GO that refer to genes with NO GO annotation'. Once indexed, curators who have expertise in appropriate disciplines enter pertinent information. MGI makes use of several controlled vocabularies that ensure uniform data encoding, enable robust analysis and support the construction of complex queries. These vocabularies range from pick-lists to structured vocabularies such as the GO. All data associations are supported with statements of evidence as well as access to source publications.

miRiaD: A Text Mining Tool for Detecting Associations of microRNAs with Diseases.

PubMed

Gupta, Samir; Ross, Karen E; Tudor, Catalina O; Wu, Cathy H; Schmidt, Carl J; Vijay-Shanker, K

2016-04-29

MicroRNAs are increasingly being appreciated as critical players in human diseases, and questions concerning the role of microRNAs arise in many areas of biomedical research. There are several manually curated databases of microRNA-disease associations gathered from the biomedical literature; however, it is difficult for curators of these databases to keep up with the explosion of publications in the microRNA-disease field. Moreover, automated literature mining tools that assist manual curation of microRNA-disease associations currently capture only one microRNA property (expression) in the context of one disease (cancer). Thus, there is a clear need to develop more sophisticated automated literature mining tools that capture a variety of microRNA properties and relations in the context of multiple diseases to provide researchers with fast access to the most recent published information and to streamline and accelerate manual curation. We have developed miRiaD (microRNAs in association with Disease), a text-mining tool that automatically extracts associations between microRNAs and diseases from the literature. These associations are often not directly linked, and the intermediate relations are often highly informative for the biomedical researcher. Thus, miRiaD extracts the miR-disease pairs together with an explanation for their association. We also developed a procedure that assigns scores to sentences, marking their informativeness, based on the microRNA-disease relation observed within the sentence. miRiaD was applied to the entire Medline corpus, identifying 8301 PMIDs with miR-disease associations. These abstracts and the miR-disease associations are available for browsing at http://biotm.cis.udel.edu/miRiaD . We evaluated the recall and precision of miRiaD with respect to information of high interest to public microRNA-disease database curators (expression and target gene associations), obtaining a recall of 88.46-90.78. When we expanded the evaluation to include sentences with a wide range of microRNA-disease information that may be of interest to biomedical researchers, miRiaD also performed very well with a F-score of 89.4. The informativeness ranking of sentences was evaluated in terms of nDCG (0.977) and correlation metrics (0.678-0.727) when compared to an annotator's ranked list. miRiaD, a high performance system that can capture a wide variety of microRNA-disease related information, extends beyond the scope of existing microRNA-disease resources. It can be incorporated into manual curation pipelines and serve as a resource for biomedical researchers interested in the role of microRNAs in disease. In our ongoing work we are developing an improved miRiaD web interface that will facilitate complex queries about microRNA-disease relationships, such as "In what diseases does microRNA regulation of apoptosis play a role?" or "Is there overlap in the sets of genes targeted by microRNAs in different types of dementia?"."

Biocuration at the Saccharomyces genome database.

PubMed

Skrzypek, Marek S; Nash, Robert S

2015-08-01

Saccharomyces Genome Database is an online resource dedicated to managing information about the biology and genetics of the model organism, yeast (Saccharomyces cerevisiae). This information is derived primarily from scientific publications through a process of human curation that involves manual extraction of data and their organization into a comprehensive system of knowledge. This system provides a foundation for further analysis of experimental data coming from research on yeast as well as other organisms. In this review we will demonstrate how biocuration and biocurators add a key component, the biological context, to our understanding of how genes, proteins, genomes and cells function and interact. We will explain the role biocurators play in sifting through the wealth of biological data to incorporate and connect key information. We will also discuss the many ways we assist researchers with their various research needs. We hope to convince the reader that manual curation is vital in converting the flood of data into organized and interconnected knowledge, and that biocurators play an essential role in the integration of scientific information into a coherent model of the cell. © 2015 Wiley Periodicals, Inc.

Biocuration at the Saccharomyces Genome Database

PubMed Central

Skrzypek, Marek S.; Nash, Robert S.

2015-01-01

Saccharomyces Genome Database is an online resource dedicated to managing information about the biology and genetics of the model organism, yeast (Saccharomyces cerevisiae). This information is derived primarily from scientific publications through a process of human curation that involves manual extraction of data and their organization into a comprehensive system of knowledge. This system provides a foundation for further analysis of experimental data coming from research on yeast as well as other organisms. In this review we will demonstrate how biocuration and biocurators add a key component, the biological context, to our understanding of how genes, proteins, genomes and cells function and interact. We will explain the role biocurators play in sifting through the wealth of biological data to incorporate and connect key information. We will also discuss the many ways we assist researchers with their various research needs. We hope to convince the reader that manual curation is vital in converting the flood of data into organized and interconnected knowledge, and that biocurators play an essential role in the integration of scientific information into a coherent model of the cell. PMID:25997651

S2RSLDB: a comprehensive manually curated, internet-accessible database of the sigma-2 receptor selective ligands.

PubMed

Nastasi, Giovanni; Miceli, Carla; Pittalà, Valeria; Modica, Maria N; Prezzavento, Orazio; Romeo, Giuseppe; Rescifina, Antonio; Marrazzo, Agostino; Amata, Emanuele

2017-01-01

Sigma (σ) receptors are accepted as a particular receptor class consisting of two subtypes: sigma-1 (σ 1 ) and sigma-2 (σ 2 ). The two receptor subtypes have specific drug actions, pharmacological profiles and molecular characteristics. The σ 2 receptor is overexpressed in several tumor cell lines, and its ligands are currently under investigation for their role in tumor diagnosis and treatment. The σ 2 receptor structure has not been disclosed, and researchers rely on σ 2 receptor radioligand binding assay to understand the receptor's pharmacological behavior and design new lead compounds. Here we present the sigma-2 Receptor Selective Ligands Database (S2RSLDB) a manually curated database of the σ 2 receptor selective ligands containing more than 650 compounds. The database is built with chemical structure information, radioligand binding affinity data, computed physicochemical properties, and experimental radioligand binding procedures. The S2RSLDB is freely available online without account login and having a powerful search engine the user may build complex queries, sort tabulated results, generate color coded 2D and 3D graphs and download the data for additional screening. The collection here reported is extremely useful for the development of new ligands endowed of σ 2 receptor affinity, selectivity, and appropriate physicochemical properties. The database will be updated yearly and in the near future, an online submission form will be available to help with keeping the database widely spread in the research community and continually updated. The database is available at http://www.researchdsf.unict.it/S2RSLDB.

Exploring Short Linear Motifs Using the ELM Database and Tools.

PubMed

Gouw, Marc; Sámano-Sánchez, Hugo; Van Roey, Kim; Diella, Francesca; Gibson, Toby J; Dinkel, Holger

2017-06-27

The Eukaryotic Linear Motif (ELM) resource is dedicated to the characterization and prediction of short linear motifs (SLiMs). SLiMs are compact, degenerate peptide segments found in many proteins and essential to almost all cellular processes. However, despite their abundance, SLiMs remain largely uncharacterized. The ELM database is a collection of manually annotated SLiM instances curated from experimental literature. In this article we illustrate how to browse and search the database for curated SLiM data, and cover the different types of data integrated in the resource. We also cover how to use this resource in order to predict SLiMs in known as well as novel proteins, and how to interpret the results generated by the ELM prediction pipeline. The ELM database is a very rich resource, and in the following protocols we give helpful examples to demonstrate how this knowledge can be used to improve your own research. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.

EcoCyc: a comprehensive database resource for Escherichia coli

PubMed Central

Keseler, Ingrid M.; Collado-Vides, Julio; Gama-Castro, Socorro; Ingraham, John; Paley, Suzanne; Paulsen, Ian T.; Peralta-Gil, Martín; Karp, Peter D.

2005-01-01

The EcoCyc database (http://EcoCyc.org/) is a comprehensive source of information on the biology of the prototypical model organism Escherichia coli K12. The mission for EcoCyc is to contain both computable descriptions of, and detailed comments describing, all genes, proteins, pathways and molecular interactions in E.coli. Through ongoing manual curation, extensive information such as summary comments, regulatory information, literature citations and evidence types has been extracted from 8862 publications and added to Version 8.5 of the EcoCyc database. The EcoCyc database can be accessed through a World Wide Web interface, while the downloadable Pathway Tools software and data files enable computational exploration of the data and provide enhanced querying capabilities that web interfaces cannot support. For example, EcoCyc contains carefully curated information that can be used as training sets for bioinformatics prediction of entities such as promoters, operons, genetic networks, transcription factor binding sites, metabolic pathways, functionally related genes, protein complexes and protein–ligand interactions. PMID:15608210

An overview of the BioCreative 2012 Workshop Track III: interactive text mining task

PubMed Central

Arighi, Cecilia N.; Carterette, Ben; Cohen, K. Bretonnel; Krallinger, Martin; Wilbur, W. John; Fey, Petra; Dodson, Robert; Cooper, Laurel; Van Slyke, Ceri E.; Dahdul, Wasila; Mabee, Paula; Li, Donghui; Harris, Bethany; Gillespie, Marc; Jimenez, Silvia; Roberts, Phoebe; Matthews, Lisa; Becker, Kevin; Drabkin, Harold; Bello, Susan; Licata, Luana; Chatr-aryamontri, Andrew; Schaeffer, Mary L.; Park, Julie; Haendel, Melissa; Van Auken, Kimberly; Li, Yuling; Chan, Juancarlos; Muller, Hans-Michael; Cui, Hong; Balhoff, James P.; Chi-Yang Wu, Johnny; Lu, Zhiyong; Wei, Chih-Hsuan; Tudor, Catalina O.; Raja, Kalpana; Subramani, Suresh; Natarajan, Jeyakumar; Cejuela, Juan Miguel; Dubey, Pratibha; Wu, Cathy

2013-01-01

In many databases, biocuration primarily involves literature curation, which usually involves retrieving relevant articles, extracting information that will translate into annotations and identifying new incoming literature. As the volume of biological literature increases, the use of text mining to assist in biocuration becomes increasingly relevant. A number of groups have developed tools for text mining from a computer science/linguistics perspective, and there are many initiatives to curate some aspect of biology from the literature. Some biocuration efforts already make use of a text mining tool, but there have not been many broad-based systematic efforts to study which aspects of a text mining tool contribute to its usefulness for a curation task. Here, we report on an effort to bring together text mining tool developers and database biocurators to test the utility and usability of tools. Six text mining systems presenting diverse biocuration tasks participated in a formal evaluation, and appropriate biocurators were recruited for testing. The performance results from this evaluation indicate that some of the systems were able to improve efficiency of curation by speeding up the curation task significantly (∼1.7- to 2.5-fold) over manual curation. In addition, some of the systems were able to improve annotation accuracy when compared with the performance on the manually curated set. In terms of inter-annotator agreement, the factors that contributed to significant differences for some of the systems included the expertise of the biocurator on the given curation task, the inherent difficulty of the curation and attention to annotation guidelines. After the task, annotators were asked to complete a survey to help identify strengths and weaknesses of the various systems. The analysis of this survey highlights how important task completion is to the biocurators’ overall experience of a system, regardless of the system’s high score on design, learnability and usability. In addition, strategies to refine the annotation guidelines and systems documentation, to adapt the tools to the needs and query types the end user might have and to evaluate performance in terms of efficiency, user interface, result export and traditional evaluation metrics have been analyzed during this task. This analysis will help to plan for a more intense study in BioCreative IV. PMID:23327936

An overview of the BioCreative 2012 Workshop Track III: interactive text mining task.

PubMed

Arighi, Cecilia N; Carterette, Ben; Cohen, K Bretonnel; Krallinger, Martin; Wilbur, W John; Fey, Petra; Dodson, Robert; Cooper, Laurel; Van Slyke, Ceri E; Dahdul, Wasila; Mabee, Paula; Li, Donghui; Harris, Bethany; Gillespie, Marc; Jimenez, Silvia; Roberts, Phoebe; Matthews, Lisa; Becker, Kevin; Drabkin, Harold; Bello, Susan; Licata, Luana; Chatr-aryamontri, Andrew; Schaeffer, Mary L; Park, Julie; Haendel, Melissa; Van Auken, Kimberly; Li, Yuling; Chan, Juancarlos; Muller, Hans-Michael; Cui, Hong; Balhoff, James P; Chi-Yang Wu, Johnny; Lu, Zhiyong; Wei, Chih-Hsuan; Tudor, Catalina O; Raja, Kalpana; Subramani, Suresh; Natarajan, Jeyakumar; Cejuela, Juan Miguel; Dubey, Pratibha; Wu, Cathy

2013-01-01

In many databases, biocuration primarily involves literature curation, which usually involves retrieving relevant articles, extracting information that will translate into annotations and identifying new incoming literature. As the volume of biological literature increases, the use of text mining to assist in biocuration becomes increasingly relevant. A number of groups have developed tools for text mining from a computer science/linguistics perspective, and there are many initiatives to curate some aspect of biology from the literature. Some biocuration efforts already make use of a text mining tool, but there have not been many broad-based systematic efforts to study which aspects of a text mining tool contribute to its usefulness for a curation task. Here, we report on an effort to bring together text mining tool developers and database biocurators to test the utility and usability of tools. Six text mining systems presenting diverse biocuration tasks participated in a formal evaluation, and appropriate biocurators were recruited for testing. The performance results from this evaluation indicate that some of the systems were able to improve efficiency of curation by speeding up the curation task significantly (∼1.7- to 2.5-fold) over manual curation. In addition, some of the systems were able to improve annotation accuracy when compared with the performance on the manually curated set. In terms of inter-annotator agreement, the factors that contributed to significant differences for some of the systems included the expertise of the biocurator on the given curation task, the inherent difficulty of the curation and attention to annotation guidelines. After the task, annotators were asked to complete a survey to help identify strengths and weaknesses of the various systems. The analysis of this survey highlights how important task completion is to the biocurators' overall experience of a system, regardless of the system's high score on design, learnability and usability. In addition, strategies to refine the annotation guidelines and systems documentation, to adapt the tools to the needs and query types the end user might have and to evaluate performance in terms of efficiency, user interface, result export and traditional evaluation metrics have been analyzed during this task. This analysis will help to plan for a more intense study in BioCreative IV.

ChimerDB 3.0: an enhanced database for fusion genes from cancer transcriptome and literature data mining.

PubMed

Lee, Myunggyo; Lee, Kyubum; Yu, Namhee; Jang, Insu; Choi, Ikjung; Kim, Pora; Jang, Ye Eun; Kim, Byounggun; Kim, Sunkyu; Lee, Byungwook; Kang, Jaewoo; Lee, Sanghyuk

2017-01-04

Fusion gene is an important class of therapeutic targets and prognostic markers in cancer. ChimerDB is a comprehensive database of fusion genes encompassing analysis of deep sequencing data and manual curations. In this update, the database coverage was enhanced considerably by adding two new modules of The Cancer Genome Atlas (TCGA) RNA-Seq analysis and PubMed abstract mining. ChimerDB 3.0 is composed of three modules of ChimerKB, ChimerPub and ChimerSeq. ChimerKB represents a knowledgebase including 1066 fusion genes with manual curation that were compiled from public resources of fusion genes with experimental evidences. ChimerPub includes 2767 fusion genes obtained from text mining of PubMed abstracts. ChimerSeq module is designed to archive the fusion candidates from deep sequencing data. Importantly, we have analyzed RNA-Seq data of the TCGA project covering 4569 patients in 23 cancer types using two reliable programs of FusionScan and TopHat-Fusion. The new user interface supports diverse search options and graphic representation of fusion gene structure. ChimerDB 3.0 is available at http://ercsb.ewha.ac.kr/fusiongene/. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

GeneSigDB—a curated database of gene expression signatures

PubMed Central

Culhane, Aedín C.; Schwarzl, Thomas; Sultana, Razvan; Picard, Kermshlise C.; Picard, Shaita C.; Lu, Tim H.; Franklin, Katherine R.; French, Simon J.; Papenhausen, Gerald; Correll, Mick; Quackenbush, John

2010-01-01

The primary objective of most gene expression studies is the identification of one or more gene signatures; lists of genes whose transcriptional levels are uniquely associated with a specific biological phenotype. Whilst thousands of experimentally derived gene signatures are published, their potential value to the community is limited by their computational inaccessibility. Gene signatures are embedded in published article figures, tables or in supplementary materials, and are frequently presented using non-standard gene or probeset nomenclature. We present GeneSigDB (http://compbio.dfci.harvard.edu/genesigdb) a manually curated database of gene expression signatures. GeneSigDB release 1.0 focuses on cancer and stem cells gene signatures and was constructed from more than 850 publications from which we manually transcribed 575 gene signatures. Most gene signatures (n = 560) were successfully mapped to the genome to extract standardized lists of EnsEMBL gene identifiers. GeneSigDB provides the original gene signature, the standardized gene list and a fully traceable gene mapping history for each gene from the original transcribed data table through to the standardized list of genes. The GeneSigDB web portal is easy to search, allows users to compare their own gene list to those in the database, and download gene signatures in most common gene identifier formats. PMID:19934259

Global Metabolic Reconstruction and Metabolic Gene Evolution in the Cattle Genome

PubMed Central

Kim, Woonsu; Park, Hyesun; Seo, Seongwon

2016-01-01

The sequence of cattle genome provided a valuable opportunity to systematically link genetic and metabolic traits of cattle. The objectives of this study were 1) to reconstruct genome-scale cattle-specific metabolic pathways based on the most recent and updated cattle genome build and 2) to identify duplicated metabolic genes in the cattle genome for better understanding of metabolic adaptations in cattle. A bioinformatic pipeline of an organism for amalgamating genomic annotations from multiple sources was updated. Using this, an amalgamated cattle genome database based on UMD_3.1, was created. The amalgamated cattle genome database is composed of a total of 33,292 genes: 19,123 consensus genes between NCBI and Ensembl databases, 8,410 and 5,493 genes only found in NCBI or Ensembl, respectively, and 266 genes from NCBI scaffolds. A metabolic reconstruction of the cattle genome and cattle pathway genome database (PGDB) was also developed using Pathway Tools, followed by an intensive manual curation. The manual curation filled or revised 68 pathway holes, deleted 36 metabolic pathways, and added 23 metabolic pathways. Consequently, the curated cattle PGDB contains 304 metabolic pathways, 2,460 reactions including 2,371 enzymatic reactions, and 4,012 enzymes. Furthermore, this study identified eight duplicated genes in 12 metabolic pathways in the cattle genome compared to human and mouse. Some of these duplicated genes are related with specific hormone biosynthesis and detoxifications. The updated genome-scale metabolic reconstruction is a useful tool for understanding biology and metabolic characteristics in cattle. There has been significant improvements in the quality of cattle genome annotations and the MetaCyc database. The duplicated metabolic genes in the cattle genome compared to human and mouse implies evolutionary changes in the cattle genome and provides a useful information for further research on understanding metabolic adaptations of cattle. PMID:26992093

Comprehensive coverage of cardiovascular disease data in the disease portals at the Rat Genome Database.

PubMed

Wang, Shur-Jen; Laulederkind, Stanley J F; Hayman, G Thomas; Petri, Victoria; Smith, Jennifer R; Tutaj, Marek; Nigam, Rajni; Dwinell, Melinda R; Shimoyama, Mary

2016-08-01

Cardiovascular diseases are complex diseases caused by a combination of genetic and environmental factors. To facilitate progress in complex disease research, the Rat Genome Database (RGD) provides the community with a disease portal where genome objects and biological data related to cardiovascular diseases are systematically organized. The purpose of this study is to present biocuration at RGD, including disease, genetic, and pathway data. The RGD curation team uses controlled vocabularies/ontologies to organize data curated from the published literature or imported from disease and pathway databases. These organized annotations are associated with genes, strains, and quantitative trait loci (QTLs), thus linking functional annotations to genome objects. Screen shots from the web pages are used to demonstrate the organization of annotations at RGD. The human cardiovascular disease genes identified by annotations were grouped according to data sources and their annotation profiles were compared by in-house tools and other enrichment tools available to the public. The analysis results show that the imported cardiovascular disease genes from ClinVar and OMIM are functionally different from the RGD manually curated genes in terms of pathway and Gene Ontology annotations. The inclusion of disease genes from other databases enriches the collection of disease genes not only in quantity but also in quality. Copyright © 2016 the American Physiological Society.

IMPPAT: A curated database of Indian Medicinal Plants, Phytochemistry And Therapeutics.

PubMed

Mohanraj, Karthikeyan; Karthikeyan, Bagavathy Shanmugam; Vivek-Ananth, R P; Chand, R P Bharath; Aparna, S R; Mangalapandi, Pattulingam; Samal, Areejit

2018-03-12

Phytochemicals of medicinal plants encompass a diverse chemical space for drug discovery. India is rich with a flora of indigenous medicinal plants that have been used for centuries in traditional Indian medicine to treat human maladies. A comprehensive online database on the phytochemistry of Indian medicinal plants will enable computational approaches towards natural product based drug discovery. In this direction, we present, IMPPAT, a manually curated database of 1742 Indian Medicinal Plants, 9596 Phytochemicals, And 1124 Therapeutic uses spanning 27074 plant-phytochemical associations and 11514 plant-therapeutic associations. Notably, the curation effort led to a non-redundant in silico library of 9596 phytochemicals with standard chemical identifiers and structure information. Using cheminformatic approaches, we have computed the physicochemical, ADMET (absorption, distribution, metabolism, excretion, toxicity) and drug-likeliness properties of the IMPPAT phytochemicals. We show that the stereochemical complexity and shape complexity of IMPPAT phytochemicals differ from libraries of commercial compounds or diversity-oriented synthesis compounds while being similar to other libraries of natural products. Within IMPPAT, we have filtered a subset of 960 potential druggable phytochemicals, of which majority have no significant similarity to existing FDA approved drugs, and thus, rendering them as good candidates for prospective drugs. IMPPAT database is openly accessible at: https://cb.imsc.res.in/imppat .

Effective biomedical document classification for identifying publications relevant to the mouse Gene Expression Database (GXD).

PubMed

Jiang, Xiangying; Ringwald, Martin; Blake, Judith; Shatkay, Hagit

2017-01-01

The Gene Expression Database (GXD) is a comprehensive online database within the Mouse Genome Informatics resource, aiming to provide available information about endogenous gene expression during mouse development. The information stems primarily from many thousands of biomedical publications that database curators must go through and read. Given the very large number of biomedical papers published each year, automatic document classification plays an important role in biomedical research. Specifically, an effective and efficient document classifier is needed for supporting the GXD annotation workflow. We present here an effective yet relatively simple classification scheme, which uses readily available tools while employing feature selection, aiming to assist curators in identifying publications relevant to GXD. We examine the performance of our method over a large manually curated dataset, consisting of more than 25 000 PubMed abstracts, of which about half are curated as relevant to GXD while the other half as irrelevant to GXD. In addition to text from title-and-abstract, we also consider image captions, an important information source that we integrate into our method. We apply a captions-based classifier to a subset of about 3300 documents, for which the full text of the curated articles is available. The results demonstrate that our proposed approach is robust and effectively addresses the GXD document classification. Moreover, using information obtained from image captions clearly improves performance, compared to title and abstract alone, affirming the utility of image captions as a substantial evidence source for automatically determining the relevance of biomedical publications to a specific subject area. www.informatics.jax.org. © The Author(s) 2017. Published by Oxford University Press.

BrEPS 2.0: Optimization of sequence pattern prediction for enzyme annotation.

PubMed

Dudek, Christian-Alexander; Dannheim, Henning; Schomburg, Dietmar

2017-01-01

The prediction of gene functions is crucial for a large number of different life science areas. Faster high throughput sequencing techniques generate more and larger datasets. The manual annotation by classical wet-lab experiments is not suitable for these large amounts of data. We showed earlier that the automatic sequence pattern-based BrEPS protocol, based on manually curated sequences, can be used for the prediction of enzymatic functions of genes. The growing sequence databases provide the opportunity for more reliable patterns, but are also a challenge for the implementation of automatic protocols. We reimplemented and optimized the BrEPS pattern generation to be applicable for larger datasets in an acceptable timescale. Primary improvement of the new BrEPS protocol is the enhanced data selection step. Manually curated annotations from Swiss-Prot are used as reliable source for function prediction of enzymes observed on protein level. The pool of sequences is extended by highly similar sequences from TrEMBL and SwissProt. This allows us to restrict the selection of Swiss-Prot entries, without losing the diversity of sequences needed to generate significant patterns. Additionally, a supporting pattern type was introduced by extending the patterns at semi-conserved positions with highly similar amino acids. Extended patterns have an increased complexity, increasing the chance to match more sequences, without losing the essential structural information of the pattern. To enhance the usability of the database, we introduced enzyme function prediction based on consensus EC numbers and IUBMB enzyme nomenclature. BrEPS is part of the Braunschweig Enzyme Database (BRENDA) and is available on a completely redesigned website and as download. The database can be downloaded and used with the BrEPScmd command line tool for large scale sequence analysis. The BrEPS website and downloads for the database creation tool, command line tool and database are freely accessible at http://breps.tu-bs.de.

BrEPS 2.0: Optimization of sequence pattern prediction for enzyme annotation

PubMed Central

Schomburg, Dietmar

2017-01-01

The prediction of gene functions is crucial for a large number of different life science areas. Faster high throughput sequencing techniques generate more and larger datasets. The manual annotation by classical wet-lab experiments is not suitable for these large amounts of data. We showed earlier that the automatic sequence pattern-based BrEPS protocol, based on manually curated sequences, can be used for the prediction of enzymatic functions of genes. The growing sequence databases provide the opportunity for more reliable patterns, but are also a challenge for the implementation of automatic protocols. We reimplemented and optimized the BrEPS pattern generation to be applicable for larger datasets in an acceptable timescale. Primary improvement of the new BrEPS protocol is the enhanced data selection step. Manually curated annotations from Swiss-Prot are used as reliable source for function prediction of enzymes observed on protein level. The pool of sequences is extended by highly similar sequences from TrEMBL and SwissProt. This allows us to restrict the selection of Swiss-Prot entries, without losing the diversity of sequences needed to generate significant patterns. Additionally, a supporting pattern type was introduced by extending the patterns at semi-conserved positions with highly similar amino acids. Extended patterns have an increased complexity, increasing the chance to match more sequences, without losing the essential structural information of the pattern. To enhance the usability of the database, we introduced enzyme function prediction based on consensus EC numbers and IUBMB enzyme nomenclature. BrEPS is part of the Braunschweig Enzyme Database (BRENDA) and is available on a completely redesigned website and as download. The database can be downloaded and used with the BrEPScmd command line tool for large scale sequence analysis. The BrEPS website and downloads for the database creation tool, command line tool and database are freely accessible at http://breps.tu-bs.de. PMID:28750104

Updated regulation curation model at the Saccharomyces Genome Database

PubMed Central

Engel, Stacia R; Skrzypek, Marek S; Hellerstedt, Sage T; Wong, Edith D; Nash, Robert S; Weng, Shuai; Binkley, Gail; Sheppard, Travis K; Karra, Kalpana; Cherry, J Michael

2018-01-01

Abstract The Saccharomyces Genome Database (SGD) provides comprehensive, integrated biological information for the budding yeast Saccharomyces cerevisiae, along with search and analysis tools to explore these data, enabling the discovery of functional relationships between sequence and gene products in fungi and higher organisms. We have recently expanded our data model for regulation curation to address regulation at the protein level in addition to transcription, and are presenting the expanded data on the ‘Regulation’ pages at SGD. These pages include a summary describing the context under which the regulator acts, manually curated and high-throughput annotations showing the regulatory relationships for that gene and a graphical visualization of its regulatory network and connected networks. For genes whose products regulate other genes or proteins, the Regulation page includes Gene Ontology enrichment analysis of the biological processes in which those targets participate. For DNA-binding transcription factors, we also provide other information relevant to their regulatory function, such as DNA binding site motifs and protein domains. As with other data types at SGD, all regulatory relationships and accompanying data are available through YeastMine, SGD’s data warehouse based on InterMine. Database URL: http://www.yeastgenome.org PMID:29688362

The Genomes OnLine Database (GOLD) v.5: a metadata management system based on a four level (meta)genome project classification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reddy, Tatiparthi B. K.; Thomas, Alex D.; Stamatis, Dimitri

The Genomes OnLine Database (GOLD; http://www.genomesonline.org) is a comprehensive online resource to catalog and monitor genetic studies worldwide. GOLD provides up-to-date status on complete and ongoing sequencing projects along with a broad array of curated metadata. Within this paper, we report version 5 (v.5) of the database. The newly designed database schema and web user interface supports several new features including the implementation of a four level (meta)genome project classification system and a simplified intuitive web interface to access reports and launch search tools. The database currently hosts information for about 19 200 studies, 56 000 Biosamples, 56 000 sequencingmore » projects and 39 400 analysis projects. More than just a catalog of worldwide genome projects, GOLD is a manually curated, quality-controlled metadata warehouse. The problems encountered in integrating disparate and varying quality data into GOLD are briefly highlighted. Lastly, GOLD fully supports and follows the Genomic Standards Consortium (GSC) Minimum Information standards.« less

Gene and protein nomenclature in public databases

PubMed Central

Fundel, Katrin; Zimmer, Ralf

2006-01-01

Background Frequently, several alternative names are in use for biological objects such as genes and proteins. Applications like manual literature search, automated text-mining, named entity identification, gene/protein annotation, and linking of knowledge from different information sources require the knowledge of all used names referring to a given gene or protein. Various organism-specific or general public databases aim at organizing knowledge about genes and proteins. These databases can be used for deriving gene and protein name dictionaries. So far, little is known about the differences between databases in terms of size, ambiguities and overlap. Results We compiled five gene and protein name dictionaries for each of the five model organisms (yeast, fly, mouse, rat, and human) from different organism-specific and general public databases. We analyzed the degree of ambiguity of gene and protein names within and between dictionaries, to a lexicon of common English words and domain-related non-gene terms, and we compared different data sources in terms of size of extracted dictionaries and overlap of synonyms between those. The study shows that the number of genes/proteins and synonyms covered in individual databases varies significantly for a given organism, and that the degree of ambiguity of synonyms varies significantly between different organisms. Furthermore, it shows that, despite considerable efforts of co-curation, the overlap of synonyms in different data sources is rather moderate and that the degree of ambiguity of gene names with common English words and domain-related non-gene terms varies depending on the considered organism. Conclusion In conclusion, these results indicate that the combination of data contained in different databases allows the generation of gene and protein name dictionaries that contain significantly more used names than dictionaries obtained from individual data sources. Furthermore, curation of combined dictionaries considerably increases size and decreases ambiguity. The entries of the curated synonym dictionary are available for manual querying, editing, and PubMed- or Google-search via the ProThesaurus-wiki. For automated querying via custom software, we offer a web service and an exemplary client application. PMID:16899134

MicroScope—an integrated microbial resource for the curation and comparative analysis of genomic and metabolic data

PubMed Central

Vallenet, David; Belda, Eugeni; Calteau, Alexandra; Cruveiller, Stéphane; Engelen, Stefan; Lajus, Aurélie; Le Fèvre, François; Longin, Cyrille; Mornico, Damien; Roche, David; Rouy, Zoé; Salvignol, Gregory; Scarpelli, Claude; Thil Smith, Adam Alexander; Weiman, Marion; Médigue, Claudine

2013-01-01

MicroScope is an integrated platform dedicated to both the methodical updating of microbial genome annotation and to comparative analysis. The resource provides data from completed and ongoing genome projects (automatic and expert annotations), together with data sources from post-genomic experiments (i.e. transcriptomics, mutant collections) allowing users to perfect and improve the understanding of gene functions. MicroScope (http://www.genoscope.cns.fr/agc/microscope) combines tools and graphical interfaces to analyse genomes and to perform the manual curation of gene annotations in a comparative context. Since its first publication in January 2006, the system (previously named MaGe for Magnifying Genomes) has been continuously extended both in terms of data content and analysis tools. The last update of MicroScope was published in 2009 in the Database journal. Today, the resource contains data for >1600 microbial genomes, of which ∼300 are manually curated and maintained by biologists (1200 personal accounts today). Expert annotations are continuously gathered in the MicroScope database (∼50 000 a year), contributing to the improvement of the quality of microbial genomes annotations. Improved data browsing and searching tools have been added, original tools useful in the context of expert annotation have been developed and integrated and the website has been significantly redesigned to be more user-friendly. Furthermore, in the context of the European project Microme (Framework Program 7 Collaborative Project), MicroScope is becoming a resource providing for the curation and analysis of both genomic and metabolic data. An increasing number of projects are related to the study of environmental bacterial (meta)genomes that are able to metabolize a large variety of chemical compounds that may be of high industrial interest. PMID:23193269

The Pfam protein families database.

PubMed

Punta, Marco; Coggill, Penny C; Eberhardt, Ruth Y; Mistry, Jaina; Tate, John; Boursnell, Chris; Pang, Ningze; Forslund, Kristoffer; Ceric, Goran; Clements, Jody; Heger, Andreas; Holm, Liisa; Sonnhammer, Erik L L; Eddy, Sean R; Bateman, Alex; Finn, Robert D

2012-01-01

Pfam is a widely used database of protein families, currently containing more than 13,000 manually curated protein families as of release 26.0. Pfam is available via servers in the UK (http://pfam.sanger.ac.uk/), the USA (http://pfam.janelia.org/) and Sweden (http://pfam.sbc.su.se/). Here, we report on changes that have occurred since our 2010 NAR paper (release 24.0). Over the last 2 years, we have generated 1840 new families and increased coverage of the UniProt Knowledgebase (UniProtKB) to nearly 80%. Notably, we have taken the step of opening up the annotation of our families to the Wikipedia community, by linking Pfam families to relevant Wikipedia pages and encouraging the Pfam and Wikipedia communities to improve and expand those pages. We continue to improve the Pfam website and add new visualizations, such as the 'sunburst' representation of taxonomic distribution of families. In this work we additionally address two topics that will be of particular interest to the Pfam community. First, we explain the definition and use of family-specific, manually curated gathering thresholds. Second, we discuss some of the features of domains of unknown function (also known as DUFs), which constitute a rapidly growing class of families within Pfam.

TMDB: a literature-curated database for small molecular compounds found from tea.

PubMed

Yue, Yi; Chu, Gang-Xiu; Liu, Xue-Shi; Tang, Xing; Wang, Wei; Liu, Guang-Jin; Yang, Tao; Ling, Tie-Jun; Wang, Xiao-Gang; Zhang, Zheng-Zhu; Xia, Tao; Wan, Xiao-Chun; Bao, Guan-Hu

2014-09-16

Tea is one of the most consumed beverages worldwide. The healthy effects of tea are attributed to a wealthy of different chemical components from tea. Thousands of studies on the chemical constituents of tea had been reported. However, data from these individual reports have not been collected into a single database. The lack of a curated database of related information limits research in this field, and thus a cohesive database system should necessarily be constructed for data deposit and further application. The Tea Metabolome database (TMDB), a manually curated and web-accessible database, was developed to provide detailed, searchable descriptions of small molecular compounds found in Camellia spp. esp. in the plant Camellia sinensis and compounds in its manufactured products (different kinds of tea infusion). TMDB is currently the most complete and comprehensive curated collection of tea compounds data in the world. It contains records for more than 1393 constituents found in tea with information gathered from 364 published books, journal articles, and electronic databases. It also contains experimental 1H NMR and 13C NMR data collected from the purified reference compounds or collected from other database resources such as HMDB. TMDB interface allows users to retrieve tea compounds entries by keyword search using compound name, formula, occurrence, and CAS register number. Each entry in the TMDB contains an average of 24 separate data fields including its original plant species, compound structure, formula, molecular weight, name, CAS registry number, compound types, compound uses including healthy benefits, reference literatures, NMR, MS data, and the corresponding ID from databases such as HMDB and Pubmed. Users can also contribute novel regulatory entries by using a web-based submission page. The TMDB database is freely accessible from the URL of http://pcsb.ahau.edu.cn:8080/TCDB/index.jsp. The TMDB is designed to address the broad needs of tea biochemists, natural products chemists, nutritionists, and members of tea related research community. The TMDB database provides a solid platform for collection, standardization, and searching of compounds information found in tea. As such this database will be a comprehensive repository for tea biochemistry and tea health research community.

NPInter v3.0: an upgraded database of noncoding RNA-associated interactions

PubMed Central

Hao, Yajing; Wu, Wei; Li, Hui; Yuan, Jiao; Luo, Jianjun; Zhao, Yi; Chen, Runsheng

2016-01-01

Despite the fact that a large quantity of noncoding RNAs (ncRNAs) have been identified, their functions remain unclear. To enable researchers to have a better understanding of ncRNAs’ functions, we updated the NPInter database to version 3.0, which contains experimentally verified interactions between ncRNAs (excluding tRNAs and rRNAs), especially long noncoding RNAs (lncRNAs) and other biomolecules (proteins, mRNAs, miRNAs and genomic DNAs). In NPInter v3.0, interactions pertaining to ncRNAs are not only manually curated from scientific literature but also curated from high-throughput technologies. In addition, we also curated lncRNA–miRNA interactions from in silico predictions supported by AGO CLIP-seq data. When compared with NPInter v2.0, the interactions are more informative (with additional information on tissues or cell lines, binding sites, conservation, co-expression values and other features) and more organized (with divisions on data sets by data sources, tissues or cell lines, experiments and other criteria). NPInter v3.0 expands the data set to 491,416 interactions in 188 tissues (or cell lines) from 68 kinds of experimental technologies. NPInter v3.0 also improves the user interface and adds new web services, including a local UCSC Genome Browser to visualize binding sites. Additionally, NPInter v3.0 defined a high-confidence set of interactions and predicted the functions of lncRNAs in human and mouse based on the interactions curated in the database. NPInter v3.0 is available at http://www.bioinfo.org/NPInter/. Database URL: http://www.bioinfo.org/NPInter/ PMID:27087310

PHI-base: a new interface and further additions for the multi-species pathogen–host interactions database

PubMed Central

Urban, Martin; Cuzick, Alayne; Rutherford, Kim; Irvine, Alistair; Pedro, Helder; Pant, Rashmi; Sadanadan, Vidyendra; Khamari, Lokanath; Billal, Santoshkumar; Mohanty, Sagar; Hammond-Kosack, Kim E.

2017-01-01

The pathogen–host interactions database (PHI-base) is available at www.phi-base.org. PHI-base contains expertly curated molecular and biological information on genes proven to affect the outcome of pathogen–host interactions reported in peer reviewed research articles. In addition, literature that indicates specific gene alterations that did not affect the disease interaction phenotype are curated to provide complete datasets for comparative purposes. Viruses are not included. Here we describe a revised PHI-base Version 4 data platform with improved search, filtering and extended data display functions. A PHIB-BLAST search function is provided and a link to PHI-Canto, a tool for authors to directly curate their own published data into PHI-base. The new release of PHI-base Version 4.2 (October 2016) has an increased data content containing information from 2219 manually curated references. The data provide information on 4460 genes from 264 pathogens tested on 176 hosts in 8046 interactions. Prokaryotic and eukaryotic pathogens are represented in almost equal numbers. Host species belong ∼70% to plants and 30% to other species of medical and/or environmental importance. Additional data types included into PHI-base 4 are the direct targets of pathogen effector proteins in experimental and natural host organisms. The curation problems encountered and the future directions of the PHI-base project are briefly discussed. PMID:27915230

BioM2MetDisease: a manually curated database for associations between microRNAs, metabolites, small molecules and metabolic diseases

PubMed Central

Xu, Yanjun; Yang, Haixiu; Wu, Tan; Dong, Qun; Sun, Zeguo; Shang, Desi; Li, Feng; Xu, Yingqi; Su, Fei; Liu, Siyao

2017-01-01

Abstract BioM2MetDisease is a manually curated database that aims to provide a comprehensive and experimentally supported resource of associations between metabolic diseases and various biomolecules. Recently, metabolic diseases such as diabetes have become one of the leading threats to people’s health. Metabolic disease associated with alterations of multiple types of biomolecules such as miRNAs and metabolites. An integrated and high-quality data source that collection of metabolic disease associated biomolecules is essential for exploring the underlying molecular mechanisms and discovering novel therapeutics. Here, we developed the BioM2MetDisease database, which currently documents 2681 entries of relationships between 1147 biomolecules (miRNAs, metabolites and small molecules/drugs) and 78 metabolic diseases across 14 species. Each entry includes biomolecule category, species, biomolecule name, disease name, dysregulation pattern, experimental technique, a brief description of metabolic disease-biomolecule relationships, the reference, additional annotation information etc. BioM2MetDisease provides a user-friendly interface to explore and retrieve all data conveniently. A submission page was also offered for researchers to submit new associations between biomolecules and metabolic diseases. BioM2MetDisease provides a comprehensive resource for studying biology molecules act in metabolic diseases, and it is helpful for understanding the molecular mechanisms and developing novel therapeutics for metabolic diseases. Database URL: http://www.bio-bigdata.com/BioM2MetDisease/ PMID:28605773

Mouse Tumor Biology (MTB): a database of mouse models for human cancer.

PubMed

Bult, Carol J; Krupke, Debra M; Begley, Dale A; Richardson, Joel E; Neuhauser, Steven B; Sundberg, John P; Eppig, Janan T

2015-01-01

The Mouse Tumor Biology (MTB; http://tumor.informatics.jax.org) database is a unique online compendium of mouse models for human cancer. MTB provides online access to expertly curated information on diverse mouse models for human cancer and interfaces for searching and visualizing data associated with these models. The information in MTB is designed to facilitate the selection of strains for cancer research and is a platform for mining data on tumor development and patterns of metastases. MTB curators acquire data through manual curation of peer-reviewed scientific literature and from direct submissions by researchers. Data in MTB are also obtained from other bioinformatics resources including PathBase, the Gene Expression Omnibus and ArrayExpress. Recent enhancements to MTB improve the association between mouse models and human genes commonly mutated in a variety of cancers as identified in large-scale cancer genomics studies, provide new interfaces for exploring regions of the mouse genome associated with cancer phenotypes and incorporate data and information related to Patient-Derived Xenograft models of human cancers. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Caveat emptor: limitations of the automated reconstruction of metabolic pathways in Plasmodium.

PubMed

Ginsburg, Hagai

2009-01-01

The functional reconstruction of metabolic pathways from an annotated genome is a tedious and demanding enterprise. Automation of this endeavor using bioinformatics algorithms could cope with the ever-increasing number of sequenced genomes and accelerate the process. Here, the manual reconstruction of metabolic pathways in the functional genomic database of Plasmodium falciparum--Malaria Parasite Metabolic Pathways--is described and compared with pathways generated automatically as they appear in PlasmoCyc, metaSHARK and the Kyoto Encyclopedia for Genes and Genomes. A critical evaluation of this comparison discloses that the automatic reconstruction of pathways generates manifold paths that need an expert manual verification to accept some and reject most others based on manually curated gene annotation.

VIOLIN: vaccine investigation and online information network.

PubMed

Xiang, Zuoshuang; Todd, Thomas; Ku, Kim P; Kovacic, Bethany L; Larson, Charles B; Chen, Fang; Hodges, Andrew P; Tian, Yuying; Olenzek, Elizabeth A; Zhao, Boyang; Colby, Lesley A; Rush, Howard G; Gilsdorf, Janet R; Jourdian, George W; He, Yongqun

2008-01-01

Vaccines are among the most efficacious and cost-effective tools for reducing morbidity and mortality caused by infectious diseases. The vaccine investigation and online information network (VIOLIN) is a web-based central resource, allowing easy curation, comparison and analysis of vaccine-related research data across various human pathogens (e.g. Haemophilus influenzae, human immunodeficiency virus (HIV) and Plasmodium falciparum) of medical importance and across humans, other natural hosts and laboratory animals. Vaccine-related peer-reviewed literature data have been downloaded into the database from PubMed and are searchable through various literature search programs. Vaccine data are also annotated, edited and submitted to the database through a web-based interactive system that integrates efficient computational literature mining and accurate manual curation. Curated information includes general microbial pathogenesis and host protective immunity, vaccine preparation and characteristics, stimulated host responses after vaccination and protection efficacy after challenge. Vaccine-related pathogen and host genes are also annotated and available for searching through customized BLAST programs. All VIOLIN data are available for download in an eXtensible Markup Language (XML)-based data exchange format. VIOLIN is expected to become a centralized source of vaccine information and to provide investigators in basic and clinical sciences with curated data and bioinformatics tools for vaccine research and development. VIOLIN is publicly available at http://www.violinet.org.

Comprehensive annotation of secondary metabolite biosynthetic genes and gene clusters of Aspergillus nidulans, A. fumigatus, A. niger and A. oryzae

PubMed Central

2013-01-01

Background Secondary metabolite production, a hallmark of filamentous fungi, is an expanding area of research for the Aspergilli. These compounds are potent chemicals, ranging from deadly toxins to therapeutic antibiotics to potential anti-cancer drugs. The genome sequences for multiple Aspergilli have been determined, and provide a wealth of predictive information about secondary metabolite production. Sequence analysis and gene overexpression strategies have enabled the discovery of novel secondary metabolites and the genes involved in their biosynthesis. The Aspergillus Genome Database (AspGD) provides a central repository for gene annotation and protein information for Aspergillus species. These annotations include Gene Ontology (GO) terms, phenotype data, gene names and descriptions and they are crucial for interpreting both small- and large-scale data and for aiding in the design of new experiments that further Aspergillus research. Results We have manually curated Biological Process GO annotations for all genes in AspGD with recorded functions in secondary metabolite production, adding new GO terms that specifically describe each secondary metabolite. We then leveraged these new annotations to predict roles in secondary metabolism for genes lacking experimental characterization. As a starting point for manually annotating Aspergillus secondary metabolite gene clusters, we used antiSMASH (antibiotics and Secondary Metabolite Analysis SHell) and SMURF (Secondary Metabolite Unknown Regions Finder) algorithms to identify potential clusters in A. nidulans, A. fumigatus, A. niger and A. oryzae, which we subsequently refined through manual curation. Conclusions This set of 266 manually curated secondary metabolite gene clusters will facilitate the investigation of novel Aspergillus secondary metabolites. PMID:23617571

Improved annotation of the insect vector of citrus greening disease: biocuration by a diverse genomics community

PubMed Central

Hosmani, Prashant S.; Villalobos-Ayala, Krystal; Miller, Sherry; Shippy, Teresa; Flores, Mirella; Rosendale, Andrew; Cordola, Chris; Bell, Tracey; Mann, Hannah; DeAvila, Gabe; DeAvila, Daniel; Moore, Zachary; Buller, Kyle; Ciolkevich, Kathryn; Nandyal, Samantha; Mahoney, Robert; Van Voorhis, Joshua; Dunlevy, Megan; Farrow, David; Hunter, David; Morgan, Taylar; Shore, Kayla; Guzman, Victoria; Izsak, Allison; Dixon, Danielle E.; Cridge, Andrew; Cano, Liliana; Cao, Xiaolong; Jiang, Haobo; Leng, Nan; Johnson, Shannon; Cantarel, Brandi L.; Richards, Stephen; English, Adam; Shatters, Robert G.; Childers, Chris; Chen, Mei-Ju; Hunter, Wayne; Cilia, Michelle; Mueller, Lukas A.; Munoz-Torres, Monica; Nelson, David; Poelchau, Monica F.; Benoit, Joshua B.; Wiersma-Koch, Helen; D’Elia, Tom; Brown, Susan J.

2017-01-01

Abstract The Asian citrus psyllid (Diaphorina citri Kuwayama) is the insect vector of the bacterium Candidatus Liberibacter asiaticus (CLas), the pathogen associated with citrus Huanglongbing (HLB, citrus greening). HLB threatens citrus production worldwide. Suppression or reduction of the insect vector using chemical insecticides has been the primary method to inhibit the spread of citrus greening disease. Accurate structural and functional annotation of the Asian citrus psyllid genome, as well as a clear understanding of the interactions between the insect and CLas, are required for development of new molecular-based HLB control methods. A draft assembly of the D. citri genome has been generated and annotated with automated pipelines. However, knowledge transfer from well-curated reference genomes such as that of Drosophila melanogaster to newly sequenced ones is challenging due to the complexity and diversity of insect genomes. To identify and improve gene models as potential targets for pest control, we manually curated several gene families with a focus on genes that have key functional roles in D. citri biology and CLas interactions. This community effort produced 530 manually curated gene models across developmental, physiological, RNAi regulatory and immunity-related pathways. As previously shown in the pea aphid, RNAi machinery genes putatively involved in the microRNA pathway have been specifically duplicated. A comprehensive transcriptome enabled us to identify a number of gene families that are either missing or misassembled in the draft genome. In order to develop biocuration as a training experience, we included undergraduate and graduate students from multiple institutions, as well as experienced annotators from the insect genomics research community. The resulting gene set (OGS v1.0) combines both automatically predicted and manually curated gene models. Database URL: https://citrusgreening.org/ PMID:29220441

HoPaCI-DB: host-Pseudomonas and Coxiella interaction database

PubMed Central

Bleves, Sophie; Dunger, Irmtraud; Walter, Mathias C.; Frangoulidis, Dimitrios; Kastenmüller, Gabi; Voulhoux, Romé; Ruepp, Andreas

2014-01-01

Bacterial infectious diseases are the result of multifactorial processes affected by the interplay between virulence factors and host targets. The host-Pseudomonas and Coxiella interaction database (HoPaCI-DB) is a publicly available manually curated integrative database (http://mips.helmholtz-muenchen.de/HoPaCI/) of host–pathogen interaction data from Pseudomonas aeruginosa and Coxiella burnetii. The resource provides structured information on 3585 experimentally validated interactions between molecules, bioprocesses and cellular structures extracted from the scientific literature. Systematic annotation and interactive graphical representation of disease networks make HoPaCI-DB a versatile knowledge base for biologists and network biology approaches. PMID:24137008

Liverome: a curated database of liver cancer-related gene signatures with self-contained context information.

PubMed

Lee, Langho; Wang, Kai; Li, Gang; Xie, Zhi; Wang, Yuli; Xu, Jiangchun; Sun, Shaoxian; Pocalyko, David; Bhak, Jong; Kim, Chulhong; Lee, Kee-Ho; Jang, Ye Jin; Yeom, Young Il; Yoo, Hyang-Sook; Hwang, Seungwoo

2011-11-30

Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide. A number of molecular profiling studies have investigated the changes in gene and protein expression that are associated with various clinicopathological characteristics of HCC and generated a wealth of scattered information, usually in the form of gene signature tables. A database of the published HCC gene signatures would be useful to liver cancer researchers seeking to retrieve existing differential expression information on a candidate gene and to make comparisons between signatures for prioritization of common genes. A challenge in constructing such database is that a direct import of the signatures as appeared in articles would lead to a loss or ambiguity of their context information that is essential for a correct biological interpretation of a gene's expression change. This challenge arises because designation of compared sample groups is most often abbreviated, ad hoc, or even missing from published signature tables. Without manual curation, the context information becomes lost, leading to uninformative database contents. Although several databases of gene signatures are available, none of them contains informative form of signatures nor shows comprehensive coverage on liver cancer. Thus we constructed Liverome, a curated database of liver cancer-related gene signatures with self-contained context information. Liverome's data coverage is more than three times larger than any other signature database, consisting of 143 signatures taken from 98 HCC studies, mostly microarray and proteome, and involving 6,927 genes. The signatures were post-processed into an informative and uniform representation and annotated with an itemized summary so that all context information is unambiguously self-contained within the database. The signatures were further informatively named and meaningfully organized according to ten functional categories for guided browsing. Its web interface enables a straightforward retrieval of known differential expression information on a query gene and a comparison of signatures to prioritize common genes. The utility of Liverome-collected data is shown by case studies in which useful biological insights on HCC are produced. Liverome database provides a comprehensive collection of well-curated HCC gene signatures and straightforward interfaces for gene search and signature comparison as well. Liverome is available at http://liverome.kobic.re.kr.

PHI-base: a new interface and further additions for the multi-species pathogen-host interactions database.

PubMed

Urban, Martin; Cuzick, Alayne; Rutherford, Kim; Irvine, Alistair; Pedro, Helder; Pant, Rashmi; Sadanadan, Vidyendra; Khamari, Lokanath; Billal, Santoshkumar; Mohanty, Sagar; Hammond-Kosack, Kim E

2017-01-04

The pathogen-host interactions database (PHI-base) is available at www.phi-base.org PHI-base contains expertly curated molecular and biological information on genes proven to affect the outcome of pathogen-host interactions reported in peer reviewed research articles. In addition, literature that indicates specific gene alterations that did not affect the disease interaction phenotype are curated to provide complete datasets for comparative purposes. Viruses are not included. Here we describe a revised PHI-base Version 4 data platform with improved search, filtering and extended data display functions. A PHIB-BLAST search function is provided and a link to PHI-Canto, a tool for authors to directly curate their own published data into PHI-base. The new release of PHI-base Version 4.2 (October 2016) has an increased data content containing information from 2219 manually curated references. The data provide information on 4460 genes from 264 pathogens tested on 176 hosts in 8046 interactions. Prokaryotic and eukaryotic pathogens are represented in almost equal numbers. Host species belong ∼70% to plants and 30% to other species of medical and/or environmental importance. Additional data types included into PHI-base 4 are the direct targets of pathogen effector proteins in experimental and natural host organisms. The curation problems encountered and the future directions of the PHI-base project are briefly discussed. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

A literature search tool for intelligent extraction of disease-associated genes.

PubMed

Jung, Jae-Yoon; DeLuca, Todd F; Nelson, Tristan H; Wall, Dennis P

2014-01-01

To extract disorder-associated genes from the scientific literature in PubMed with greater sensitivity for literature-based support than existing methods. We developed a PubMed query to retrieve disorder-related, original research articles. Then we applied a rule-based text-mining algorithm with keyword matching to extract target disorders, genes with significant results, and the type of study described by the article. We compared our resulting candidate disorder genes and supporting references with existing databases. We demonstrated that our candidate gene set covers nearly all genes in manually curated databases, and that the references supporting the disorder-gene link are more extensive and accurate than other general purpose gene-to-disorder association databases. We implemented a novel publication search tool to find target articles, specifically focused on links between disorders and genotypes. Through comparison against gold-standard manually updated gene-disorder databases and comparison with automated databases of similar functionality we show that our tool can search through the entirety of PubMed to extract the main gene findings for human diseases rapidly and accurately.

Mycobacteriophage genome database.

PubMed

Joseph, Jerrine; Rajendran, Vasanthi; Hassan, Sameer; Kumar, Vanaja

2011-01-01

Mycobacteriophage genome database (MGDB) is an exclusive repository of the 64 completely sequenced mycobacteriophages with annotated information. It is a comprehensive compilation of the various gene parameters captured from several databases pooled together to empower mycobacteriophage researchers. The MGDB (Version No.1.0) comprises of 6086 genes from 64 mycobacteriophages classified into 72 families based on ACLAME database. Manual curation was aided by information available from public databases which was enriched further by analysis. Its web interface allows browsing as well as querying the classification. The main objective is to collect and organize the complexity inherent to mycobacteriophage protein classification in a rational way. The other objective is to browse the existing and new genomes and describe their functional annotation. The database is available for free at http://mpgdb.ibioinformatics.org/mpgdb.php.

The Pfam protein families database

PubMed Central

Punta, Marco; Coggill, Penny C.; Eberhardt, Ruth Y.; Mistry, Jaina; Tate, John; Boursnell, Chris; Pang, Ningze; Forslund, Kristoffer; Ceric, Goran; Clements, Jody; Heger, Andreas; Holm, Liisa; Sonnhammer, Erik L. L.; Eddy, Sean R.; Bateman, Alex; Finn, Robert D.

2012-01-01

Pfam is a widely used database of protein families, currently containing more than 13 000 manually curated protein families as of release 26.0. Pfam is available via servers in the UK (http://pfam.sanger.ac.uk/), the USA (http://pfam.janelia.org/) and Sweden (http://pfam.sbc.su.se/). Here, we report on changes that have occurred since our 2010 NAR paper (release 24.0). Over the last 2 years, we have generated 1840 new families and increased coverage of the UniProt Knowledgebase (UniProtKB) to nearly 80%. Notably, we have taken the step of opening up the annotation of our families to the Wikipedia community, by linking Pfam families to relevant Wikipedia pages and encouraging the Pfam and Wikipedia communities to improve and expand those pages. We continue to improve the Pfam website and add new visualizations, such as the ‘sunburst’ representation of taxonomic distribution of families. In this work we additionally address two topics that will be of particular interest to the Pfam community. First, we explain the definition and use of family-specific, manually curated gathering thresholds. Second, we discuss some of the features of domains of unknown function (also known as DUFs), which constitute a rapidly growing class of families within Pfam. PMID:22127870

Automating document classification for the Immune Epitope Database

PubMed Central

Wang, Peng; Morgan, Alexander A; Zhang, Qing; Sette, Alessandro; Peters, Bjoern

2007-01-01

Background The Immune Epitope Database contains information on immune epitopes curated manually from the scientific literature. Like similar projects in other knowledge domains, significant effort is spent on identifying which articles are relevant for this purpose. Results We here report our experience in automating this process using Naïve Bayes classifiers trained on 20,910 abstracts classified by domain experts. Improvements on the basic classifier performance were made by a) utilizing information stored in PubMed beyond the abstract itself b) applying standard feature selection criteria and c) extracting domain specific feature patterns that e.g. identify peptides sequences. We have implemented the classifier into the curation process determining if abstracts are clearly relevant, clearly irrelevant, or if no certain classification can be made, in which case the abstracts are manually classified. Testing this classification scheme on an independent dataset, we achieve 95% sensitivity and specificity in the 51.1% of abstracts that were automatically classified. Conclusion By implementing text classification, we have sped up the reference selection process without sacrificing sensitivity or specificity of the human expert classification. This study provides both practical recommendations for users of text classification tools, as well as a large dataset which can serve as a benchmark for tool developers. PMID:17655769

BioM2MetDisease: a manually curated database for associations between microRNAs, metabolites, small molecules and metabolic diseases.

PubMed

Xu, Yanjun; Yang, Haixiu; Wu, Tan; Dong, Qun; Sun, Zeguo; Shang, Desi; Li, Feng; Xu, Yingqi; Su, Fei; Liu, Siyao; Zhang, Yunpeng; Li, Xia

2017-01-01

BioM2MetDisease is a manually curated database that aims to provide a comprehensive and experimentally supported resource of associations between metabolic diseases and various biomolecules. Recently, metabolic diseases such as diabetes have become one of the leading threats to people’s health. Metabolic disease associated with alterations of multiple types of biomolecules such as miRNAs and metabolites. An integrated and high-quality data source that collection of metabolic disease associated biomolecules is essential for exploring the underlying molecular mechanisms and discovering novel therapeutics. Here, we developed the BioM2MetDisease database, which currently documents 2681 entries of relationships between 1147 biomolecules (miRNAs, metabolites and small molecules/drugs) and 78 metabolic diseases across 14 species. Each entry includes biomolecule category, species, biomolecule name, disease name, dysregulation pattern, experimental technique, a brief description of metabolic disease-biomolecule relationships, the reference, additional annotation information etc. BioM2MetDisease provides a user-friendly interface to explore and retrieve all data conveniently. A submission page was also offered for researchers to submit new associations between biomolecules and metabolic diseases. BioM2MetDisease provides a comprehensive resource for studying biology molecules act in metabolic diseases, and it is helpful for understanding the molecular mechanisms and developing novel therapeutics for metabolic diseases. http://www.bio-bigdata.com/BioM2MetDisease/. © The Author(s) 2017. Published by Oxford University Press.

Classification of Chemical Compounds to Support Complex Queries in a Pathway Database

PubMed Central

Weidemann, Andreas; Kania, Renate; Peiss, Christian; Rojas, Isabel

2004-01-01

Data quality in biological databases has become a topic of great discussion. To provide high quality data and to deal with the vast amount of biochemical data, annotators and curators need to be supported by software that carries out part of their work in an (semi-) automatic manner. The detection of errors and inconsistencies is a part that requires the knowledge of domain experts, thus in most cases it is done manually, making it very expensive and time-consuming. This paper presents two tools to partially support the curation of data on biochemical pathways. The tool enables the automatic classification of chemical compounds based on their respective SMILES strings. Such classification allows the querying and visualization of biochemical reactions at different levels of abstraction, according to the level of detail at which the reaction participants are described. Chemical compounds can be classified in a flexible manner based on different criteria. The support of the process of data curation is provided by facilitating the detection of compounds that are identified as different but that are actually the same. This is also used to identify similar reactions and, in turn, pathways. PMID:18629066

HPIDB 2.0: a curated database for host–pathogen interactions

PubMed Central

Ammari, Mais G.; Gresham, Cathy R.; McCarthy, Fiona M.; Nanduri, Bindu

2016-01-01

Identification and analysis of host–pathogen interactions (HPI) is essential to study infectious diseases. However, HPI data are sparse in existing molecular interaction databases, especially for agricultural host–pathogen systems. Therefore, resources that annotate, predict and display the HPI that underpin infectious diseases are critical for developing novel intervention strategies. HPIDB 2.0 (http://www.agbase.msstate.edu/hpi/main.html) is a resource for HPI data, and contains 45, 238 manually curated entries in the current release. Since the first description of the database in 2010, multiple enhancements to HPIDB data and interface services were made that are described here. Notably, HPIDB 2.0 now provides targeted biocuration of molecular interaction data. As a member of the International Molecular Exchange consortium, annotations provided by HPIDB 2.0 curators meet community standards to provide detailed contextual experimental information and facilitate data sharing. Moreover, HPIDB 2.0 provides access to rapidly available community annotations that capture minimum molecular interaction information to address immediate researcher needs for HPI network analysis. In addition to curation, HPIDB 2.0 integrates HPI from existing external sources and contains tools to infer additional HPI where annotated data are scarce. Compared to other interaction databases, our data collection approach ensures HPIDB 2.0 users access the most comprehensive HPI data from a wide range of pathogens and their hosts (594 pathogen and 70 host species, as of February 2016). Improvements also include enhanced search capacity, addition of Gene Ontology functional information, and implementation of network visualization. The changes made to HPIDB 2.0 content and interface ensure that users, especially agricultural researchers, are able to easily access and analyse high quality, comprehensive HPI data. All HPIDB 2.0 data are updated regularly, are publically available for direct download, and are disseminated to other molecular interaction resources. Database URL: http://www.agbase.msstate.edu/hpi/main.html PMID:27374121

MIPS: curated databases and comprehensive secondary data resources in 2010.

PubMed

Mewes, H Werner; Ruepp, Andreas; Theis, Fabian; Rattei, Thomas; Walter, Mathias; Frishman, Dmitrij; Suhre, Karsten; Spannagl, Manuel; Mayer, Klaus F X; Stümpflen, Volker; Antonov, Alexey

2011-01-01

The Munich Information Center for Protein Sequences (MIPS at the Helmholtz Center for Environmental Health, Neuherberg, Germany) has many years of experience in providing annotated collections of biological data. Selected data sets of high relevance, such as model genomes, are subjected to careful manual curation, while the bulk of high-throughput data is annotated by automatic means. High-quality reference resources developed in the past and still actively maintained include Saccharomyces cerevisiae, Neurospora crassa and Arabidopsis thaliana genome databases as well as several protein interaction data sets (MPACT, MPPI and CORUM). More recent projects are PhenomiR, the database on microRNA-related phenotypes, and MIPS PlantsDB for integrative and comparative plant genome research. The interlinked resources SIMAP and PEDANT provide homology relationships as well as up-to-date and consistent annotation for 38,000,000 protein sequences. PPLIPS and CCancer are versatile tools for proteomics and functional genomics interfacing to a database of compilations from gene lists extracted from literature. A novel literature-mining tool, EXCERBT, gives access to structured information on classified relations between genes, proteins, phenotypes and diseases extracted from Medline abstracts by semantic analysis. All databases described here, as well as the detailed descriptions of our projects can be accessed through the MIPS WWW server (http://mips.helmholtz-muenchen.de).

MIPS: curated databases and comprehensive secondary data resources in 2010

PubMed Central

Mewes, H. Werner; Ruepp, Andreas; Theis, Fabian; Rattei, Thomas; Walter, Mathias; Frishman, Dmitrij; Suhre, Karsten; Spannagl, Manuel; Mayer, Klaus F.X.; Stümpflen, Volker; Antonov, Alexey

2011-01-01

The Munich Information Center for Protein Sequences (MIPS at the Helmholtz Center for Environmental Health, Neuherberg, Germany) has many years of experience in providing annotated collections of biological data. Selected data sets of high relevance, such as model genomes, are subjected to careful manual curation, while the bulk of high-throughput data is annotated by automatic means. High-quality reference resources developed in the past and still actively maintained include Saccharomyces cerevisiae, Neurospora crassa and Arabidopsis thaliana genome databases as well as several protein interaction data sets (MPACT, MPPI and CORUM). More recent projects are PhenomiR, the database on microRNA-related phenotypes, and MIPS PlantsDB for integrative and comparative plant genome research. The interlinked resources SIMAP and PEDANT provide homology relationships as well as up-to-date and consistent annotation for 38 000 000 protein sequences. PPLIPS and CCancer are versatile tools for proteomics and functional genomics interfacing to a database of compilations from gene lists extracted from literature. A novel literature-mining tool, EXCERBT, gives access to structured information on classified relations between genes, proteins, phenotypes and diseases extracted from Medline abstracts by semantic analysis. All databases described here, as well as the detailed descriptions of our projects can be accessed through the MIPS WWW server (http://mips.helmholtz-muenchen.de). PMID:21109531

The immune epitope database (IEDB) 3.0

PubMed Central

Vita, Randi; Overton, James A.; Greenbaum, Jason A.; Ponomarenko, Julia; Clark, Jason D.; Cantrell, Jason R.; Wheeler, Daniel K.; Gabbard, Joseph L.; Hix, Deborah; Sette, Alessandro; Peters, Bjoern

2015-01-01

The IEDB, www.iedb.org, contains information on immune epitopes—the molecular targets of adaptive immune responses—curated from the published literature and submitted by National Institutes of Health funded epitope discovery efforts. From 2004 to 2012 the IEDB curation of journal articles published since 1960 has caught up to the present day, with >95% of relevant published literature manually curated amounting to more than 15 000 journal articles and more than 704 000 experiments to date. The revised curation target since 2012 has been to make recent research findings quickly available in the IEDB and thereby ensure that it continues to be an up-to-date resource. Having gathered a comprehensive dataset in the IEDB, a complete redesign of the query and reporting interface has been performed in the IEDB 3.0 release to improve how end users can access this information in an intuitive and biologically accurate manner. We here present this most recent release of the IEDB and describe the user testing procedures as well as the use of external ontologies that have enabled it. PMID:25300482

EVLncRNAs: a manually curated database for long non-coding RNAs validated by low-throughput experiments.

PubMed

Zhou, Bailing; Zhao, Huiying; Yu, Jiafeng; Guo, Chengang; Dou, Xianghua; Song, Feng; Hu, Guodong; Cao, Zanxia; Qu, Yuanxu; Yang, Yuedong; Zhou, Yaoqi; Wang, Jihua

2018-01-04

Long non-coding RNAs (lncRNAs) play important functional roles in various biological processes. Early databases were utilized to deposit all lncRNA candidates produced by high-throughput experimental and/or computational techniques to facilitate classification, assessment and validation. As more lncRNAs are validated by low-throughput experiments, several databases were established for experimentally validated lncRNAs. However, these databases are small in scale (with a few hundreds of lncRNAs only) and specific in their focuses (plants, diseases or interactions). Thus, it is highly desirable to have a comprehensive dataset for experimentally validated lncRNAs as a central repository for all of their structures, functions and phenotypes. Here, we established EVLncRNAs by curating lncRNAs validated by low-throughput experiments (up to 1 May 2016) and integrating specific databases (lncRNAdb, LncRANDisease, Lnc2Cancer and PLNIncRBase) with additional functional and disease-specific information not covered previously. The current version of EVLncRNAs contains 1543 lncRNAs from 77 species that is 2.9 times larger than the current largest database for experimentally validated lncRNAs. Seventy-four percent lncRNA entries are partially or completely new, comparing to all existing experimentally validated databases. The established database allows users to browse, search and download as well as to submit experimentally validated lncRNAs. The database is available at http://biophy.dzu.edu.cn/EVLncRNAs. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

EVLncRNAs: a manually curated database for long non-coding RNAs validated by low-throughput experiments

PubMed Central

Zhao, Huiying; Yu, Jiafeng; Guo, Chengang; Dou, Xianghua; Song, Feng; Hu, Guodong; Cao, Zanxia; Qu, Yuanxu

2018-01-01

Abstract Long non-coding RNAs (lncRNAs) play important functional roles in various biological processes. Early databases were utilized to deposit all lncRNA candidates produced by high-throughput experimental and/or computational techniques to facilitate classification, assessment and validation. As more lncRNAs are validated by low-throughput experiments, several databases were established for experimentally validated lncRNAs. However, these databases are small in scale (with a few hundreds of lncRNAs only) and specific in their focuses (plants, diseases or interactions). Thus, it is highly desirable to have a comprehensive dataset for experimentally validated lncRNAs as a central repository for all of their structures, functions and phenotypes. Here, we established EVLncRNAs by curating lncRNAs validated by low-throughput experiments (up to 1 May 2016) and integrating specific databases (lncRNAdb, LncRANDisease, Lnc2Cancer and PLNIncRBase) with additional functional and disease-specific information not covered previously. The current version of EVLncRNAs contains 1543 lncRNAs from 77 species that is 2.9 times larger than the current largest database for experimentally validated lncRNAs. Seventy-four percent lncRNA entries are partially or completely new, comparing to all existing experimentally validated databases. The established database allows users to browse, search and download as well as to submit experimentally validated lncRNAs. The database is available at http://biophy.dzu.edu.cn/EVLncRNAs. PMID:28985416

JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles

PubMed Central

Mathelier, Anthony; Zhao, Xiaobei; Zhang, Allen W.; Parcy, François; Worsley-Hunt, Rebecca; Arenillas, David J.; Buchman, Sorana; Chen, Chih-yu; Chou, Alice; Ienasescu, Hans; Lim, Jonathan; Shyr, Casper; Tan, Ge; Zhou, Michelle; Lenhard, Boris; Sandelin, Albin; Wasserman, Wyeth W.

2014-01-01

JASPAR (http://jaspar.genereg.net) is the largest open-access database of matrix-based nucleotide profiles describing the binding preference of transcription factors from multiple species. The fifth major release greatly expands the heart of JASPAR—the JASPAR CORE subcollection, which contains curated, non-redundant profiles—with 135 new curated profiles (74 in vertebrates, 8 in Drosophila melanogaster, 10 in Caenorhabditis elegans and 43 in Arabidopsis thaliana; a 30% increase in total) and 43 older updated profiles (36 in vertebrates, 3 in D. melanogaster and 4 in A. thaliana; a 9% update in total). The new and updated profiles are mainly derived from published chromatin immunoprecipitation-seq experimental datasets. In addition, the web interface has been enhanced with advanced capabilities in browsing, searching and subsetting. Finally, the new JASPAR release is accompanied by a new BioPython package, a new R tool package and a new R/Bioconductor data package to facilitate access for both manual and automated methods. PMID:24194598

JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles.

PubMed

Mathelier, Anthony; Zhao, Xiaobei; Zhang, Allen W; Parcy, François; Worsley-Hunt, Rebecca; Arenillas, David J; Buchman, Sorana; Chen, Chih-yu; Chou, Alice; Ienasescu, Hans; Lim, Jonathan; Shyr, Casper; Tan, Ge; Zhou, Michelle; Lenhard, Boris; Sandelin, Albin; Wasserman, Wyeth W

2014-01-01

JASPAR (http://jaspar.genereg.net) is the largest open-access database of matrix-based nucleotide profiles describing the binding preference of transcription factors from multiple species. The fifth major release greatly expands the heart of JASPAR-the JASPAR CORE subcollection, which contains curated, non-redundant profiles-with 135 new curated profiles (74 in vertebrates, 8 in Drosophila melanogaster, 10 in Caenorhabditis elegans and 43 in Arabidopsis thaliana; a 30% increase in total) and 43 older updated profiles (36 in vertebrates, 3 in D. melanogaster and 4 in A. thaliana; a 9% update in total). The new and updated profiles are mainly derived from published chromatin immunoprecipitation-seq experimental datasets. In addition, the web interface has been enhanced with advanced capabilities in browsing, searching and subsetting. Finally, the new JASPAR release is accompanied by a new BioPython package, a new R tool package and a new R/Bioconductor data package to facilitate access for both manual and automated methods.

Canto: an online tool for community literature curation.

PubMed

Rutherford, Kim M; Harris, Midori A; Lock, Antonia; Oliver, Stephen G; Wood, Valerie

2014-06-15

Detailed curation of published molecular data is essential for any model organism database. Community curation enables researchers to contribute data from their papers directly to databases, supplementing the activity of professional curators and improving coverage of a growing body of literature. We have developed Canto, a web-based tool that provides an intuitive curation interface for both curators and researchers, to support community curation in the fission yeast database, PomBase. Canto supports curation using OBO ontologies, and can be easily configured for use with any species. Canto code and documentation are available under an Open Source license from http://curation.pombase.org/. Canto is a component of the Generic Model Organism Database (GMOD) project (http://www.gmod.org/). © The Author 2014. Published by Oxford University Press.

Geroprotectors.org: a new, structured and curated database of current therapeutic interventions in aging and age-related disease.

PubMed

Moskalev, Alexey; Chernyagina, Elizaveta; de Magalhães, João Pedro; Barardo, Diogo; Thoppil, Harikrishnan; Shaposhnikov, Mikhail; Budovsky, Arie; Fraifeld, Vadim E; Garazha, Andrew; Tsvetkov, Vasily; Bronovitsky, Evgeny; Bogomolov, Vladislav; Scerbacov, Alexei; Kuryan, Oleg; Gurinovich, Roman; Jellen, Leslie C; Kennedy, Brian; Mamoshina, Polina; Dobrovolskaya, Evgeniya; Aliper, Alex; Kaminsky, Dmitry; Zhavoronkov, Alex

2015-09-01

As the level of interest in aging research increases, there is a growing number of geroprotectors, or therapeutic interventions that aim to extend the healthy lifespan and repair or reduce aging-related damage in model organisms and, eventually, in humans. There is a clear need for a manually-curated database of geroprotectors to compile and index their effects on aging and age-related diseases and link these effects to relevant studies and multiple biochemical and drug databases. Here, we introduce the first such resource, Geroprotectors (http://geroprotectors.org). Geroprotectors is a public, rapidly explorable database that catalogs over 250 experiments involving over 200 known or candidate geroprotectors that extend lifespan in model organisms. Each compound has a comprehensive profile complete with biochemistry, mechanisms, and lifespan effects in various model organisms, along with information ranging from chemical structure, side effects, and toxicity to FDA drug status. These are presented in a visually intuitive, efficient framework fit for casual browsing or in-depth research alike. Data are linked to the source studies or databases, providing quick and convenient access to original data. The Geroprotectors database facilitates cross-study, cross-organism, and cross-discipline analysis and saves countless hours of inefficient literature and web searching. Geroprotectors is a one-stop, knowledge-sharing, time-saving resource for researchers seeking healthy aging solutions.

Geroprotectors.org: a new, structured and curated database of current therapeutic interventions in aging and age-related disease

PubMed Central

Moskalev, Alexey; Chernyagina, Elizaveta; de Magalhães, João Pedro; Barardo, Diogo; Thoppil, Harikrishnan; Shaposhnikov, Mikhail; Budovsky, Arie; Fraifeld, Vadim E.; Garazha, Andrew; Tsvetkov, Vasily; Bronovitsky, Evgeny; Bogomolov, Vladislav; Scerbacov, Alexei; Kuryan, Oleg; Gurinovich, Roman; Jellen, Leslie C.; Kennedy, Brian; Mamoshina, Polina; Dobrovolskaya, Evgeniya; Aliper, Alex; Kaminsky, Dmitry; Zhavoronkov, Alex

2015-01-01

As the level of interest in aging research increases, there is a growing number of geroprotectors, or therapeutic interventions that aim to extend the healthy lifespan and repair or reduce aging-related damage in model organisms and, eventually, in humans. There is a clear need for a manually-curated database of geroprotectors to compile and index their effects on aging and age-related diseases and link these effects to relevant studies and multiple biochemical and drug databases. Here, we introduce the first such resource, Geroprotectors (http://geroprotectors.org). Geroprotectors is a public, rapidly explorable database that catalogs over 250 experiments involving over 200 known or candidate geroprotectors that extend lifespan in model organisms. Each compound has a comprehensive profile complete with biochemistry, mechanisms, and lifespan effects in various model organisms, along with information ranging from chemical structure, side effects, and toxicity to FDA drug status. These are presented in a visually intuitive, efficient framework fit for casual browsing or in-depth research alike. Data are linked to the source studies or databases, providing quick and convenient access to original data. The Geroprotectors database facilitates cross-study, cross-organism, and cross-discipline analysis and saves countless hours of inefficient literature and web searching. Geroprotectors is a one-stop, knowledge-sharing, time-saving resource for researchers seeking healthy aging solutions. PMID:26342919

Supervised Learning for Detection of Duplicates in Genomic Sequence Databases.

PubMed

Chen, Qingyu; Zobel, Justin; Zhang, Xiuzhen; Verspoor, Karin

2016-01-01

First identified as an issue in 1996, duplication in biological databases introduces redundancy and even leads to inconsistency when contradictory information appears. The amount of data makes purely manual de-duplication impractical, and existing automatic systems cannot detect duplicates as precisely as can experts. Supervised learning has the potential to address such problems by building automatic systems that learn from expert curation to detect duplicates precisely and efficiently. While machine learning is a mature approach in other duplicate detection contexts, it has seen only preliminary application in genomic sequence databases. We developed and evaluated a supervised duplicate detection method based on an expert curated dataset of duplicates, containing over one million pairs across five organisms derived from genomic sequence databases. We selected 22 features to represent distinct attributes of the database records, and developed a binary model and a multi-class model. Both models achieve promising performance; under cross-validation, the binary model had over 90% accuracy in each of the five organisms, while the multi-class model maintains high accuracy and is more robust in generalisation. We performed an ablation study to quantify the impact of different sequence record features, finding that features derived from meta-data, sequence identity, and alignment quality impact performance most strongly. The study demonstrates machine learning can be an effective additional tool for de-duplication of genomic sequence databases. All Data are available as described in the supplementary material.

MIPS: analysis and annotation of proteins from whole genomes in 2005

PubMed Central

Mewes, H. W.; Frishman, D.; Mayer, K. F. X.; Münsterkötter, M.; Noubibou, O.; Pagel, P.; Rattei, T.; Oesterheld, M.; Ruepp, A.; Stümpflen, V.

2006-01-01

The Munich Information Center for Protein Sequences (MIPS at the GSF), Neuherberg, Germany, provides resources related to genome information. Manually curated databases for several reference organisms are maintained. Several of these databases are described elsewhere in this and other recent NAR database issues. In a complementary effort, a comprehensive set of >400 genomes automatically annotated with the PEDANT system are maintained. The main goal of our current work on creating and maintaining genome databases is to extend gene centered information to information on interactions within a generic comprehensive framework. We have concentrated our efforts along three lines (i) the development of suitable comprehensive data structures and database technology, communication and query tools to include a wide range of different types of information enabling the representation of complex information such as functional modules or networks Genome Research Environment System, (ii) the development of databases covering computable information such as the basic evolutionary relations among all genes, namely SIMAP, the sequence similarity matrix and the CABiNet network analysis framework and (iii) the compilation and manual annotation of information related to interactions such as protein–protein interactions or other types of relations (e.g. MPCDB, MPPI, CYGD). All databases described and the detailed descriptions of our projects can be accessed through the MIPS WWW server (). PMID:16381839

MIPS: analysis and annotation of proteins from whole genomes in 2005.

PubMed

Mewes, H W; Frishman, D; Mayer, K F X; Münsterkötter, M; Noubibou, O; Pagel, P; Rattei, T; Oesterheld, M; Ruepp, A; Stümpflen, V

2006-01-01

The Munich Information Center for Protein Sequences (MIPS at the GSF), Neuherberg, Germany, provides resources related to genome information. Manually curated databases for several reference organisms are maintained. Several of these databases are described elsewhere in this and other recent NAR database issues. In a complementary effort, a comprehensive set of >400 genomes automatically annotated with the PEDANT system are maintained. The main goal of our current work on creating and maintaining genome databases is to extend gene centered information to information on interactions within a generic comprehensive framework. We have concentrated our efforts along three lines (i) the development of suitable comprehensive data structures and database technology, communication and query tools to include a wide range of different types of information enabling the representation of complex information such as functional modules or networks Genome Research Environment System, (ii) the development of databases covering computable information such as the basic evolutionary relations among all genes, namely SIMAP, the sequence similarity matrix and the CABiNet network analysis framework and (iii) the compilation and manual annotation of information related to interactions such as protein-protein interactions or other types of relations (e.g. MPCDB, MPPI, CYGD). All databases described and the detailed descriptions of our projects can be accessed through the MIPS WWW server (http://mips.gsf.de).

IMGMD: A platform for the integration and standardisation of In silico Microbial Genome-scale Metabolic Models.

PubMed

Ye, Chao; Xu, Nan; Dong, Chuan; Ye, Yuannong; Zou, Xuan; Chen, Xiulai; Guo, Fengbiao; Liu, Liming

2017-04-07

Genome-scale metabolic models (GSMMs) constitute a platform that combines genome sequences and detailed biochemical information to quantify microbial physiology at the system level. To improve the unity, integrity, correctness, and format of data in published GSMMs, a consensus IMGMD database was built in the LAMP (Linux + Apache + MySQL + PHP) system by integrating and standardizing 328 GSMMs constructed for 139 microorganisms. The IMGMD database can help microbial researchers download manually curated GSMMs, rapidly reconstruct standard GSMMs, design pathways, and identify metabolic targets for strategies on strain improvement. Moreover, the IMGMD database facilitates the integration of wet-lab and in silico data to gain an additional insight into microbial physiology. The IMGMD database is freely available, without any registration requirements, at http://imgmd.jiangnan.edu.cn/database.

GeneSigDB: a manually curated database and resource for analysis of gene expression signatures

PubMed Central

Culhane, Aedín C.; Schröder, Markus S.; Sultana, Razvan; Picard, Shaita C.; Martinelli, Enzo N.; Kelly, Caroline; Haibe-Kains, Benjamin; Kapushesky, Misha; St Pierre, Anne-Alyssa; Flahive, William; Picard, Kermshlise C.; Gusenleitner, Daniel; Papenhausen, Gerald; O'Connor, Niall; Correll, Mick; Quackenbush, John

2012-01-01

GeneSigDB (http://www.genesigdb.org or http://compbio.dfci.harvard.edu/genesigdb/) is a database of gene signatures that have been extracted and manually curated from the published literature. It provides a standardized resource of published prognostic, diagnostic and other gene signatures of cancer and related disease to the community so they can compare the predictive power of gene signatures or use these in gene set enrichment analysis. Since GeneSigDB release 1.0, we have expanded from 575 to 3515 gene signatures, which were collected and transcribed from 1604 published articles largely focused on gene expression in cancer, stem cells, immune cells, development and lung disease. We have made substantial upgrades to the GeneSigDB website to improve accessibility and usability, including adding a tag cloud browse function, facetted navigation and a ‘basket’ feature to store genes or gene signatures of interest. Users can analyze GeneSigDB gene signatures, or upload their own gene list, to identify gene signatures with significant gene overlap and results can be viewed on a dynamic editable heatmap that can be downloaded as a publication quality image. All data in GeneSigDB can be downloaded in numerous formats including .gmt file format for gene set enrichment analysis or as a R/Bioconductor data file. GeneSigDB is available from http://www.genesigdb.org. PMID:22110038

Recommendations for Locus-Specific Databases and Their Curation

PubMed Central

Cotton, R.G.H.; Auerbach, A.D.; Beckmann, J.S.; Blumenfeld, O.O.; Brookes, A.J.; Brown, A.F.; Carrera, P.; Cox, D.W.; Gottlieb, B.; Greenblatt, M.S.; Hilbert, P.; Lehvaslaiho, H.; Liang, P.; Marsh, S.; Nebert, D.W.; Povey, S.; Rossetti, S.; Scriver, C.R.; Summar, M.; Tolan, D.R.; Verma, I.C.; Vihinen, M.; den Dunnen, J.T.

2009-01-01

Expert curation and complete collection of mutations in genes that affect human health is essential for proper genetic healthcare and research. Expert curation is given by the curators of gene-specific mutation databases or locus-specific databases (LSDBs). While there are over 700 such databases, they vary in their content, completeness, time available for curation, and the expertise of the curator. Curation and LSDBs have been discussed, written about, and protocols have been provided for over 10 years, but there have been no formal recommendations for the ideal form of these entities. This work initiates a discussion on this topic to assist future efforts in human genetics. Further discussion is welcome. PMID:18157828

Recommendations for locus-specific databases and their curation.

PubMed

Cotton, R G H; Auerbach, A D; Beckmann, J S; Blumenfeld, O O; Brookes, A J; Brown, A F; Carrera, P; Cox, D W; Gottlieb, B; Greenblatt, M S; Hilbert, P; Lehvaslaiho, H; Liang, P; Marsh, S; Nebert, D W; Povey, S; Rossetti, S; Scriver, C R; Summar, M; Tolan, D R; Verma, I C; Vihinen, M; den Dunnen, J T

2008-01-01

Expert curation and complete collection of mutations in genes that affect human health is essential for proper genetic healthcare and research. Expert curation is given by the curators of gene-specific mutation databases or locus-specific databases (LSDBs). While there are over 700 such databases, they vary in their content, completeness, time available for curation, and the expertise of the curator. Curation and LSDBs have been discussed, written about, and protocols have been provided for over 10 years, but there have been no formal recommendations for the ideal form of these entities. This work initiates a discussion on this topic to assist future efforts in human genetics. Further discussion is welcome. (c) 2007 Wiley-Liss, Inc.

DISEASES: text mining and data integration of disease-gene associations.

PubMed

Pletscher-Frankild, Sune; Pallejà, Albert; Tsafou, Kalliopi; Binder, Janos X; Jensen, Lars Juhl

2015-03-01

Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease-gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a web interface at http://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

LOCATE: a mouse protein subcellular localization database

PubMed Central

Fink, J. Lynn; Aturaliya, Rajith N.; Davis, Melissa J.; Zhang, Fasheng; Hanson, Kelly; Teasdale, Melvena S.; Kai, Chikatoshi; Kawai, Jun; Carninci, Piero; Hayashizaki, Yoshihide; Teasdale, Rohan D.

2006-01-01

We present here LOCATE, a curated, web-accessible database that houses data describing the membrane organization and subcellular localization of proteins from the FANTOM3 Isoform Protein Sequence set. Membrane organization is predicted by the high-throughput, computational pipeline MemO. The subcellular locations of selected proteins from this set were determined by a high-throughput, immunofluorescence-based assay and by manually reviewing >1700 peer-reviewed publications. LOCATE represents the first effort to catalogue the experimentally verified subcellular location and membrane organization of mammalian proteins using a high-throughput approach and provides localization data for ∼40% of the mouse proteome. It is available at . PMID:16381849

Lynx: a database and knowledge extraction engine for integrative medicine.

PubMed

Sulakhe, Dinanath; Balasubramanian, Sandhya; Xie, Bingqing; Feng, Bo; Taylor, Andrew; Wang, Sheng; Berrocal, Eduardo; Dave, Utpal; Xu, Jinbo; Börnigen, Daniela; Gilliam, T Conrad; Maltsev, Natalia

2014-01-01

We have developed Lynx (http://lynx.ci.uchicago.edu)--a web-based database and a knowledge extraction engine, supporting annotation and analysis of experimental data and generation of weighted hypotheses on molecular mechanisms contributing to human phenotypes and disorders of interest. Its underlying knowledge base (LynxKB) integrates various classes of information from >35 public databases and private collections, as well as manually curated data from our group and collaborators. Lynx provides advanced search capabilities and a variety of algorithms for enrichment analysis and network-based gene prioritization to assist the user in extracting meaningful knowledge from LynxKB and experimental data, whereas its service-oriented architecture provides public access to LynxKB and its analytical tools via user-friendly web services and interfaces.

The BioC-BioGRID corpus: full text articles annotated for curation of protein–protein and genetic interactions

PubMed Central

Kim, Sun; Chatr-aryamontri, Andrew; Chang, Christie S.; Oughtred, Rose; Rust, Jennifer; Wilbur, W. John; Comeau, Donald C.; Dolinski, Kara; Tyers, Mike

2017-01-01

A great deal of information on the molecular genetics and biochemistry of model organisms has been reported in the scientific literature. However, this data is typically described in free text form and is not readily amenable to computational analyses. To this end, the BioGRID database systematically curates the biomedical literature for genetic and protein interaction data. This data is provided in a standardized computationally tractable format and includes structured annotation of experimental evidence. BioGRID curation necessarily involves substantial human effort by expert curators who must read each publication to extract the relevant information. Computational text-mining methods offer the potential to augment and accelerate manual curation. To facilitate the development of practical text-mining strategies, a new challenge was organized in BioCreative V for the BioC task, the collaborative Biocurator Assistant Task. This was a non-competitive, cooperative task in which the participants worked together to build BioC-compatible modules into an integrated pipeline to assist BioGRID curators. As an integral part of this task, a test collection of full text articles was developed that contained both biological entity annotations (gene/protein and organism/species) and molecular interaction annotations (protein–protein and genetic interactions (PPIs and GIs)). This collection, which we call the BioC-BioGRID corpus, was annotated by four BioGRID curators over three rounds of annotation and contains 120 full text articles curated in a dataset representing two major model organisms, namely budding yeast and human. The BioC-BioGRID corpus contains annotations for 6409 mentions of genes and their Entrez Gene IDs, 186 mentions of organism names and their NCBI Taxonomy IDs, 1867 mentions of PPIs and 701 annotations of PPI experimental evidence statements, 856 mentions of GIs and 399 annotations of GI evidence statements. The purpose, characteristics and possible future uses of the BioC-BioGRID corpus are detailed in this report. Database URL: http://bioc.sourceforge.net/BioC-BioGRID.html PMID:28077563

A framework for organizing cancer-related variations from existing databases, publications and NGS data using a High-performance Integrated Virtual Environment (HIVE).

PubMed

Wu, Tsung-Jung; Shamsaddini, Amirhossein; Pan, Yang; Smith, Krista; Crichton, Daniel J; Simonyan, Vahan; Mazumder, Raja

2014-01-01

Years of sequence feature curation by UniProtKB/Swiss-Prot, PIR-PSD, NCBI-CDD, RefSeq and other database biocurators has led to a rich repository of information on functional sites of genes and proteins. This information along with variation-related annotation can be used to scan human short sequence reads from next-generation sequencing (NGS) pipelines for presence of non-synonymous single-nucleotide variations (nsSNVs) that affect functional sites. This and similar workflows are becoming more important because thousands of NGS data sets are being made available through projects such as The Cancer Genome Atlas (TCGA), and researchers want to evaluate their biomarkers in genomic data. BioMuta, an integrated sequence feature database, provides a framework for automated and manual curation and integration of cancer-related sequence features so that they can be used in NGS analysis pipelines. Sequence feature information in BioMuta is collected from the Catalogue of Somatic Mutations in Cancer (COSMIC), ClinVar, UniProtKB and through biocuration of information available from publications. Additionally, nsSNVs identified through automated analysis of NGS data from TCGA are also included in the database. Because of the petabytes of data and information present in NGS primary repositories, a platform HIVE (High-performance Integrated Virtual Environment) for storing, analyzing, computing and curating NGS data and associated metadata has been developed. Using HIVE, 31 979 nsSNVs were identified in TCGA-derived NGS data from breast cancer patients. All variations identified through this process are stored in a Curated Short Read archive, and the nsSNVs from the tumor samples are included in BioMuta. Currently, BioMuta has 26 cancer types with 13 896 small-scale and 308 986 large-scale study-derived variations. Integration of variation data allows identifications of novel or common nsSNVs that can be prioritized in validation studies. Database URL: BioMuta: http://hive.biochemistry.gwu.edu/tools/biomuta/index.php; CSR: http://hive.biochemistry.gwu.edu/dna.cgi?cmd=csr; HIVE: http://hive.biochemistry.gwu.edu.

Lynx: a database and knowledge extraction engine for integrative medicine

PubMed Central

Sulakhe, Dinanath; Balasubramanian, Sandhya; Xie, Bingqing; Feng, Bo; Taylor, Andrew; Wang, Sheng; Berrocal, Eduardo; Dave, Utpal; Xu, Jinbo; Börnigen, Daniela; Gilliam, T. Conrad; Maltsev, Natalia

2014-01-01

We have developed Lynx (http://lynx.ci.uchicago.edu)—a web-based database and a knowledge extraction engine, supporting annotation and analysis of experimental data and generation of weighted hypotheses on molecular mechanisms contributing to human phenotypes and disorders of interest. Its underlying knowledge base (LynxKB) integrates various classes of information from >35 public databases and private collections, as well as manually curated data from our group and collaborators. Lynx provides advanced search capabilities and a variety of algorithms for enrichment analysis and network-based gene prioritization to assist the user in extracting meaningful knowledge from LynxKB and experimental data, whereas its service-oriented architecture provides public access to LynxKB and its analytical tools via user-friendly web services and interfaces. PMID:24270788

MetaRNA-Seq: An Interactive Tool to Browse and Annotate Metadata from RNA-Seq Studies.

PubMed

Kumar, Pankaj; Halama, Anna; Hayat, Shahina; Billing, Anja M; Gupta, Manish; Yousri, Noha A; Smith, Gregory M; Suhre, Karsten

2015-01-01

The number of RNA-Seq studies has grown in recent years. The design of RNA-Seq studies varies from very simple (e.g., two-condition case-control) to very complicated (e.g., time series involving multiple samples at each time point with separate drug treatments). Most of these publically available RNA-Seq studies are deposited in NCBI databases, but their metadata are scattered throughout four different databases: Sequence Read Archive (SRA), Biosample, Bioprojects, and Gene Expression Omnibus (GEO). Although the NCBI web interface is able to provide all of the metadata information, it often requires significant effort to retrieve study- or project-level information by traversing through multiple hyperlinks and going to another page. Moreover, project- and study-level metadata lack manual or automatic curation by categories, such as disease type, time series, case-control, or replicate type, which are vital to comprehending any RNA-Seq study. Here we describe "MetaRNA-Seq," a new tool for interactively browsing, searching, and annotating RNA-Seq metadata with the capability of semiautomatic curation at the study level.

The DrugAge database of aging-related drugs.

PubMed

Barardo, Diogo; Thornton, Daniel; Thoppil, Harikrishnan; Walsh, Michael; Sharifi, Samim; Ferreira, Susana; Anžič, Andreja; Fernandes, Maria; Monteiro, Patrick; Grum, Tjaša; Cordeiro, Rui; De-Souza, Evandro Araújo; Budovsky, Arie; Araujo, Natali; Gruber, Jan; Petrascheck, Michael; Fraifeld, Vadim E; Zhavoronkov, Alexander; Moskalev, Alexey; de Magalhães, João Pedro

2017-06-01

Aging is a major worldwide medical challenge. Not surprisingly, identifying drugs and compounds that extend lifespan in model organisms is a growing research area. Here, we present DrugAge (http://genomics.senescence.info/drugs/), a curated database of lifespan-extending drugs and compounds. At the time of writing, DrugAge contains 1316 entries featuring 418 different compounds from studies across 27 model organisms, including worms, flies, yeast and mice. Data were manually curated from 324 publications. Using drug-gene interaction data, we also performed a functional enrichment analysis of targets of lifespan-extending drugs. Enriched terms include various functional categories related to glutathione and antioxidant activity, ion transport and metabolic processes. In addition, we found a modest but significant overlap between targets of lifespan-extending drugs and known aging-related genes, suggesting that some but not most aging-related pathways have been targeted pharmacologically in longevity studies. DrugAge is freely available online for the scientific community and will be an important resource for biogerontologists. © 2017 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.

Lnc2Meth: a manually curated database of regulatory relationships between long non-coding RNAs and DNA methylation associated with human disease

PubMed Central

Zhi, Hui; Li, Xin; Wang, Peng; Gao, Yue; Gao, Baoqing; Zhou, Dianshuang; Zhang, Yan; Guo, Maoni; Yue, Ming; Shen, Weitao

2018-01-01

Abstract Lnc2Meth (http://www.bio-bigdata.com/Lnc2Meth/), an interactive resource to identify regulatory relationships between human long non-coding RNAs (lncRNAs) and DNA methylation, is not only a manually curated collection and annotation of experimentally supported lncRNAs-DNA methylation associations but also a platform that effectively integrates tools for calculating and identifying the differentially methylated lncRNAs and protein-coding genes (PCGs) in diverse human diseases. The resource provides: (i) advanced search possibilities, e.g. retrieval of the database by searching the lncRNA symbol of interest, DNA methylation patterns, regulatory mechanisms and disease types; (ii) abundant computationally calculated DNA methylation array profiles for the lncRNAs and PCGs; (iii) the prognostic values for each hit transcript calculated from the patients clinical data; (iv) a genome browser to display the DNA methylation landscape of the lncRNA transcripts for a specific type of disease; (v) tools to re-annotate probes to lncRNA loci and identify the differential methylation patterns for lncRNAs and PCGs with user-supplied external datasets; (vi) an R package (LncDM) to complete the differentially methylated lncRNAs identification and visualization with local computers. Lnc2Meth provides a timely and valuable resource that can be applied to significantly expand our understanding of the regulatory relationships between lncRNAs and DNA methylation in various human diseases. PMID:29069510

GapBlaster-A Graphical Gap Filler for Prokaryote Genomes.

PubMed

de Sá, Pablo H C G; Miranda, Fábio; Veras, Adonney; de Melo, Diego Magalhães; Soares, Siomar; Pinheiro, Kenny; Guimarães, Luis; Azevedo, Vasco; Silva, Artur; Ramos, Rommel T J

2016-01-01

The advent of NGS (Next Generation Sequencing) technologies has resulted in an exponential increase in the number of complete genomes available in biological databases. This advance has allowed the development of several computational tools enabling analyses of large amounts of data in each of the various steps, from processing and quality filtering to gap filling and manual curation. The tools developed for gap closure are very useful as they result in more complete genomes, which will influence downstream analyses of genomic plasticity and comparative genomics. However, the gap filling step remains a challenge for genome assembly, often requiring manual intervention. Here, we present GapBlaster, a graphical application to evaluate and close gaps. GapBlaster was developed via Java programming language. The software uses contigs obtained in the assembly of the genome to perform an alignment against a draft of the genome/scaffold, using BLAST or Mummer to close gaps. Then, all identified alignments of contigs that extend through the gaps in the draft sequence are presented to the user for further evaluation via the GapBlaster graphical interface. GapBlaster presents significant results compared to other similar software and has the advantage of offering a graphical interface for manual curation of the gaps. GapBlaster program, the user guide and the test datasets are freely available at https://sourceforge.net/projects/gapblaster2015/. It requires Sun JDK 8 and Blast or Mummer.

PathNER: a tool for systematic identification of biological pathway mentions in the literature

PubMed Central

2013-01-01

Background Biological pathways are central to many biomedical studies and are frequently discussed in the literature. Several curated databases have been established to collate the knowledge of molecular processes constituting pathways. Yet, there has been little focus on enabling systematic detection of pathway mentions in the literature. Results We developed a tool, named PathNER (Pathway Named Entity Recognition), for the systematic identification of pathway mentions in the literature. PathNER is based on soft dictionary matching and rules, with the dictionary generated from public pathway databases. The rules utilise general pathway-specific keywords, syntactic information and gene/protein mentions. Detection results from both components are merged. On a gold-standard corpus, PathNER achieved an F1-score of 84%. To illustrate its potential, we applied PathNER on a collection of articles related to Alzheimer's disease to identify associated pathways, highlighting cases that can complement an existing manually curated knowledgebase. Conclusions In contrast to existing text-mining efforts that target the automatic reconstruction of pathway details from molecular interactions mentioned in the literature, PathNER focuses on identifying specific named pathway mentions. These mentions can be used to support large-scale curation and pathway-related systems biology applications, as demonstrated in the example of Alzheimer's disease. PathNER is implemented in Java and made freely available online at http://sourceforge.net/projects/pathner/. PMID:24555844

STOP using just GO: a multi-ontology hypothesis generation tool for high throughput experimentation

PubMed Central

2013-01-01

Background Gene Ontology (GO) enrichment analysis remains one of the most common methods for hypothesis generation from high throughput datasets. However, we believe that researchers strive to test other hypotheses that fall outside of GO. Here, we developed and evaluated a tool for hypothesis generation from gene or protein lists using ontological concepts present in manually curated text that describes those genes and proteins. Results As a consequence we have developed the method Statistical Tracking of Ontological Phrases (STOP) that expands the realm of testable hypotheses in gene set enrichment analyses by integrating automated annotations of genes to terms from over 200 biomedical ontologies. While not as precise as manually curated terms, we find that the additional enriched concepts have value when coupled with traditional enrichment analyses using curated terms. Conclusion Multiple ontologies have been developed for gene and protein annotation, by using a dataset of both manually curated GO terms and automatically recognized concepts from curated text we can expand the realm of hypotheses that can be discovered. The web application STOP is available at http://mooneygroup.org/stop/. PMID:23409969

Enhanced annotations and features for comparing thousands of Pseudomonas genomes in the Pseudomonas genome database.

PubMed

Winsor, Geoffrey L; Griffiths, Emma J; Lo, Raymond; Dhillon, Bhavjinder K; Shay, Julie A; Brinkman, Fiona S L

2016-01-04

The Pseudomonas Genome Database (http://www.pseudomonas.com) is well known for the application of community-based annotation approaches for producing a high-quality Pseudomonas aeruginosa PAO1 genome annotation, and facilitating whole-genome comparative analyses with other Pseudomonas strains. To aid analysis of potentially thousands of complete and draft genome assemblies, this database and analysis platform was upgraded to integrate curated genome annotations and isolate metadata with enhanced tools for larger scale comparative analysis and visualization. Manually curated gene annotations are supplemented with improved computational analyses that help identify putative drug targets and vaccine candidates or assist with evolutionary studies by identifying orthologs, pathogen-associated genes and genomic islands. The database schema has been updated to integrate isolate metadata that will facilitate more powerful analysis of genomes across datasets in the future. We continue to place an emphasis on providing high-quality updates to gene annotations through regular review of the scientific literature and using community-based approaches including a major new Pseudomonas community initiative for the assignment of high-quality gene ontology terms to genes. As we further expand from thousands of genomes, we plan to provide enhancements that will aid data visualization and analysis arising from whole-genome comparative studies including more pan-genome and population-based approaches. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

MicRhoDE: a curated database for the analysis of microbial rhodopsin diversity and evolution

PubMed Central

Boeuf, Dominique; Audic, Stéphane; Brillet-Guéguen, Loraine; Caron, Christophe; Jeanthon, Christian

2015-01-01

Microbial rhodopsins are a diverse group of photoactive transmembrane proteins found in all three domains of life and in viruses. Today, microbial rhodopsin research is a flourishing research field in which new understandings of rhodopsin diversity, function and evolution are contributing to broader microbiological and molecular knowledge. Here, we describe MicRhoDE, a comprehensive, high-quality and freely accessible database that facilitates analysis of the diversity and evolution of microbial rhodopsins. Rhodopsin sequences isolated from a vast array of marine and terrestrial environments were manually collected and curated. To each rhodopsin sequence are associated related metadata, including predicted spectral tuning of the protein, putative activity and function, taxonomy for sequences that can be linked to a 16S rRNA gene, sampling date and location, and supporting literature. The database currently covers 7857 aligned sequences from more than 450 environmental samples or organisms. Based on a robust phylogenetic analysis, we introduce an operational classification system with multiple phylogenetic levels ranging from superclusters to species-level operational taxonomic units. An integrated pipeline for online sequence alignment and phylogenetic tree construction is also provided. With a user-friendly interface and integrated online bioinformatics tools, this unique resource should be highly valuable for upcoming studies of the biogeography, diversity, distribution and evolution of microbial rhodopsins. Database URL: http://micrhode.sb-roscoff.fr. PMID:26286928

MicRhoDE: a curated database for the analysis of microbial rhodopsin diversity and evolution.

PubMed

Boeuf, Dominique; Audic, Stéphane; Brillet-Guéguen, Loraine; Caron, Christophe; Jeanthon, Christian

2015-01-01

Microbial rhodopsins are a diverse group of photoactive transmembrane proteins found in all three domains of life and in viruses. Today, microbial rhodopsin research is a flourishing research field in which new understandings of rhodopsin diversity, function and evolution are contributing to broader microbiological and molecular knowledge. Here, we describe MicRhoDE, a comprehensive, high-quality and freely accessible database that facilitates analysis of the diversity and evolution of microbial rhodopsins. Rhodopsin sequences isolated from a vast array of marine and terrestrial environments were manually collected and curated. To each rhodopsin sequence are associated related metadata, including predicted spectral tuning of the protein, putative activity and function, taxonomy for sequences that can be linked to a 16S rRNA gene, sampling date and location, and supporting literature. The database currently covers 7857 aligned sequences from more than 450 environmental samples or organisms. Based on a robust phylogenetic analysis, we introduce an operational classification system with multiple phylogenetic levels ranging from superclusters to species-level operational taxonomic units. An integrated pipeline for online sequence alignment and phylogenetic tree construction is also provided. With a user-friendly interface and integrated online bioinformatics tools, this unique resource should be highly valuable for upcoming studies of the biogeography, diversity, distribution and evolution of microbial rhodopsins. Database URL: http://micrhode.sb-roscoff.fr. © The Author(s) 2015. Published by Oxford University Press.

Ambiguity and variability of database and software names in bioinformatics.

PubMed

Duck, Geraint; Kovacevic, Aleksandar; Robertson, David L; Stevens, Robert; Nenadic, Goran

2015-01-01

There are numerous options available to achieve various tasks in bioinformatics, but until recently, there were no tools that could systematically identify mentions of databases and tools within the literature. In this paper we explore the variability and ambiguity of database and software name mentions and compare dictionary and machine learning approaches to their identification. Through the development and analysis of a corpus of 60 full-text documents manually annotated at the mention level, we report high variability and ambiguity in database and software mentions. On a test set of 25 full-text documents, a baseline dictionary look-up achieved an F-score of 46 %, highlighting not only variability and ambiguity but also the extensive number of new resources introduced. A machine learning approach achieved an F-score of 63 % (with precision of 74 %) and 70 % (with precision of 83 %) for strict and lenient matching respectively. We characterise the issues with various mention types and propose potential ways of capturing additional database and software mentions in the literature. Our analyses show that identification of mentions of databases and tools is a challenging task that cannot be achieved by relying on current manually-curated resource repositories. Although machine learning shows improvement and promise (primarily in precision), more contextual information needs to be taken into account to achieve a good degree of accuracy.

Building a genome database using an object-oriented approach.

PubMed

Barbasiewicz, Anna; Liu, Lin; Lang, B Franz; Burger, Gertraud

2002-01-01

GOBASE is a relational database that integrates data associated with mitochondria and chloroplasts. The most important data in GOBASE, i. e., molecular sequences and taxonomic information, are obtained from the public sequence data repository at the National Center for Biotechnology Information (NCBI), and are validated by our experts. Maintaining a curated genomic database comes with a towering labor cost, due to the shear volume of available genomic sequences and the plethora of annotation errors and omissions in records retrieved from public repositories. Here we describe our approach to increase automation of the database population process, thereby reducing manual intervention. As a first step, we used Unified Modeling Language (UML) to construct a list of potential errors. Each case was evaluated independently, and an expert solution was devised, and represented as a diagram. Subsequently, the UML diagrams were used as templates for writing object-oriented automation programs in the Java programming language.

Text Mining Genotype-Phenotype Relationships from Biomedical Literature for Database Curation and Precision Medicine.

PubMed

Singhal, Ayush; Simmons, Michael; Lu, Zhiyong

2016-11-01

The practice of precision medicine will ultimately require databases of genes and mutations for healthcare providers to reference in order to understand the clinical implications of each patient's genetic makeup. Although the highest quality databases require manual curation, text mining tools can facilitate the curation process, increasing accuracy, coverage, and productivity. However, to date there are no available text mining tools that offer high-accuracy performance for extracting such triplets from biomedical literature. In this paper we propose a high-performance machine learning approach to automate the extraction of disease-gene-variant triplets from biomedical literature. Our approach is unique because we identify the genes and protein products associated with each mutation from not just the local text content, but from a global context as well (from the Internet and from all literature in PubMed). Our approach also incorporates protein sequence validation and disease association using a novel text-mining-based machine learning approach. We extract disease-gene-variant triplets from all abstracts in PubMed related to a set of ten important diseases (breast cancer, prostate cancer, pancreatic cancer, lung cancer, acute myeloid leukemia, Alzheimer's disease, hemochromatosis, age-related macular degeneration (AMD), diabetes mellitus, and cystic fibrosis). We then evaluate our approach in two ways: (1) a direct comparison with the state of the art using benchmark datasets; (2) a validation study comparing the results of our approach with entries in a popular human-curated database (UniProt) for each of the previously mentioned diseases. In the benchmark comparison, our full approach achieves a 28% improvement in F1-measure (from 0.62 to 0.79) over the state-of-the-art results. For the validation study with UniProt Knowledgebase (KB), we present a thorough analysis of the results and errors. Across all diseases, our approach returned 272 triplets (disease-gene-variant) that overlapped with entries in UniProt and 5,384 triplets without overlap in UniProt. Analysis of the overlapping triplets and of a stratified sample of the non-overlapping triplets revealed accuracies of 93% and 80% for the respective categories (cumulative accuracy, 77%). We conclude that our process represents an important and broadly applicable improvement to the state of the art for curation of disease-gene-variant relationships.

Automated hierarchical classification of protein domain subfamilies based on functionally-divergent residue signatures

PubMed Central

2012-01-01

Background The NCBI Conserved Domain Database (CDD) consists of a collection of multiple sequence alignments of protein domains that are at various stages of being manually curated into evolutionary hierarchies based on conserved and divergent sequence and structural features. These domain models are annotated to provide insights into the relationships between sequence, structure and function via web-based BLAST searches. Results Here we automate the generation of conserved domain (CD) hierarchies using a combination of heuristic and Markov chain Monte Carlo (MCMC) sampling procedures and starting from a (typically very large) multiple sequence alignment. This procedure relies on statistical criteria to define each hierarchy based on the conserved and divergent sequence patterns associated with protein functional-specialization. At the same time this facilitates the sequence and structural annotation of residues that are functionally important. These statistical criteria also provide a means to objectively assess the quality of CD hierarchies, a non-trivial task considering that the protein subgroups are often very distantly related—a situation in which standard phylogenetic methods can be unreliable. Our aim here is to automatically generate (typically sub-optimal) hierarchies that, based on statistical criteria and visual comparisons, are comparable to manually curated hierarchies; this serves as the first step toward the ultimate goal of obtaining optimal hierarchical classifications. A plot of runtimes for the most time-intensive (non-parallelizable) part of the algorithm indicates a nearly linear time complexity so that, even for the extremely large Rossmann fold protein class, results were obtained in about a day. Conclusions This approach automates the rapid creation of protein domain hierarchies and thus will eliminate one of the most time consuming aspects of conserved domain database curation. At the same time, it also facilitates protein domain annotation by identifying those pattern residues that most distinguish each protein domain subgroup from other related subgroups. PMID:22726767

Argo: enabling the development of bespoke workflows and services for disease annotation.

PubMed

Batista-Navarro, Riza; Carter, Jacob; Ananiadou, Sophia

2016-01-01

Argo (http://argo.nactem.ac.uk) is a generic text mining workbench that can cater to a variety of use cases, including the semi-automatic annotation of literature. It enables its technical users to build their own customised text mining solutions by providing a wide array of interoperable and configurable elementary components that can be seamlessly integrated into processing workflows. With Argo's graphical annotation interface, domain experts can then make use of the workflows' automatically generated output to curate information of interest.With the continuously rising need to understand the aetiology of diseases as well as the demand for their informed diagnosis and personalised treatment, the curation of disease-relevant information from medical and clinical documents has become an indispensable scientific activity. In the Fifth BioCreative Challenge Evaluation Workshop (BioCreative V), there was substantial interest in the mining of literature for disease-relevant information. Apart from a panel discussion focussed on disease annotations, the chemical-disease relations (CDR) track was also organised to foster the sharing and advancement of disease annotation tools and resources.This article presents the application of Argo's capabilities to the literature-based annotation of diseases. As part of our participation in BioCreative V's User Interactive Track (IAT), we demonstrated and evaluated Argo's suitability to the semi-automatic curation of chronic obstructive pulmonary disease (COPD) phenotypes. Furthermore, the workbench facilitated the development of some of the CDR track's top-performing web services for normalising disease mentions against the Medical Subject Headings (MeSH) database. In this work, we highlight Argo's support for developing various types of bespoke workflows ranging from ones which enabled us to easily incorporate information from various databases, to those which train and apply machine learning-based concept recognition models, through to user-interactive ones which allow human curators to manually provide their corrections to automatically generated annotations. Our participation in the BioCreative V challenges shows Argo's potential as an enabling technology for curating disease and phenotypic information from literature.Database URL: http://argo.nactem.ac.uk. © The Author(s) 2016. Published by Oxford University Press.

Argo: enabling the development of bespoke workflows and services for disease annotation

PubMed Central

Batista-Navarro, Riza; Carter, Jacob; Ananiadou, Sophia

2016-01-01

Argo (http://argo.nactem.ac.uk) is a generic text mining workbench that can cater to a variety of use cases, including the semi-automatic annotation of literature. It enables its technical users to build their own customised text mining solutions by providing a wide array of interoperable and configurable elementary components that can be seamlessly integrated into processing workflows. With Argo's graphical annotation interface, domain experts can then make use of the workflows' automatically generated output to curate information of interest. With the continuously rising need to understand the aetiology of diseases as well as the demand for their informed diagnosis and personalised treatment, the curation of disease-relevant information from medical and clinical documents has become an indispensable scientific activity. In the Fifth BioCreative Challenge Evaluation Workshop (BioCreative V), there was substantial interest in the mining of literature for disease-relevant information. Apart from a panel discussion focussed on disease annotations, the chemical-disease relations (CDR) track was also organised to foster the sharing and advancement of disease annotation tools and resources. This article presents the application of Argo’s capabilities to the literature-based annotation of diseases. As part of our participation in BioCreative V’s User Interactive Track (IAT), we demonstrated and evaluated Argo’s suitability to the semi-automatic curation of chronic obstructive pulmonary disease (COPD) phenotypes. Furthermore, the workbench facilitated the development of some of the CDR track’s top-performing web services for normalising disease mentions against the Medical Subject Headings (MeSH) database. In this work, we highlight Argo’s support for developing various types of bespoke workflows ranging from ones which enabled us to easily incorporate information from various databases, to those which train and apply machine learning-based concept recognition models, through to user-interactive ones which allow human curators to manually provide their corrections to automatically generated annotations. Our participation in the BioCreative V challenges shows Argo’s potential as an enabling technology for curating disease and phenotypic information from literature. Database URL: http://argo.nactem.ac.uk PMID:27189607

Kalium: a database of potassium channel toxins from scorpion venom.

PubMed

Kuzmenkov, Alexey I; Krylov, Nikolay A; Chugunov, Anton O; Grishin, Eugene V; Vassilevski, Alexander A

2016-01-01

Kalium (http://kaliumdb.org/) is a manually curated database that accumulates data on potassium channel toxins purified from scorpion venom (KTx). This database is an open-access resource, and provides easy access to pages of other databases of interest, such as UniProt, PDB, NCBI Taxonomy Browser, and PubMed. General achievements of Kalium are a strict and easy regulation of KTx classification based on the unified nomenclature supported by researchers in the field, removal of peptides with partial sequence and entries supported by transcriptomic information only, classification of β-family toxins, and addition of a novel λ-family. Molecules presented in the database can be processed by the Clustal Omega server using a one-click option. Molecular masses of mature peptides are calculated and available activity data are compiled for all KTx. We believe that Kalium is not only of high interest to professional toxinologists, but also of general utility to the scientific community.Database URL:http://kaliumdb.org/. © The Author(s) 2016. Published by Oxford University Press.

Entitymetrics: Measuring the Impact of Entities

PubMed Central

Ding, Ying; Song, Min; Han, Jia; Yu, Qi; Yan, Erjia; Lin, Lili; Chambers, Tamy

2013-01-01

This paper proposes entitymetrics to measure the impact of knowledge units. Entitymetrics highlight the importance of entities embedded in scientific literature for further knowledge discovery. In this paper, we use Metformin, a drug for diabetes, as an example to form an entity-entity citation network based on literature related to Metformin. We then calculate the network features and compare the centrality ranks of biological entities with results from Comparative Toxicogenomics Database (CTD). The comparison demonstrates the usefulness of entitymetrics to detect most of the outstanding interactions manually curated in CTD. PMID:24009660

Automatic categorization of diverse experimental information in the bioscience literature

PubMed Central

2012-01-01

Background Curation of information from bioscience literature into biological knowledge databases is a crucial way of capturing experimental information in a computable form. During the biocuration process, a critical first step is to identify from all published literature the papers that contain results for a specific data type the curator is interested in annotating. This step normally requires curators to manually examine many papers to ascertain which few contain information of interest and thus, is usually time consuming. We developed an automatic method for identifying papers containing these curation data types among a large pool of published scientific papers based on the machine learning method Support Vector Machine (SVM). This classification system is completely automatic and can be readily applied to diverse experimental data types. It has been in use in production for automatic categorization of 10 different experimental datatypes in the biocuration process at WormBase for the past two years and it is in the process of being adopted in the biocuration process at FlyBase and the Saccharomyces Genome Database (SGD). We anticipate that this method can be readily adopted by various databases in the biocuration community and thereby greatly reducing time spent on an otherwise laborious and demanding task. We also developed a simple, readily automated procedure to utilize training papers of similar data types from different bodies of literature such as C. elegans and D. melanogaster to identify papers with any of these data types for a single database. This approach has great significance because for some data types, especially those of low occurrence, a single corpus often does not have enough training papers to achieve satisfactory performance. Results We successfully tested the method on ten data types from WormBase, fifteen data types from FlyBase and three data types from Mouse Genomics Informatics (MGI). It is being used in the curation work flow at WormBase for automatic association of newly published papers with ten data types including RNAi, antibody, phenotype, gene regulation, mutant allele sequence, gene expression, gene product interaction, overexpression phenotype, gene interaction, and gene structure correction. Conclusions Our methods are applicable to a variety of data types with training set containing several hundreds to a few thousand documents. It is completely automatic and, thus can be readily incorporated to different workflow at different literature-based databases. We believe that the work presented here can contribute greatly to the tremendous task of automating the important yet labor-intensive biocuration effort. PMID:22280404

Automatic categorization of diverse experimental information in the bioscience literature.

PubMed

Fang, Ruihua; Schindelman, Gary; Van Auken, Kimberly; Fernandes, Jolene; Chen, Wen; Wang, Xiaodong; Davis, Paul; Tuli, Mary Ann; Marygold, Steven J; Millburn, Gillian; Matthews, Beverley; Zhang, Haiyan; Brown, Nick; Gelbart, William M; Sternberg, Paul W

2012-01-26

Curation of information from bioscience literature into biological knowledge databases is a crucial way of capturing experimental information in a computable form. During the biocuration process, a critical first step is to identify from all published literature the papers that contain results for a specific data type the curator is interested in annotating. This step normally requires curators to manually examine many papers to ascertain which few contain information of interest and thus, is usually time consuming. We developed an automatic method for identifying papers containing these curation data types among a large pool of published scientific papers based on the machine learning method Support Vector Machine (SVM). This classification system is completely automatic and can be readily applied to diverse experimental data types. It has been in use in production for automatic categorization of 10 different experimental datatypes in the biocuration process at WormBase for the past two years and it is in the process of being adopted in the biocuration process at FlyBase and the Saccharomyces Genome Database (SGD). We anticipate that this method can be readily adopted by various databases in the biocuration community and thereby greatly reducing time spent on an otherwise laborious and demanding task. We also developed a simple, readily automated procedure to utilize training papers of similar data types from different bodies of literature such as C. elegans and D. melanogaster to identify papers with any of these data types for a single database. This approach has great significance because for some data types, especially those of low occurrence, a single corpus often does not have enough training papers to achieve satisfactory performance. We successfully tested the method on ten data types from WormBase, fifteen data types from FlyBase and three data types from Mouse Genomics Informatics (MGI). It is being used in the curation work flow at WormBase for automatic association of newly published papers with ten data types including RNAi, antibody, phenotype, gene regulation, mutant allele sequence, gene expression, gene product interaction, overexpression phenotype, gene interaction, and gene structure correction. Our methods are applicable to a variety of data types with training set containing several hundreds to a few thousand documents. It is completely automatic and, thus can be readily incorporated to different workflow at different literature-based databases. We believe that the work presented here can contribute greatly to the tremendous task of automating the important yet labor-intensive biocuration effort.

dbDSM: a manually curated database for deleterious synonymous mutations.

PubMed

Wen, Pengbo; Xiao, Peng; Xia, Junfeng

2016-06-15

Synonymous mutations (SMs), which changed the sequence of a gene without directly altering the amino acid sequence of the encoded protein, were thought to have no functional consequences for a long time. They are often assumed to be neutral in models of mutation and selection and were completely ignored in many studies. However, accumulating experimental evidence has demonstrated that these mutations exert their impact on gene functions via splicing accuracy, mRNA stability, translation fidelity, protein folding and expression, and some of these mutations are implicated in human diseases. To the best of our knowledge, there is still no database specially focusing on disease-related SMs. We have developed a new database called dbDSM (database of Deleterious Synonymous Mutation), a continually updated database that collects, curates and manages available human disease-related SM data obtained from published literature. In the current release, dbDSM collects 1936 SM-disease association entries, including 1289 SMs and 443 human diseases from ClinVar, GRASP, GWAS Catalog, GWASdb, PolymiRTS database, PubMed database and Web of Knowledge. Additionally, we provided users a link to download all the data in the dbDSM and a link to submit novel data into the database. We hope dbDSM will be a useful resource for investigating the roles of SMs in human disease. dbDSM is freely available online at http://bioinfo.ahu.edu.cn:8080/dbDSM/index.jsp with all major browser supported. jfxia@ahu.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

dbAMEPNI: a database of alanine mutagenic effects for protein–nucleic acid interactions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Ling; Xiong, Yi; Gao, Hongyun

Protein–nucleic acid interactions play essential roles in various biological activities such as gene regulation, transcription, DNA repair and DNA packaging. Understanding the effects of amino acid substitutions on protein–nucleic acid binding affinities can help elucidate the molecular mechanism of protein–nucleic acid recognition. Until now, no comprehensive and updated database of quantitative binding data on alanine mutagenic effects for protein–nucleic acid interactions is publicly accessible. Thus, we developed a new database of Alanine Mutagenic Effects for Protein-Nucleic Acid Interactions (dbAMEPNI). dbAMEPNI is a manually curated, literature-derived database, comprising over 577 alanine mutagenic data with experimentally determined binding affinities for protein–nucleic acidmore » complexes. Here, it contains several important parameters, such as dissociation constant (Kd), Gibbs free energy change (ΔΔG), experimental conditions and structural parameters of mutant residues. In addition, the database provides an extended dataset of 282 single alanine mutations with only qualitative data (or descriptive effects) of thermodynamic information.« less

dbAMEPNI: a database of alanine mutagenic effects for protein–nucleic acid interactions

DOE PAGES

Liu, Ling; Xiong, Yi; Gao, Hongyun; ...

2018-04-02

Protein–nucleic acid interactions play essential roles in various biological activities such as gene regulation, transcription, DNA repair and DNA packaging. Understanding the effects of amino acid substitutions on protein–nucleic acid binding affinities can help elucidate the molecular mechanism of protein–nucleic acid recognition. Until now, no comprehensive and updated database of quantitative binding data on alanine mutagenic effects for protein–nucleic acid interactions is publicly accessible. Thus, we developed a new database of Alanine Mutagenic Effects for Protein-Nucleic Acid Interactions (dbAMEPNI). dbAMEPNI is a manually curated, literature-derived database, comprising over 577 alanine mutagenic data with experimentally determined binding affinities for protein–nucleic acidmore » complexes. Here, it contains several important parameters, such as dissociation constant (Kd), Gibbs free energy change (ΔΔG), experimental conditions and structural parameters of mutant residues. In addition, the database provides an extended dataset of 282 single alanine mutations with only qualitative data (or descriptive effects) of thermodynamic information.« less

BGDB: a database of bivalent genes.

PubMed

Li, Qingyan; Lian, Shuabin; Dai, Zhiming; Xiang, Qian; Dai, Xianhua

2013-01-01

Bivalent gene is a gene marked with both H3K4me3 and H3K27me3 epigenetic modification in the same area, and is proposed to play a pivotal role related to pluripotency in embryonic stem (ES) cells. Identification of these bivalent genes and understanding their functions are important for further research of lineage specification and embryo development. So far, lots of genome-wide histone modification data were generated in mouse and human ES cells. These valuable data make it possible to identify bivalent genes, but no comprehensive data repositories or analysis tools are available for bivalent genes currently. In this work, we develop BGDB, the database of bivalent genes. The database contains 6897 bivalent genes in human and mouse ES cells, which are manually collected from scientific literature. Each entry contains curated information, including genomic context, sequences, gene ontology and other relevant information. The web services of BGDB database were implemented with PHP + MySQL + JavaScript, and provide diverse query functions. Database URL: http://dailab.sysu.edu.cn/bgdb/

Lnc2Meth: a manually curated database of regulatory relationships between long non-coding RNAs and DNA methylation associated with human disease.

PubMed

Zhi, Hui; Li, Xin; Wang, Peng; Gao, Yue; Gao, Baoqing; Zhou, Dianshuang; Zhang, Yan; Guo, Maoni; Yue, Ming; Shen, Weitao; Ning, Shangwei; Jin, Lianhong; Li, Xia

2018-01-04

Lnc2Meth (http://www.bio-bigdata.com/Lnc2Meth/), an interactive resource to identify regulatory relationships between human long non-coding RNAs (lncRNAs) and DNA methylation, is not only a manually curated collection and annotation of experimentally supported lncRNAs-DNA methylation associations but also a platform that effectively integrates tools for calculating and identifying the differentially methylated lncRNAs and protein-coding genes (PCGs) in diverse human diseases. The resource provides: (i) advanced search possibilities, e.g. retrieval of the database by searching the lncRNA symbol of interest, DNA methylation patterns, regulatory mechanisms and disease types; (ii) abundant computationally calculated DNA methylation array profiles for the lncRNAs and PCGs; (iii) the prognostic values for each hit transcript calculated from the patients clinical data; (iv) a genome browser to display the DNA methylation landscape of the lncRNA transcripts for a specific type of disease; (v) tools to re-annotate probes to lncRNA loci and identify the differential methylation patterns for lncRNAs and PCGs with user-supplied external datasets; (vi) an R package (LncDM) to complete the differentially methylated lncRNAs identification and visualization with local computers. Lnc2Meth provides a timely and valuable resource that can be applied to significantly expand our understanding of the regulatory relationships between lncRNAs and DNA methylation in various human diseases. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

The Halophile protein database.

PubMed

Sharma, Naveen; Farooqi, Mohammad Samir; Chaturvedi, Krishna Kumar; Lal, Shashi Bhushan; Grover, Monendra; Rai, Anil; Pandey, Pankaj

2014-01-01

Halophilic archaea/bacteria adapt to different salt concentration, namely extreme, moderate and low. These type of adaptations may occur as a result of modification of protein structure and other changes in different cell organelles. Thus proteins may play an important role in the adaptation of halophilic archaea/bacteria to saline conditions. The Halophile protein database (HProtDB) is a systematic attempt to document the biochemical and biophysical properties of proteins from halophilic archaea/bacteria which may be involved in adaptation of these organisms to saline conditions. In this database, various physicochemical properties such as molecular weight, theoretical pI, amino acid composition, atomic composition, estimated half-life, instability index, aliphatic index and grand average of hydropathicity (Gravy) have been listed. These physicochemical properties play an important role in identifying the protein structure, bonding pattern and function of the specific proteins. This database is comprehensive, manually curated, non-redundant catalogue of proteins. The database currently contains 59 897 proteins properties extracted from 21 different strains of halophilic archaea/bacteria. The database can be accessed through link. Database URL: http://webapp.cabgrid.res.in/protein/ © The Author(s) 2014. Published by Oxford University Press.

CellFinder: a cell data repository

PubMed Central

Stachelscheid, Harald; Seltmann, Stefanie; Lekschas, Fritz; Fontaine, Jean-Fred; Mah, Nancy; Neves, Mariana; Andrade-Navarro, Miguel A.; Leser, Ulf; Kurtz, Andreas

2014-01-01

CellFinder (http://www.cellfinder.org) is a comprehensive one-stop resource for molecular data characterizing mammalian cells in different tissues and in different development stages. It is built from carefully selected data sets stemming from other curated databases and the biomedical literature. To date, CellFinder describes 3394 cell types and 50 951 cell lines. The database currently contains 3055 microscopic and anatomical images, 205 whole-genome expression profiles of 194 cell/tissue types from RNA-seq and microarrays and 553 905 protein expressions for 535 cells/tissues. Text mining of a corpus of >2000 publications followed by manual curation confirmed expression information on ∼900 proteins and genes. CellFinder’s data model is capable to seamlessly represent entities from single cells to the organ level, to incorporate mappings between homologous entities in different species and to describe processes of cell development and differentiation. Its ontological backbone currently consists of 204 741 ontology terms incorporated from 10 different ontologies unified under the novel CELDA ontology. CellFinder’s web portal allows searching, browsing and comparing the stored data, interactive construction of developmental trees and navigating the partonomic hierarchy of cells and tissues through a unique body browser designed for life scientists and clinicians. PMID:24304896

The immune epitope database: a historical retrospective of the first decade.

PubMed

Salimi, Nima; Fleri, Ward; Peters, Bjoern; Sette, Alessandro

2012-10-01

As the amount of biomedical information available in the literature continues to increase, databases that aggregate this information continue to grow in importance and scope. The population of databases can occur either through fully automated text mining approaches or through manual curation by human subject experts. We here report our experiences in populating the National Institute of Allergy and Infectious Diseases sponsored Immune Epitope Database and Analysis Resource (IEDB, http://iedb.org), which was created in 2003, and as of 2012 captures the epitope information from approximately 99% of all papers published to date that describe immune epitopes (with the exception of cancer and HIV data). This was achieved using a hybrid model based on automated document categorization and extensive human expert involvement. This task required automated scanning of over 22 million PubMed abstracts followed by classification and curation of over 13 000 references, including over 7000 infectious disease-related manuscripts, over 1000 allergy-related manuscripts, roughly 4000 related to autoimmunity, and 1000 transplant/alloantigen-related manuscripts. The IEDB curation involves an unprecedented level of detail, capturing for each paper the actual experiments performed for each different epitope structure. Key to enabling this process was the extensive use of ontologies to ensure rigorous and consistent data representation as well as interoperability with other bioinformatics resources, including the Protein Data Bank, Chemical Entities of Biological Interest, and the NIAID Bioinformatics Resource Centers. A growing fraction of the IEDB data derives from direct submissions by research groups engaged in epitope discovery, and is being facilitated by the implementation of novel data submission tools. The present explosion of information contained in biological databases demands effective query and display capabilities to optimize the user experience. Accordingly, the development of original ways to query the database, on the basis of ontologically driven hierarchical trees, and display of epitope data in aggregate in a biologically intuitive yet rigorous fashion is now at the forefront of the IEDB efforts. We also highlight advances made in the realm of epitope analysis and predictive tools available in the IEDB. © 2012 The Authors. Immunology © 2012 Blackwell Publishing Ltd.

Collaborative biocuration--text-mining development task for document prioritization for curation.

PubMed

Wiegers, Thomas C; Davis, Allan Peter; Mattingly, Carolyn J

2012-01-01

The Critical Assessment of Information Extraction systems in Biology (BioCreAtIvE) challenge evaluation is a community-wide effort for evaluating text mining and information extraction systems for the biological domain. The 'BioCreative Workshop 2012' subcommittee identified three areas, or tracks, that comprised independent, but complementary aspects of data curation in which they sought community input: literature triage (Track I); curation workflow (Track II) and text mining/natural language processing (NLP) systems (Track III). Track I participants were invited to develop tools or systems that would effectively triage and prioritize articles for curation and present results in a prototype web interface. Training and test datasets were derived from the Comparative Toxicogenomics Database (CTD; http://ctdbase.org) and consisted of manuscripts from which chemical-gene-disease data were manually curated. A total of seven groups participated in Track I. For the triage component, the effectiveness of participant systems was measured by aggregate gene, disease and chemical 'named-entity recognition' (NER) across articles; the effectiveness of 'information retrieval' (IR) was also measured based on 'mean average precision' (MAP). Top recall scores for gene, disease and chemical NER were 49, 65 and 82%, respectively; the top MAP score was 80%. Each participating group also developed a prototype web interface; these interfaces were evaluated based on functionality and ease-of-use by CTD's biocuration project manager. In this article, we present a detailed description of the challenge and a summary of the results.

HEROD: a human ethnic and regional specific omics database.

PubMed

Zeng, Xian; Tao, Lin; Zhang, Peng; Qin, Chu; Chen, Shangying; He, Weidong; Tan, Ying; Xia Liu, Hong; Yang, Sheng Yong; Chen, Zhe; Jiang, Yu Yang; Chen, Yu Zong

2017-10-15

Genetic and gene expression variations within and between populations and across geographical regions have substantial effects on the biological phenotypes, diseases, and therapeutic response. The development of precision medicines can be facilitated by the OMICS studies of the patients of specific ethnicity and geographic region. However, there is an inadequate facility for broadly and conveniently accessing the ethnic and regional specific OMICS data. Here, we introduced a new free database, HEROD, a human ethnic and regional specific OMICS database. Its first version contains the gene expression data of 53 070 patients of 169 diseases in seven ethnic populations from 193 cities/regions in 49 nations curated from the Gene Expression Omnibus (GEO), the ArrayExpress Archive of Functional Genomics Data (ArrayExpress), the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). Geographic region information of curated patients was mainly manually extracted from referenced publications of each original study. These data can be accessed and downloaded via keyword search, World map search, and menu-bar search of disease name, the international classification of disease code, geographical region, location of sample collection, ethnic population, gender, age, sample source organ, patient type (patient or healthy), sample type (disease or normal tissue) and assay type on the web interface. The HEROD database is freely accessible at http://bidd2.nus.edu.sg/herod/index.php. The database and web interface are implemented in MySQL, PHP and HTML with all major browsers supported. phacyz@nus.edu.sg. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

The Web-Based DNA Vaccine Database DNAVaxDB and Its Usage for Rational DNA Vaccine Design.

PubMed

Racz, Rebecca; He, Yongqun

2016-01-01

A DNA vaccine is a vaccine that uses a mammalian expression vector to express one or more protein antigens and is administered in vivo to induce an adaptive immune response. Since the 1990s, a significant amount of research has been performed on DNA vaccines and the mechanisms behind them. To meet the needs of the DNA vaccine research community, we created DNAVaxDB ( http://www.violinet.org/dnavaxdb ), the first Web-based database and analysis resource of experimentally verified DNA vaccines. All the data in DNAVaxDB, which includes plasmids, antigens, vaccines, and sources, is manually curated and experimentally verified. This chapter goes over the detail of DNAVaxDB system and shows how the DNA vaccine database, combined with the Vaxign vaccine design tool, can be used for rational design of a DNA vaccine against a pathogen, such as Mycobacterium bovis.

GEAR: A database of Genomic Elements Associated with drug Resistance.

PubMed

Wang, Yin-Ying; Chen, Wei-Hua; Xiao, Pei-Pei; Xie, Wen-Bin; Luo, Qibin; Bork, Peer; Zhao, Xing-Ming

2017-03-15

Drug resistance is becoming a serious problem that leads to the failure of standard treatments, which is generally developed because of genetic mutations of certain molecules. Here, we present GEAR (A database of Genomic Elements Associated with drug Resistance) that aims to provide comprehensive information about genomic elements (including genes, single-nucleotide polymorphisms and microRNAs) that are responsible for drug resistance. Right now, GEAR contains 1631 associations between 201 human drugs and 758 genes, 106 associations between 29 human drugs and 66 miRNAs, and 44 associations between 17 human drugs and 22 SNPs. These relationships are firstly extracted from primary literature with text mining and then manually curated. The drug resistome deposited in GEAR provides insights into the genetic factors underlying drug resistance. In addition, new indications and potential drug combinations can be identified based on the resistome. The GEAR database can be freely accessed through http://gear.comp-sysbio.org.

GEAR: A database of Genomic Elements Associated with drug Resistance

PubMed Central

Wang, Yin-Ying; Chen, Wei-Hua; Xiao, Pei-Pei; Xie, Wen-Bin; Luo, Qibin; Bork, Peer; Zhao, Xing-Ming

2017-01-01

Drug resistance is becoming a serious problem that leads to the failure of standard treatments, which is generally developed because of genetic mutations of certain molecules. Here, we present GEAR (A database of Genomic Elements Associated with drug Resistance) that aims to provide comprehensive information about genomic elements (including genes, single-nucleotide polymorphisms and microRNAs) that are responsible for drug resistance. Right now, GEAR contains 1631 associations between 201 human drugs and 758 genes, 106 associations between 29 human drugs and 66 miRNAs, and 44 associations between 17 human drugs and 22 SNPs. These relationships are firstly extracted from primary literature with text mining and then manually curated. The drug resistome deposited in GEAR provides insights into the genetic factors underlying drug resistance. In addition, new indications and potential drug combinations can be identified based on the resistome. The GEAR database can be freely accessed through http://gear.comp-sysbio.org. PMID:28294141

Morphosyntactic annotation of CHILDES transcripts*

PubMed Central

SAGAE, KENJI; DAVIS, ERIC; LAVIE, ALON; MACWHINNEY, BRIAN; WINTNER, SHULY

2014-01-01

Corpora of child language are essential for research in child language acquisition and psycholinguistics. Linguistic annotation of the corpora provides researchers with better means for exploring the development of grammatical constructions and their usage. We describe a project whose goal is to annotate the English section of the CHILDES database with grammatical relations in the form of labeled dependency structures. We have produced a corpus of over 18,800 utterances (approximately 65,000 words) with manually curated gold-standard grammatical relation annotations. Using this corpus, we have developed a highly accurate data-driven parser for the English CHILDES data, which we used to automatically annotate the remainder of the English section of CHILDES. We have also extended the parser to Spanish, and are currently working on supporting more languages. The parser and the manually and automatically annotated data are freely available for research purposes. PMID:20334720

Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research.

PubMed

Bravo, Àlex; Piñero, Janet; Queralt-Rosinach, Núria; Rautschka, Michael; Furlong, Laura I

2015-02-21

Current biomedical research needs to leverage and exploit the large amount of information reported in scientific publications. Automated text mining approaches, in particular those aimed at finding relationships between entities, are key for identification of actionable knowledge from free text repositories. We present the BeFree system aimed at identifying relationships between biomedical entities with a special focus on genes and their associated diseases. By exploiting morpho-syntactic information of the text, BeFree is able to identify gene-disease, drug-disease and drug-target associations with state-of-the-art performance. The application of BeFree to real-case scenarios shows its effectiveness in extracting information relevant for translational research. We show the value of the gene-disease associations extracted by BeFree through a number of analyses and integration with other data sources. BeFree succeeds in identifying genes associated to a major cause of morbidity worldwide, depression, which are not present in other public resources. Moreover, large-scale extraction and analysis of gene-disease associations, and integration with current biomedical knowledge, provided interesting insights on the kind of information that can be found in the literature, and raised challenges regarding data prioritization and curation. We found that only a small proportion of the gene-disease associations discovered by using BeFree is collected in expert-curated databases. Thus, there is a pressing need to find alternative strategies to manual curation, in order to review, prioritize and curate text-mining data and incorporate it into domain-specific databases. We present our strategy for data prioritization and discuss its implications for supporting biomedical research and applications. BeFree is a novel text mining system that performs competitively for the identification of gene-disease, drug-disease and drug-target associations. Our analyses show that mining only a small fraction of MEDLINE results in a large dataset of gene-disease associations, and only a small proportion of this dataset is actually recorded in curated resources (2%), raising several issues on data prioritization and curation. We propose that joint analysis of text mined data with data curated by experts appears as a suitable approach to both assess data quality and highlight novel and interesting information.

DDRprot: a database of DNA damage response-related proteins.

PubMed

Andrés-León, Eduardo; Cases, Ildefonso; Arcas, Aida; Rojas, Ana M

2016-01-01

The DNA Damage Response (DDR) signalling network is an essential system that protects the genome's integrity. The DDRprot database presented here is a resource that integrates manually curated information on the human DDR network and its sub-pathways. For each particular DDR protein, we present detailed information about its function. If involved in post-translational modifications (PTMs) with each other, we depict the position of the modified residue/s in the three-dimensional structures, when resolved structures are available for the proteins. All this information is linked to the original publication from where it was obtained. Phylogenetic information is also shown, including time of emergence and conservation across 47 selected species, family trees and sequence alignments of homologues. The DDRprot database can be queried by different criteria: pathways, species, evolutionary age or involvement in (PTM). Sequence searches using hidden Markov models can be also used.Database URL: http://ddr.cbbio.es. © The Author(s) 2016. Published by Oxford University Press.

RNAimmuno: A database of the nonspecific immunological effects of RNA interference and microRNA reagents

PubMed Central

Olejniczak, Marta; Galka-Marciniak, Paulina; Polak, Katarzyna; Fligier, Andrzej; Krzyzosiak, Wlodzimierz J.

2012-01-01

The RNAimmuno database was created to provide easy access to information regarding the nonspecific effects generated in cells by RNA interference triggers and microRNA regulators. Various RNAi and microRNA reagents, which differ in length and structure, often cause non-sequence-specific immune responses, in addition to triggering the intended sequence-specific effects. The activation of the cellular sensors of foreign RNA or DNA may lead to the induction of type I interferon and proinflammatory cytokine release. Subsequent changes in the cellular transcriptome and proteome may result in adverse effects, including cell death during therapeutic treatments or the misinterpretation of experimental results in research applications. The manually curated RNAimmuno database gathers the majority of the published data regarding the immunological side effects that are caused in investigated cell lines, tissues, and model organisms by different reagents. The database is accessible at http://rnaimmuno.ibch.poznan.pl and may be helpful in the further application and development of RNAi- and microRNA-based technologies. PMID:22411954

RNAimmuno: a database of the nonspecific immunological effects of RNA interference and microRNA reagents.

PubMed

Olejniczak, Marta; Galka-Marciniak, Paulina; Polak, Katarzyna; Fligier, Andrzej; Krzyzosiak, Wlodzimierz J

2012-05-01

The RNAimmuno database was created to provide easy access to information regarding the nonspecific effects generated in cells by RNA interference triggers and microRNA regulators. Various RNAi and microRNA reagents, which differ in length and structure, often cause non-sequence-specific immune responses, in addition to triggering the intended sequence-specific effects. The activation of the cellular sensors of foreign RNA or DNA may lead to the induction of type I interferon and proinflammatory cytokine release. Subsequent changes in the cellular transcriptome and proteome may result in adverse effects, including cell death during therapeutic treatments or the misinterpretation of experimental results in research applications. The manually curated RNAimmuno database gathers the majority of the published data regarding the immunological side effects that are caused in investigated cell lines, tissues, and model organisms by different reagents. The database is accessible at http://rnaimmuno.ibch.poznan.pl and may be helpful in the further application and development of RNAi- and microRNA-based technologies.

Gene regulation knowledge commons: community action takes care of DNA binding transcription factors

PubMed Central

Tripathi, Sushil; Vercruysse, Steven; Chawla, Konika; Christie, Karen R.; Blake, Judith A.; Huntley, Rachael P.; Orchard, Sandra; Hermjakob, Henning; Thommesen, Liv; Lægreid, Astrid; Kuiper, Martin

2016-01-01

A large gap remains between the amount of knowledge in scientific literature and the fraction that gets curated into standardized databases, despite many curation initiatives. Yet the availability of comprehensive knowledge in databases is crucial for exploiting existing background knowledge, both for designing follow-up experiments and for interpreting new experimental data. Structured resources also underpin the computational integration and modeling of regulatory pathways, which further aids our understanding of regulatory dynamics. We argue how cooperation between the scientific community and professional curators can increase the capacity of capturing precise knowledge from literature. We demonstrate this with a project in which we mobilize biological domain experts who curate large amounts of DNA binding transcription factors, and show that they, although new to the field of curation, can make valuable contributions by harvesting reported knowledge from scientific papers. Such community curation can enhance the scientific epistemic process. Database URL: http://www.tfcheckpoint.org PMID:27270715

Can we replace curation with information extraction software?

PubMed

Karp, Peter D

2016-01-01

Can we use programs for automated or semi-automated information extraction from scientific texts as practical alternatives to professional curation? I show that error rates of current information extraction programs are too high to replace professional curation today. Furthermore, current IEP programs extract single narrow slivers of information, such as individual protein interactions; they cannot extract the large breadth of information extracted by professional curators for databases such as EcoCyc. They also cannot arbitrate among conflicting statements in the literature as curators can. Therefore, funding agencies should not hobble the curation efforts of existing databases on the assumption that a problem that has stymied Artificial Intelligence researchers for more than 60 years will be solved tomorrow. Semi-automated extraction techniques appear to have significantly more potential based on a review of recent tools that enhance curator productivity. But a full cost-benefit analysis for these tools is lacking. Without such analysis it is possible to expend significant effort developing information-extraction tools that automate small parts of the overall curation workflow without achieving a significant decrease in curation costs.Database URL. © The Author(s) 2016. Published by Oxford University Press.

The CompTox Chemistry Dashboard: a community data resource for environmental chemistry.

PubMed

Williams, Antony J; Grulke, Christopher M; Edwards, Jeff; McEachran, Andrew D; Mansouri, Kamel; Baker, Nancy C; Patlewicz, Grace; Shah, Imran; Wambaugh, John F; Judson, Richard S; Richard, Ann M

2017-11-28

Despite an abundance of online databases providing access to chemical data, there is increasing demand for high-quality, structure-curated, open data to meet the various needs of the environmental sciences and computational toxicology communities. The U.S. Environmental Protection Agency's (EPA) web-based CompTox Chemistry Dashboard is addressing these needs by integrating diverse types of relevant domain data through a cheminformatics layer, built upon a database of curated substances linked to chemical structures. These data include physicochemical, environmental fate and transport, exposure, usage, in vivo toxicity, and in vitro bioassay data, surfaced through an integration hub with link-outs to additional EPA data and public domain online resources. Batch searching allows for direct chemical identifier (ID) mapping and downloading of multiple data streams in several different formats. This facilitates fast access to available structure, property, toxicity, and bioassay data for collections of chemicals (hundreds to thousands at a time). Advanced search capabilities are available to support, for example, non-targeted analysis and identification of chemicals using mass spectrometry. The contents of the chemistry database, presently containing ~ 760,000 substances, are available as public domain data for download. The chemistry content underpinning the Dashboard has been aggregated over the past 15 years by both manual and auto-curation techniques within EPA's DSSTox project. DSSTox chemical content is subject to strict quality controls to enforce consistency among chemical substance-structure identifiers, as well as list curation review to ensure accurate linkages of DSSTox substances to chemical lists and associated data. The Dashboard, publicly launched in April 2016, has expanded considerably in content and user traffic over the past year. It is continuously evolving with the growth of DSSTox into high-interest or data-rich domains of interest to EPA, such as chemicals on the Toxic Substances Control Act listing, while providing the user community with a flexible and dynamic web-based platform for integration, processing, visualization and delivery of data and resources. The Dashboard provides support for a broad array of research and regulatory programs across the worldwide community of toxicologists and environmental scientists.

BioModels Database: a repository of mathematical models of biological processes.

PubMed

Chelliah, Vijayalakshmi; Laibe, Camille; Le Novère, Nicolas

2013-01-01

BioModels Database is a public online resource that allows storing and sharing of published, peer-reviewed quantitative, dynamic models of biological processes. The model components and behaviour are thoroughly checked to correspond the original publication and manually curated to ensure reliability. Furthermore, the model elements are annotated with terms from controlled vocabularies as well as linked to relevant external data resources. This greatly helps in model interpretation and reuse. Models are stored in SBML format, accepted in SBML and CellML formats, and are available for download in various other common formats such as BioPAX, Octave, SciLab, VCML, XPP and PDF, in addition to SBML. The reaction network diagram of the models is also available in several formats. BioModels Database features a search engine, which provides simple and more advanced searches. Features such as online simulation and creation of smaller models (submodels) from the selected model elements of a larger one are provided. BioModels Database can be accessed both via a web interface and programmatically via web services. New models are available in BioModels Database at regular releases, about every 4 months.

HIM-herbal ingredients in-vivo metabolism database.

PubMed

Kang, Hong; Tang, Kailin; Liu, Qi; Sun, Yi; Huang, Qi; Zhu, Ruixin; Gao, Jun; Zhang, Duanfeng; Huang, Chenggang; Cao, Zhiwei

2013-05-31

Herbal medicine has long been viewed as a valuable asset for potential new drug discovery and herbal ingredients' metabolites, especially the in vivo metabolites were often found to gain better pharmacological, pharmacokinetic and even better safety profiles compared to their parent compounds. However, these herbal metabolite information is still scattered and waiting to be collected. HIM database manually collected so far the most comprehensive available in-vivo metabolism information for herbal active ingredients, as well as their corresponding bioactivity, organs and/or tissues distribution, toxicity, ADME and the clinical research profile. Currently HIM contains 361 ingredients and 1104 corresponding in-vivo metabolites from 673 reputable herbs. Tools of structural similarity, substructure search and Lipinski's Rule of Five are also provided. Various links were made to PubChem, PubMed, TCM-ID (Traditional Chinese Medicine Information database) and HIT (Herbal ingredients' targets databases). A curated database HIM is set up for the in vivo metabolites information of the active ingredients for Chinese herbs, together with their corresponding bioactivity, toxicity and ADME profile. HIM is freely accessible to academic researchers at http://www.bioinformatics.org.cn/.

LMSD: LIPID MAPS structure database

PubMed Central

Sud, Manish; Fahy, Eoin; Cotter, Dawn; Brown, Alex; Dennis, Edward A.; Glass, Christopher K.; Merrill, Alfred H.; Murphy, Robert C.; Raetz, Christian R. H.; Russell, David W.; Subramaniam, Shankar

2007-01-01

The LIPID MAPS Structure Database (LMSD) is a relational database encompassing structures and annotations of biologically relevant lipids. Structures of lipids in the database come from four sources: (i) LIPID MAPS Consortium's core laboratories and partners; (ii) lipids identified by LIPID MAPS experiments; (iii) computationally generated structures for appropriate lipid classes; (iv) biologically relevant lipids manually curated from LIPID BANK, LIPIDAT and other public sources. All the lipid structures in LMSD are drawn in a consistent fashion. In addition to a classification-based retrieval of lipids, users can search LMSD using either text-based or structure-based search options. The text-based search implementation supports data retrieval by any combination of these data fields: LIPID MAPS ID, systematic or common name, mass, formula, category, main class, and subclass data fields. The structure-based search, in conjunction with optional data fields, provides the capability to perform a substructure search or exact match for the structure drawn by the user. Search results, in addition to structure and annotations, also include relevant links to external databases. The LMSD is publicly available at PMID:17098933

BGDB: a database of bivalent genes

PubMed Central

Li, Qingyan; Lian, Shuabin; Dai, Zhiming; Xiang, Qian; Dai, Xianhua

2013-01-01

Bivalent gene is a gene marked with both H3K4me3 and H3K27me3 epigenetic modification in the same area, and is proposed to play a pivotal role related to pluripotency in embryonic stem (ES) cells. Identification of these bivalent genes and understanding their functions are important for further research of lineage specification and embryo development. So far, lots of genome-wide histone modification data were generated in mouse and human ES cells. These valuable data make it possible to identify bivalent genes, but no comprehensive data repositories or analysis tools are available for bivalent genes currently. In this work, we develop BGDB, the database of bivalent genes. The database contains 6897 bivalent genes in human and mouse ES cells, which are manually collected from scientific literature. Each entry contains curated information, including genomic context, sequences, gene ontology and other relevant information. The web services of BGDB database were implemented with PHP + MySQL + JavaScript, and provide diverse query functions. Database URL: http://dailab.sysu.edu.cn/bgdb/ PMID:23894186

Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae

PubMed Central

Reguly, Teresa; Breitkreutz, Ashton; Boucher, Lorrie; Breitkreutz, Bobby-Joe; Hon, Gary C; Myers, Chad L; Parsons, Ainslie; Friesen, Helena; Oughtred, Rose; Tong, Amy; Stark, Chris; Ho, Yuen; Botstein, David; Andrews, Brenda; Boone, Charles; Troyanskya, Olga G; Ideker, Trey; Dolinski, Kara; Batada, Nizar N; Tyers, Mike

2006-01-01

Background The study of complex biological networks and prediction of gene function has been enabled by high-throughput (HTP) methods for detection of genetic and protein interactions. Sparse coverage in HTP datasets may, however, distort network properties and confound predictions. Although a vast number of well substantiated interactions are recorded in the scientific literature, these data have not yet been distilled into networks that enable system-level inference. Results We describe here a comprehensive database of genetic and protein interactions, and associated experimental evidence, for the budding yeast Saccharomyces cerevisiae, as manually curated from over 31,793 abstracts and online publications. This literature-curated (LC) dataset contains 33,311 interactions, on the order of all extant HTP datasets combined. Surprisingly, HTP protein-interaction datasets currently achieve only around 14% coverage of the interactions in the literature. The LC network nevertheless shares attributes with HTP networks, including scale-free connectivity and correlations between interactions, abundance, localization, and expression. We find that essential genes or proteins are enriched for interactions with other essential genes or proteins, suggesting that the global network may be functionally unified. This interconnectivity is supported by a substantial overlap of protein and genetic interactions in the LC dataset. We show that the LC dataset considerably improves the predictive power of network-analysis approaches. The full LC dataset is available at the BioGRID () and SGD () databases. Conclusion Comprehensive datasets of biological interactions derived from the primary literature provide critical benchmarks for HTP methods, augment functional prediction, and reveal system-level attributes of biological networks. PMID:16762047

Reconstruction of metabolic pathways for the cattle genome

PubMed Central

Seo, Seongwon; Lewin, Harris A

2009-01-01

Background Metabolic reconstruction of microbial, plant and animal genomes is a necessary step toward understanding the evolutionary origins of metabolism and species-specific adaptive traits. The aims of this study were to reconstruct conserved metabolic pathways in the cattle genome and to identify metabolic pathways with missing genes and proteins. The MetaCyc database and PathwayTools software suite were chosen for this work because they are widely used and easy to implement. Results An amalgamated cattle genome database was created using the NCBI and Ensembl cattle genome databases (based on build 3.1) as data sources. PathwayTools was used to create a cattle-specific pathway genome database, which was followed by comprehensive manual curation for the reconstruction of metabolic pathways. The curated database, CattleCyc 1.0, consists of 217 metabolic pathways. A total of 64 mammalian-specific metabolic pathways were modified from the reference pathways in MetaCyc, and two pathways previously identified but missing from MetaCyc were added. Comparative analysis of metabolic pathways revealed the absence of mammalian genes for 22 metabolic enzymes whose activity was reported in the literature. We also identified six human metabolic protein-coding genes for which the cattle ortholog is missing from the sequence assembly. Conclusion CattleCyc is a powerful tool for understanding the biology of ruminants and other cetartiodactyl species. In addition, the approach used to develop CattleCyc provides a framework for the metabolic reconstruction of other newly sequenced mammalian genomes. It is clear that metabolic pathway analysis strongly reflects the quality of the underlying genome annotations. Thus, having well-annotated genomes from many mammalian species hosted in BioCyc will facilitate the comparative analysis of metabolic pathways among different species and a systems approach to comparative physiology. PMID:19284618

Ranking support vector machine for multiple kernels output combination in protein-protein interaction extraction from biomedical literature.

PubMed

Yang, Zhihao; Lin, Yuan; Wu, Jiajin; Tang, Nan; Lin, Hongfei; Li, Yanpeng

2011-10-01

Knowledge about protein-protein interactions (PPIs) unveils the molecular mechanisms of biological processes. However, the volume and content of published biomedical literature on protein interactions is expanding rapidly, making it increasingly difficult for interaction database curators to detect and curate protein interaction information manually. We present a multiple kernel learning-based approach for automatic PPI extraction from biomedical literature. The approach combines the following kernels: feature-based, tree, and graph and combines their output with Ranking support vector machine (SVM). Experimental evaluations show that the features in individual kernels are complementary and the kernel combined with Ranking SVM achieves better performance than those of the individual kernels, equal weight combination and optimal weight combination. Our approach can achieve state-of-the-art performance with respect to the comparable evaluations, with 64.88% F-score and 88.02% AUC on the AImed corpus. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Human Ageing Genomic Resources: new and updated databases

PubMed Central

Tacutu, Robi; Thornton, Daniel; Johnson, Emily; Budovsky, Arie; Barardo, Diogo; Craig, Thomas; Diana, Eugene; Lehmann, Gilad; Toren, Dmitri; Wang, Jingwei; Fraifeld, Vadim E

2018-01-01

Abstract In spite of a growing body of research and data, human ageing remains a poorly understood process. Over 10 years ago we developed the Human Ageing Genomic Resources (HAGR), a collection of databases and tools for studying the biology and genetics of ageing. Here, we present HAGR’s main functionalities, highlighting new additions and improvements. HAGR consists of six core databases: (i) the GenAge database of ageing-related genes, in turn composed of a dataset of >300 human ageing-related genes and a dataset with >2000 genes associated with ageing or longevity in model organisms; (ii) the AnAge database of animal ageing and longevity, featuring >4000 species; (iii) the GenDR database with >200 genes associated with the life-extending effects of dietary restriction; (iv) the LongevityMap database of human genetic association studies of longevity with >500 entries; (v) the DrugAge database with >400 ageing or longevity-associated drugs or compounds; (vi) the CellAge database with >200 genes associated with cell senescence. All our databases are manually curated by experts and regularly updated to ensure a high quality data. Cross-links across our databases and to external resources help researchers locate and integrate relevant information. HAGR is freely available online (http://genomics.senescence.info/). PMID:29121237

Data Curation: Improving Environmental Health Data Quality.

PubMed

Yang, Lin; Li, Jiao; Hou, Li; Qian, Qing

2015-01-01

With the growing recognition of the influence of climate change on human health, scientists' attention to analyzing the relationship between meteorological factors and adverse health effects. However, the paucity of high quality integrated data is one of the great challenges, especially when scientific studies rely on data-intensive computing. This paper aims to design an appropriate curation process to address this problem. We present a data curation workflow that: (i) follows the guidance of DCC Curation Lifecycle Model; (ii) combines manual curation with automatic curation; (iii) and solves environmental health data curation problem. The workflow was applied to a medical knowledge service system and showed that it was capable of improving work efficiency and data quality.

BiGG Models: A platform for integrating, standardizing and sharing genome-scale models

DOE PAGES

King, Zachary A.; Lu, Justin; Drager, Andreas; ...

2015-10-17

In this study, genome-scale metabolic models are mathematically structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scalemore » metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data.« less

BiGG Models: A platform for integrating, standardizing and sharing genome-scale models

PubMed Central

King, Zachary A.; Lu, Justin; Dräger, Andreas; Miller, Philip; Federowicz, Stephen; Lerman, Joshua A.; Ebrahim, Ali; Palsson, Bernhard O.; Lewis, Nathan E.

2016-01-01

Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data. PMID:26476456

The Pathogen-Host Interactions database (PHI-base): additions and future developments

PubMed Central

Urban, Martin; Pant, Rashmi; Raghunath, Arathi; Irvine, Alistair G.; Pedro, Helder; Hammond-Kosack, Kim E.

2015-01-01

Rapidly evolving pathogens cause a diverse array of diseases and epidemics that threaten crop yield, food security as well as human, animal and ecosystem health. To combat infection greater comparative knowledge is required on the pathogenic process in multiple species. The Pathogen-Host Interactions database (PHI-base) catalogues experimentally verified pathogenicity, virulence and effector genes from bacterial, fungal and protist pathogens. Mutant phenotypes are associated with gene information. The included pathogens infect a wide range of hosts including humans, animals, plants, insects, fish and other fungi. The current version, PHI-base 3.6, available at http://www.phi-base.org, stores information on 2875 genes, 4102 interactions, 110 host species, 160 pathogenic species (103 plant, 3 fungal and 54 animal infecting species) and 181 diseases drawn from 1243 references. Phenotypic and gene function information has been obtained by manual curation of the peer-reviewed literature. A controlled vocabulary consisting of nine high-level phenotype terms permits comparisons and data analysis across the taxonomic space. PHI-base phenotypes were mapped via their associated gene information to reference genomes available in Ensembl Genomes. Virulence genes and hotspots can be visualized directly in genome browsers. Future plans for PHI-base include development of tools facilitating community-led curation and inclusion of the corresponding host target(s). PMID:25414340

Toward an integrated knowledge environment to support modern oncology.

PubMed

Blake, Patrick M; Decker, David A; Glennon, Timothy M; Liang, Yong Michael; Losko, Sascha; Navin, Nicholas; Suh, K Stephen

2011-01-01

Around the world, teams of researchers continue to develop a wide range of systems to capture, store, and analyze data including treatment, patient outcomes, tumor registries, next-generation sequencing, single-nucleotide polymorphism, copy number, gene expression, drug chemistry, drug safety, and toxicity. Scientists mine, curate, and manually annotate growing mountains of data to produce high-quality databases, while clinical information is aggregated in distant systems. Databases are currently scattered, and relationships between variables coded in disparate datasets are frequently invisible. The challenge is to evolve oncology informatics from a "systems" orientation of standalone platforms and silos into an "integrated knowledge environments" that will connect "knowable" research data with patient clinical information. The aim of this article is to review progress toward an integrated knowledge environment to support modern oncology with a focus on supporting scientific discovery and improving cancer care.

i5k | National Agricultural Library

Science.gov Websites

genome browser, and the Apollo manual curation service. Over 50 arthropod genomes are now part of the i5k (done by Dan Hughes at Baylor) with manual annotations by the research community (done via Web Apollo with manual annotations by the research community (via the Apollo manual annotation software). insects

Resource Disambiguator for the Web: Extracting Biomedical Resources and Their Citations from the Scientific Literature.

PubMed

Ozyurt, Ibrahim Burak; Grethe, Jeffrey S; Martone, Maryann E; Bandrowski, Anita E

2016-01-01

The NIF Registry developed and maintained by the Neuroscience Information Framework is a cooperative project aimed at cataloging research resources, e.g., software tools, databases and tissue banks, funded largely by governments and available as tools to research scientists. Although originally conceived for neuroscience, the NIF Registry has over the years broadened in the scope to include research resources of general relevance to biomedical research. The current number of research resources listed by the Registry numbers over 13K. The broadening in scope to biomedical science led us to re-christen the NIF Registry platform as SciCrunch. The NIF/SciCrunch Registry has been cataloging the resource landscape since 2006; as such, it serves as a valuable dataset for tracking the breadth, fate and utilization of these resources. Our experience shows research resources like databases are dynamic objects, that can change location and scope over time. Although each record is entered manually and human-curated, the current size of the registry requires tools that can aid in curation efforts to keep content up to date, including when and where such resources are used. To address this challenge, we have developed an open source tool suite, collectively termed RDW: Resource Disambiguator for the (Web). RDW is designed to help in the upkeep and curation of the registry as well as in enhancing the content of the registry by automated extraction of resource candidates from the literature. The RDW toolkit includes a URL extractor from papers, resource candidate screen, resource URL change tracker, resource content change tracker. Curators access these tools via a web based user interface. Several strategies are used to optimize these tools, including supervised and unsupervised learning algorithms as well as statistical text analysis. The complete tool suite is used to enhance and maintain the resource registry as well as track the usage of individual resources through an innovative literature citation index honed for research resources. Here we present an overview of the Registry and show how the RDW tools are used in curation and usage tracking.

Resource Disambiguator for the Web: Extracting Biomedical Resources and Their Citations from the Scientific Literature

PubMed Central

Ozyurt, Ibrahim Burak; Grethe, Jeffrey S.; Martone, Maryann E.; Bandrowski, Anita E.

2016-01-01

The NIF Registry developed and maintained by the Neuroscience Information Framework is a cooperative project aimed at cataloging research resources, e.g., software tools, databases and tissue banks, funded largely by governments and available as tools to research scientists. Although originally conceived for neuroscience, the NIF Registry has over the years broadened in the scope to include research resources of general relevance to biomedical research. The current number of research resources listed by the Registry numbers over 13K. The broadening in scope to biomedical science led us to re-christen the NIF Registry platform as SciCrunch. The NIF/SciCrunch Registry has been cataloging the resource landscape since 2006; as such, it serves as a valuable dataset for tracking the breadth, fate and utilization of these resources. Our experience shows research resources like databases are dynamic objects, that can change location and scope over time. Although each record is entered manually and human-curated, the current size of the registry requires tools that can aid in curation efforts to keep content up to date, including when and where such resources are used. To address this challenge, we have developed an open source tool suite, collectively termed RDW: Resource Disambiguator for the (Web). RDW is designed to help in the upkeep and curation of the registry as well as in enhancing the content of the registry by automated extraction of resource candidates from the literature. The RDW toolkit includes a URL extractor from papers, resource candidate screen, resource URL change tracker, resource content change tracker. Curators access these tools via a web based user interface. Several strategies are used to optimize these tools, including supervised and unsupervised learning algorithms as well as statistical text analysis. The complete tool suite is used to enhance and maintain the resource registry as well as track the usage of individual resources through an innovative literature citation index honed for research resources. Here we present an overview of the Registry and show how the RDW tools are used in curation and usage tracking. PMID:26730820

CORAL: aligning conserved core regions across domain families.

PubMed

Fong, Jessica H; Marchler-Bauer, Aron

2009-08-01

Homologous protein families share highly conserved sequence and structure regions that are frequent targets for comparative analysis of related proteins and families. Many protein families, such as the curated domain families in the Conserved Domain Database (CDD), exhibit similar structural cores. To improve accuracy in aligning such protein families, we propose a profile-profile method CORAL that aligns individual core regions as gap-free units. CORAL computes optimal local alignment of two profiles with heuristics to preserve continuity within core regions. We benchmarked its performance on curated domains in CDD, which have pre-defined core regions, against COMPASS, HHalign and PSI-BLAST, using structure superpositions and comprehensive curator-optimized alignments as standards of truth. CORAL improves alignment accuracy on core regions over general profile methods, returning a balanced score of 0.57 for over 80% of all domain families in CDD, compared with the highest balanced score of 0.45 from other methods. Further, CORAL provides E-values to aid in detecting homologous protein families and, by respecting block boundaries, produces alignments with improved 'readability' that facilitate manual refinement. CORAL will be included in future versions of the NCBI Cn3D/CDTree software, which can be downloaded at http://www.ncbi.nlm.nih.gov/Structure/cdtree/cdtree.shtml. Supplementary data are available at Bioinformatics online.

Consensus coding sequence (CCDS) database: a standardized set of human and mouse protein-coding regions supported by expert curation

PubMed Central

Pujar, Shashikant; O’Leary, Nuala A; Farrell, Catherine M; Mudge, Jonathan M; Wallin, Craig; Diekhans, Mark; Barnes, If; Bennett, Ruth; Berry, Andrew E; Cox, Eric; Davidson, Claire; Goldfarb, Tamara; Gonzalez, Jose M; Hunt, Toby; Jackson, John; Joardar, Vinita; Kay, Mike P; Kodali, Vamsi K; McAndrews, Monica; McGarvey, Kelly M; Murphy, Michael; Rajput, Bhanu; Rangwala, Sanjida H; Riddick, Lillian D; Seal, Ruth L; Webb, David; Zhu, Sophia; Aken, Bronwen L; Bult, Carol J; Frankish, Adam; Pruitt, Kim D

2018-01-01

Abstract The Consensus Coding Sequence (CCDS) project provides a dataset of protein-coding regions that are identically annotated on the human and mouse reference genome assembly in genome annotations produced independently by NCBI and the Ensembl group at EMBL-EBI. This dataset is the product of an international collaboration that includes NCBI, Ensembl, HUGO Gene Nomenclature Committee, Mouse Genome Informatics and University of California, Santa Cruz. Identically annotated coding regions, which are generated using an automated pipeline and pass multiple quality assurance checks, are assigned a stable and tracked identifier (CCDS ID). Additionally, coordinated manual review by expert curators from the CCDS collaboration helps in maintaining the integrity and high quality of the dataset. The CCDS data are available through an interactive web page (https://www.ncbi.nlm.nih.gov/CCDS/CcdsBrowse.cgi) and an FTP site (ftp://ftp.ncbi.nlm.nih.gov/pub/CCDS/). In this paper, we outline the ongoing work, growth and stability of the CCDS dataset and provide updates on new collaboration members and new features added to the CCDS user interface. We also present expert curation scenarios, with specific examples highlighting the importance of an accurate reference genome assembly and the crucial role played by input from the research community. PMID:29126148

An online database for plant image analysis software tools.

PubMed

Lobet, Guillaume; Draye, Xavier; Périlleux, Claire

2013-10-09

Recent years have seen an increase in methods for plant phenotyping using image analyses. These methods require new software solutions for data extraction and treatment. These solutions are instrumental in supporting various research pipelines, ranging from the localisation of cellular compounds to the quantification of tree canopies. However, due to the variety of existing tools and the lack of central repository, it is challenging for researchers to identify the software that is best suited for their research. We present an online, manually curated, database referencing more than 90 plant image analysis software solutions. The website, plant-image-analysis.org, presents each software in a uniform and concise manner enabling users to identify the available solutions for their experimental needs. The website also enables user feedback, evaluations and new software submissions. The plant-image-analysis.org database provides an overview of existing plant image analysis software. The aim of such a toolbox is to help users to find solutions, and to provide developers a way to exchange and communicate about their work.

MetaboLights: An Open-Access Database Repository for Metabolomics Data.

PubMed

Kale, Namrata S; Haug, Kenneth; Conesa, Pablo; Jayseelan, Kalaivani; Moreno, Pablo; Rocca-Serra, Philippe; Nainala, Venkata Chandrasekhar; Spicer, Rachel A; Williams, Mark; Li, Xuefei; Salek, Reza M; Griffin, Julian L; Steinbeck, Christoph

2016-03-24

MetaboLights is the first general purpose, open-access database repository for cross-platform and cross-species metabolomics research at the European Bioinformatics Institute (EMBL-EBI). Based upon the open-source ISA framework, MetaboLights provides Metabolomics Standard Initiative (MSI) compliant metadata and raw experimental data associated with metabolomics experiments. Users can upload their study datasets into the MetaboLights Repository. These studies are then automatically assigned a stable and unique identifier (e.g., MTBLS1) that can be used for publication reference. The MetaboLights Reference Layer associates metabolites with metabolomics studies in the archive and is extensively annotated with data fields such as structural and chemical information, NMR and MS spectra, target species, metabolic pathways, and reactions. The database is manually curated with no specific release schedules. MetaboLights is also recommended by journals for metabolomics data deposition. This unit provides a guide to using MetaboLights, downloading experimental data, and depositing metabolomics datasets using user-friendly submission tools. Copyright © 2016 John Wiley & Sons, Inc.

PubSearch and PubFetch: a simple management system for semiautomated retrieval and annotation of biological information from the literature.

PubMed

Yoo, Danny; Xu, Iris; Berardini, Tanya Z; Rhee, Seung Yon; Narayanasamy, Vijay; Twigger, Simon

2006-03-01

For most systems in biology, a large body of literature exists that describes the complexity of the system based on experimental results. Manual review of this literature to extract targeted information into biological databases is difficult and time consuming. To address this problem, we developed PubSearch and PubFetch, which store literature, keyword, and gene information in a relational database, index the literature with keywords and gene names, and provide a Web user interface for annotating the genes from experimental data found in the associated literature. A set of protocols is provided in this unit for installing, populating, running, and using PubSearch and PubFetch. In addition, we provide support protocols for performing controlled vocabulary annotations. Intended users of PubSearch and PubFetch are database curators and biology researchers interested in tracking the literature and capturing information about genes of interest in a more effective way than with conventional spreadsheets and lab notebooks.

Non-Coding RNA Analysis Using the Rfam Database.

PubMed

Kalvari, Ioanna; Nawrocki, Eric P; Argasinska, Joanna; Quinones-Olvera, Natalia; Finn, Robert D; Bateman, Alex; Petrov, Anton I

2018-06-01

Rfam is a database of non-coding RNA families in which each family is represented by a multiple sequence alignment, a consensus secondary structure, and a covariance model. Using a combination of manual and literature-based curation and a custom software pipeline, Rfam converts descriptions of RNA families found in the scientific literature into computational models that can be used to annotate RNAs belonging to those families in any DNA or RNA sequence. Valuable research outputs that are often locked up in figures and supplementary information files are encapsulated in Rfam entries and made accessible through the Rfam Web site. The data produced by Rfam have a broad application, from genome annotation to providing training sets for algorithm development. This article gives an overview of how to search and navigate the Rfam Web site, and how to annotate sequences with RNA families. The Rfam database is freely available at http://rfam.org. © 2018 by John Wiley & Sons, Inc. Copyright © 2018 John Wiley & Sons, Inc.

OriDB, the DNA replication origin database updated and extended.

PubMed

Siow, Cheuk C; Nieduszynska, Sian R; Müller, Carolin A; Nieduszynski, Conrad A

2012-01-01

OriDB (http://www.oridb.org/) is a database containing collated genome-wide mapping studies of confirmed and predicted replication origin sites. The original database collated and curated Saccharomyces cerevisiae origin mapping studies. Here, we report that the OriDB database and web site have been revamped to improve user accessibility to curated data sets, to greatly increase the number of curated origin mapping studies, and to include the collation of replication origin sites in the fission yeast Schizosaccharomyces pombe. The revised database structure underlies these improvements and will facilitate further expansion in the future. The updated OriDB for S. cerevisiae is available at http://cerevisiae.oridb.org/ and for S. pombe at http://pombe.oridb.org/.

A semi-automated methodology for finding lipid-related GO terms.

PubMed

Fan, Mengyuan; Low, Hong Sang; Wenk, Markus R; Wong, Limsoon

2014-01-01

Although semantic similarity in Gene Ontology (GO) and other approaches may be used to find similar GO terms, there is yet a method to systematically find a class of GO terms sharing a common property with high accuracy (e.g., involving human curation). We have developed a methodology to address this issue and applied it to identify lipid-related GO terms, owing to the important and varied roles of lipids in many biological processes. Our methodology finds lipid-related GO terms in a semi-automated manner, requiring only moderate manual curation. We first obtain a list of lipid-related gold-standard GO terms by keyword search and manual curation. Then, based on the hypothesis that co-annotated GO terms share similar properties, we develop a machine learning method that expands the list of lipid-related terms from the gold standard. Those terms predicted most likely to be lipid related are examined by a human curator following specific curation rules to confirm the class labels. The structure of GO is also exploited to help reduce the curation effort. The prediction and curation cycle is repeated until no further lipid-related term is found. Our approach has covered a high proportion, if not all, of lipid-related terms with relatively high efficiency. http://compbio.ddns.comp.nus.edu.sg/∼lipidgo. © The Author(s) 2014. Published by Oxford University Press.

MoonProt: a database for proteins that are known to moonlight

PubMed Central

Mani, Mathew; Chen, Chang; Amblee, Vaishak; Liu, Haipeng; Mathur, Tanu; Zwicke, Grant; Zabad, Shadi; Patel, Bansi; Thakkar, Jagravi; Jeffery, Constance J.

2015-01-01

Moonlighting proteins comprise a class of multifunctional proteins in which a single polypeptide chain performs multiple biochemical functions that are not due to gene fusions, multiple RNA splice variants or pleiotropic effects. The known moonlighting proteins perform a variety of diverse functions in many different cell types and species, and information about their structures and functions is scattered in many publications. We have constructed the manually curated, searchable, internet-based MoonProt Database (http://www.moonlightingproteins.org) with information about the over 200 proteins that have been experimentally verified to be moonlighting proteins. The availability of this organized information provides a more complete picture of what is currently known about moonlighting proteins. The database will also aid researchers in other fields, including determining the functions of genes identified in genome sequencing projects, interpreting data from proteomics projects and annotating protein sequence and structural databases. In addition, information about the structures and functions of moonlighting proteins can be helpful in understanding how novel protein functional sites evolved on an ancient protein scaffold, which can also help in the design of proteins with novel functions. PMID:25324305

The MAR databases: development and implementation of databases specific for marine metagenomics

PubMed Central

Klemetsen, Terje; Raknes, Inge A; Fu, Juan; Agafonov, Alexander; Balasundaram, Sudhagar V; Tartari, Giacomo; Robertsen, Espen

2018-01-01

Abstract We introduce the marine databases; MarRef, MarDB and MarCat (https://mmp.sfb.uit.no/databases/), which are publicly available resources that promote marine research and innovation. These data resources, which have been implemented in the Marine Metagenomics Portal (MMP) (https://mmp.sfb.uit.no/), are collections of richly annotated and manually curated contextual (metadata) and sequence databases representing three tiers of accuracy. While MarRef is a database for completely sequenced marine prokaryotic genomes, which represent a marine prokaryote reference genome database, MarDB includes all incomplete sequenced prokaryotic genomes regardless level of completeness. The last database, MarCat, represents a gene (protein) catalog of uncultivable (and cultivable) marine genes and proteins derived from marine metagenomics samples. The first versions of MarRef and MarDB contain 612 and 3726 records, respectively. Each record is built up of 106 metadata fields including attributes for sampling, sequencing, assembly and annotation in addition to the organism and taxonomic information. Currently, MarCat contains 1227 records with 55 metadata fields. Ontologies and controlled vocabularies are used in the contextual databases to enhance consistency. The user-friendly web interface lets the visitors browse, filter and search in the contextual databases and perform BLAST searches against the corresponding sequence databases. All contextual and sequence databases are freely accessible and downloadable from https://s1.sfb.uit.no/public/mar/. PMID:29106641

The National NeuroAIDS Tissue Consortium (NNTC) Database: an integrated database for HIV-related studies

PubMed Central

Cserhati, Matyas F.; Pandey, Sanjit; Beaudoin, James J.; Baccaglini, Lorena; Guda, Chittibabu; Fox, Howard S.

2015-01-01

We herein present the National NeuroAIDS Tissue Consortium-Data Coordinating Center (NNTC-DCC) database, which is the only available database for neuroAIDS studies that contains data in an integrated, standardized form. This database has been created in conjunction with the NNTC, which provides human tissue and biofluid samples to individual researchers to conduct studies focused on neuroAIDS. The database contains experimental datasets from 1206 subjects for the following categories (which are further broken down into subcategories): gene expression, genotype, proteins, endo-exo-chemicals, morphometrics and other (miscellaneous) data. The database also contains a wide variety of downloadable data and metadata for 95 HIV-related studies covering 170 assays from 61 principal investigators. The data represent 76 tissue types, 25 measurement types, and 38 technology types, and reaches a total of 33 017 407 data points. We used the ISA platform to create the database and develop a searchable web interface for querying the data. A gene search tool is also available, which searches for NCBI GEO datasets associated with selected genes. The database is manually curated with many user-friendly features, and is cross-linked to the NCBI, HUGO and PubMed databases. A free registration is required for qualified users to access the database. Database URL: http://nntc-dcc.unmc.edu PMID:26228431

OpenTrials: towards a collaborative open database of all available information on all clinical trials.

PubMed

Goldacre, Ben; Gray, Jonathan

2016-04-08

OpenTrials is a collaborative and open database for all available structured data and documents on all clinical trials, threaded together by individual trial. With a versatile and expandable data schema, it is initially designed to host and match the following documents and data for each trial: registry entries; links, abstracts, or texts of academic journal papers; portions of regulatory documents describing individual trials; structured data on methods and results extracted by systematic reviewers or other researchers; clinical study reports; and additional documents such as blank consent forms, blank case report forms, and protocols. The intention is to create an open, freely re-usable index of all such information and to increase discoverability, facilitate research, identify inconsistent data, enable audits on the availability and completeness of this information, support advocacy for better data and drive up standards around open data in evidence-based medicine. The project has phase I funding. This will allow us to create a practical data schema and populate the database initially through web-scraping, basic record linkage techniques, crowd-sourced curation around selected drug areas, and import of existing sources of structured and documents. It will also allow us to create user-friendly web interfaces onto the data and conduct user engagement workshops to optimise the database and interface designs. Where other projects have set out to manually and perfectly curate a narrow range of information on a smaller number of trials, we aim to use a broader range of techniques and attempt to match a very large quantity of information on all trials. We are currently seeking feedback and additional sources of structured data.

THPdb: Database of FDA-approved peptide and protein therapeutics.

PubMed

Usmani, Salman Sadullah; Bedi, Gursimran; Samuel, Jesse S; Singh, Sandeep; Kalra, Sourav; Kumar, Pawan; Ahuja, Anjuman Arora; Sharma, Meenu; Gautam, Ankur; Raghava, Gajendra P S

2017-01-01

THPdb (http://crdd.osdd.net/raghava/thpdb/) is a manually curated repository of Food and Drug Administration (FDA) approved therapeutic peptides and proteins. The information in THPdb has been compiled from 985 research publications, 70 patents and other resources like DrugBank. The current version of the database holds a total of 852 entries, providing comprehensive information on 239 US-FDA approved therapeutic peptides and proteins and their 380 drug variants. The information on each peptide and protein includes their sequences, chemical properties, composition, disease area, mode of activity, physical appearance, category or pharmacological class, pharmacodynamics, route of administration, toxicity, target of activity, etc. In addition, we have annotated the structure of most of the protein and peptides. A number of user-friendly tools have been integrated to facilitate easy browsing and data analysis. To assist scientific community, a web interface and mobile App have also been developed.

Lynx web services for annotations and systems analysis of multi-gene disorders.

PubMed

Sulakhe, Dinanath; Taylor, Andrew; Balasubramanian, Sandhya; Feng, Bo; Xie, Bingqing; Börnigen, Daniela; Dave, Utpal J; Foster, Ian T; Gilliam, T Conrad; Maltsev, Natalia

2014-07-01

Lynx is a web-based integrated systems biology platform that supports annotation and analysis of experimental data and generation of weighted hypotheses on molecular mechanisms contributing to human phenotypes and disorders of interest. Lynx has integrated multiple classes of biomedical data (genomic, proteomic, pathways, phenotypic, toxicogenomic, contextual and others) from various public databases as well as manually curated data from our group and collaborators (LynxKB). Lynx provides tools for gene list enrichment analysis using multiple functional annotations and network-based gene prioritization. Lynx provides access to the integrated database and the analytical tools via REST based Web Services (http://lynx.ci.uchicago.edu/webservices.html). This comprises data retrieval services for specific functional annotations, services to search across the complete LynxKB (powered by Lucene), and services to access the analytical tools built within the Lynx platform. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

The Degradome database: mammalian proteases and diseases of proteolysis.

PubMed

Quesada, Víctor; Ordóñez, Gonzalo R; Sánchez, Luis M; Puente, Xose S; López-Otín, Carlos

2009-01-01

The degradome is defined as the complete set of proteases present in an organism. The recent availability of whole genomic sequences from multiple organisms has led us to predict the contents of the degradomes of several mammalian species. To ensure the fidelity of these predictions, our methods have included manual curation of individual sequences and, when necessary, direct cloning and sequencing experiments. The results of these studies in human, chimpanzee, mouse and rat have been incorporated into the Degradome database, which can be accessed through a web interface at http://degradome.uniovi.es. The annotations about each individual protease can be retrieved by browsing catalytic classes and families or by searching specific terms. This web site also provides detailed information about genetic diseases of proteolysis, a growing field of great importance for multiple users. Finally, the user can find additional information about protease structures, protease inhibitors, ancillary domains of proteases and differences between mammalian degradomes.

The Degradome database: mammalian proteases and diseases of proteolysis

PubMed Central

Quesada, Víctor; Ordóñez, Gonzalo R.; Sánchez, Luis M.; Puente, Xose S.; López-Otín, Carlos

2009-01-01

The degradome is defined as the complete set of proteases present in an organism. The recent availability of whole genomic sequences from multiple organisms has led us to predict the contents of the degradomes of several mammalian species. To ensure the fidelity of these predictions, our methods have included manual curation of individual sequences and, when necessary, direct cloning and sequencing experiments. The results of these studies in human, chimpanzee, mouse and rat have been incorporated into the Degradome database, which can be accessed through a web interface at http://degradome.uniovi.es. The annotations about each individual protease can be retrieved by browsing catalytic classes and families or by searching specific terms. This web site also provides detailed information about genetic diseases of proteolysis, a growing field of great importance for multiple users. Finally, the user can find additional information about protease structures, protease inhibitors, ancillary domains of proteases and differences between mammalian degradomes. PMID:18776217

The National NeuroAIDS Tissue Consortium (NNTC) Database: an integrated database for HIV-related studies.

PubMed

Cserhati, Matyas F; Pandey, Sanjit; Beaudoin, James J; Baccaglini, Lorena; Guda, Chittibabu; Fox, Howard S

2015-01-01

We herein present the National NeuroAIDS Tissue Consortium-Data Coordinating Center (NNTC-DCC) database, which is the only available database for neuroAIDS studies that contains data in an integrated, standardized form. This database has been created in conjunction with the NNTC, which provides human tissue and biofluid samples to individual researchers to conduct studies focused on neuroAIDS. The database contains experimental datasets from 1206 subjects for the following categories (which are further broken down into subcategories): gene expression, genotype, proteins, endo-exo-chemicals, morphometrics and other (miscellaneous) data. The database also contains a wide variety of downloadable data and metadata for 95 HIV-related studies covering 170 assays from 61 principal investigators. The data represent 76 tissue types, 25 measurement types, and 38 technology types, and reaches a total of 33,017,407 data points. We used the ISA platform to create the database and develop a searchable web interface for querying the data. A gene search tool is also available, which searches for NCBI GEO datasets associated with selected genes. The database is manually curated with many user-friendly features, and is cross-linked to the NCBI, HUGO and PubMed databases. A free registration is required for qualified users to access the database. © The Author(s) 2015. Published by Oxford University Press.

Text mining facilitates database curation - extraction of mutation-disease associations from Bio-medical literature.

PubMed

Ravikumar, Komandur Elayavilli; Wagholikar, Kavishwar B; Li, Dingcheng; Kocher, Jean-Pierre; Liu, Hongfang

2015-06-06

Advances in the next generation sequencing technology has accelerated the pace of individualized medicine (IM), which aims to incorporate genetic/genomic information into medicine. One immediate need in interpreting sequencing data is the assembly of information about genetic variants and their corresponding associations with other entities (e.g., diseases or medications). Even with dedicated effort to capture such information in biological databases, much of this information remains 'locked' in the unstructured text of biomedical publications. There is a substantial lag between the publication and the subsequent abstraction of such information into databases. Multiple text mining systems have been developed, but most of them focus on the sentence level association extraction with performance evaluation based on gold standard text annotations specifically prepared for text mining systems. We developed and evaluated a text mining system, MutD, which extracts protein mutation-disease associations from MEDLINE abstracts by incorporating discourse level analysis, using a benchmark data set extracted from curated database records. MutD achieves an F-measure of 64.3% for reconstructing protein mutation disease associations in curated database records. Discourse level analysis component of MutD contributed to a gain of more than 10% in F-measure when compared against the sentence level association extraction. Our error analysis indicates that 23 of the 64 precision errors are true associations that were not captured by database curators and 68 of the 113 recall errors are caused by the absence of associated disease entities in the abstract. After adjusting for the defects in the curated database, the revised F-measure of MutD in association detection reaches 81.5%. Our quantitative analysis reveals that MutD can effectively extract protein mutation disease associations when benchmarking based on curated database records. The analysis also demonstrates that incorporating discourse level analysis significantly improved the performance of extracting the protein-mutation-disease association. Future work includes the extension of MutD for full text articles.

NPACT: Naturally Occurring Plant-based Anti-cancer Compound-Activity-Target database

PubMed Central

Mangal, Manu; Sagar, Parul; Singh, Harinder; Raghava, Gajendra P. S.; Agarwal, Subhash M.

2013-01-01

Plant-derived molecules have been highly valued by biomedical researchers and pharmaceutical companies for developing drugs, as they are thought to be optimized during evolution. Therefore, we have collected and compiled a central resource Naturally Occurring Plant-based Anti-cancer Compound-Activity-Target database (NPACT, http://crdd.osdd.net/raghava/npact/) that gathers the information related to experimentally validated plant-derived natural compounds exhibiting anti-cancerous activity (in vitro and in vivo), to complement the other databases. It currently contains 1574 compound entries, and each record provides information on their structure, manually curated published data on in vitro and in vivo experiments along with reference for users referral, inhibitory values (IC50/ED50/EC50/GI50), properties (physical, elemental and topological), cancer types, cell lines, protein targets, commercial suppliers and drug likeness of compounds. NPACT can easily be browsed or queried using various options, and an online similarity tool has also been made available. Further, to facilitate retrieval of existing data, each record is hyperlinked to similar databases like SuperNatural, Herbal Ingredients’ Targets, Comparative Toxicogenomics Database, PubChem and NCI-60 GI50 data. PMID:23203877

Human Ageing Genomic Resources: Integrated databases and tools for the biology and genetics of ageing

PubMed Central

Tacutu, Robi; Craig, Thomas; Budovsky, Arie; Wuttke, Daniel; Lehmann, Gilad; Taranukha, Dmitri; Costa, Joana; Fraifeld, Vadim E.; de Magalhães, João Pedro

2013-01-01

The Human Ageing Genomic Resources (HAGR, http://genomics.senescence.info) is a freely available online collection of research databases and tools for the biology and genetics of ageing. HAGR features now several databases with high-quality manually curated data: (i) GenAge, a database of genes associated with ageing in humans and model organisms; (ii) AnAge, an extensive collection of longevity records and complementary traits for >4000 vertebrate species; and (iii) GenDR, a newly incorporated database, containing both gene mutations that interfere with dietary restriction-mediated lifespan extension and consistent gene expression changes induced by dietary restriction. Since its creation about 10 years ago, major efforts have been undertaken to maintain the quality of data in HAGR, while further continuing to develop, improve and extend it. This article briefly describes the content of HAGR and details the major updates since its previous publications, in terms of both structure and content. The completely redesigned interface, more intuitive and more integrative of HAGR resources, is also presented. Altogether, we hope that through its improvements, the current version of HAGR will continue to provide users with the most comprehensive and accessible resources available today in the field of biogerontology. PMID:23193293

The BioGRID interaction database: 2017 update

PubMed Central

Chatr-aryamontri, Andrew; Oughtred, Rose; Boucher, Lorrie; Rust, Jennifer; Chang, Christie; Kolas, Nadine K.; O'Donnell, Lara; Oster, Sara; Theesfeld, Chandra; Sellam, Adnane; Stark, Chris; Breitkreutz, Bobby-Joe; Dolinski, Kara; Tyers, Mike

2017-01-01

The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the annotation and archival of protein, genetic and chemical interactions for all major model organism species and humans. As of September 2016 (build 3.4.140), the BioGRID contains 1 072 173 genetic and protein interactions, and 38 559 post-translational modifications, as manually annotated from 48 114 publications. This dataset represents interaction records for 66 model organisms and represents a 30% increase compared to the previous 2015 BioGRID update. BioGRID curates the biomedical literature for major model organism species, including humans, with a recent emphasis on central biological processes and specific human diseases. To facilitate network-based approaches to drug discovery, BioGRID now incorporates 27 501 chemical–protein interactions for human drug targets, as drawn from the DrugBank database. A new dynamic interaction network viewer allows the easy navigation and filtering of all genetic and protein interaction data, as well as for bioactive compounds and their established targets. BioGRID data are directly downloadable without restriction in a variety of standardized formats and are freely distributed through partner model organism databases and meta-databases. PMID:27980099

LocSigDB: a database of protein localization signals

PubMed Central

Negi, Simarjeet; Pandey, Sanjit; Srinivasan, Satish M.; Mohammed, Akram; Guda, Chittibabu

2015-01-01

LocSigDB (http://genome.unmc.edu/LocSigDB/) is a manually curated database of experimental protein localization signals for eight distinct subcellular locations; primarily in a eukaryotic cell with brief coverage of bacterial proteins. Proteins must be localized at their appropriate subcellular compartment to perform their desired function. Mislocalization of proteins to unintended locations is a causative factor for many human diseases; therefore, collection of known sorting signals will help support many important areas of biomedical research. By performing an extensive literature study, we compiled a collection of 533 experimentally determined localization signals, along with the proteins that harbor such signals. Each signal in the LocSigDB is annotated with its localization, source, PubMed references and is linked to the proteins in UniProt database along with the organism information that contain the same amino acid pattern as the given signal. From LocSigDB webserver, users can download the whole database or browse/search for data using an intuitive query interface. To date, LocSigDB is the most comprehensive compendium of protein localization signals for eight distinct subcellular locations. Database URL: http://genome.unmc.edu/LocSigDB/ PMID:25725059

Kin-Driver: a database of driver mutations in protein kinases.

PubMed

Simonetti, Franco L; Tornador, Cristian; Nabau-Moretó, Nuria; Molina-Vila, Miguel A; Marino-Buslje, Cristina

2014-01-01

Somatic mutations in protein kinases (PKs) are frequent driver events in many human tumors, while germ-line mutations are associated with hereditary diseases. Here we present Kin-driver, the first database that compiles driver mutations in PKs with experimental evidence demonstrating their functional role. Kin-driver is a manual expert-curated database that pays special attention to activating mutations (AMs) and can serve as a validation set to develop new generation tools focused on the prediction of gain-of-function driver mutations. It also offers an easy and intuitive environment to facilitate the visualization and analysis of mutations in PKs. Because all mutations are mapped onto a multiple sequence alignment, analogue positions between kinases can be identified and tentative new mutations can be proposed for studying by transferring annotation. Finally, our database can also be of use to clinical and translational laboratories, helping them to identify uncommon AMs that can correlate with response to new antitumor drugs. The website was developed using PHP and JavaScript, which are supported by all major browsers; the database was built using MySQL server. Kin-driver is available at: http://kin-driver.leloir.org.ar/ © The Author(s) 2014. Published by Oxford University Press.

Automated PDF highlighting to support faster curation of literature for Parkinson's and Alzheimer's disease.

PubMed

Wu, Honghan; Oellrich, Anika; Girges, Christine; de Bono, Bernard; Hubbard, Tim J P; Dobson, Richard J B

2017-01-01

Neurodegenerative disorders such as Parkinson's and Alzheimer's disease are devastating and costly illnesses, a source of major global burden. In order to provide successful interventions for patients and reduce costs, both causes and pathological processes need to be understood. The ApiNATOMY project aims to contribute to our understanding of neurodegenerative disorders by manually curating and abstracting data from the vast body of literature amassed on these illnesses. As curation is labour-intensive, we aimed to speed up the process by automatically highlighting those parts of the PDF document of primary importance to the curator. Using techniques similar to those of summarisation, we developed an algorithm that relies on linguistic, semantic and spatial features. Employing this algorithm on a test set manually corrected for tool imprecision, we achieved a macro F 1 -measure of 0.51, which is an increase of 132% compared to the best bag-of-words baseline model. A user based evaluation was also conducted to assess the usefulness of the methodology on 40 unseen publications, which reveals that in 85% of cases all highlighted sentences are relevant to the curation task and in about 65% of the cases, the highlights are sufficient to support the knowledge curation task without needing to consult the full text. In conclusion, we believe that these are promising results for a step in automating the recognition of curation-relevant sentences. Refining our approach to pre-digest papers will lead to faster processing and cost reduction in the curation process. https://github.com/KHP-Informatics/NapEasy. © The Author(s) 2017. Published by Oxford University Press.

Planform: an application and database of graph-encoded planarian regenerative experiments.

PubMed

Lobo, Daniel; Malone, Taylor J; Levin, Michael

2013-04-15

Understanding the mechanisms governing the regeneration capabilities of many organisms is a fundamental interest in biology and medicine. An ever-increasing number of manipulation and molecular experiments are attempting to discover a comprehensive model for regeneration, with the planarian flatworm being one of the most important model species. Despite much effort, no comprehensive, constructive, mechanistic models exist yet, and it is now clear that computational tools are needed to mine this huge dataset. However, until now, there is no database of regenerative experiments, and the current genotype-phenotype ontologies and databases are based on textual descriptions, which are not understandable by computers. To overcome these difficulties, we present here Planform (Planarian formalization), a manually curated database and software tool for planarian regenerative experiments, based on a mathematical graph formalism. The database contains more than a thousand experiments from the main publications in the planarian literature. The software tool provides the user with a graphical interface to easily interact with and mine the database. The presented system is a valuable resource for the regeneration community and, more importantly, will pave the way for the application of novel artificial intelligence tools to extract knowledge from this dataset. The database and software tool are freely available at http://planform.daniel-lobo.com.

TIPdb: a database of anticancer, antiplatelet, and antituberculosis phytochemicals from indigenous plants in Taiwan.

PubMed

Lin, Ying-Chi; Wang, Chia-Chi; Chen, Ih-Sheng; Jheng, Jhao-Liang; Li, Jih-Heng; Tung, Chun-Wei

2013-01-01

The unique geographic features of Taiwan are attributed to the rich indigenous and endemic plant species in Taiwan. These plants serve as resourceful bank for biologically active phytochemicals. Given that these plant-derived chemicals are prototypes of potential drugs for diseases, databases connecting the chemical structures and pharmacological activities may facilitate drug development. To enhance the utility of the data, it is desirable to develop a database of chemical compounds and corresponding activities from indigenous plants in Taiwan. A database of anticancer, antiplatelet, and antituberculosis phytochemicals from indigenous plants in Taiwan was constructed. The database, TIPdb, is composed of a standardized format of published anticancer, antiplatelet, and antituberculosis phytochemicals from indigenous plants in Taiwan. A browse function was implemented for users to browse the database in a taxonomy-based manner. Search functions can be utilized to filter records of interest by botanical name, part, chemical class, or compound name. The structured and searchable database TIPdb was constructed to serve as a comprehensive and standardized resource for anticancer, antiplatelet, and antituberculosis compounds search. The manually curated chemical structures and activities provide a great opportunity to develop quantitative structure-activity relationship models for the high-throughput screening of potential anticancer, antiplatelet, and antituberculosis drugs.

TIPdb: A Database of Anticancer, Antiplatelet, and Antituberculosis Phytochemicals from Indigenous Plants in Taiwan

PubMed Central

Lin, Ying-Chi; Wang, Chia-Chi; Chen, Ih-Sheng; Jheng, Jhao-Liang; Li, Jih-Heng; Tung, Chun-Wei

2013-01-01

The unique geographic features of Taiwan are attributed to the rich indigenous and endemic plant species in Taiwan. These plants serve as resourceful bank for biologically active phytochemicals. Given that these plant-derived chemicals are prototypes of potential drugs for diseases, databases connecting the chemical structures and pharmacological activities may facilitate drug development. To enhance the utility of the data, it is desirable to develop a database of chemical compounds and corresponding activities from indigenous plants in Taiwan. A database of anticancer, antiplatelet, and antituberculosis phytochemicals from indigenous plants in Taiwan was constructed. The database, TIPdb, is composed of a standardized format of published anticancer, antiplatelet, and antituberculosis phytochemicals from indigenous plants in Taiwan. A browse function was implemented for users to browse the database in a taxonomy-based manner. Search functions can be utilized to filter records of interest by botanical name, part, chemical class, or compound name. The structured and searchable database TIPdb was constructed to serve as a comprehensive and standardized resource for anticancer, antiplatelet, and antituberculosis compounds search. The manually curated chemical structures and activities provide a great opportunity to develop quantitative structure-activity relationship models for the high-throughput screening of potential anticancer, antiplatelet, and antituberculosis drugs. PMID:23766708

Cordova: Web-based management of genetic variation data

PubMed Central

Ephraim, Sean S.; Anand, Nikhil; DeLuca, Adam P.; Taylor, Kyle R.; Kolbe, Diana L.; Simpson, Allen C.; Azaiez, Hela; Sloan, Christina M.; Shearer, A. Eliot; Hallier, Andrea R.; Casavant, Thomas L.; Scheetz, Todd E.; Smith, Richard J. H.; Braun, Terry A.

2014-01-01

Summary: Cordova is an out-of-the-box solution for building and maintaining an online database of genetic variations integrated with pathogenicity prediction results from popular algorithms. Our primary motivation for developing this system is to aid researchers and clinician–scientists in determining the clinical significance of genetic variations. To achieve this goal, Cordova provides an interface to review and manually or computationally curate genetic variation data as well as share it for clinical diagnostics and the advancement of research. Availability and implementation: Cordova is open source under the MIT license and is freely available for download at https://github.com/clcg/cordova. Contact: sean.ephraim@gmail.com or terry-braun@uiowa.edu PMID:25123904

Dataset of potential targets for Mycobacterium tuberculosis H37Rv through comparative genome analysis.

PubMed

Asif, Siddiqui M; Asad, Amir; Faizan, Ahmad; Anjali, Malik S; Arvind, Arya; Neelesh, Kapoor; Hirdesh, Kumar; Sanjay, Kumar

2009-12-31

Mycobacterium tuberculosis is the causative agent of the disease, tuberculosis and H37Rv is the most studied clinical strain. We use comparative genome analysis of Mycobacterium tuberculosis H37Rv and human for the identification of potential targets dataset. We used DEG (Database of Essential Genes) to identify essential genes in the H37Rv strain. The analysis shows that 628 of the 3989 genes in Mycobacterium tuberculosis H37Rv were found to be essential of which 324 genes lack similarity to the human genome. Subsequently hypothetical proteins were removed through manual curation. This further resulted in a dataset of 135 proteins with essential function and no homology to human.

The UniProtKB guide to the human proteome

PubMed Central

Breuza, Lionel; Poux, Sylvain; Estreicher, Anne; Famiglietti, Maria Livia; Magrane, Michele; Tognolli, Michael; Bridge, Alan; Baratin, Delphine; Redaschi, Nicole

2016-01-01

Advances in high-throughput and advanced technologies allow researchers to routinely perform whole genome and proteome analysis. For this purpose, they need high-quality resources providing comprehensive gene and protein sets for their organisms of interest. Using the example of the human proteome, we will describe the content of a complete proteome in the UniProt Knowledgebase (UniProtKB). We will show how manual expert curation of UniProtKB/Swiss-Prot is complemented by expert-driven automatic annotation to build a comprehensive, high-quality and traceable resource. We will also illustrate how the complexity of the human proteome is captured and structured in UniProtKB. Database URL: www.uniprot.org PMID:26896845

HAMAP in 2013, new developments in the protein family classification and annotation system

PubMed Central

Pedruzzi, Ivo; Rivoire, Catherine; Auchincloss, Andrea H.; Coudert, Elisabeth; Keller, Guillaume; de Castro, Edouard; Baratin, Delphine; Cuche, Béatrice A.; Bougueleret, Lydie; Poux, Sylvain; Redaschi, Nicole; Xenarios, Ioannis; Bridge, Alan

2013-01-01

HAMAP (High-quality Automated and Manual Annotation of Proteins—available at http://hamap.expasy.org/) is a system for the classification and annotation of protein sequences. It consists of a collection of manually curated family profiles for protein classification, and associated annotation rules that specify annotations that apply to family members. HAMAP was originally developed to support the manual curation of UniProtKB/Swiss-Prot records describing microbial proteins. Here we describe new developments in HAMAP, including the extension of HAMAP to eukaryotic proteins, the use of HAMAP in the automated annotation of UniProtKB/TrEMBL, providing high-quality annotation for millions of protein sequences, and the future integration of HAMAP into a unified system for UniProtKB annotation, UniRule. HAMAP is continuously updated by expert curators with new family profiles and annotation rules as new protein families are characterized. The collection of HAMAP family classification profiles and annotation rules can be browsed and viewed on the HAMAP website, which also provides an interface to scan user sequences against HAMAP profiles. PMID:23193261

The Pathogen-Host Interactions database (PHI-base): additions and future developments.

PubMed

Urban, Martin; Pant, Rashmi; Raghunath, Arathi; Irvine, Alistair G; Pedro, Helder; Hammond-Kosack, Kim E

2015-01-01

Rapidly evolving pathogens cause a diverse array of diseases and epidemics that threaten crop yield, food security as well as human, animal and ecosystem health. To combat infection greater comparative knowledge is required on the pathogenic process in multiple species. The Pathogen-Host Interactions database (PHI-base) catalogues experimentally verified pathogenicity, virulence and effector genes from bacterial, fungal and protist pathogens. Mutant phenotypes are associated with gene information. The included pathogens infect a wide range of hosts including humans, animals, plants, insects, fish and other fungi. The current version, PHI-base 3.6, available at http://www.phi-base.org, stores information on 2875 genes, 4102 interactions, 110 host species, 160 pathogenic species (103 plant, 3 fungal and 54 animal infecting species) and 181 diseases drawn from 1243 references. Phenotypic and gene function information has been obtained by manual curation of the peer-reviewed literature. A controlled vocabulary consisting of nine high-level phenotype terms permits comparisons and data analysis across the taxonomic space. PHI-base phenotypes were mapped via their associated gene information to reference genomes available in Ensembl Genomes. Virulence genes and hotspots can be visualized directly in genome browsers. Future plans for PHI-base include development of tools facilitating community-led curation and inclusion of the corresponding host target(s). © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Entomopathogen ID: a curated sequence resource for entomopathogenic fungi

USDA-ARS?s Scientific Manuscript database

We report the development of a publicly accessible, curated database of Hypocrealean entomopathogenic fungi sequence data. The goal is to provide a platform for users to easily access sequence data from reference strains. The database can be used to accurately identify unknown entomopathogenic fungi...

MaizeGDB: New tools and resource

USDA-ARS?s Scientific Manuscript database

MaizeGDB, the USDA-ARS genetics and genomics database, is a highly curated, community-oriented informatics service to researchers focused on the crop plant and model organism Zea mays. MaizeGDB facilitates maize research by curating, integrating, and maintaining a database that serves as the central...

DiMeX: A Text Mining System for Mutation-Disease Association Extraction.

PubMed

Mahmood, A S M Ashique; Wu, Tsung-Jung; Mazumder, Raja; Vijay-Shanker, K

2016-01-01

The number of published articles describing associations between mutations and diseases is increasing at a fast pace. There is a pressing need to gather such mutation-disease associations into public knowledge bases, but manual curation slows down the growth of such databases. We have addressed this problem by developing a text-mining system (DiMeX) to extract mutation to disease associations from publication abstracts. DiMeX consists of a series of natural language processing modules that preprocess input text and apply syntactic and semantic patterns to extract mutation-disease associations. DiMeX achieves high precision and recall with F-scores of 0.88, 0.91 and 0.89 when evaluated on three different datasets for mutation-disease associations. DiMeX includes a separate component that extracts mutation mentions in text and associates them with genes. This component has been also evaluated on different datasets and shown to achieve state-of-the-art performance. The results indicate that our system outperforms the existing mutation-disease association tools, addressing the low precision problems suffered by most approaches. DiMeX was applied on a large set of abstracts from Medline to extract mutation-disease associations, as well as other relevant information including patient/cohort size and population data. The results are stored in a database that can be queried and downloaded at http://biotm.cis.udel.edu/dimex/. We conclude that this high-throughput text-mining approach has the potential to significantly assist researchers and curators to enrich mutation databases.

DiMeX: A Text Mining System for Mutation-Disease Association Extraction

PubMed Central

Mahmood, A. S. M. Ashique; Wu, Tsung-Jung; Mazumder, Raja; Vijay-Shanker, K.

2016-01-01

The number of published articles describing associations between mutations and diseases is increasing at a fast pace. There is a pressing need to gather such mutation-disease associations into public knowledge bases, but manual curation slows down the growth of such databases. We have addressed this problem by developing a text-mining system (DiMeX) to extract mutation to disease associations from publication abstracts. DiMeX consists of a series of natural language processing modules that preprocess input text and apply syntactic and semantic patterns to extract mutation-disease associations. DiMeX achieves high precision and recall with F-scores of 0.88, 0.91 and 0.89 when evaluated on three different datasets for mutation-disease associations. DiMeX includes a separate component that extracts mutation mentions in text and associates them with genes. This component has been also evaluated on different datasets and shown to achieve state-of-the-art performance. The results indicate that our system outperforms the existing mutation-disease association tools, addressing the low precision problems suffered by most approaches. DiMeX was applied on a large set of abstracts from Medline to extract mutation-disease associations, as well as other relevant information including patient/cohort size and population data. The results are stored in a database that can be queried and downloaded at http://biotm.cis.udel.edu/dimex/. We conclude that this high-throughput text-mining approach has the potential to significantly assist researchers and curators to enrich mutation databases. PMID:27073839

FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining.

PubMed

Bachman, John A; Gyori, Benjamin M; Sorger, Peter K

2018-06-28

For automated reading of scientific publications to extract useful information about molecular mechanisms it is critical that genes, proteins and other entities be correctly associated with uniform identifiers, a process known as named entity linking or "grounding." Correct grounding is essential for resolving relationships among mined information, curated interaction databases, and biological datasets. The accuracy of this process is largely dependent on the availability of machine-readable resources associating synonyms and abbreviations commonly found in biomedical literature with uniform identifiers. In a task involving automated reading of ∼215,000 articles using the REACH event extraction software we found that grounding was disproportionately inaccurate for multi-protein families (e.g., "AKT") and complexes with multiple subunits (e.g."NF- κB"). To address this problem we constructed FamPlex, a manually curated resource defining protein families and complexes as they are commonly encountered in biomedical text. In FamPlex the gene-level constituents of families and complexes are defined in a flexible format allowing for multi-level, hierarchical membership. To create FamPlex, text strings corresponding to entities were identified empirically from literature and linked manually to uniform identifiers; these identifiers were also mapped to equivalent entries in multiple related databases. FamPlex also includes curated prefix and suffix patterns that improve named entity recognition and event extraction. Evaluation of REACH extractions on a test corpus of ∼54,000 articles showed that FamPlex significantly increased grounding accuracy for families and complexes (from 15 to 71%). The hierarchical organization of entities in FamPlex also made it possible to integrate otherwise unconnected mechanistic information across families, subfamilies, and individual proteins. Applications of FamPlex to the TRIPS/DRUM reading system and the Biocreative VI Bioentity Normalization Task dataset demonstrated the utility of FamPlex in other settings. FamPlex is an effective resource for improving named entity recognition, grounding, and relationship resolution in automated reading of biomedical text. The content in FamPlex is available in both tabular and Open Biomedical Ontology formats at https://github.com/sorgerlab/famplex under the Creative Commons CC0 license and has been integrated into the TRIPS/DRUM and REACH reading systems.

GAMOLA2, a Comprehensive Software Package for the Annotation and Curation of Draft and Complete Microbial Genomes

PubMed Central

Altermann, Eric; Lu, Jingli; McCulloch, Alan

2017-01-01

Expert curated annotation remains one of the critical steps in achieving a reliable biological relevant annotation. Here we announce the release of GAMOLA2, a user friendly and comprehensive software package to process, annotate and curate draft and complete bacterial, archaeal, and viral genomes. GAMOLA2 represents a wrapping tool to combine gene model determination, functional Blast, COG, Pfam, and TIGRfam analyses with structural predictions including detection of tRNAs, rRNA genes, non-coding RNAs, signal protein cleavage sites, transmembrane helices, CRISPR repeats and vector sequence contaminations. GAMOLA2 has already been validated in a wide range of bacterial and archaeal genomes, and its modular concept allows easy addition of further functionality in future releases. A modified and adapted version of the Artemis Genome Viewer (Sanger Institute) has been developed to leverage the additional features and underlying information provided by the GAMOLA2 analysis, and is part of the software distribution. In addition to genome annotations, GAMOLA2 features, among others, supplemental modules that assist in the creation of custom Blast databases, annotation transfers between genome versions, and the preparation of Genbank files for submission via the NCBI Sequin tool. GAMOLA2 is intended to be run under a Linux environment, whereas the subsequent visualization and manual curation in Artemis is mobile and platform independent. The development of GAMOLA2 is ongoing and community driven. New functionality can easily be added upon user requests, ensuring that GAMOLA2 provides information relevant to microbiologists. The software is available free of charge for academic use. PMID:28386247

GAMOLA2, a Comprehensive Software Package for the Annotation and Curation of Draft and Complete Microbial Genomes.

PubMed

Altermann, Eric; Lu, Jingli; McCulloch, Alan

2017-01-01

Expert curated annotation remains one of the critical steps in achieving a reliable biological relevant annotation. Here we announce the release of GAMOLA2, a user friendly and comprehensive software package to process, annotate and curate draft and complete bacterial, archaeal, and viral genomes. GAMOLA2 represents a wrapping tool to combine gene model determination, functional Blast, COG, Pfam, and TIGRfam analyses with structural predictions including detection of tRNAs, rRNA genes, non-coding RNAs, signal protein cleavage sites, transmembrane helices, CRISPR repeats and vector sequence contaminations. GAMOLA2 has already been validated in a wide range of bacterial and archaeal genomes, and its modular concept allows easy addition of further functionality in future releases. A modified and adapted version of the Artemis Genome Viewer (Sanger Institute) has been developed to leverage the additional features and underlying information provided by the GAMOLA2 analysis, and is part of the software distribution. In addition to genome annotations, GAMOLA2 features, among others, supplemental modules that assist in the creation of custom Blast databases, annotation transfers between genome versions, and the preparation of Genbank files for submission via the NCBI Sequin tool. GAMOLA2 is intended to be run under a Linux environment, whereas the subsequent visualization and manual curation in Artemis is mobile and platform independent. The development of GAMOLA2 is ongoing and community driven. New functionality can easily be added upon user requests, ensuring that GAMOLA2 provides information relevant to microbiologists. The software is available free of charge for academic use.

HitPredict version 4: comprehensive reliability scoring of physical protein-protein interactions from more than 100 species.

PubMed

López, Yosvany; Nakai, Kenta; Patil, Ashwini

2015-01-01

HitPredict is a consolidated resource of experimentally identified, physical protein-protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein-protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of physical, genetic and predicted interactions. Automated integration of interactions is further complicated by varying levels of accuracy of database content and lack of adherence to standard formats. To address these issues, the latest version of HitPredict provides a manually curated dataset of 398 696 physical associations between 70 808 proteins from 105 species. Manual confirmation was used to resolve all issues encountered during data integration. For improved reliability assessment, this version combines a new score derived from the experimental information of the interactions with the original score based on the features of the interacting proteins. The combined interaction score performs better than either of the individual scores in HitPredict as well as the reliability score of another similar database. HitPredict provides a web interface to search proteins and visualize their interactions, and the data can be downloaded for offline analysis. Data usability has been enhanced by mapping protein identifiers across multiple reference databases. Thus, the latest version of HitPredict provides a significantly larger, more reliable and usable dataset of protein-protein interactions from several species for the study of gene groups. Database URL: http://hintdb.hgc.jp/htp. © The Author(s) 2015. Published by Oxford University Press.

Autophagy Regulatory Network - a systems-level bioinformatics resource for studying the mechanism and regulation of autophagy.

PubMed

Türei, Dénes; Földvári-Nagy, László; Fazekas, Dávid; Módos, Dezső; Kubisch, János; Kadlecsik, Tamás; Demeter, Amanda; Lenti, Katalin; Csermely, Péter; Vellai, Tibor; Korcsmáros, Tamás

2015-01-01

Autophagy is a complex cellular process having multiple roles, depending on tissue, physiological, or pathological conditions. Major post-translational regulators of autophagy are well known, however, they have not yet been collected comprehensively. The precise and context-dependent regulation of autophagy necessitates additional regulators, including transcriptional and post-transcriptional components that are listed in various datasets. Prompted by the lack of systems-level autophagy-related information, we manually collected the literature and integrated external resources to gain a high coverage autophagy database. We developed an online resource, Autophagy Regulatory Network (ARN; http://autophagy-regulation.org), to provide an integrated and systems-level database for autophagy research. ARN contains manually curated, imported, and predicted interactions of autophagy components (1,485 proteins with 4,013 interactions) in humans. We listed 413 transcription factors and 386 miRNAs that could regulate autophagy components or their protein regulators. We also connected the above-mentioned autophagy components and regulators with signaling pathways from the SignaLink 2 resource. The user-friendly website of ARN allows researchers without computational background to search, browse, and download the database. The database can be downloaded in SQL, CSV, BioPAX, SBML, PSI-MI, and in a Cytoscape CYS file formats. ARN has the potential to facilitate the experimental validation of novel autophagy components and regulators. In addition, ARN helps the investigation of transcription factors, miRNAs and signaling pathways implicated in the control of the autophagic pathway. The list of such known and predicted regulators could be important in pharmacological attempts against cancer and neurodegenerative diseases.

CEBS: a comprehensive annotated database of toxicological data

PubMed Central

Lea, Isabel A.; Gong, Hui; Paleja, Anand; Rashid, Asif; Fostel, Jennifer

2017-01-01

The Chemical Effects in Biological Systems database (CEBS) is a comprehensive and unique toxicology resource that compiles individual and summary animal data from the National Toxicology Program (NTP) testing program and other depositors into a single electronic repository. CEBS has undergone significant updates in recent years and currently contains over 11 000 test articles (exposure agents) and over 8000 studies including all available NTP carcinogenicity, short-term toxicity and genetic toxicity studies. Study data provided to CEBS are manually curated, accessioned and subject to quality assurance review prior to release to ensure high quality. The CEBS database has two main components: data collection and data delivery. To accommodate the breadth of data produced by NTP, the CEBS data collection component is an integrated relational design that allows the flexibility to capture any type of electronic data (to date). The data delivery component of the database comprises a series of dedicated user interface tables containing pre-processed data that support each component of the user interface. The user interface has been updated to include a series of nine Guided Search tools that allow access to NTP summary and conclusion data and larger non-NTP datasets. The CEBS database can be accessed online at http://www.niehs.nih.gov/research/resources/databases/cebs/. PMID:27899660

HIPdb: a database of experimentally validated HIV inhibiting peptides.

PubMed

Qureshi, Abid; Thakur, Nishant; Kumar, Manoj

2013-01-01

Besides antiretroviral drugs, peptides have also demonstrated potential to inhibit the Human immunodeficiency virus (HIV). For example, T20 has been discovered to effectively block the HIV entry and was approved by the FDA as a novel anti-HIV peptide (AHP). We have collated all experimental information on AHPs at a single platform. HIPdb is a manually curated database of experimentally verified HIV inhibiting peptides targeting various steps or proteins involved in the life cycle of HIV e.g. fusion, integration, reverse transcription etc. This database provides experimental information of 981 peptides. These are of varying length obtained from natural as well as synthetic sources and tested on different cell lines. Important fields included are peptide sequence, length, source, target, cell line, inhibition/IC(50), assay and reference. The database provides user friendly browse, search, sort and filter options. It also contains useful services like BLAST and 'Map' for alignment with user provided sequences. In addition, predicted structure and physicochemical properties of the peptides are also included. HIPdb database is freely available at http://crdd.osdd.net/servers/hipdb. Comprehensive information of this database will be helpful in selecting/designing effective anti-HIV peptides. Thus it may prove a useful resource to researchers for peptide based therapeutics development.

Consensus coding sequence (CCDS) database: a standardized set of human and mouse protein-coding regions supported by expert curation.

PubMed

Pujar, Shashikant; O'Leary, Nuala A; Farrell, Catherine M; Loveland, Jane E; Mudge, Jonathan M; Wallin, Craig; Girón, Carlos G; Diekhans, Mark; Barnes, If; Bennett, Ruth; Berry, Andrew E; Cox, Eric; Davidson, Claire; Goldfarb, Tamara; Gonzalez, Jose M; Hunt, Toby; Jackson, John; Joardar, Vinita; Kay, Mike P; Kodali, Vamsi K; Martin, Fergal J; McAndrews, Monica; McGarvey, Kelly M; Murphy, Michael; Rajput, Bhanu; Rangwala, Sanjida H; Riddick, Lillian D; Seal, Ruth L; Suner, Marie-Marthe; Webb, David; Zhu, Sophia; Aken, Bronwen L; Bruford, Elspeth A; Bult, Carol J; Frankish, Adam; Murphy, Terence; Pruitt, Kim D

2018-01-04

The Consensus Coding Sequence (CCDS) project provides a dataset of protein-coding regions that are identically annotated on the human and mouse reference genome assembly in genome annotations produced independently by NCBI and the Ensembl group at EMBL-EBI. This dataset is the product of an international collaboration that includes NCBI, Ensembl, HUGO Gene Nomenclature Committee, Mouse Genome Informatics and University of California, Santa Cruz. Identically annotated coding regions, which are generated using an automated pipeline and pass multiple quality assurance checks, are assigned a stable and tracked identifier (CCDS ID). Additionally, coordinated manual review by expert curators from the CCDS collaboration helps in maintaining the integrity and high quality of the dataset. The CCDS data are available through an interactive web page (https://www.ncbi.nlm.nih.gov/CCDS/CcdsBrowse.cgi) and an FTP site (ftp://ftp.ncbi.nlm.nih.gov/pub/CCDS/). In this paper, we outline the ongoing work, growth and stability of the CCDS dataset and provide updates on new collaboration members and new features added to the CCDS user interface. We also present expert curation scenarios, with specific examples highlighting the importance of an accurate reference genome assembly and the crucial role played by input from the research community. Published by Oxford University Press on behalf of Nucleic Acids Research 2017.

The MAR databases: development and implementation of databases specific for marine metagenomics.

PubMed

Klemetsen, Terje; Raknes, Inge A; Fu, Juan; Agafonov, Alexander; Balasundaram, Sudhagar V; Tartari, Giacomo; Robertsen, Espen; Willassen, Nils P

2018-01-04

We introduce the marine databases; MarRef, MarDB and MarCat (https://mmp.sfb.uit.no/databases/), which are publicly available resources that promote marine research and innovation. These data resources, which have been implemented in the Marine Metagenomics Portal (MMP) (https://mmp.sfb.uit.no/), are collections of richly annotated and manually curated contextual (metadata) and sequence databases representing three tiers of accuracy. While MarRef is a database for completely sequenced marine prokaryotic genomes, which represent a marine prokaryote reference genome database, MarDB includes all incomplete sequenced prokaryotic genomes regardless level of completeness. The last database, MarCat, represents a gene (protein) catalog of uncultivable (and cultivable) marine genes and proteins derived from marine metagenomics samples. The first versions of MarRef and MarDB contain 612 and 3726 records, respectively. Each record is built up of 106 metadata fields including attributes for sampling, sequencing, assembly and annotation in addition to the organism and taxonomic information. Currently, MarCat contains 1227 records with 55 metadata fields. Ontologies and controlled vocabularies are used in the contextual databases to enhance consistency. The user-friendly web interface lets the visitors browse, filter and search in the contextual databases and perform BLAST searches against the corresponding sequence databases. All contextual and sequence databases are freely accessible and downloadable from https://s1.sfb.uit.no/public/mar/. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Improving transcriptome construction in non-model organisms: integrating manual and automated gene definition in Emiliania huxleyi.

PubMed

Feldmesser, Ester; Rosenwasser, Shilo; Vardi, Assaf; Ben-Dor, Shifra

2014-02-22

The advent of Next Generation Sequencing technologies and corresponding bioinformatics tools allows the definition of transcriptomes in non-model organisms. Non-model organisms are of great ecological and biotechnological significance, and consequently the understanding of their unique metabolic pathways is essential. Several methods that integrate de novo assembly with genome-based assembly have been proposed. Yet, there are many open challenges in defining genes, particularly where genomes are not available or incomplete. Despite the large numbers of transcriptome assemblies that have been performed, quality control of the transcript building process, particularly on the protein level, is rarely performed if ever. To test and improve the quality of the automated transcriptome reconstruction, we used manually defined and curated genes, several of them experimentally validated. Several approaches to transcript construction were utilized, based on the available data: a draft genome, high quality RNAseq reads, and ESTs. In order to maximize the contribution of the various data, we integrated methods including de novo and genome based assembly, as well as EST clustering. After each step a set of manually curated genes was used for quality assessment of the transcripts. The interplay between the automated pipeline and the quality control indicated which additional processes were required to improve the transcriptome reconstruction. We discovered that E. huxleyi has a very high percentage of non-canonical splice junctions, and relatively high rates of intron retention, which caused unique issues with the currently available tools. While individual tools missed genes and artificially joined overlapping transcripts, combining the results of several tools improved the completeness and quality considerably. The final collection, created from the integration of several quality control and improvement rounds, was compared to the manually defined set both on the DNA and protein levels, and resulted in an improvement of 20% versus any of the read-based approaches alone. To the best of our knowledge, this is the first time that an automated transcript definition is subjected to quality control using manually defined and curated genes and thereafter the process is improved. We recommend using a set of manually curated genes to troubleshoot transcriptome reconstruction.

neXtA5: accelerating annotation of articles via automated approaches in neXtProt.

PubMed

Mottin, Luc; Gobeill, Julien; Pasche, Emilie; Michel, Pierre-André; Cusin, Isabelle; Gaudet, Pascale; Ruch, Patrick

2016-01-01

The rapid increase in the number of published articles poses a challenge for curated databases to remain up-to-date. To help the scientific community and database curators deal with this issue, we have developed an application, neXtA5, which prioritizes the literature for specific curation requirements. Our system, neXtA5, is a curation service composed of three main elements. The first component is a named-entity recognition module, which annotates MEDLINE over some predefined axes. This report focuses on three axes: Diseases, the Molecular Function and Biological Process sub-ontologies of the Gene Ontology (GO). The automatic annotations are then stored in a local database, BioMed, for each annotation axis. Additional entities such as species and chemical compounds are also identified. The second component is an existing search engine, which retrieves the most relevant MEDLINE records for any given query. The third component uses the content of BioMed to generate an axis-specific ranking, which takes into account the density of named-entities as stored in the Biomed database. The two ranked lists are ultimately merged using a linear combination, which has been specifically tuned to support the annotation of each axis. The fine-tuning of the coefficients is formally reported for each axis-driven search. Compared with PubMed, which is the system used by most curators, the improvement is the following: +231% for Diseases, +236% for Molecular Functions and +3153% for Biological Process when measuring the precision of the top-returned PMID (P0 or mean reciprocal rank). The current search methods significantly improve the search effectiveness of curators for three important curation axes. Further experiments are being performed to extend the curation types, in particular protein-protein interactions, which require specific relationship extraction capabilities. In parallel, user-friendly interfaces powered with a set of JSON web services are currently being implemented into the neXtProt annotation pipeline.Available on: http://babar.unige.ch:8082/neXtA5Database URL: http://babar.unige.ch:8082/neXtA5/fetcher.jsp. © The Author(s) 2016. Published by Oxford University Press.

neXtA5: accelerating annotation of articles via automated approaches in neXtProt

PubMed Central

Mottin, Luc; Gobeill, Julien; Pasche, Emilie; Michel, Pierre-André; Cusin, Isabelle; Gaudet, Pascale; Ruch, Patrick

2016-01-01

The rapid increase in the number of published articles poses a challenge for curated databases to remain up-to-date. To help the scientific community and database curators deal with this issue, we have developed an application, neXtA5, which prioritizes the literature for specific curation requirements. Our system, neXtA5, is a curation service composed of three main elements. The first component is a named-entity recognition module, which annotates MEDLINE over some predefined axes. This report focuses on three axes: Diseases, the Molecular Function and Biological Process sub-ontologies of the Gene Ontology (GO). The automatic annotations are then stored in a local database, BioMed, for each annotation axis. Additional entities such as species and chemical compounds are also identified. The second component is an existing search engine, which retrieves the most relevant MEDLINE records for any given query. The third component uses the content of BioMed to generate an axis-specific ranking, which takes into account the density of named-entities as stored in the Biomed database. The two ranked lists are ultimately merged using a linear combination, which has been specifically tuned to support the annotation of each axis. The fine-tuning of the coefficients is formally reported for each axis-driven search. Compared with PubMed, which is the system used by most curators, the improvement is the following: +231% for Diseases, +236% for Molecular Functions and +3153% for Biological Process when measuring the precision of the top-returned PMID (P0 or mean reciprocal rank). The current search methods significantly improve the search effectiveness of curators for three important curation axes. Further experiments are being performed to extend the curation types, in particular protein–protein interactions, which require specific relationship extraction capabilities. In parallel, user-friendly interfaces powered with a set of JSON web services are currently being implemented into the neXtProt annotation pipeline. Available on: http://babar.unige.ch:8082/neXtA5 Database URL: http://babar.unige.ch:8082/neXtA5/fetcher.jsp PMID:27374119

Ambiguity of non-systematic chemical identifiers within and between small-molecule databases.

PubMed

Akhondi, Saber A; Muresan, Sorel; Williams, Antony J; Kors, Jan A

2015-01-01

A wide range of chemical compound databases are currently available for pharmaceutical research. To retrieve compound information, including structures, researchers can query these chemical databases using non-systematic identifiers. These are source-dependent identifiers (e.g., brand names, generic names), which are usually assigned to the compound at the point of registration. The correctness of non-systematic identifiers (i.e., whether an identifier matches the associated structure) can only be assessed manually, which is cumbersome, but it is possible to automatically check their ambiguity (i.e., whether an identifier matches more than one structure). In this study we have quantified the ambiguity of non-systematic identifiers within and between eight widely used chemical databases. We also studied the effect of chemical structure standardization on reducing the ambiguity of non-systematic identifiers. The ambiguity of non-systematic identifiers within databases varied from 0.1 to 15.2 % (median 2.5 %). Standardization reduced the ambiguity only to a small extent for most databases. A wide range of ambiguity existed for non-systematic identifiers that are shared between databases (17.7-60.2 %, median of 40.3 %). Removing stereochemistry information provided the largest reduction in ambiguity across databases (median reduction 13.7 percentage points). Ambiguity of non-systematic identifiers within chemical databases is generally low, but ambiguity of non-systematic identifiers that are shared between databases, is high. Chemical structure standardization reduces the ambiguity to a limited extent. Our findings can help to improve database integration, curation, and maintenance.

probeBase—an online resource for rRNA-targeted oligonucleotide probes and primers: new features 2016

PubMed Central

Greuter, Daniel; Loy, Alexander; Horn, Matthias; Rattei, Thomas

2016-01-01

probeBase http://www.probebase.net is a manually maintained and curated database of rRNA-targeted oligonucleotide probes and primers. Contextual information and multiple options for evaluating in silico hybridization performance against the most recent rRNA sequence databases are provided for each oligonucleotide entry, which makes probeBase an important and frequently used resource for microbiology research and diagnostics. Here we present a major update of probeBase, which was last featured in the NAR Database Issue 2007. This update describes a complete remodeling of the database architecture and environment to accommodate computationally efficient access. Improved search functions, sequence match tools and data output now extend the opportunities for finding suitable hierarchical probe sets that target an organism or taxon at different taxonomic levels. To facilitate the identification of complementary probe sets for organisms represented by short rRNA sequence reads generated by amplicon sequencing or metagenomic analysis with next generation sequencing technologies such as Illumina and IonTorrent, we introduce a novel tool that recovers surrogate near full-length rRNA sequences for short query sequences and finds matching oligonucleotides in probeBase. PMID:26586809

Cordova: web-based management of genetic variation data.

PubMed

Ephraim, Sean S; Anand, Nikhil; DeLuca, Adam P; Taylor, Kyle R; Kolbe, Diana L; Simpson, Allen C; Azaiez, Hela; Sloan, Christina M; Shearer, A Eliot; Hallier, Andrea R; Casavant, Thomas L; Scheetz, Todd E; Smith, Richard J H; Braun, Terry A

2014-12-01

Cordova is an out-of-the-box solution for building and maintaining an online database of genetic variations integrated with pathogenicity prediction results from popular algorithms. Our primary motivation for developing this system is to aid researchers and clinician-scientists in determining the clinical significance of genetic variations. To achieve this goal, Cordova provides an interface to review and manually or computationally curate genetic variation data as well as share it for clinical diagnostics and the advancement of research. Cordova is open source under the MIT license and is freely available for download at https://github.com/clcg/cordova. Published by Oxford University Press. This work is written by US Government employees and is in the public domain in the US.

Improving the Discoverability and Availability of Sample Data and Imagery in NASA's Astromaterials Curation Digital Repository Using a New Common Architecture for Sample Databases

NASA Technical Reports Server (NTRS)

Todd, N. S.; Evans, C.

2015-01-01

The Astromaterials Acquisition and Curation Office at NASA's Johnson Space Center (JSC) is the designated facility for curating all of NASA's extraterrestrial samples. The suite of collections includes the lunar samples from the Apollo missions, cosmic dust particles falling into the Earth's atmosphere, meteorites collected in Antarctica, comet and interstellar dust particles from the Stardust mission, asteroid particles from the Japanese Hayabusa mission, and solar wind atoms collected during the Genesis mission. To support planetary science research on these samples, NASA's Astromaterials Curation Office hosts the Astromaterials Curation Digital Repository, which provides descriptions of the missions and collections, and critical information about each individual sample. Our office is implementing several informatics initiatives with the goal of better serving the planetary research community. One of these initiatives aims to increase the availability and discoverability of sample data and images through the use of a newly designed common architecture for Astromaterials Curation databases.

The Protein-DNA Interface database

PubMed Central

2010-01-01

The Protein-DNA Interface database (PDIdb) is a repository containing relevant structural information of Protein-DNA complexes solved by X-ray crystallography and available at the Protein Data Bank. The database includes a simple functional classification of the protein-DNA complexes that consists of three hierarchical levels: Class, Type and Subtype. This classification has been defined and manually curated by humans based on the information gathered from several sources that include PDB, PubMed, CATH, SCOP and COPS. The current version of the database contains only structures with resolution of 2.5 Å or higher, accounting for a total of 922 entries. The major aim of this database is to contribute to the understanding of the main rules that underlie the molecular recognition process between DNA and proteins. To this end, the database is focused on each specific atomic interface rather than on the separated binding partners. Therefore, each entry in this database consists of a single and independent protein-DNA interface. We hope that PDIdb will be useful to many researchers working in fields such as the prediction of transcription factor binding sites in DNA, the study of specificity determinants that mediate enzyme recognition events, engineering and design of new DNA binding proteins with distinct binding specificity and affinity, among others. Finally, due to its friendly and easy-to-use web interface, we hope that PDIdb will also serve educational and teaching purposes. PMID:20482798

The Protein-DNA Interface database.

PubMed

Norambuena, Tomás; Melo, Francisco

2010-05-18

The Protein-DNA Interface database (PDIdb) is a repository containing relevant structural information of Protein-DNA complexes solved by X-ray crystallography and available at the Protein Data Bank. The database includes a simple functional classification of the protein-DNA complexes that consists of three hierarchical levels: Class, Type and Subtype. This classification has been defined and manually curated by humans based on the information gathered from several sources that include PDB, PubMed, CATH, SCOP and COPS. The current version of the database contains only structures with resolution of 2.5 A or higher, accounting for a total of 922 entries. The major aim of this database is to contribute to the understanding of the main rules that underlie the molecular recognition process between DNA and proteins. To this end, the database is focused on each specific atomic interface rather than on the separated binding partners. Therefore, each entry in this database consists of a single and independent protein-DNA interface.We hope that PDIdb will be useful to many researchers working in fields such as the prediction of transcription factor binding sites in DNA, the study of specificity determinants that mediate enzyme recognition events, engineering and design of new DNA binding proteins with distinct binding specificity and affinity, among others. Finally, due to its friendly and easy-to-use web interface, we hope that PDIdb will also serve educational and teaching purposes.

DIANA-TarBase v7.0: indexing more than half a million experimentally supported miRNA:mRNA interactions.

PubMed

Vlachos, Ioannis S; Paraskevopoulou, Maria D; Karagkouni, Dimitra; Georgakilas, Georgios; Vergoulis, Thanasis; Kanellos, Ilias; Anastasopoulos, Ioannis-Laertis; Maniou, Sofia; Karathanou, Konstantina; Kalfakakou, Despina; Fevgas, Athanasios; Dalamagas, Theodore; Hatzigeorgiou, Artemis G

2015-01-01

microRNAs (miRNAs) are short non-coding RNA species, which act as potent gene expression regulators. Accurate identification of miRNA targets is crucial to understanding their function. Currently, hundreds of thousands of miRNA:gene interactions have been experimentally identified. However, this wealth of information is fragmented and hidden in thousands of manuscripts and raw next-generation sequencing data sets. DIANA-TarBase was initially released in 2006 and it was the first database aiming to catalog published experimentally validated miRNA:gene interactions. DIANA-TarBase v7.0 (http://www.microrna.gr/tarbase) aims to provide for the first time hundreds of thousands of high-quality manually curated experimentally validated miRNA:gene interactions, enhanced with detailed meta-data. DIANA-TarBase v7.0 enables users to easily identify positive or negative experimental results, the utilized experimental methodology, experimental conditions including cell/tissue type and treatment. The new interface provides also advanced information ranging from the binding site location, as identified experimentally as well as in silico, to the primer sequences used for cloning experiments. More than half a million miRNA:gene interactions have been curated from published experiments on 356 different cell types from 24 species, corresponding to 9- to 250-fold more entries than any other relevant database. DIANA-TarBase v7.0 is freely available. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Correcting Inconsistencies and Errors in Bacterial Genome Metadata Using an Automated Curation Tool in Excel (AutoCurE).

PubMed

Schmedes, Sarah E; King, Jonathan L; Budowle, Bruce

2015-01-01

Whole-genome data are invaluable for large-scale comparative genomic studies. Current sequencing technologies have made it feasible to sequence entire bacterial genomes with relative ease and time with a substantially reduced cost per nucleotide, hence cost per genome. More than 3,000 bacterial genomes have been sequenced and are available at the finished status. Publically available genomes can be readily downloaded; however, there are challenges to verify the specific supporting data contained within the download and to identify errors and inconsistencies that may be present within the organizational data content and metadata. AutoCurE, an automated tool for bacterial genome database curation in Excel, was developed to facilitate local database curation of supporting data that accompany downloaded genomes from the National Center for Biotechnology Information. AutoCurE provides an automated approach to curate local genomic databases by flagging inconsistencies or errors by comparing the downloaded supporting data to the genome reports to verify genome name, RefSeq accession numbers, the presence of archaea, BioProject/UIDs, and sequence file descriptions. Flags are generated for nine metadata fields if there are inconsistencies between the downloaded genomes and genomes reports and if erroneous or missing data are evident. AutoCurE is an easy-to-use tool for local database curation for large-scale genome data prior to downstream analyses.

The BioGRID Interaction Database: 2011 update

PubMed Central

Stark, Chris; Breitkreutz, Bobby-Joe; Chatr-aryamontri, Andrew; Boucher, Lorrie; Oughtred, Rose; Livstone, Michael S.; Nixon, Julie; Van Auken, Kimberly; Wang, Xiaodong; Shi, Xiaoqi; Reguly, Teresa; Rust, Jennifer M.; Winter, Andrew; Dolinski, Kara; Tyers, Mike

2011-01-01

The Biological General Repository for Interaction Datasets (BioGRID) is a public database that archives and disseminates genetic and protein interaction data from model organisms and humans (http://www.thebiogrid.org). BioGRID currently holds 347 966 interactions (170 162 genetic, 177 804 protein) curated from both high-throughput data sets and individual focused studies, as derived from over 23 000 publications in the primary literature. Complete coverage of the entire literature is maintained for budding yeast (Saccharomyces cerevisiae), fission yeast (Schizosaccharomyces pombe) and thale cress (Arabidopsis thaliana), and efforts to expand curation across multiple metazoan species are underway. The BioGRID houses 48 831 human protein interactions that have been curated from 10 247 publications. Current curation drives are focused on particular areas of biology to enable insights into conserved networks and pathways that are relevant to human health. The BioGRID 3.0 web interface contains new search and display features that enable rapid queries across multiple data types and sources. An automated Interaction Management System (IMS) is used to prioritize, coordinate and track curation across international sites and projects. BioGRID provides interaction data to several model organism databases, resources such as Entrez-Gene and other interaction meta-databases. The entire BioGRID 3.0 data collection may be downloaded in multiple file formats, including PSI MI XML. Source code for BioGRID 3.0 is freely available without any restrictions. PMID:21071413

Guidelines for the functional annotation of microRNAs using the Gene Ontology

PubMed Central

D'Eustachio, Peter; Smith, Jennifer R.; Zampetaki, Anna

2016-01-01

MicroRNA regulation of developmental and cellular processes is a relatively new field of study, and the available research data have not been organized to enable its inclusion in pathway and network analysis tools. The association of gene products with terms from the Gene Ontology is an effective method to analyze functional data, but until recently there has been no substantial effort dedicated to applying Gene Ontology terms to microRNAs. Consequently, when performing functional analysis of microRNA data sets, researchers have had to rely instead on the functional annotations associated with the genes encoding microRNA targets. In consultation with experts in the field of microRNA research, we have created comprehensive recommendations for the Gene Ontology curation of microRNAs. This curation manual will enable provision of a high-quality, reliable set of functional annotations for the advancement of microRNA research. Here we describe the key aspects of the work, including development of the Gene Ontology to represent this data, standards for describing the data, and guidelines to support curators making these annotations. The full microRNA curation guidelines are available on the GO Consortium wiki (http://wiki.geneontology.org/index.php/MicroRNA_GO_annotation_manual). PMID:26917558

Library of molecular associations: curating the complex molecular basis of liver diseases.

PubMed

Buchkremer, Stefan; Hendel, Jasmin; Krupp, Markus; Weinmann, Arndt; Schlamp, Kai; Maass, Thorsten; Staib, Frank; Galle, Peter R; Teufel, Andreas

2010-03-20

Systems biology approaches offer novel insights into the development of chronic liver diseases. Current genomic databases supporting systems biology analyses are mostly based on microarray data. Although these data often cover genome wide expression, the validity of single microarray experiments remains questionable. However, for systems biology approaches addressing the interactions of molecular networks comprehensive but also highly validated data are necessary. We have therefore generated the first comprehensive database for published molecular associations in human liver diseases. It is based on PubMed published abstracts and aimed to close the gap between genome wide coverage of low validity from microarray data and individual highly validated data from PubMed. After an initial text mining process, the extracted abstracts were all manually validated to confirm content and potential genetic associations and may therefore be highly trusted. All data were stored in a publicly available database, Library of Molecular Associations http://www.medicalgenomics.org/databases/loma/news, currently holding approximately 1260 confirmed molecular associations for chronic liver diseases such as HCC, CCC, liver fibrosis, NASH/fatty liver disease, AIH, PBC, and PSC. We furthermore transformed these data into a powerful resource for molecular liver research by connecting them to multiple biomedical information resources. Together, this database is the first available database providing a comprehensive view and analysis options for published molecular associations on multiple liver diseases.

Text Mining to Support Gene Ontology Curation and Vice Versa.

PubMed

Ruch, Patrick

2017-01-01

In this chapter, we explain how text mining can support the curation of molecular biology databases dealing with protein functions. We also show how curated data can play a disruptive role in the developments of text mining methods. We review a decade of efforts to improve the automatic assignment of Gene Ontology (GO) descriptors, the reference ontology for the characterization of genes and gene products. To illustrate the high potential of this approach, we compare the performances of an automatic text categorizer and show a large improvement of +225 % in both precision and recall on benchmarked data. We argue that automatic text categorization functions can ultimately be embedded into a Question-Answering (QA) system to answer questions related to protein functions. Because GO descriptors can be relatively long and specific, traditional QA systems cannot answer such questions. A new type of QA system, so-called Deep QA which uses machine learning methods trained with curated contents, is thus emerging. Finally, future advances of text mining instruments are directly dependent on the availability of high-quality annotated contents at every curation step. Databases workflows must start recording explicitly all the data they curate and ideally also some of the data they do not curate.

Design of Community Resource Inventories as a Component of Scalable Earth Science Infrastructure: Experience of the Earthcube CINERGI Project

NASA Astrophysics Data System (ADS)

Zaslavsky, I.; Richard, S. M.; Valentine, D. W., Jr.; Grethe, J. S.; Hsu, L.; Malik, T.; Bermudez, L. E.; Gupta, A.; Lehnert, K. A.; Whitenack, T.; Ozyurt, I. B.; Condit, C.; Calderon, R.; Musil, L.

2014-12-01

EarthCube is envisioned as a cyberinfrastructure that fosters new, transformational geoscience by enabling sharing, understanding and scientifically-sound and efficient re-use of formerly unconnected data resources, software, models, repositories, and computational power. Its purpose is to enable science enterprise and workforce development via an extensible and adaptable collaboration and resource integration framework. A key component of this vision is development of comprehensive inventories supporting resource discovery and re-use across geoscience domains. The goal of the EarthCube CINERGI (Community Inventory of EarthCube Resources for Geoscience Interoperability) project is to create a methodology and assemble a large inventory of high-quality information resources with standard metadata descriptions and traceable provenance. The inventory is compiled from metadata catalogs maintained by geoscience data facilities, as well as from user contributions. The latter mechanism relies on community resource viewers: online applications that support update and curation of metadata records. Once harvested into CINERGI, metadata records from domain catalogs and community resource viewers are loaded into a staging database implemented in MongoDB, and validated for compliance with ISO 19139 metadata schema. Several types of metadata defects detected by the validation engine are automatically corrected with help of several information extractors or flagged for manual curation. The metadata harvesting, validation and processing components generate provenance statements using W3C PROV notation, which are stored in a Neo4J database. Thus curated metadata, along with the provenance information, is re-published and accessed programmatically and via a CINERGI online application. This presentation focuses on the role of resource inventories in a scalable and adaptable information infrastructure, and on the CINERGI metadata pipeline and its implementation challenges. Key project components are described at the project's website (http://workspace.earthcube.org/cinergi), which also provides access to the initial resource inventory, the inventory metadata model, metadata entry forms and a collection of the community resource viewers.

Enhancing a Pathway-Genome Database (PGDB) to capture subcellular localization of metabolites and enzymes: the nucleotide-sugar biosynthetic pathways of Populus trichocarpa.

PubMed

Nag, Ambarish; Karpinets, Tatiana V; Chang, Christopher H; Bar-Peled, Maor

2012-01-01

Understanding how cellular metabolism works and is regulated requires that the underlying biochemical pathways be adequately represented and integrated with large metabolomic data sets to establish a robust network model. Genetically engineering energy crops to be less recalcitrant to saccharification requires detailed knowledge of plant polysaccharide structures and a thorough understanding of the metabolic pathways involved in forming and regulating cell-wall synthesis. Nucleotide-sugars are building blocks for synthesis of cell wall polysaccharides. The biosynthesis of nucleotide-sugars is catalyzed by a multitude of enzymes that reside in different subcellular organelles, and precise representation of these pathways requires accurate capture of this biological compartmentalization. The lack of simple localization cues in genomic sequence data and annotations however leads to missing compartmentalization information for eukaryotes in automatically generated databases, such as the Pathway-Genome Databases (PGDBs) of the SRI Pathway Tools software that drives much biochemical knowledge representation on the internet. In this report, we provide an informal mechanism using the existing Pathway Tools framework to integrate protein and metabolite sub-cellular localization data with the existing representation of the nucleotide-sugar metabolic pathways in a prototype PGDB for Populus trichocarpa. The enhanced pathway representations have been successfully used to map SNP abundance data to individual nucleotide-sugar biosynthetic genes in the PGDB. The manually curated pathway representations are more conducive to the construction of a computational platform that will allow the simulation of natural and engineered nucleotide-sugar precursor fluxes into specific recalcitrant polysaccharide(s). Database URL: The curated Populus PGDB is available in the BESC public portal at http://cricket.ornl.gov/cgi-bin/beocyc_home.cgi and the nucleotide-sugar biosynthetic pathways can be directly accessed at http://cricket.ornl.gov:1555/PTR/new-image?object=SUGAR-NUCLEOTIDES.

Enhancing a Pathway-Genome Database (PGDB) to capture subcellular localization of metabolites and enzymes: the nucleotide-sugar biosynthetic pathways of Populus trichocarpa

PubMed Central

Nag, Ambarish; Karpinets, Tatiana V.; Chang, Christopher H.; Bar-Peled, Maor

2012-01-01

Understanding how cellular metabolism works and is regulated requires that the underlying biochemical pathways be adequately represented and integrated with large metabolomic data sets to establish a robust network model. Genetically engineering energy crops to be less recalcitrant to saccharification requires detailed knowledge of plant polysaccharide structures and a thorough understanding of the metabolic pathways involved in forming and regulating cell-wall synthesis. Nucleotide-sugars are building blocks for synthesis of cell wall polysaccharides. The biosynthesis of nucleotide-sugars is catalyzed by a multitude of enzymes that reside in different subcellular organelles, and precise representation of these pathways requires accurate capture of this biological compartmentalization. The lack of simple localization cues in genomic sequence data and annotations however leads to missing compartmentalization information for eukaryotes in automatically generated databases, such as the Pathway-Genome Databases (PGDBs) of the SRI Pathway Tools software that drives much biochemical knowledge representation on the internet. In this report, we provide an informal mechanism using the existing Pathway Tools framework to integrate protein and metabolite sub-cellular localization data with the existing representation of the nucleotide-sugar metabolic pathways in a prototype PGDB for Populus trichocarpa. The enhanced pathway representations have been successfully used to map SNP abundance data to individual nucleotide-sugar biosynthetic genes in the PGDB. The manually curated pathway representations are more conducive to the construction of a computational platform that will allow the simulation of natural and engineered nucleotide-sugar precursor fluxes into specific recalcitrant polysaccharide(s). Database URL: The curated Populus PGDB is available in the BESC public portal at http://cricket.ornl.gov/cgi-bin/beocyc_home.cgi and the nucleotide-sugar biosynthetic pathways can be directly accessed at http://cricket.ornl.gov:1555/PTR/new-image?object=SUGAR-NUCLEOTIDES. PMID:22465851

EXTRACT: Interactive extraction of environment metadata and term suggestion for metagenomic sample annotation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pafilis, Evangelos; Buttigieg, Pier Luigi; Ferrell, Barbra

The microbial and molecular ecology research communities have made substantial progress on developing standards for annotating samples with environment metadata. However, sample manual annotation is a highly labor intensive process and requires familiarity with the terminologies used. We have therefore developed an interactive annotation tool, EXTRACT, which helps curators identify and extract standard-compliant terms for annotation of metagenomic records and other samples. Behind its web-based user interface, the system combines published methods for named entity recognition of environment, organism, tissue and disease terms. The evaluators in the BioCreative V Interactive Annotation Task found the system to be intuitive, useful, wellmore » documented and sufficiently accurate to be helpful in spotting relevant text passages and extracting organism and environment terms. Here the comparison of fully manual and text-mining-assisted curation revealed that EXTRACT speeds up annotation by 15–25% and helps curators to detect terms that would otherwise have been missed.« less

EXTRACT: Interactive extraction of environment metadata and term suggestion for metagenomic sample annotation

DOE PAGES

Pafilis, Evangelos; Buttigieg, Pier Luigi; Ferrell, Barbra; ...

2016-01-01

The microbial and molecular ecology research communities have made substantial progress on developing standards for annotating samples with environment metadata. However, sample manual annotation is a highly labor intensive process and requires familiarity with the terminologies used. We have therefore developed an interactive annotation tool, EXTRACT, which helps curators identify and extract standard-compliant terms for annotation of metagenomic records and other samples. Behind its web-based user interface, the system combines published methods for named entity recognition of environment, organism, tissue and disease terms. The evaluators in the BioCreative V Interactive Annotation Task found the system to be intuitive, useful, wellmore » documented and sufficiently accurate to be helpful in spotting relevant text passages and extracting organism and environment terms. Here the comparison of fully manual and text-mining-assisted curation revealed that EXTRACT speeds up annotation by 15–25% and helps curators to detect terms that would otherwise have been missed.« less

Using support vector machines to improve elemental ion identification in macromolecular crystal structures

DOE PAGES

Morshed, Nader; Echols, Nathaniel; Adams, Paul D.

2015-04-25

In the process of macromolecular model building, crystallographers must examine electron density for isolated atoms and differentiate sites containing structured solvent molecules from those containing elemental ions. This task requires specific knowledge of metal-binding chemistry and scattering properties and is prone to error. A method has previously been described to identify ions based on manually chosen criteria for a number of elements. Here, the use of support vector machines (SVMs) to automatically classify isolated atoms as either solvent or one of various ions is described. Two data sets of protein crystal structures, one containing manually curated structures deposited with anomalousmore » diffraction data and another with automatically filtered, high-resolution structures, were constructed. On the manually curated data set, an SVM classifier was able to distinguish calcium from manganese, zinc, iron and nickel, as well as all five of these ions from water molecules, with a high degree of accuracy. Additionally, SVMs trained on the automatically curated set of high-resolution structures were able to successfully classify most common elemental ions in an independent validation test set. This method is readily extensible to other elemental ions and can also be used in conjunction with previous methods based on a priori expectations of the chemical environment and X-ray scattering.« less

Assessment of the significance of patent-derived information for the early identification of compound-target interaction hypotheses.

PubMed

Senger, Stefan

2017-04-21

Patents are an important source of information for effective decision making in drug discovery. Encouragingly, freely accessible patent-chemistry databases are now in the public domain. However, at present there is still a wide gap between relatively low coverage-high quality manually-curated data sources and high coverage data sources that use text mining and automated extraction of chemical structures. To secure much needed funding for further research and an improved infrastructure, hard evidence is required to demonstrate the significance of patent-derived information in drug discovery. Surprisingly little such evidence has been reported so far. To address this, the present study attempts to quantify the relevance of patents for formulating and substantiating hypotheses for compound-target interactions. A manually-curated set of 130 compound-target interaction pairs annotated with what are considered to be the earliest patent and publication has been produced. The analysis of this set revealed that in stark contrast to what has been reported for novel chemical structures, only about 10% of the compound-target interaction pairs could be found in publications in the scientific literature within one year of being reported in patents. The average delay across all interaction pairs is close to 4 years. In an attempt to benchmark current capabilities, it was also examined how much of the benefit of using patent-derived information can be retained when a bioannotated version of SureChEMBL is used as secondary source for the patent literature. Encouragingly, this approach found the patents in the annotated set for 72% of the compound-target interaction pairs. Similarly, the effect of using the bioactivity database ChEMBL as secondary source for the scientific literature was studied. Here, the publications from the annotated set were only found for 46% of the compound-target interaction pairs. Patent-derived information is a significant enabler for formulating compound-target interaction hypotheses even in cases where the respective interaction is later reported in the scientific literature. The findings of this study clearly highlight the significance of future investments in the development and provision of databases and tools that will allow scientists to search patent information in a comprehensive, reliable, and efficient manner.

FreeSolv: A database of experimental and calculated hydration free energies, with input files

PubMed Central

Mobley, David L.; Guthrie, J. Peter

2014-01-01

This work provides a curated database of experimental and calculated hydration free energies for small neutral molecules in water, along with molecular structures, input files, references, and annotations. We call this the Free Solvation Database, or FreeSolv. Experimental values were taken from prior literature and will continue to be curated, with updated experimental references and data added as they become available. Calculated values are based on alchemical free energy calculations using molecular dynamics simulations. These used the GAFF small molecule force field in TIP3P water with AM1-BCC charges. Values were calculated with the GROMACS simulation package, with full details given in references cited within the database itself. This database builds in part on a previous, 504-molecule database containing similar information. However, additional curation of both experimental data and calculated values has been done here, and the total number of molecules is now up to 643. Additional information is now included in the database, such as SMILES strings, PubChem compound IDs, accurate reference DOIs, and others. One version of the database is provided in the Supporting Information of this article, but as ongoing updates are envisioned, the database is now versioned and hosted online. In addition to providing the database, this work describes its construction process. The database is available free-of-charge via http://www.escholarship.org/uc/item/6sd403pz. PMID:24928188

TubercuList--10 years after.

PubMed

Lew, Jocelyne M; Kapopoulou, Adamandia; Jones, Louis M; Cole, Stewart T

2011-01-01

TubercuList (http://tuberculist.epfl.ch/), the relational database that presents genome-derived information about H37Rv, the paradigm strain of Mycobacterium tuberculosis, has been active for ten years and now presents its twentieth release. Here, we describe some of the recent changes that have resulted from manual annotation with information from the scientific literature. Through manual curation, TubercuList strives to provide current gene-based information and is thus distinguished from other online sources of genome sequence data for M. tuberculosis. New, mostly small, genes have been discovered and the coordinates of some existing coding sequences have been changed when bioinformatics or experimental data suggest that this is required. Nucleotides that are polymorphic between different sources of H37Rv are annotated and gene essentiality data have been updated. A host of functional information has been gleaned from the literature and many new activities of proteins and RNAs have been included. To facilitate basic and translational research, TubercuList also provides links to other specialized databases that present diverse datasets such as 3D-structures, expression profiles, drug development criteria and drug resistance information, in addition to direct access to PubMed articles pertinent to particular genes. TubercuList has been and remains a highly valuable tool for the tuberculosis research community with >75,000 visitors per month. Copyright © 2010 Elsevier Ltd. All rights reserved.

EpiGeNet: A Graph Database of Interdependencies Between Genetic and Epigenetic Events in Colorectal Cancer.

PubMed

Balaur, Irina; Saqi, Mansoor; Barat, Ana; Lysenko, Artem; Mazein, Alexander; Rawlings, Christopher J; Ruskin, Heather J; Auffray, Charles

2017-10-01

The development of colorectal cancer (CRC)-the third most common cancer type-has been associated with deregulations of cellular mechanisms stimulated by both genetic and epigenetic events. StatEpigen is a manually curated and annotated database, containing information on interdependencies between genetic and epigenetic signals, and specialized currently for CRC research. Although StatEpigen provides a well-developed graphical user interface for information retrieval, advanced queries involving associations between multiple concepts can benefit from more detailed graph representation of the integrated data. This can be achieved by using a graph database (NoSQL) approach. Data were extracted from StatEpigen and imported to our newly developed EpiGeNet, a graph database for storage and querying of conditional relationships between molecular (genetic and epigenetic) events observed at different stages of colorectal oncogenesis. We illustrate the enhanced capability of EpiGeNet for exploration of different queries related to colorectal tumor progression; specifically, we demonstrate the query process for (i) stage-specific molecular events, (ii) most frequently observed genetic and epigenetic interdependencies in colon adenoma, and (iii) paths connecting key genes reported in CRC and associated events. The EpiGeNet framework offers improved capability for management and visualization of data on molecular events specific to CRC initiation and progression.

The systematic annotation of the three main GPCR families in Reactome.

PubMed

Jassal, Bijay; Jupe, Steven; Caudy, Michael; Birney, Ewan; Stein, Lincoln; Hermjakob, Henning; D'Eustachio, Peter

2010-07-29

Reactome is an open-source, freely available database of human biological pathways and processes. A major goal of our work is to provide an integrated view of cellular signalling processes that spans from ligand-receptor interactions to molecular readouts at the level of metabolic and transcriptional events. To this end, we have built the first catalogue of all human G protein-coupled receptors (GPCRs) known to bind endogenous or natural ligands. The UniProt database has records for 797 proteins classified as GPCRs and sorted into families A/1, B/2 and C/3 on the basis of amino acid sequence. To these records we have added details from the IUPHAR database and our own manual curation of relevant literature to create reactions in which 563 GPCRs bind ligands and also interact with specific G-proteins to initiate signalling cascades. We believe the remaining 234 GPCRs are true orphans. The Reactome GPCR pathway can be viewed as a detailed interactive diagram and can be exported in many forms. It provides a template for the orthology-based inference of GPCR reactions for diverse model organism species, and can be overlaid with protein-protein interaction and gene expression datasets to facilitate overrepresentation studies and other forms of pathway analysis. Database URL: http://www.reactome.org.

PlantRNA, a database for tRNAs of photosynthetic eukaryotes.

PubMed

Cognat, Valérie; Pawlak, Gaël; Duchêne, Anne-Marie; Daujat, Magali; Gigant, Anaïs; Salinas, Thalia; Michaud, Morgane; Gutmann, Bernard; Giegé, Philippe; Gobert, Anthony; Maréchal-Drouard, Laurence

2013-01-01

PlantRNA database (http://plantrna.ibmp.cnrs.fr/) compiles transfer RNA (tRNA) gene sequences retrieved from fully annotated plant nuclear, plastidial and mitochondrial genomes. The set of annotated tRNA gene sequences has been manually curated for maximum quality and confidence. The novelty of this database resides in the inclusion of biological information relevant to the function of all the tRNAs entered in the library. This includes 5'- and 3'-flanking sequences, A and B box sequences, region of transcription initiation and poly(T) transcription termination stretches, tRNA intron sequences, aminoacyl-tRNA synthetases and enzymes responsible for tRNA maturation and modification. Finally, data on mitochondrial import of nuclear-encoded tRNAs as well as the bibliome for the respective tRNAs and tRNA-binding proteins are also included. The current annotation concerns complete genomes from 11 organisms: five flowering plants (Arabidopsis thaliana, Oryza sativa, Populus trichocarpa, Medicago truncatula and Brachypodium distachyon), a moss (Physcomitrella patens), two green algae (Chlamydomonas reinhardtii and Ostreococcus tauri), one glaucophyte (Cyanophora paradoxa), one brown alga (Ectocarpus siliculosus) and a pennate diatom (Phaeodactylum tricornutum). The database will be regularly updated and implemented with new plant genome annotations so as to provide extensive information on tRNA biology to the research community.

The IntAct molecular interaction database in 2012

PubMed Central

Kerrien, Samuel; Aranda, Bruno; Breuza, Lionel; Bridge, Alan; Broackes-Carter, Fiona; Chen, Carol; Duesbury, Margaret; Dumousseau, Marine; Feuermann, Marc; Hinz, Ursula; Jandrasits, Christine; Jimenez, Rafael C.; Khadake, Jyoti; Mahadevan, Usha; Masson, Patrick; Pedruzzi, Ivo; Pfeiffenberger, Eric; Porras, Pablo; Raghunath, Arathi; Roechert, Bernd; Orchard, Sandra; Hermjakob, Henning

2012-01-01

IntAct is an open-source, open data molecular interaction database populated by data either curated from the literature or from direct data depositions. Two levels of curation are now available within the database, with both IMEx-level annotation and less detailed MIMIx-compatible entries currently supported. As from September 2011, IntAct contains approximately 275 000 curated binary interaction evidences from over 5000 publications. The IntAct website has been improved to enhance the search process and in particular the graphical display of the results. New data download formats are also available, which will facilitate the inclusion of IntAct's data in the Semantic Web. IntAct is an active contributor to the IMEx consortium (http://www.imexconsortium.org). IntAct source code and data are freely available at http://www.ebi.ac.uk/intact. PMID:22121220

DEMO: ECOTOX Knowledgebase

EPA Science Inventory

The ECOTOXicology Knowledgebase (ECOTOX), is a comprehensive, curated database that summarizes toxicology data fromsingle chemical exposure studies to aquatic life, terrestrial plants, and wildlife. The ECOTOX Knowledgebase currently has curated data from over 47,000 references a...

Text mining for the biocuration workflow

PubMed Central

Hirschman, Lynette; Burns, Gully A. P. C; Krallinger, Martin; Arighi, Cecilia; Cohen, K. Bretonnel; Valencia, Alfonso; Wu, Cathy H.; Chatr-Aryamontri, Andrew; Dowell, Karen G.; Huala, Eva; Lourenço, Anália; Nash, Robert; Veuthey, Anne-Lise; Wiegers, Thomas; Winter, Andrew G.

2012-01-01

Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documented successes for improving biocuration throughput using text mining. Our initial investigations took place for the workshop on ‘Text Mining for the BioCuration Workflow’ at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community. PMID:22513129

Text mining for the biocuration workflow.

PubMed

Hirschman, Lynette; Burns, Gully A P C; Krallinger, Martin; Arighi, Cecilia; Cohen, K Bretonnel; Valencia, Alfonso; Wu, Cathy H; Chatr-Aryamontri, Andrew; Dowell, Karen G; Huala, Eva; Lourenço, Anália; Nash, Robert; Veuthey, Anne-Lise; Wiegers, Thomas; Winter, Andrew G

2012-01-01

Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documented successes for improving biocuration throughput using text mining. Our initial investigations took place for the workshop on 'Text Mining for the BioCuration Workflow' at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community.

TISSUES 2.0: an integrative web resource on mammalian tissue expression

PubMed Central

Palasca, Oana; Santos, Alberto; Stolte, Christian; Gorodkin, Jan; Jensen, Lars Juhl

2018-01-01

Abstract Physiological and molecular similarities between organisms make it possible to translate findings from simpler experimental systems—model organisms—into more complex ones, such as human. This translation facilitates the understanding of biological processes under normal or disease conditions. Researchers aiming to identify the similarities and differences between organisms at the molecular level need resources collecting multi-organism tissue expression data. We have developed a database of gene–tissue associations in human, mouse, rat and pig by integrating multiple sources of evidence: transcriptomics covering all four species and proteomics (human only), manually curated and mined from the scientific literature. Through a scoring scheme, these associations are made comparable across all sources of evidence and across organisms. Furthermore, the scoring produces a confidence score assigned to each of the associations. The TISSUES database (version 2.0) is publicly accessible through a user-friendly web interface and as part of the STRING app for Cytoscape. In addition, we analyzed the agreement between datasets, across and within organisms, and identified that the agreement is mainly affected by the quality of the datasets rather than by the technologies used or organisms compared. Database URL: http://tissues.jensenlab.org/ PMID:29617745

PRGdb: a bioinformatics platform for plant resistance gene analysis

PubMed Central

Sanseverino, Walter; Roma, Guglielmo; De Simone, Marco; Faino, Luigi; Melito, Sara; Stupka, Elia; Frusciante, Luigi; Ercolano, Maria Raffaella

2010-01-01

PRGdb is a web accessible open-source (http://www.prgdb.org) database that represents the first bioinformatic resource providing a comprehensive overview of resistance genes (R-genes) in plants. PRGdb holds more than 16 000 known and putative R-genes belonging to 192 plant species challenged by 115 different pathogens and linked with useful biological information. The complete database includes a set of 73 manually curated reference R-genes, 6308 putative R-genes collected from NCBI and 10463 computationally predicted putative R-genes. Thanks to a user-friendly interface, data can be examined using different query tools. A home-made prediction pipeline called Disease Resistance Analysis and Gene Orthology (DRAGO), based on reference R-gene sequence data, was developed to search for plant resistance genes in public datasets such as Unigene and Genbank. New putative R-gene classes containing unknown domain combinations were discovered and characterized. The development of the PRG platform represents an important starting point to conduct various experimental tasks. The inferred cross-link between genomic and phenotypic information allows access to a large body of information to find answers to several biological questions. The database structure also permits easy integration with other data types and opens up prospects for future implementations. PMID:19906694

Viral genome analysis and knowledge management.

PubMed

Kuiken, Carla; Yoon, Hyejin; Abfalterer, Werner; Gaschen, Brian; Lo, Chienchi; Korber, Bette

2013-01-01

One of the challenges of genetic data analysis is to combine information from sources that are distributed around the world and accessible through a wide array of different methods and interfaces. The HIV database and its footsteps, the hepatitis C virus (HCV) and hemorrhagic fever virus (HFV) databases, have made it their mission to make different data types easily available to their users. This involves a large amount of behind-the-scenes processing, including quality control and analysis of the sequences and their annotation. Gene and protein sequences are distilled from the sequences that are stored in GenBank; to this end, both submitter annotation and script-generated sequences are used. Alignments of both nucleotide and amino acid sequences are generated, manually curated, distilled into an alignment model, and regenerated in an iterative cycle that results in ever better new alignments. Annotation of epidemiological and clinical information is parsed, checked, and added to the database. User interfaces are updated, and new interfaces are added based upon user requests. Vital for its success, the database staff are heavy users of the system, which enables them to fix bugs and find opportunities for improvement. In this chapter we describe some of the infrastructure that keeps these heavily used analysis platforms alive and vital after nearly 25 years of use. The database/analysis platforms described in this chapter can be accessed at http://hiv.lanl.gov http://hcv.lanl.gov http://hfv.lanl.gov.

HNdb: an integrated database of gene and protein information on head and neck squamous cell carcinoma

PubMed Central

Henrique, Tiago; José Freitas da Silveira, Nelson; Henrique Cunha Volpato, Arthur; Mioto, Mayra Mataruco; Carolina Buzzo Stefanini, Ana; Bachir Fares, Adil; Gustavo da Silva Castro Andrade, João; Masson, Carolina; Verónica Mendoza López, Rossana; Daumas Nunes, Fabio; Paulo Kowalski, Luis; Severino, Patricia; Tajara, Eloiza Helena

2016-01-01

The total amount of scientific literature has grown rapidly in recent years. Specifically, there are several million citations in the field of cancer. This makes it difficult, if not impossible, to manually retrieve relevant information on the mechanisms that govern tumor behavior or the neoplastic process. Furthermore, cancer is a complex disease or, more accurately, a set of diseases. The heterogeneity that permeates many tumors is particularly evident in head and neck (HN) cancer, one of the most common types of cancer worldwide. In this study, we present HNdb, a free database that aims to provide a unified and comprehensive resource of information on genes and proteins involved in HN squamous cell carcinoma, covering data on genomics, transcriptomics, proteomics, literature citations and also cross-references of external databases. Different literature searches of MEDLINE abstracts were performed using specific Medical Subject Headings (MeSH terms) for oral, oropharyngeal, hypopharyngeal and laryngeal squamous cell carcinomas. A curated gene-to-publication assignment yielded a total of 1370 genes related to HN cancer. The diversity of results allowed identifying novel and mostly unexplored gene associations, revealing, for example, that processes linked to response to steroid hormone stimulus are significantly enriched in genes related to HN carcinomas. Thus, our database expands the possibilities for gene networks investigation, providing potential hypothesis to be tested. Database URL: http://www.gencapo.famerp.br/hndb PMID:27013077

MeDReaders: a database for transcription factors that bind to methylated DNA.

PubMed

Wang, Guohua; Luo, Ximei; Wang, Jianan; Wan, Jun; Xia, Shuli; Zhu, Heng; Qian, Jiang; Wang, Yadong

2018-01-04

Understanding the molecular principles governing interactions between transcription factors (TFs) and DNA targets is one of the main subjects for transcriptional regulation. Recently, emerging evidence demonstrated that some TFs could bind to DNA motifs containing highly methylated CpGs both in vitro and in vivo. Identification of such TFs and elucidation of their physiological roles now become an important stepping-stone toward understanding the mechanisms underlying the methylation-mediated biological processes, which have crucial implications for human disease and disease development. Hence, we constructed a database, named as MeDReaders, to collect information about methylated DNA binding activities. A total of 731 TFs, which could bind to methylated DNA sequences, were manually curated in human and mouse studies reported in the literature. In silico approaches were applied to predict methylated and unmethylated motifs of 292 TFs by integrating whole genome bisulfite sequencing (WGBS) and ChIP-Seq datasets in six human cell lines and one mouse cell line extracted from ENCODE and GEO database. MeDReaders database will provide a comprehensive resource for further studies and aid related experiment designs. The database implemented unified access for users to most TFs involved in such methylation-associated binding actives. The website is available at http://medreader.org/. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

The Candidate Cancer Gene Database: a database of cancer driver genes from forward genetic screens in mice.

PubMed

Abbott, Kenneth L; Nyre, Erik T; Abrahante, Juan; Ho, Yen-Yi; Isaksson Vogel, Rachel; Starr, Timothy K

2015-01-01

Identification of cancer driver gene mutations is crucial for advancing cancer therapeutics. Due to the overwhelming number of passenger mutations in the human tumor genome, it is difficult to pinpoint causative driver genes. Using transposon mutagenesis in mice many laboratories have conducted forward genetic screens and identified thousands of candidate driver genes that are highly relevant to human cancer. Unfortunately, this information is difficult to access and utilize because it is scattered across multiple publications using different mouse genome builds and strength metrics. To improve access to these findings and facilitate meta-analyses, we developed the Candidate Cancer Gene Database (CCGD, http://ccgd-starrlab.oit.umn.edu/). The CCGD is a manually curated database containing a unified description of all identified candidate driver genes and the genomic location of transposon common insertion sites (CISs) from all currently published transposon-based screens. To demonstrate relevance to human cancer, we performed a modified gene set enrichment analysis using KEGG pathways and show that human cancer pathways are highly enriched in the database. We also used hierarchical clustering to identify pathways enriched in blood cancers compared to solid cancers. The CCGD is a novel resource available to scientists interested in the identification of genetic drivers of cancer. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

The Microbe Directory: An annotated, searchable inventory of microbes' characteristics.

PubMed

Shaaban, Heba; Westfall, David A; Mohammad, Rawhi; Danko, David; Bezdan, Daniela; Afshinnekoo, Ebrahim; Segata, Nicola; Mason, Christopher E

2018-01-05

The Microbe Directory is a collective research effort to profile and annotate more than 7,500 unique microbial species from the MetaPhlAn2 database that includes bacteria, archaea, viruses, fungi, and protozoa. By collecting and summarizing data on various microbes' characteristics, the project comprises a database that can be used downstream of large-scale metagenomic taxonomic analyses, allowing one to interpret and explore their taxonomic classifications to have a deeper understanding of the microbial ecosystem they are studying. Such characteristics include, but are not limited to: optimal pH, optimal temperature, Gram stain, biofilm-formation, spore-formation, antimicrobial resistance, and COGEM class risk rating. The database has been manually curated by trained student-researchers from Weill Cornell Medicine and CUNY-Hunter College, and its analysis remains an ongoing effort with open-source capabilities so others can contribute. Available in SQL, JSON, and CSV (i.e. Excel) formats, the Microbe Directory can be queried for the aforementioned parameters by a microorganism's taxonomy. In addition to the raw database, The Microbe Directory has an online counterpart ( https://microbe.directory/) that provides a user-friendly interface for storage, retrieval, and analysis into which other microbial database projects could be incorporated. The Microbe Directory was primarily designed to serve as a resource for researchers conducting metagenomic analyses, but its online web interface should also prove useful to any individual who wishes to learn more about any particular microbe.

NRF2-ome: an integrated web resource to discover protein interaction and regulatory networks of NRF2.

PubMed

Türei, Dénes; Papp, Diána; Fazekas, Dávid; Földvári-Nagy, László; Módos, Dezső; Lenti, Katalin; Csermely, Péter; Korcsmáros, Tamás

2013-01-01

NRF2 is the master transcriptional regulator of oxidative and xenobiotic stress responses. NRF2 has important roles in carcinogenesis, inflammation, and neurodegenerative diseases. We developed an online resource, NRF2-ome, to provide an integrated and systems-level database for NRF2. The database contains manually curated and predicted interactions of NRF2 as well as data from external interaction databases. We integrated NRF2 interactome with NRF2 target genes, NRF2 regulating TFs, and miRNAs. We connected NRF2-ome to signaling pathways to allow mapping upstream NRF2 regulatory components that could directly or indirectly influence NRF2 activity totaling 35,967 protein-protein and signaling interactions. The user-friendly website allows researchers without computational background to search, browse, and download the database. The database can be downloaded in SQL, CSV, BioPAX, SBML, PSI-MI, and in a Cytoscape CYS file formats. We illustrated the applicability of the website by suggesting a posttranscriptional negative feedback of NRF2 by MAFG protein and raised the possibility of a connection between NRF2 and the JAK/STAT pathway through STAT1 and STAT3. NRF2-ome can also be used as an evaluation tool to help researchers and drug developers to understand the hidden regulatory mechanisms in the complex network of NRF2.

TBC2health: a database of experimentally validated health-beneficial effects of tea bioactive compounds.

PubMed

Zhang, Shihua; Xuan, Hongdong; Zhang, Liang; Fu, Sicong; Wang, Yijun; Yang, Hua; Tai, Yuling; Song, Youhong; Zhang, Jinsong; Ho, Chi-Tang; Li, Shaowen; Wan, Xiaochun

2017-09-01

Tea is one of the most consumed beverages in the world. Considerable studies show the exceptional health benefits (e.g. antioxidation, cancer prevention) of tea owing to its various bioactive components. However, data from these extensively published papers had not been made available in a central database. To lay a foundation in improving the understanding of healthy tea functions, we established a TBC2health database that currently documents 1338 relationships between 497 tea bioactive compounds and 206 diseases (or phenotypes) manually culled from over 300 published articles. Each entry in TBC2health contains comprehensive information about a bioactive relationship that can be accessed in three aspects: (i) compound information, (ii) disease (or phenotype) information and (iii) evidence and reference. Using the curated bioactive relationships, a bipartite network was reconstructed and the corresponding network (or sub-network) visualization and topological analyses are provided for users. This database has a user-friendly interface for entry browse, search and download. In addition, TBC2health provides a submission page and several useful tools (e.g. BLAST, molecular docking) to facilitate use of the database. Consequently, TBC2health can serve as a valuable bioinformatics platform for the exploration of beneficial effects of tea on human health. TBC2health is freely available at http://camellia.ahau.edu.cn/TBC2health. © The Author 2016. Published by Oxford University Press.

DB-PABP: a database of polyanion-binding proteins

PubMed Central

Fang, Jianwen; Dong, Yinghua; Salamat-Miller, Nazila; Russell Middaugh, C.

2008-01-01

The interactions between polyanions (PAs) and polyanion-binding proteins (PABPs) have been found to play significant roles in many essential biological processes including intracellular organization, transport and protein folding. Furthermore, many neurodegenerative disease-related proteins are PABPs. Thus, a better understanding of PA/PABP interactions may not only enhance our understandings of biological systems but also provide new clues to these deadly diseases. The literature in this field is widely scattered, suggesting the need for a comprehensive and searchable database of PABPs. The DB-PABP is a comprehensive, manually curated and searchable database of experimentally characterized PABPs. It is freely available and can be accessed online at http://pabp.bcf.ku.edu/DB_PABP/. The DB-PABP was implemented as a MySQL relational database. An interactive web interface was created using Java Server Pages (JSP). The search page of the database is organized into a main search form and a section for utilities. The main search form enables custom searches via four menus: protein names, polyanion names, the source species of the proteins and the methods used to discover the interactions. Available utilities include a commonality matrix, a function of listing PABPs by the number of interacting polyanions and a string search for author surnames. The DB-PABP is maintained at the University of Kansas. We encourage users to provide feedback and submit new data and references. PMID:17916573

DB-PABP: a database of polyanion-binding proteins.

PubMed

Fang, Jianwen; Dong, Yinghua; Salamat-Miller, Nazila; Middaugh, C Russell

2008-01-01

The interactions between polyanions (PAs) and polyanion-binding proteins (PABPs) have been found to play significant roles in many essential biological processes including intracellular organization, transport and protein folding. Furthermore, many neurodegenerative disease-related proteins are PABPs. Thus, a better understanding of PA/PABP interactions may not only enhance our understandings of biological systems but also provide new clues to these deadly diseases. The literature in this field is widely scattered, suggesting the need for a comprehensive and searchable database of PABPs. The DB-PABP is a comprehensive, manually curated and searchable database of experimentally characterized PABPs. It is freely available and can be accessed online at http://pabp.bcf.ku.edu/DB_PABP/. The DB-PABP was implemented as a MySQL relational database. An interactive web interface was created using Java Server Pages (JSP). The search page of the database is organized into a main search form and a section for utilities. The main search form enables custom searches via four menus: protein names, polyanion names, the source species of the proteins and the methods used to discover the interactions. Available utilities include a commonality matrix, a function of listing PABPs by the number of interacting polyanions and a string search for author surnames. The DB-PABP is maintained at the University of Kansas. We encourage users to provide feedback and submit new data and references.

SSER: Species specific essential reactions database.

PubMed

Labena, Abraham A; Ye, Yuan-Nong; Dong, Chuan; Zhang, Fa-Z; Guo, Feng-Biao

2017-04-19

Essential reactions are vital components of cellular networks. They are the foundations of synthetic biology and are potential candidate targets for antimetabolic drug design. Especially if a single reaction is catalyzed by multiple enzymes, then inhibiting the reaction would be a better option than targeting the enzymes or the corresponding enzyme-encoding gene. The existing databases such as BRENDA, BiGG, KEGG, Bio-models, Biosilico, and many others offer useful and comprehensive information on biochemical reactions. But none of these databases especially focus on essential reactions. Therefore, building a centralized repository for this class of reactions would be of great value. Here, we present a species-specific essential reactions database (SSER). The current version comprises essential biochemical and transport reactions of twenty-six organisms which are identified via flux balance analysis (FBA) combined with manual curation on experimentally validated metabolic network models. Quantitative data on the number of essential reactions, number of the essential reactions associated with their respective enzyme-encoding genes and shared essential reactions across organisms are the main contents of the database. SSER would be a prime source to obtain essential reactions data and related gene and metabolite information and it can significantly facilitate the metabolic network models reconstruction and analysis, and drug target discovery studies. Users can browse, search, compare and download the essential reactions of organisms of their interest through the website http://cefg.uestc.edu.cn/sser .

Using support vector machines to improve elemental ion identification in macromolecular crystal structures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morshed, Nader; Lawrence Berkeley National Laboratory, Berkeley, CA 94720; Echols, Nathaniel, E-mail: nechols@lbl.gov

2015-05-01

A method to automatically identify possible elemental ions in X-ray crystal structures has been extended to use support vector machine (SVM) classifiers trained on selected structures in the PDB, with significantly improved sensitivity over manually encoded heuristics. In the process of macromolecular model building, crystallographers must examine electron density for isolated atoms and differentiate sites containing structured solvent molecules from those containing elemental ions. This task requires specific knowledge of metal-binding chemistry and scattering properties and is prone to error. A method has previously been described to identify ions based on manually chosen criteria for a number of elements. Here,more » the use of support vector machines (SVMs) to automatically classify isolated atoms as either solvent or one of various ions is described. Two data sets of protein crystal structures, one containing manually curated structures deposited with anomalous diffraction data and another with automatically filtered, high-resolution structures, were constructed. On the manually curated data set, an SVM classifier was able to distinguish calcium from manganese, zinc, iron and nickel, as well as all five of these ions from water molecules, with a high degree of accuracy. Additionally, SVMs trained on the automatically curated set of high-resolution structures were able to successfully classify most common elemental ions in an independent validation test set. This method is readily extensible to other elemental ions and can also be used in conjunction with previous methods based on a priori expectations of the chemical environment and X-ray scattering.« less

TGF-beta signaling proteins and the Protein Ontology.

PubMed

Arighi, Cecilia N; Liu, Hongfang; Natale, Darren A; Barker, Winona C; Drabkin, Harold; Blake, Judith A; Smith, Barry; Wu, Cathy H

2009-05-06

The Protein Ontology (PRO) is designed as a formal and principled Open Biomedical Ontologies (OBO) Foundry ontology for proteins. The components of PRO extend from a classification of proteins on the basis of evolutionary relationships at the homeomorphic level to the representation of the multiple protein forms of a gene, including those resulting from alternative splicing, cleavage and/or post-translational modifications. Focusing specifically on the TGF-beta signaling proteins, we describe the building, curation, usage and dissemination of PRO. PRO is manually curated on the basis of PrePRO, an automatically generated file with content derived from standard protein data sources. Manual curation ensures that the treatment of the protein classes and the internal and external relationships conform to the PRO framework. The current release of PRO is based upon experimental data from mouse and human proteins wherein equivalent protein forms are represented by single terms. In addition to the PRO ontology, the annotation of PRO terms is released as a separate PRO association file, which contains, for each given PRO term, an annotation from the experimentally characterized sub-types as well as the corresponding database identifiers and sequence coordinates. The annotations are added in the form of relationship to other ontologies. Whenever possible, equivalent forms in other species are listed to facilitate cross-species comparison. Splice and allelic variants, gene fusion products and modified protein forms are all represented as entities in the ontology. Therefore, PRO provides for the representation of protein entities and a resource for describing the associated data. This makes PRO useful both for proteomics studies where isoforms and modified forms must be differentiated, and for studies of biological pathways, where representations need to take account of the different ways in which the cascade of events may depend on specific protein modifications. PRO provides a framework for the formal representation of protein classes and protein forms in the OBO Foundry. It is designed to enable data retrieval and integration and machine reasoning at the molecular level of proteins, thereby facilitating cross-species comparisons, pathway analysis, disease modeling and the generation of new hypotheses.

Identification of Putative Nuclear Receptors and Steroidogenic Enzymes in Murray-Darling Rainbowfish (Melanotaenia fluviatilis) Using RNA-Seq and De Novo Transcriptome Assembly.

PubMed

Bain, Peter A; Papanicolaou, Alexie; Kumar, Anupama

2015-01-01

Murray-Darling rainbowfish (Melanotaenia fluviatilis [Castelnau, 1878]; Atheriniformes: Melanotaeniidae) is a small-bodied teleost currently under development in Australasia as a test species for aquatic toxicological studies. To date, efforts towards the development of molecular biomarkers of contaminant exposure have been hindered by the lack of available sequence data. To address this, we sequenced messenger RNA from brain, liver and gonads of mature male and female fish and generated a high-quality draft transcriptome using a de novo assembly approach. 149,742 clusters of putative transcripts were obtained, encompassing 43,841 non-redundant protein-coding regions. Deduced amino acid sequences were annotated by functional inference based on similarity with sequences from manually curated protein sequence databases. The draft assembly contained protein-coding regions homologous to 95.7% of the complete cohort of predicted proteins from the taxonomically related species, Oryzias latipes (Japanese medaka). The mean length of rainbowfish protein-coding sequences relative to their medaka homologues was 92.1%, indicating that despite the limited number of tissues sampled a large proportion of the total expected number of protein-coding genes was captured in the study. Because of our interest in the effects of environmental contaminants on endocrine pathways, we manually curated subsets of coding regions for putative nuclear receptors and steroidogenic enzymes in the rainbowfish transcriptome, revealing 61 candidate nuclear receptors encompassing all known subfamilies, and 41 putative steroidogenic enzymes representing all major steroidogenic enzymes occurring in teleosts. The transcriptome presented here will be a valuable resource for researchers interested in biomarker development, protein structure and function, and contaminant-response genomics in Murray-Darling rainbowfish.

ConsPred: a rule-based (re-)annotation framework for prokaryotic genomes.

PubMed

Weinmaier, Thomas; Platzer, Alexander; Frank, Jeroen; Hellinger, Hans-Jörg; Tischler, Patrick; Rattei, Thomas

2016-11-01

The rapidly growing number of available prokaryotic genome sequences requires fully automated and high-quality software solutions for their initial and re-annotation. Here we present ConsPred, a prokaryotic genome annotation framework that performs intrinsic gene predictions, homology searches, predictions of non-coding genes as well as CRISPR repeats and integrates all evidence into a consensus annotation. ConsPred achieves comprehensive, high-quality annotations based on rules and priorities, similar to decision-making in manual curation and avoids conflicting predictions. Parameters controlling the annotation process are configurable by the user. ConsPred has been used in the institutions of the authors for longer than 5 years and can easily be extended and adapted to specific needs. The ConsPred algorithm for producing a consensus from the varying scores of multiple gene prediction programs approaches manual curation in accuracy. Its rule-based approach for choosing final predictions avoids overriding previous manual curations. ConsPred is implemented in Java, Perl and Shell and is freely available under the Creative Commons license as a stand-alone in-house pipeline or as an Amazon Machine Image for cloud computing, see https://sourceforge.net/projects/conspred/. thomas.rattei@univie.ac.atSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Curated protein information in the Saccharomyces genome database.

PubMed

Hellerstedt, Sage T; Nash, Robert S; Weng, Shuai; Paskov, Kelley M; Wong, Edith D; Karra, Kalpana; Engel, Stacia R; Cherry, J Michael

2017-01-01

Due to recent advancements in the production of experimental proteomic data, the Saccharomyces genome database (SGD; www.yeastgenome.org ) has been expanding our protein curation activities to make new data types available to our users. Because of broad interest in post-translational modifications (PTM) and their importance to protein function and regulation, we have recently started incorporating expertly curated PTM information on individual protein pages. Here we also present the inclusion of new abundance and protein half-life data obtained from high-throughput proteome studies. These new data types have been included with the aim to facilitate cellular biology research. : www.yeastgenome.org. © The Author(s) 2017. Published by Oxford University Press.

Ginseng Genome Database: an open-access platform for genomics of Panax ginseng.

PubMed

Jayakodi, Murukarthick; Choi, Beom-Soon; Lee, Sang-Choon; Kim, Nam-Hoon; Park, Jee Young; Jang, Woojong; Lakshmanan, Meiyappan; Mohan, Shobhana V G; Lee, Dong-Yup; Yang, Tae-Jin

2018-04-12

The ginseng (Panax ginseng C.A. Meyer) is a perennial herbaceous plant that has been used in traditional oriental medicine for thousands of years. Ginsenosides, which have significant pharmacological effects on human health, are the foremost bioactive constituents in this plant. Having realized the importance of this plant to humans, an integrated omics resource becomes indispensable to facilitate genomic research, molecular breeding and pharmacological study of this herb. The first draft genome sequences of P. ginseng cultivar "Chunpoong" were reported recently. Here, using the draft genome, transcriptome, and functional annotation datasets of P. ginseng, we have constructed the Ginseng Genome Database http://ginsengdb.snu.ac.kr /, the first open-access platform to provide comprehensive genomic resources of P. ginseng. The current version of this database provides the most up-to-date draft genome sequence (of approximately 3000 Mbp of scaffold sequences) along with the structural and functional annotations for 59,352 genes and digital expression of genes based on transcriptome data from different tissues, growth stages and treatments. In addition, tools for visualization and the genomic data from various analyses are provided. All data in the database were manually curated and integrated within a user-friendly query page. This database provides valuable resources for a range of research fields related to P. ginseng and other species belonging to the Apiales order as well as for plant research communities in general. Ginseng genome database can be accessed at http://ginsengdb.snu.ac.kr /.

ProCarDB: a database of bacterial carotenoids.

PubMed

Nupur, L N U; Vats, Asheema; Dhanda, Sandeep Kumar; Raghava, Gajendra P S; Pinnaka, Anil Kumar; Kumar, Ashwani

2016-05-26

Carotenoids have important functions in bacteria, ranging from harvesting light energy to neutralizing oxidants and acting as virulence factors. However, information pertaining to the carotenoids is scattered throughout the literature. Furthermore, information about the genes/proteins involved in the biosynthesis of carotenoids has tremendously increased in the post-genomic era. A web server providing the information about microbial carotenoids in a structured manner is required and will be a valuable resource for the scientific community working with microbial carotenoids. Here, we have created a manually curated, open access, comprehensive compilation of bacterial carotenoids named as ProCarDB- Prokaryotic Carotenoid Database. ProCarDB includes 304 unique carotenoids arising from 50 biosynthetic pathways distributed among 611 prokaryotes. ProCarDB provides important information on carotenoids, such as 2D and 3D structures, molecular weight, molecular formula, SMILES, InChI, InChIKey, IUPAC name, KEGG Id, PubChem Id, and ChEBI Id. The database also provides NMR data, UV-vis absorption data, IR data, MS data and HPLC data that play key roles in the identification of carotenoids. An important feature of this database is the extension of biosynthetic pathways from the literature and through the presence of the genes/enzymes in different organisms. The information contained in the database was mined from published literature and databases such as KEGG, PubChem, ChEBI, LipidBank, LPSN, and Uniprot. The database integrates user-friendly browsing and searching with carotenoid analysis tools to help the user. We believe that this database will serve as a major information centre for researchers working on bacterial carotenoids.

Managing biological networks by using text mining and computer-aided curation

NASA Astrophysics Data System (ADS)

Yu, Seok Jong; Cho, Yongseong; Lee, Min-Ho; Lim, Jongtae; Yoo, Jaesoo

2015-11-01

In order to understand a biological mechanism in a cell, a researcher should collect a huge number of protein interactions with experimental data from experiments and the literature. Text mining systems that extract biological interactions from papers have been used to construct biological networks for a few decades. Even though the text mining of literature is necessary to construct a biological network, few systems with a text mining tool are available for biologists who want to construct their own biological networks. We have developed a biological network construction system called BioKnowledge Viewer that can generate a biological interaction network by using a text mining tool and biological taggers. It also Boolean simulation software to provide a biological modeling system to simulate the model that is made with the text mining tool. A user can download PubMed articles and construct a biological network by using the Multi-level Knowledge Emergence Model (KMEM), MetaMap, and A Biomedical Named Entity Recognizer (ABNER) as a text mining tool. To evaluate the system, we constructed an aging-related biological network that consist 9,415 nodes (genes) by using manual curation. With network analysis, we found that several genes, including JNK, AP-1, and BCL-2, were highly related in aging biological network. We provide a semi-automatic curation environment so that users can obtain a graph database for managing text mining results that are generated in the server system and can navigate the network with BioKnowledge Viewer, which is freely available at http://bioknowledgeviewer.kisti.re.kr.

WikiGenomes: an open web application for community consumption and curation of gene annotation data in Wikidata

DOE Office of Scientific and Technical Information (OSTI.GOV)

Putman, Tim E.; Lelong, Sebastien; Burgstaller-Muehlbacher, Sebastian

With the advancement of genome-sequencing technologies, new genomes are being sequenced daily. Although these sequences are deposited in publicly available data warehouses, their functional and genomic annotations (beyond genes which are predicted automatically) mostly reside in the text of primary publications. Professional curators are hard at work extracting those annotations from the literature for the most studied organisms and depositing them in structured databases. However, the resources don’t exist to fund the comprehensive curation of the thousands of newly sequenced organisms in this manner. Here, we describe WikiGenomes (wikigenomes.org), a web application that facilitates the consumption and curation of genomicmore » data by the entire scientific community. WikiGenomes is based on Wikidata, an openly editable knowledge graph with the goal of aggregating published knowledge into a free and open database. WikiGenomes empowers the individual genomic researcher to contribute their expertise to the curation effort and integrates the knowledge into Wikidata, enabling it to be accessed by anyone without restriction.« less

WikiGenomes: an open web application for community consumption and curation of gene annotation data in Wikidata

DOE PAGES

Putman, Tim E.; Lelong, Sebastien; Burgstaller-Muehlbacher, Sebastian; ...

2017-03-06

With the advancement of genome-sequencing technologies, new genomes are being sequenced daily. Although these sequences are deposited in publicly available data warehouses, their functional and genomic annotations (beyond genes which are predicted automatically) mostly reside in the text of primary publications. Professional curators are hard at work extracting those annotations from the literature for the most studied organisms and depositing them in structured databases. However, the resources don’t exist to fund the comprehensive curation of the thousands of newly sequenced organisms in this manner. Here, we describe WikiGenomes (wikigenomes.org), a web application that facilitates the consumption and curation of genomicmore » data by the entire scientific community. WikiGenomes is based on Wikidata, an openly editable knowledge graph with the goal of aggregating published knowledge into a free and open database. WikiGenomes empowers the individual genomic researcher to contribute their expertise to the curation effort and integrates the knowledge into Wikidata, enabling it to be accessed by anyone without restriction.« less

Earth System Model Development and Analysis using FRE-Curator and Live Access Servers: On-demand analysis of climate model output with data provenance.

NASA Astrophysics Data System (ADS)

Radhakrishnan, A.; Balaji, V.; Schweitzer, R.; Nikonov, S.; O'Brien, K.; Vahlenkamp, H.; Burger, E. F.

2016-12-01

There are distinct phases in the development cycle of an Earth system model. During the model development phase, scientists make changes to code and parameters and require rapid access to results for evaluation. During the production phase, scientists may make an ensemble of runs with different settings, and produce large quantities of output, that must be further analyzed and quality controlled for scientific papers and submission to international projects such as the Climate Model Intercomparison Project (CMIP). During this phase, provenance is a key concern:being able to track back from outputs to inputs. We will discuss one of the paths taken at GFDL in delivering tools across this lifecycle, offering on-demand analysis of data by integrating the use of GFDL's in-house FRE-Curator, Unidata's THREDDS and NOAA PMEL's Live Access Servers (LAS).Experience over this lifecycle suggests that a major difficulty in developing analysis capabilities is only partially the scientific content, but often devoted to answering the questions "where is the data?" and "how do I get to it?". "FRE-Curator" is the name of a database-centric paradigm used at NOAA GFDL to ingest information about the model runs into an RDBMS (Curator database). The components of FRE-Curator are integrated into Flexible Runtime Environment workflow and can be invoked during climate model simulation. The front end to FRE-Curator, known as the Model Development Database Interface (MDBI) provides an in-house web-based access to GFDL experiments: metadata, analysis output and more. In order to provide on-demand visualization, MDBI uses Live Access Servers which is a highly configurable web server designed to provide flexible access to geo-referenced scientific data, that makes use of OPeNDAP. Model output saved in GFDL's tape archive, the size of the database and experiments, continuous model development initiatives with more dynamic configurations add complexity and challenges in providing an on-demand visualization experience to our GFDL users.

A high level interface to SCOP and ASTRAL implemented in python.

PubMed

Casbon, James A; Crooks, Gavin E; Saqi, Mansoor A S

2006-01-10

Benchmarking algorithms in structural bioinformatics often involves the construction of datasets of proteins with given sequence and structural properties. The SCOP database is a manually curated structural classification which groups together proteins on the basis of structural similarity. The ASTRAL compendium provides non redundant subsets of SCOP domains on the basis of sequence similarity such that no two domains in a given subset share more than a defined degree of sequence similarity. Taken together these two resources provide a 'ground truth' for assessing structural bioinformatics algorithms. We present a small and easy to use API written in python to enable construction of datasets from these resources. We have designed a set of python modules to provide an abstraction of the SCOP and ASTRAL databases. The modules are designed to work as part of the Biopython distribution. Python users can now manipulate and use the SCOP hierarchy from within python programs, and use ASTRAL to return sequences of domains in SCOP, as well as clustered representations of SCOP from ASTRAL. The modules make the analysis and generation of datasets for use in structural genomics easier and more principled.

The Comparative Toxicogenomics Database: update 2017.

PubMed

Davis, Allan Peter; Grondin, Cynthia J; Johnson, Robin J; Sciaky, Daniela; King, Benjamin L; McMorran, Roy; Wiegers, Jolene; Wiegers, Thomas C; Mattingly, Carolyn J

2017-01-04

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) provides information about interactions between chemicals and gene products, and their relationships to diseases. Core CTD content (chemical-gene, chemical-disease and gene-disease interactions manually curated from the literature) are integrated with each other as well as with select external datasets to generate expanded networks and predict novel associations. Today, core CTD includes more than 30.5 million toxicogenomic connections relating chemicals/drugs, genes/proteins, diseases, taxa, Gene Ontology (GO) annotations, pathways, and gene interaction modules. In this update, we report a 33% increase in our core data content since 2015, describe our new exposure module (that harmonizes exposure science information with core toxicogenomic data) and introduce a novel dataset of GO-disease inferences (that identify common molecular underpinnings for seemingly unrelated pathologies). These advancements centralize and contextualize real-world chemical exposures with molecular pathways to help scientists generate testable hypotheses in an effort to understand the etiology and mechanisms underlying environmentally influenced diseases. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Genomes OnLine Database (GOLD) v.6: data updates and feature enhancements

PubMed Central

Mukherjee, Supratim; Stamatis, Dimitri; Bertsch, Jon; Ovchinnikova, Galina; Verezemska, Olena; Isbandi, Michelle; Thomas, Alex D.; Ali, Rida; Sharma, Kaushal; Kyrpides, Nikos C.; Reddy, T. B. K.

2017-01-01

The Genomes Online Database (GOLD) (https://gold.jgi.doe.gov) is a manually curated data management system that catalogs sequencing projects with associated metadata from around the world. In the current version of GOLD (v.6), all projects are organized based on a four level classification system in the form of a Study, Organism (for isolates) or Biosample (for environmental samples), Sequencing Project and Analysis Project. Currently, GOLD provides information for 26 117 Studies, 239 100 Organisms, 15 887 Biosamples, 97 212 Sequencing Projects and 78 579 Analysis Projects. These are integrated with over 312 metadata fields from which 58 are controlled vocabularies with 2067 terms. The web interface facilitates submission of a diverse range of Sequencing Projects (such as isolate genome, single-cell genome, metagenome, metatranscriptome) and complex Analysis Projects (such as genome from metagenome, or combined assembly from multiple Sequencing Projects). GOLD provides a seamless interface with the Integrated Microbial Genomes (IMG) system and supports and promotes the Genomic Standards Consortium (GSC) Minimum Information standards. This paper describes the data updates and additional features added during the last two years. PMID:27794040

CancerHSP: anticancer herbs database of systems pharmacology

NASA Astrophysics Data System (ADS)

Tao, Weiyang; Li, Bohui; Gao, Shuo; Bai, Yaofei; Shar, Piar Ali; Zhang, Wenjuan; Guo, Zihu; Sun, Ke; Fu, Yingxue; Huang, Chao; Zheng, Chunli; Mu, Jiexin; Pei, Tianli; Wang, Yuan; Li, Yan; Wang, Yonghua

2015-06-01

The numerous natural products and their bioactivity potentially afford an extraordinary resource for new drug discovery and have been employed in cancer treatment. However, the underlying pharmacological mechanisms of most natural anticancer compounds remain elusive, which has become one of the major obstacles in developing novel effective anticancer agents. Here, to address these unmet needs, we developed an anticancer herbs database of systems pharmacology (CancerHSP), which records anticancer herbs related information through manual curation. Currently, CancerHSP contains 2439 anticancer herbal medicines with 3575 anticancer ingredients. For each ingredient, the molecular structure and nine key ADME parameters are provided. Moreover, we also provide the anticancer activities of these compounds based on 492 different cancer cell lines. Further, the protein targets of the compounds are predicted by state-of-art methods or collected from literatures. CancerHSP will help reveal the molecular mechanisms of natural anticancer products and accelerate anticancer drug development, especially facilitate future investigations on drug repositioning and drug discovery. CancerHSP is freely available on the web at http://lsp.nwsuaf.edu.cn/CancerHSP.php.

76 FR 30997 - National Transit Database: Amendments to Urbanized Area Annual Reporting Manual

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-27

... Transit Database: Amendments to Urbanized Area Annual Reporting Manual AGENCY: Federal Transit Administration (FTA), DOT. ACTION: Notice of Amendments to 2011 National Transit Database Urbanized Area Annual... Administration's (FTA) 2011 National Transit Database (NTD) Urbanized Area Annual Reporting Manual (Annual Manual...

HIstome--a relational knowledgebase of human histone proteins and histone modifying enzymes.

PubMed

Khare, Satyajeet P; Habib, Farhat; Sharma, Rahul; Gadewal, Nikhil; Gupta, Sanjay; Galande, Sanjeev

2012-01-01

Histones are abundant nuclear proteins that are essential for the packaging of eukaryotic DNA into chromosomes. Different histone variants, in combination with their modification 'code', control regulation of gene expression in diverse cellular processes. Several enzymes that catalyze the addition and removal of multiple histone modifications have been discovered in the past decade, enabling investigations of their role(s) in normal cellular processes and diverse pathological conditions. This sudden influx of data, however, has resulted in need of an updated knowledgebase that compiles, organizes and presents curated scientific information to the user in an easily accessible format. Here, we present HIstome, a browsable, manually curated, relational database that provides information about human histone proteins, their sites of modifications, variants and modifying enzymes. HIstome is a knowledgebase of 55 human histone proteins, 106 distinct sites of their post-translational modifications (PTMs) and 152 histone-modifying enzymes. Entries have been grouped into 5 types of histones, 8 types of post-translational modifications and 14 types of enzymes that catalyze addition and removal of these modifications. The resource will be useful for epigeneticists, pharmacologists and clinicians. HIstome: The Histone Infobase is available online at http://www.iiserpune.ac.in/∼coee/histome/ and http://www.actrec.gov.in/histome/.

PPDMs-a resource for mapping small molecule bioactivities from ChEMBL to Pfam-A protein domains.

PubMed

Kruger, Felix A; Gaulton, Anna; Nowotka, Michal; Overington, John P

2015-03-01

PPDMs is a resource that maps small molecule bioactivities to protein domains from the Pfam-A collection of protein families. Small molecule bioactivities mapped to protein domains add important precision to approaches that use protein sequence searches alignments to assist applications in computational drug discovery and systems and chemical biology. We have previously proposed a mapping heuristic for a subset of bioactivities stored in ChEMBL with the Pfam-A domain most likely to mediate small molecule binding. We have since refined this mapping using a manual procedure. Here, we present a resource that provides up-to-date mappings and the possibility to review assigned mappings as well as to participate in their assignment and curation. We also describe how mappings provided through the PPDMs resource are made accessible through the main schema of the ChEMBL database. The PPDMs resource and curation interface is available at https://www.ebi.ac.uk/chembl/research/ppdms/pfam_maps. The source-code for PPDMs is available under the Apache license at https://github.com/chembl/pfam_maps. Source code is available at https://github.com/chembl/pfam_map_loader to demonstrate the integration process with the main schema of ChEMBL. © The Author 2014. Published by Oxford University Press.

Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D

DOE PAGES

Preciat Gonzalez, German A.; El Assal, Lemmer R. P.; Noronha, Alberto; ...

2017-06-14

The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, manymore » algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.« less

Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D

DOE Office of Scientific and Technical Information (OSTI.GOV)

Preciat Gonzalez, German A.; El Assal, Lemmer R. P.; Noronha, Alberto

The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, manymore » algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.« less

Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D.

PubMed

Preciat Gonzalez, German A; El Assal, Lemmer R P; Noronha, Alberto; Thiele, Ines; Haraldsdóttir, Hulda S; Fleming, Ronan M T

2017-06-14

The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, many algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.

How accurate is automated gap filling of metabolic models?

PubMed

Karp, Peter D; Weaver, Daniel; Latendresse, Mario

2018-06-19

Reaction gap filling is a computational technique for proposing the addition of reactions to genome-scale metabolic models to permit those models to run correctly. Gap filling completes what are otherwise incomplete models that lack fully connected metabolic networks. The models are incomplete because they are derived from annotated genomes in which not all enzymes have been identified. Here we compare the results of applying an automated likelihood-based gap filler within the Pathway Tools software with the results of manually gap filling the same metabolic model. Both gap-filling exercises were applied to the same genome-derived qualitative metabolic reconstruction for Bifidobacterium longum subsp. longum JCM 1217, and to the same modeling conditions - anaerobic growth under four nutrients producing 53 biomass metabolites. The solution computed by the gap-filling program GenDev contained 12 reactions, but closer examination showed that solution was not minimal; two of the twelve reactions can be removed to yield a set of ten reactions that enable model growth. The manually curated solution contained 13 reactions, eight of which were shared with the 12-reaction computed solution. Thus, GenDev achieved recall of 61.5% and precision of 66.6%. These results suggest that although computational gap fillers are populating metabolic models with significant numbers of correct reactions, automatically gap-filled metabolic models also contain significant numbers of incorrect reactions. Our conclusion is that manual curation of gap-filler results is needed to obtain high-accuracy models. Many of the differences between the manual and automatic solutions resulted from using expert biological knowledge to direct the choice of reactions within the curated solution, such as reactions specific to the anaerobic lifestyle of B. longum.

Ci4SeR--curation interface for semantic resources--evaluation with adverse drug reactions.

PubMed

Souvignet, Julien; Asfari, Hadyl; Declerck, Gunnar; Lardon, Jérémy; Trombert-Paviot, Béatrice; Jaulent, Marie-Christine; Bousquet, Cédric

2014-01-01

Evaluation and validation have become a crucial problem for the development of semantic resources. We developed Ci4SeR, a Graphical User Interface to optimize the curation work (not taking into account structural aspects), suitable for any type of resource with lightweight description logic. We tested it on OntoADR, an ontology of adverse drug reactions. A single curator has reviewed 326 terms (1020 axioms) in an estimated time of 120 hours (2.71 concepts and 8.5 axioms reviewed per hour) and added 1874 new axioms (15.6 axioms per hour). Compared with previous manual endeavours, the interface allows increasing the speed-rate of reviewed concepts by 68% and axiom addition by 486%. A wider use of Ci4SeR would help semantic resources curation and improve completeness of knowledge modelling.

Rice Annotation Project Database (RAP-DB): an integrative and interactive database for rice genomics.

PubMed

Sakai, Hiroaki; Lee, Sung Shin; Tanaka, Tsuyoshi; Numa, Hisataka; Kim, Jungsok; Kawahara, Yoshihiro; Wakimoto, Hironobu; Yang, Ching-chia; Iwamoto, Masao; Abe, Takashi; Yamada, Yuko; Muto, Akira; Inokuchi, Hachiro; Ikemura, Toshimichi; Matsumoto, Takashi; Sasaki, Takuji; Itoh, Takeshi

2013-02-01

The Rice Annotation Project Database (RAP-DB, http://rapdb.dna.affrc.go.jp/) has been providing a comprehensive set of gene annotations for the genome sequence of rice, Oryza sativa (japonica group) cv. Nipponbare. Since the first release in 2005, RAP-DB has been updated several times along with the genome assembly updates. Here, we present our newest RAP-DB based on the latest genome assembly, Os-Nipponbare-Reference-IRGSP-1.0 (IRGSP-1.0), which was released in 2011. We detected 37,869 loci by mapping transcript and protein sequences of 150 monocot species. To provide plant researchers with highly reliable and up to date rice gene annotations, we have been incorporating literature-based manually curated data, and 1,626 loci currently incorporate literature-based annotation data, including commonly used gene names or gene symbols. Transcriptional activities are shown at the nucleotide level by mapping RNA-Seq reads derived from 27 samples. We also mapped the Illumina reads of a Japanese leading japonica cultivar, Koshihikari, and a Chinese indica cultivar, Guangluai-4, to the genome and show alignments together with the single nucleotide polymorphisms (SNPs) and gene functional annotations through a newly developed browser, Short-Read Assembly Browser (S-RAB). We have developed two satellite databases, Plant Gene Family Database (PGFD) and Integrative Database of Cereal Gene Phylogeny (IDCGP), which display gene family and homologous gene relationships among diverse plant species. RAP-DB and the satellite databases offer simple and user-friendly web interfaces, enabling plant and genome researchers to access the data easily and facilitating a broad range of plant research topics.

Follicle Online: an integrated database of follicle assembly, development and ovulation.

PubMed

Hua, Juan; Xu, Bo; Yang, Yifan; Ban, Rongjun; Iqbal, Furhan; Cooke, Howard J; Zhang, Yuanwei; Shi, Qinghua

2015-01-01

Folliculogenesis is an important part of ovarian function as it provides the oocytes for female reproductive life. Characterizing genes/proteins involved in folliculogenesis is fundamental for understanding the mechanisms associated with this biological function and to cure the diseases associated with folliculogenesis. A large number of genes/proteins associated with folliculogenesis have been identified from different species. However, no dedicated public resource is currently available for folliculogenesis-related genes/proteins that are validated by experiments. Here, we are reporting a database 'Follicle Online' that provides the experimentally validated gene/protein map of the folliculogenesis in a number of species. Follicle Online is a web-based database system for storing and retrieving folliculogenesis-related experimental data. It provides detailed information for 580 genes/proteins (from 23 model organisms, including Homo sapiens, Mus musculus, Rattus norvegicus, Mesocricetus auratus, Bos Taurus, Drosophila and Xenopus laevis) that have been reported to be involved in folliculogenesis, POF (premature ovarian failure) and PCOS (polycystic ovary syndrome). The literature was manually curated from more than 43,000 published articles (till 1 March 2014). The Follicle Online database is implemented in PHP + MySQL + JavaScript and this user-friendly web application provides access to the stored data. In summary, we have developed a centralized database that provides users with comprehensive information about genes/proteins involved in folliculogenesis. This database can be accessed freely and all the stored data can be viewed without any registration. Database URL: http://mcg.ustc.edu.cn/sdap1/follicle/index.php © The Author(s) 2015. Published by Oxford University Press.

ExtraTrain: a database of Extragenic regions and Transcriptional information in prokaryotic organisms

PubMed Central

Pareja, Eduardo; Pareja-Tobes, Pablo; Manrique, Marina; Pareja-Tobes, Eduardo; Bonal, Javier; Tobes, Raquel

2006-01-01

Background Transcriptional regulation processes are the principal mechanisms of adaptation in prokaryotes. In these processes, the regulatory proteins and the regulatory DNA signals located in extragenic regions are the key elements involved. As all extragenic spaces are putative regulatory regions, ExtraTrain covers all extragenic regions of available genomes and regulatory proteins from bacteria and archaea included in the UniProt database. Description ExtraTrain provides integrated and easily manageable information for 679816 extragenic regions and for the genes delimiting each of them. In addition ExtraTrain supplies a tool to explore extragenic regions, named Palinsight, oriented to detect and search palindromic patterns. This interactive visual tool is totally integrated in the database, allowing the search for regulatory signals in user defined sets of extragenic regions. The 26046 regulatory proteins included in ExtraTrain belong to the families AraC/XylS, ArsR, AsnC, Cold shock domain, CRP-FNR, DeoR, GntR, IclR, LacI, LuxR, LysR, MarR, MerR, NtrC/Fis, OmpR and TetR. The database follows the InterPro criteria to define these families. The information about regulators includes manually curated sets of references specifically associated to regulator entries. In order to achieve a sustainable and maintainable knowledge database ExtraTrain is a platform open to the contribution of knowledge by the scientific community providing a system for the incorporation of textual knowledge. Conclusion ExtraTrain is a new database for exploring Extragenic regions and Transcriptional information in bacteria and archaea. ExtraTrain database is available at . PMID:16539733

Follicle Online: an integrated database of follicle assembly, development and ovulation

PubMed Central

Hua, Juan; Xu, Bo; Yang, Yifan; Ban, Rongjun; Iqbal, Furhan; Zhang, Yuanwei; Shi, Qinghua

2015-01-01

Folliculogenesis is an important part of ovarian function as it provides the oocytes for female reproductive life. Characterizing genes/proteins involved in folliculogenesis is fundamental for understanding the mechanisms associated with this biological function and to cure the diseases associated with folliculogenesis. A large number of genes/proteins associated with folliculogenesis have been identified from different species. However, no dedicated public resource is currently available for folliculogenesis-related genes/proteins that are validated by experiments. Here, we are reporting a database ‘Follicle Online’ that provides the experimentally validated gene/protein map of the folliculogenesis in a number of species. Follicle Online is a web-based database system for storing and retrieving folliculogenesis-related experimental data. It provides detailed information for 580 genes/proteins (from 23 model organisms, including Homo sapiens, Mus musculus, Rattus norvegicus, Mesocricetus auratus, Bos Taurus, Drosophila and Xenopus laevis) that have been reported to be involved in folliculogenesis, POF (premature ovarian failure) and PCOS (polycystic ovary syndrome). The literature was manually curated from more than 43 000 published articles (till 1 March 2014). The Follicle Online database is implemented in PHP + MySQL + JavaScript and this user-friendly web application provides access to the stored data. In summary, we have developed a centralized database that provides users with comprehensive information about genes/proteins involved in folliculogenesis. This database can be accessed freely and all the stored data can be viewed without any registration. Database URL: http://mcg.ustc.edu.cn/sdap1/follicle/index.php PMID:25931457

PDB data curation.

PubMed

Wang, Yanchao; Sunderraman, Rajshekhar

2006-01-01

In this paper, we propose two architectures for curating PDB data to improve its quality. The first one, PDB Data Curation System, is developed by adding two parts, Checking Filter and Curation Engine, between User Interface and Database. This architecture supports the basic PDB data curation. The other one, PDB Data Curation System with XCML, is designed for further curation which adds four more parts, PDB-XML, PDB, OODB, Protin-OODB, into the previous one. This architecture uses XCML language to automatically check errors of PDB data that enables PDB data more consistent and accurate. These two tools can be used for cleaning existing PDB files and creating new PDB files. We also show some ideas how to add constraints and assertions with XCML to get better data. In addition, we discuss the data provenance that may affect data accuracy and consistency.

Annotation of phenotypic diversity: decoupling data curation and ontology curation using Phenex.

PubMed

Balhoff, James P; Dahdul, Wasila M; Dececchi, T Alexander; Lapp, Hilmar; Mabee, Paula M; Vision, Todd J

2014-01-01

Phenex (http://phenex.phenoscape.org/) is a desktop application for semantically annotating the phenotypic character matrix datasets common in evolutionary biology. Since its initial publication, we have added new features that address several major bottlenecks in the efficiency of the phenotype curation process: allowing curators during the data curation phase to provisionally request terms that are not yet available from a relevant ontology; supporting quality control against annotation guidelines to reduce later manual review and revision; and enabling the sharing of files for collaboration among curators. We decoupled data annotation from ontology development by creating an Ontology Request Broker (ORB) within Phenex. Curators can use the ORB to request a provisional term for use in data annotation; the provisional term can be automatically replaced with a permanent identifier once the term is added to an ontology. We added a set of annotation consistency checks to prevent common curation errors, reducing the need for later correction. We facilitated collaborative editing by improving the reliability of Phenex when used with online folder sharing services, via file change monitoring and continual autosave. With the addition of these new features, and in particular the Ontology Request Broker, Phenex users have been able to focus more effectively on data annotation. Phenoscape curators using Phenex have reported a smoother annotation workflow, with much reduced interruptions from ontology maintenance and file management issues.

HIVsirDB: a database of HIV inhibiting siRNAs.

PubMed

Tyagi, Atul; Ahmed, Firoz; Thakur, Nishant; Sharma, Arun; Raghava, Gajendra P S; Kumar, Manoj

2011-01-01

Human immunodeficiency virus (HIV) is responsible for millions of deaths every year. The current treatment involves the use of multiple antiretroviral agents that may harm patients due to their toxic nature. RNA interference (RNAi) is a potent candidate for the future treatment of HIV, uses short interfering RNA (siRNA/shRNA) for silencing HIV genes. In this study, attempts have been made to create a database HIVsirDB of siRNAs responsible for silencing HIV genes. HIVsirDB is a manually curated database of HIV inhibiting siRNAs that provides comprehensive information about each siRNA or shRNA. Information was collected and compiled from literature and public resources. This database contains around 750 siRNAs that includes 75 partially complementary siRNAs differing by one or more bases with the target sites and over 100 escape mutant sequences. HIVsirDB structure contains sixteen fields including siRNA sequence, HIV strain, targeted genome region, efficacy and conservation of target sequences. In order to facilitate user, many tools have been integrated in this database that includes; i) siRNAmap for mapping siRNAs on target sequence, ii) HIVsirblast for BLAST search against database, iii) siRNAalign for aligning siRNAs. HIVsirDB is a freely accessible database of siRNAs which can silence or degrade HIV genes. It covers 26 types of HIV strains and 28 cell types. This database will be very useful for developing models for predicting efficacy of HIV inhibiting siRNAs. In summary this is a useful resource for researchers working in the field of siRNA based HIV therapy. HIVsirDB database is accessible at http://crdd.osdd.net/raghava/hivsir/.

SpliceDisease database: linking RNA splicing and disease.

PubMed

Wang, Juan; Zhang, Jie; Li, Kaibo; Zhao, Wei; Cui, Qinghua

2012-01-01

RNA splicing is an important aspect of gene regulation in many organisms. Splicing of RNA is regulated by complicated mechanisms involving numerous RNA-binding proteins and the intricate network of interactions among them. Mutations in cis-acting splicing elements or its regulatory proteins have been shown to be involved in human diseases. Defects in pre-mRNA splicing process have emerged as a common disease-causing mechanism. Therefore, a database integrating RNA splicing and disease associations would be helpful for understanding not only the RNA splicing but also its contribution to disease. In SpliceDisease database, we manually curated 2337 splicing mutation disease entries involving 303 genes and 370 diseases, which have been supported experimentally in 898 publications. The SpliceDisease database provides information including the change of the nucleotide in the sequence, the location of the mutation on the gene, the reference Pubmed ID and detailed description for the relationship among gene mutations, splicing defects and diseases. We standardized the names of the diseases and genes and provided links for these genes to NCBI and UCSC genome browser for further annotation and genomic sequences. For the location of the mutation, we give direct links of the entry to the respective position/region in the genome browser. The users can freely browse, search and download the data in SpliceDisease at http://cmbi.bjmu.edu.cn/sdisease.

The Microbe Directory: An annotated, searchable inventory of microbes’ characteristics

PubMed Central

Mohammad, Rawhi; Danko, David; Bezdan, Daniela; Afshinnekoo, Ebrahim; Segata, Nicola; Mason, Christopher E.

2018-01-01

The Microbe Directory is a collective research effort to profile and annotate more than 7,500 unique microbial species from the MetaPhlAn2 database that includes bacteria, archaea, viruses, fungi, and protozoa. By collecting and summarizing data on various microbes’ characteristics, the project comprises a database that can be used downstream of large-scale metagenomic taxonomic analyses, allowing one to interpret and explore their taxonomic classifications to have a deeper understanding of the microbial ecosystem they are studying. Such characteristics include, but are not limited to: optimal pH, optimal temperature, Gram stain, biofilm-formation, spore-formation, antimicrobial resistance, and COGEM class risk rating. The database has been manually curated by trained student-researchers from Weill Cornell Medicine and CUNY—Hunter College, and its analysis remains an ongoing effort with open-source capabilities so others can contribute. Available in SQL, JSON, and CSV (i.e. Excel) formats, the Microbe Directory can be queried for the aforementioned parameters by a microorganism’s taxonomy. In addition to the raw database, The Microbe Directory has an online counterpart ( https://microbe.directory/) that provides a user-friendly interface for storage, retrieval, and analysis into which other microbial database projects could be incorporated. The Microbe Directory was primarily designed to serve as a resource for researchers conducting metagenomic analyses, but its online web interface should also prove useful to any individual who wishes to learn more about any particular microbe. PMID:29630066

Consensus and conflict cards for metabolic pathway databases

PubMed Central

2013-01-01

Background The metabolic network of H. sapiens and many other organisms is described in multiple pathway databases. The level of agreement between these descriptions, however, has proven to be low. We can use these different descriptions to our advantage by identifying conflicting information and combining their knowledge into a single, more accurate, and more complete description. This task is, however, far from trivial. Results We introduce the concept of Consensus and Conflict Cards (C2Cards) to provide concise overviews of what the databases do or do not agree on. Each card is centered at a single gene, EC number or reaction. These three complementary perspectives make it possible to distinguish disagreements on the underlying biology of a metabolic process from differences that can be explained by different decisions on how and in what detail to represent knowledge. As a proof-of-concept, we implemented C2CardsHuman, as a web application http://www.molgenis.org/c2cards, covering five human pathway databases. Conclusions C2Cards can contribute to ongoing reconciliation efforts by simplifying the identification of consensus and conflicts between pathway databases and lowering the threshold for experts to contribute. Several case studies illustrate the potential of the C2Cards in identifying disagreements on the underlying biology of a metabolic process. The overviews may also point out controversial biological knowledge that should be subject of further research. Finally, the examples provided emphasize the importance of manual curation and the need for a broad community involvement. PMID:23803311

Consensus and conflict cards for metabolic pathway databases.

PubMed

Stobbe, Miranda D; Swertz, Morris A; Thiele, Ines; Rengaw, Trebor; van Kampen, Antoine H C; Moerland, Perry D

2013-06-26

The metabolic network of H. sapiens and many other organisms is described in multiple pathway databases. The level of agreement between these descriptions, however, has proven to be low. We can use these different descriptions to our advantage by identifying conflicting information and combining their knowledge into a single, more accurate, and more complete description. This task is, however, far from trivial. We introduce the concept of Consensus and Conflict Cards (C₂Cards) to provide concise overviews of what the databases do or do not agree on. Each card is centered at a single gene, EC number or reaction. These three complementary perspectives make it possible to distinguish disagreements on the underlying biology of a metabolic process from differences that can be explained by different decisions on how and in what detail to represent knowledge. As a proof-of-concept, we implemented C₂Cards(Human), as a web application http://www.molgenis.org/c2cards, covering five human pathway databases. C₂Cards can contribute to ongoing reconciliation efforts by simplifying the identification of consensus and conflicts between pathway databases and lowering the threshold for experts to contribute. Several case studies illustrate the potential of the C₂Cards in identifying disagreements on the underlying biology of a metabolic process. The overviews may also point out controversial biological knowledge that should be subject of further research. Finally, the examples provided emphasize the importance of manual curation and the need for a broad community involvement.

SCPortalen: human and mouse single-cell centric database

PubMed Central

Noguchi, Shuhei; Böttcher, Michael; Hasegawa, Akira; Kouno, Tsukasa; Kato, Sachi; Tada, Yuhki; Ura, Hiroki; Abe, Kuniya; Shin, Jay W; Plessy, Charles; Carninci, Piero

2018-01-01

Abstract Published single-cell datasets are rich resources for investigators who want to address questions not originally asked by the creators of the datasets. The single-cell datasets might be obtained by different protocols and diverse analysis strategies. The main challenge in utilizing such single-cell data is how we can make the various large-scale datasets to be comparable and reusable in a different context. To challenge this issue, we developed the single-cell centric database ‘SCPortalen’ (http://single-cell.clst.riken.jp/). The current version of the database covers human and mouse single-cell transcriptomics datasets that are publicly available from the INSDC sites. The original metadata was manually curated and single-cell samples were annotated with standard ontology terms. Following that, common quality assessment procedures were conducted to check the quality of the raw sequence. Furthermore, primary data processing of the raw data followed by advanced analyses and interpretation have been performed from scratch using our pipeline. In addition to the transcriptomics data, SCPortalen provides access to single-cell image files whenever available. The target users of SCPortalen are all researchers interested in specific cell types or population heterogeneity. Through the web interface of SCPortalen users are easily able to search, explore and download the single-cell datasets of their interests. PMID:29045713

Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems

PubMed Central

Boué, Stéphanie; Talikka, Marja; Westra, Jurjen Willem; Hayes, William; Di Fabio, Anselmo; Park, Jennifer; Schlage, Walter K.; Sewer, Alain; Fields, Brett; Ansari, Sam; Martin, Florian; Veljkovic, Emilija; Kenney, Renee; Peitsch, Manuel C.; Hoeng, Julia

2015-01-01

With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com PMID:25887162

OntoBrowser: a collaborative tool for curation of ontologies by subject matter experts.

PubMed

Ravagli, Carlo; Pognan, Francois; Marc, Philippe

2017-01-01

The lack of controlled terminology and ontology usage leads to incomplete search results and poor interoperability between databases. One of the major underlying challenges of data integration is curating data to adhere to controlled terminologies and/or ontologies. Finding subject matter experts with the time and skills required to perform data curation is often problematic. In addition, existing tools are not designed for continuous data integration and collaborative curation. This results in time-consuming curation workflows that often become unsustainable. The primary objective of OntoBrowser is to provide an easy-to-use online collaborative solution for subject matter experts to map reported terms to preferred ontology (or code list) terms and facilitate ontology evolution. Additional features include web service access to data, visualization of ontologies in hierarchical/graph format and a peer review/approval workflow with alerting. The source code is freely available under the Apache v2.0 license. Source code and installation instructions are available at http://opensource.nibr.com This software is designed to run on a Java EE application server and store data in a relational database. philippe.marc@novartis.com. © The Author 2016. Published by Oxford University Press.

OntoBrowser: a collaborative tool for curation of ontologies by subject matter experts

PubMed Central

Ravagli, Carlo; Pognan, Francois

2017-01-01

Summary: The lack of controlled terminology and ontology usage leads to incomplete search results and poor interoperability between databases. One of the major underlying challenges of data integration is curating data to adhere to controlled terminologies and/or ontologies. Finding subject matter experts with the time and skills required to perform data curation is often problematic. In addition, existing tools are not designed for continuous data integration and collaborative curation. This results in time-consuming curation workflows that often become unsustainable. The primary objective of OntoBrowser is to provide an easy-to-use online collaborative solution for subject matter experts to map reported terms to preferred ontology (or code list) terms and facilitate ontology evolution. Additional features include web service access to data, visualization of ontologies in hierarchical/graph format and a peer review/approval workflow with alerting. Availability and implementation: The source code is freely available under the Apache v2.0 license. Source code and installation instructions are available at http://opensource.nibr.com. This software is designed to run on a Java EE application server and store data in a relational database. Contact: philippe.marc@novartis.com PMID:27605099

WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research.

PubMed

Slenter, Denise N; Kutmon, Martina; Hanspers, Kristina; Riutta, Anders; Windsor, Jacob; Nunes, Nuno; Mélius, Jonathan; Cirillo, Elisa; Coort, Susan L; Digles, Daniela; Ehrhart, Friederike; Giesbertz, Pieter; Kalafati, Marianthi; Martens, Marvin; Miller, Ryan; Nishida, Kozo; Rieswijk, Linda; Waagmeester, Andra; Eijssen, Lars M T; Evelo, Chris T; Pico, Alexander R; Willighagen, Egon L

2018-01-04

WikiPathways (wikipathways.org) captures the collective knowledge represented in biological pathways. By providing a database in a curated, machine readable way, omics data analysis and visualization is enabled. WikiPathways and other pathway databases are used to analyze experimental data by research groups in many fields. Due to the open and collaborative nature of the WikiPathways platform, our content keeps growing and is getting more accurate, making WikiPathways a reliable and rich pathway database. Previously, however, the focus was primarily on genes and proteins, leaving many metabolites with only limited annotation. Recent curation efforts focused on improving the annotation of metabolism and metabolic pathways by associating unmapped metabolites with database identifiers and providing more detailed interaction knowledge. Here, we report the outcomes of the continued growth and curation efforts, such as a doubling of the number of annotated metabolite nodes in WikiPathways. Furthermore, we introduce an OpenAPI documentation of our web services and the FAIR (Findable, Accessible, Interoperable and Reusable) annotation of resources to increase the interoperability of the knowledge encoded in these pathways and experimental omics data. New search options, monthly downloads, more links to metabolite databases, and new portals make pathway knowledge more effortlessly accessible to individual researchers and research communities. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

PANTHER: a browsable database of gene products organized by biological function, using curated protein family and subfamily classification.

PubMed

Thomas, Paul D; Kejariwal, Anish; Campbell, Michael J; Mi, Huaiyu; Diemer, Karen; Guo, Nan; Ladunga, Istvan; Ulitsky-Lazareva, Betty; Muruganujan, Anushya; Rabkin, Steven; Vandergriff, Jody A; Doremieux, Olivier

2003-01-01

The PANTHER database was designed for high-throughput analysis of protein sequences. One of the key features is a simplified ontology of protein function, which allows browsing of the database by biological functions. Biologist curators have associated the ontology terms with groups of protein sequences rather than individual sequences. Statistical models (Hidden Markov Models, or HMMs) are built from each of these groups. The advantage of this approach is that new sequences can be automatically classified as they become available. To ensure accurate functional classification, HMMs are constructed not only for families, but also for functionally distinct subfamilies. Multiple sequence alignments and phylogenetic trees, including curator-assigned information, are available for each family. The current version of the PANTHER database includes training sequences from all organisms in the GenBank non-redundant protein database, and the HMMs have been used to classify gene products across the entire genomes of human, and Drosophila melanogaster. The ontology terms and protein families and subfamilies, as well as Drosophila gene c;assifications, can be browsed and searched for free. Due to outstanding contractual obligations, access to human gene classifications and to protein family trees and multiple sequence alignments will temporarily require a nominal registration fee. PANTHER is publicly available on the web at http://panther.celera.com.

larvalign: Aligning Gene Expression Patterns from the Larval Brain of Drosophila melanogaster.

PubMed

Muenzing, Sascha E A; Strauch, Martin; Truman, James W; Bühler, Katja; Thum, Andreas S; Merhof, Dorit

2018-01-01

The larval brain of the fruit fly Drosophila melanogaster is a small, tractable model system for neuroscience. Genes for fluorescent marker proteins can be expressed in defined, spatially restricted neuron populations. Here, we introduce the methods for 1) generating a standard template of the larval central nervous system (CNS), 2) spatial mapping of expression patterns from different larvae into a reference space defined by the standard template. We provide a manually annotated gold standard that serves for evaluation of the registration framework involved in template generation and mapping. A method for registration quality assessment enables the automatic detection of registration errors, and a semi-automatic registration method allows one to correct registrations, which is a prerequisite for a high-quality, curated database of expression patterns. All computational methods are available within the larvalign software package: https://github.com/larvalign/larvalign/releases/tag/v1.0.

ArachnoServer 3.0: an online resource for automated discovery, analysis and annotation of spider toxins.

PubMed

Pineda, Sandy S; Chaumeil, Pierre-Alain; Kunert, Anne; Kaas, Quentin; Thang, Mike W C; Le, Lien; Nuhn, Michael; Herzig, Volker; Saez, Natalie J; Cristofori-Armstrong, Ben; Anangi, Raveendra; Senff, Sebastian; Gorse, Dominique; King, Glenn F

2018-03-15

ArachnoServer is a manually curated database that consolidates information on the sequence, structure, function and pharmacology of spider-venom toxins. Although spider venoms are complex chemical arsenals, the primary constituents are small disulfide-bridged peptides that target neuronal ion channels and receptors. Due to their high potency and selectivity, these peptides have been developed as pharmacological tools, bioinsecticides and drug leads. A new version of ArachnoServer (v3.0) has been developed that includes a bioinformatics pipeline for automated detection and analysis of peptide toxin transcripts in assembled venom-gland transcriptomes. ArachnoServer v3.0 was updated with the latest sequence, structure and functional data, the search-by-mass feature has been enhanced, and toxin cards provide additional information about each mature toxin. http://arachnoserver.org. support@arachnoserver.org. Supplementary data are available at Bioinformatics online.

Iterative non-sequential protein structural alignment.

PubMed

Salem, Saeed; Zaki, Mohammed J; Bystroff, Christopher

2009-06-01

Structural similarity between proteins gives us insights into their evolutionary relationships when there is low sequence similarity. In this paper, we present a novel approach called SNAP for non-sequential pair-wise structural alignment. Starting from an initial alignment, our approach iterates over a two-step process consisting of a superposition step and an alignment step, until convergence. We propose a novel greedy algorithm to construct both sequential and non-sequential alignments. The quality of SNAP alignments were assessed by comparing against the manually curated reference alignments in the challenging SISY and RIPC datasets. Moreover, when applied to a dataset of 4410 protein pairs selected from the CATH database, SNAP produced longer alignments with lower rmsd than several state-of-the-art alignment methods. Classification of folds using SNAP alignments was both highly sensitive and highly selective. The SNAP software along with the datasets are available online at http://www.cs.rpi.edu/~zaki/software/SNAP.

ExplorEnz: the primary source of the IUBMB enzyme list

PubMed Central

McDonald, Andrew G.; Boyce, Sinéad; Tipton, Keith F.

2009-01-01

ExplorEnz is the MySQL database that is used for the curation and dissemination of the International Union of Biochemistry and Molecular Biology (IUBMB) Enzyme Nomenclature. A simple web-based query interface is provided, along with an advanced search engine for more complex Boolean queries. The WWW front-end is accessible at http://www.enzyme-database.org, from where downloads of the database as SQL and XML are also available. An associated form-based curatorial application has been developed to facilitate the curation of enzyme data as well as the internal and public review processes that occur before an enzyme entry is made official. Suggestions for new enzyme entries, or modifications to existing ones, can be made using the forms provided at http://www.enzyme-database.org/forms.php. PMID:18776214

ETHNOS: A versatile electronic tool for the development and curation of national genetic databases

PubMed Central

2010-01-01

National and ethnic mutation databases (NEMDBs) are emerging online repositories, recording extensive information about the described genetic heterogeneity of an ethnic group or population. These resources facilitate the provision of genetic services and provide a comprehensive list of genomic variations among different populations. As such, they enhance awareness of the various genetic disorders. Here, we describe the features of the ETHNOS software, a simple but versatile tool based on a flat-file database that is specifically designed for the development and curation of NEMDBs. ETHNOS is a freely available software which runs more than half of the NEMDBs currently available. Given the emerging need for NEMDB in genetic testing services and the fact that ETHNOS is the only off-the-shelf software available for NEMDB development and curation, its adoption in subsequent NEMDB development would contribute towards data content uniformity, unlike the diverse contents and quality of the available gene (locus)-specific databases. Finally, we allude to the potential applications of NEMDBs, not only as worldwide central allele frequency repositories, but also, and most importantly, as data warehouses of individual-level genomic data, hence allowing for a comprehensive ethnicity-specific documentation of genomic variation. PMID:20650823

ETHNOS : A versatile electronic tool for the development and curation of national genetic databases.

PubMed

van Baal, Sjozef; Zlotogora, Joël; Lagoumintzis, George; Gkantouna, Vassiliki; Tzimas, Ioannis; Poulas, Konstantinos; Tsakalidis, Athanassios; Romeo, Giovanni; Patrinos, George P

2010-06-01

National and ethnic mutation databases (NEMDBs) are emerging online repositories, recording extensive information about the described genetic heterogeneity of an ethnic group or population. These resources facilitate the provision of genetic services and provide a comprehensive list of genomic variations among different populations. As such, they enhance awareness of the various genetic disorders. Here, we describe the features of the ETHNOS software, a simple but versatile tool based on a flat-file database that is specifically designed for the development and curation of NEMDBs. ETHNOS is a freely available software which runs more than half of the NEMDBs currently available. Given the emerging need for NEMDB in genetic testing services and the fact that ETHNOS is the only off-the-shelf software available for NEMDB development and curation, its adoption in subsequent NEMDB development would contribute towards data content uniformity, unlike the diverse contents and quality of the available gene (locus)-specific databases. Finally, we allude to the potential applications of NEMDBs, not only as worldwide central allele frequency repositories, but also, and most importantly, as data warehouses of individual-level genomic data, hence allowing for a comprehensive ethnicity-specific documentation of genomic variation.

The art of curation at a biological database: principles and application

USDA-ARS?s Scientific Manuscript database

The variety and quantity of data being produced by biological research has grown dramatically in recent years, resulting in an expansion of our understanding of biological systems. However, this abundance of data has brought new challenges, especially in curation. The role of biocurators is in part ...

AtlasT4SS: a curated database for type IV secretion systems.

PubMed

Souza, Rangel C; del Rosario Quispe Saji, Guadalupe; Costa, Maiana O C; Netto, Diogo S; Lima, Nicholas C B; Klein, Cecília C; Vasconcelos, Ana Tereza R; Nicolás, Marisa F

2012-08-09

The type IV secretion system (T4SS) can be classified as a large family of macromolecule transporter systems, divided into three recognized sub-families, according to the well-known functions. The major sub-family is the conjugation system, which allows transfer of genetic material, such as a nucleoprotein, via cell contact among bacteria. Also, the conjugation system can transfer genetic material from bacteria to eukaryotic cells; such is the case with the T-DNA transfer of Agrobacterium tumefaciens to host plant cells. The system of effector protein transport constitutes the second sub-family, and the third one corresponds to the DNA uptake/release system. Genome analyses have revealed numerous T4SS in Bacteria and Archaea. The purpose of this work was to organize, classify, and integrate the T4SS data into a single database, called AtlasT4SS - the first public database devoted exclusively to this prokaryotic secretion system. The AtlasT4SS is a manual curated database that describes a large number of proteins related to the type IV secretion system reported so far in Gram-negative and Gram-positive bacteria, as well as in Archaea. The database was created using the RDBMS MySQL and the Catalyst Framework based in the Perl programming language and using the Model-View-Controller (MVC) design pattern for Web. The current version holds a comprehensive collection of 1,617 T4SS proteins from 58 Bacteria (49 Gram-negative and 9 Gram-Positive), one Archaea and 11 plasmids. By applying the bi-directional best hit (BBH) relationship in pairwise genome comparison, it was possible to obtain a core set of 134 clusters of orthologous genes encoding T4SS proteins. In our database we present one way of classifying orthologous groups of T4SSs in a hierarchical classification scheme with three levels. The first level comprises four classes that are based on the organization of genetic determinants, shared homologies, and evolutionary relationships: (i) F-T4SS, (ii) P-T4SS, (iii) I-T4SS, and (iv) GI-T4SS. The second level designates a specific well-known protein families otherwise an uncharacterized protein family. Finally, in the third level, each protein of an ortholog cluster is classified according to its involvement in a specific cellular process. AtlasT4SS database is open access and is available at http://www.t4ss.lncc.br.

Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd

PubMed Central

Wang, Zichen; Monteiro, Caroline D.; Jagodnik, Kathleen M.; Fernandez, Nicolas F.; Gundersen, Gregory W.; Rouillard, Andrew D.; Jenkins, Sherry L.; Feldmann, Axel S.; Hu, Kevin S.; McDermott, Michael G.; Duan, Qiaonan; Clark, Neil R.; Jones, Matthew R.; Kou, Yan; Goff, Troy; Woodland, Holly; Amaral, Fabio M R.; Szeto, Gregory L.; Fuchs, Oliver; Schüssler-Fiorenza Rose, Sophia M.; Sharma, Shvetank; Schwartz, Uwe; Bausela, Xabier Bengoetxea; Szymkiewicz, Maciej; Maroulis, Vasileios; Salykin, Anton; Barra, Carolina M.; Kruth, Candice D.; Bongio, Nicholas J.; Mathur, Vaibhav; Todoric, Radmila D; Rubin, Udi E.; Malatras, Apostolos; Fulp, Carl T.; Galindo, John A.; Motiejunaite, Ruta; Jüschke, Christoph; Dishuck, Philip C.; Lahl, Katharina; Jafari, Mohieddin; Aibar, Sara; Zaravinos, Apostolos; Steenhuizen, Linda H.; Allison, Lindsey R.; Gamallo, Pablo; de Andres Segura, Fernando; Dae Devlin, Tyler; Pérez-García, Vicente; Ma'ayan, Avi

2016-01-01

Gene expression data are accumulating exponentially in public repositories. Reanalysis and integration of themed collections from these studies may provide new insights, but requires further human curation. Here we report a crowdsourcing project to annotate and reanalyse a large number of gene expression profiles from Gene Expression Omnibus (GEO). Through a massive open online course on Coursera, over 70 participants from over 25 countries identify and annotate 2,460 single-gene perturbation signatures, 839 disease versus normal signatures, and 906 drug perturbation signatures. All these signatures are unique and are manually validated for quality. Global analysis of these signatures confirms known associations and identifies novel associations between genes, diseases and drugs. The manually curated signatures are used as a training set to develop classifiers for extracting similar signatures from the entire GEO repository. We develop a web portal to serve these signatures for query, download and visualization. PMID:27667448

Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd.

PubMed

Wang, Zichen; Monteiro, Caroline D; Jagodnik, Kathleen M; Fernandez, Nicolas F; Gundersen, Gregory W; Rouillard, Andrew D; Jenkins, Sherry L; Feldmann, Axel S; Hu, Kevin S; McDermott, Michael G; Duan, Qiaonan; Clark, Neil R; Jones, Matthew R; Kou, Yan; Goff, Troy; Woodland, Holly; Amaral, Fabio M R; Szeto, Gregory L; Fuchs, Oliver; Schüssler-Fiorenza Rose, Sophia M; Sharma, Shvetank; Schwartz, Uwe; Bausela, Xabier Bengoetxea; Szymkiewicz, Maciej; Maroulis, Vasileios; Salykin, Anton; Barra, Carolina M; Kruth, Candice D; Bongio, Nicholas J; Mathur, Vaibhav; Todoric, Radmila D; Rubin, Udi E; Malatras, Apostolos; Fulp, Carl T; Galindo, John A; Motiejunaite, Ruta; Jüschke, Christoph; Dishuck, Philip C; Lahl, Katharina; Jafari, Mohieddin; Aibar, Sara; Zaravinos, Apostolos; Steenhuizen, Linda H; Allison, Lindsey R; Gamallo, Pablo; de Andres Segura, Fernando; Dae Devlin, Tyler; Pérez-García, Vicente; Ma'ayan, Avi

2016-09-26

Gene expression data are accumulating exponentially in public repositories. Reanalysis and integration of themed collections from these studies may provide new insights, but requires further human curation. Here we report a crowdsourcing project to annotate and reanalyse a large number of gene expression profiles from Gene Expression Omnibus (GEO). Through a massive open online course on Coursera, over 70 participants from over 25 countries identify and annotate 2,460 single-gene perturbation signatures, 839 disease versus normal signatures, and 906 drug perturbation signatures. All these signatures are unique and are manually validated for quality. Global analysis of these signatures confirms known associations and identifies novel associations between genes, diseases and drugs. The manually curated signatures are used as a training set to develop classifiers for extracting similar signatures from the entire GEO repository. We develop a web portal to serve these signatures for query, download and visualization.

75 FR 61553 - National Transit Database: Amendments to the Urbanized Area Annual Reporting Manual and to the...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-05

... Transit Database: Amendments to the Urbanized Area Annual Reporting Manual and to the Safety and Security... the 2011 National Transit Database Urbanized Area Annual Reporting Manual and Announcement of... Transit Administration's (FTA) National Transit Database (NTD) reporting requirements, including...

MimoSA: a system for minimotif annotation

PubMed Central

2010-01-01

Background Minimotifs are short peptide sequences within one protein, which are recognized by other proteins or molecules. While there are now several minimotif databases, they are incomplete. There are reports of many minimotifs in the primary literature, which have yet to be annotated, while entirely novel minimotifs continue to be published on a weekly basis. Our recently proposed function and sequence syntax for minimotifs enables us to build a general tool that will facilitate structured annotation and management of minimotif data from the biomedical literature. Results We have built the MimoSA application for minimotif annotation. The application supports management of the Minimotif Miner database, literature tracking, and annotation of new minimotifs. MimoSA enables the visualization, organization, selection and editing functions of minimotifs and their attributes in the MnM database. For the literature components, Mimosa provides paper status tracking and scoring of papers for annotation through a freely available machine learning approach, which is based on word correlation. The paper scoring algorithm is also available as a separate program, TextMine. Form-driven annotation of minimotif attributes enables entry of new minimotifs into the MnM database. Several supporting features increase the efficiency of annotation. The layered architecture of MimoSA allows for extensibility by separating the functions of paper scoring, minimotif visualization, and database management. MimoSA is readily adaptable to other annotation efforts that manually curate literature into a MySQL database. Conclusions MimoSA is an extensible application that facilitates minimotif annotation and integrates with the Minimotif Miner database. We have built MimoSA as an application that integrates dynamic abstract scoring with a high performance relational model of minimotif syntax. MimoSA's TextMine, an efficient paper-scoring algorithm, can be used to dynamically rank papers with respect to context. PMID:20565705

An Analysis of the Climate Data Initiative's Data Collection

NASA Astrophysics Data System (ADS)

Ramachandran, R.; Bugbee, K.

2015-12-01

The Climate Data Initiative (CDI) is a broad multi-agency effort of the U.S. government that seeks to leverage the extensive existing federal climate-relevant data to stimulate innovation and private-sector entrepreneurship to support national climate-change preparedness. The CDI project is a systematic effort to manually curate and share openly available climate data from various federal agencies. To date, the CDI has curated seven themes, or topics, relevant to climate change resiliency. These themes include Coastal Flooding, Food Resilience, Water, Ecosystem Vulnerability, Human Health, Energy Infrastructure, and Transportation. Each theme was curated by subject matter experts who selected datasets relevant to the topic at hand. An analysis of the entire Climate Data Initiative data collection and the data curated for each theme offers insights into which datasets are considered most relevant in addressing climate resiliency. Other aspects of the data collection will be examined including which datasets were the most visited or popular and which datasets were the most sought after for curation by the theme teams. Results from the analysis of the CDI collection will be presented in this talk.

NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins

PubMed Central

Pruitt, Kim D.; Tatusova, Tatiana; Maglott, Donna R.

2005-01-01

The National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) database (http://www.ncbi.nlm.nih.gov/RefSeq/) provides a non-redundant collection of sequences representing genomic data, transcripts and proteins. Although the goal is to provide a comprehensive dataset representing the complete sequence information for any given species, the database pragmatically includes sequence data that are currently publicly available in the archival databases. The database incorporates data from over 2400 organisms and includes over one million proteins representing significant taxonomic diversity spanning prokaryotes, eukaryotes and viruses. Nucleotide and protein sequences are explicitly linked, and the sequences are linked to other resources including the NCBI Map Viewer and Gene. Sequences are annotated to include coding regions, conserved domains, variation, references, names, database cross-references, and other features using a combined approach of collaboration and other input from the scientific community, automated annotation, propagation from GenBank and curation by NCBI staff. PMID:15608248

Plant Omics Data Center: An Integrated Web Repository for Interspecies Gene Expression Networks with NLP-Based Curation

PubMed Central

Ohyanagi, Hajime; Takano, Tomoyuki; Terashima, Shin; Kobayashi, Masaaki; Kanno, Maasa; Morimoto, Kyoko; Kanegae, Hiromi; Sasaki, Yohei; Saito, Misa; Asano, Satomi; Ozaki, Soichi; Kudo, Toru; Yokoyama, Koji; Aya, Koichiro; Suwabe, Keita; Suzuki, Go; Aoki, Koh; Kubo, Yasutaka; Watanabe, Masao; Matsuoka, Makoto; Yano, Kentaro

2015-01-01

Comprehensive integration of large-scale omics resources such as genomes, transcriptomes and metabolomes will provide deeper insights into broader aspects of molecular biology. For better understanding of plant biology, we aim to construct a next-generation sequencing (NGS)-derived gene expression network (GEN) repository for a broad range of plant species. So far we have incorporated information about 745 high-quality mRNA sequencing (mRNA-Seq) samples from eight plant species (Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, Sorghum bicolor, Vitis vinifera, Solanum tuberosum, Medicago truncatula and Glycine max) from the public short read archive, digitally profiled the entire set of gene expression profiles, and drawn GENs by using correspondence analysis (CA) to take advantage of gene expression similarities. In order to understand the evolutionary significance of the GENs from multiple species, they were linked according to the orthology of each node (gene) among species. In addition to other gene expression information, functional annotation of the genes will facilitate biological comprehension. Currently we are improving the given gene annotations with natural language processing (NLP) techniques and manual curation. Here we introduce the current status of our analyses and the web database, PODC (Plant Omics Data Center; http://bioinf.mind.meiji.ac.jp/podc/), now open to the public, providing GENs, functional annotations and additional comprehensive omics resources. PMID:25505034

SwissPalm: Protein Palmitoylation database.

PubMed

Blanc, Mathieu; David, Fabrice; Abrami, Laurence; Migliozzi, Daniel; Armand, Florence; Bürgi, Jérôme; van der Goot, Françoise Gisou

2015-01-01

Protein S-palmitoylation is a reversible post-translational modification that regulates many key biological processes, although the full extent and functions of protein S-palmitoylation remain largely unexplored. Recent developments of new chemical methods have allowed the establishment of palmitoyl-proteomes of a variety of cell lines and tissues from different species.  As the amount of information generated by these high-throughput studies is increasing, the field requires centralization and comparison of this information. Here we present SwissPalm ( http://swisspalm.epfl.ch), our open, comprehensive, manually curated resource to study protein S-palmitoylation. It currently encompasses more than 5000 S-palmitoylated protein hits from seven species, and contains more than 500 specific sites of S-palmitoylation. SwissPalm also provides curated information and filters that increase the confidence in true positive hits, and integrates predictions of S-palmitoylated cysteine scores, orthologs and isoform multiple alignments. Systems analysis of the palmitoyl-proteome screens indicate that 10% or more of the human proteome is susceptible to S-palmitoylation. Moreover, ontology and pathway analyses of the human palmitoyl-proteome reveal that key biological functions involve this reversible lipid modification. Comparative analysis finally shows a strong crosstalk between S-palmitoylation and other post-translational modifications. Through the compilation of data and continuous updates, SwissPalm will provide a powerful tool to unravel the global importance of protein S-palmitoylation.

SwissPalm: Protein Palmitoylation database

PubMed Central

Abrami, Laurence; Migliozzi, Daniel; Armand, Florence; Bürgi, Jérôme; van der Goot, Françoise Gisou

2015-01-01

Protein S-palmitoylation is a reversible post-translational modification that regulates many key biological processes, although the full extent and functions of protein S-palmitoylation remain largely unexplored. Recent developments of new chemical methods have allowed the establishment of palmitoyl-proteomes of a variety of cell lines and tissues from different species.  As the amount of information generated by these high-throughput studies is increasing, the field requires centralization and comparison of this information. Here we present SwissPalm ( http://swisspalm.epfl.ch), our open, comprehensive, manually curated resource to study protein S-palmitoylation. It currently encompasses more than 5000 S-palmitoylated protein hits from seven species, and contains more than 500 specific sites of S-palmitoylation. SwissPalm also provides curated information and filters that increase the confidence in true positive hits, and integrates predictions of S-palmitoylated cysteine scores, orthologs and isoform multiple alignments. Systems analysis of the palmitoyl-proteome screens indicate that 10% or more of the human proteome is susceptible to S-palmitoylation. Moreover, ontology and pathway analyses of the human palmitoyl-proteome reveal that key biological functions involve this reversible lipid modification. Comparative analysis finally shows a strong crosstalk between S-palmitoylation and other post-translational modifications. Through the compilation of data and continuous updates, SwissPalm will provide a powerful tool to unravel the global importance of protein S-palmitoylation. PMID:26339475

Surveying the maize community for their diversity and pedigree visualization needs to prioritize tool development and curation

USDA-ARS?s Scientific Manuscript database

The Maize Genetics and Genomics Database (MaizeGDB) team prepared a survey to identify breeders’ needs for visualizing pedigrees, diversity data, and haplotypes in order to prioritize tool development and curation efforts at MaizeGDB. The survey was distributed to the maize research community on beh...

Disease model curation improvements at Mouse Genome Informatics

PubMed Central

Bello, Susan M.; Richardson, Joel E.; Davis, Allan P.; Wiegers, Thomas C.; Mattingly, Carolyn J.; Dolan, Mary E.; Smith, Cynthia L.; Blake, Judith A.; Eppig, Janan T.

2012-01-01

Optimal curation of human diseases requires an ontology or structured vocabulary that contains terms familiar to end users, is robust enough to support multiple levels of annotation granularity, is limited to disease terms and is stable enough to avoid extensive reannotation following updates. At Mouse Genome Informatics (MGI), we currently use disease terms from Online Mendelian Inheritance in Man (OMIM) to curate mouse models of human disease. While OMIM provides highly detailed disease records that are familiar to many in the medical community, it lacks structure to support multilevel annotation. To improve disease annotation at MGI, we evaluated the merged Medical Subject Headings (MeSH) and OMIM disease vocabulary created by the Comparative Toxicogenomics Database (CTD) project. Overlaying MeSH onto OMIM provides hierarchical access to broad disease terms, a feature missing from the OMIM. We created an extended version of the vocabulary to meet the genetic disease-specific curation needs at MGI. Here we describe our evaluation of the CTD application, the extensions made by MGI and discuss the strengths and weaknesses of this approach. Database URL: http://www.informatics.jax.org/ PMID:22434831

LiverAtlas: a unique integrated knowledge database for systems-level research of liver and hepatic disease.

PubMed

Zhang, Yanqiong; Yang, Chunyuan; Wang, Shaochuang; Chen, Tao; Li, Mansheng; Wang, Xue; Li, Dongsheng; Wang, Kang; Ma, Jie; Wu, Songfeng; Zhang, Xueli; Zhu, Yunping; Wu, Jinsheng; He, Fuchu

2013-09-01

A large amount of liver-related physiological and pathological data exist in publicly available biological and bibliographic databases, which are usually far from comprehensive or integrated. Data collection, integration and mining processes pose a great challenge to scientific researchers and clinicians interested in the liver. To address these problems, we constructed LiverAtlas (http://liveratlas.hupo.org.cn), a comprehensive resource of biomedical knowledge related to the liver and various hepatic diseases by incorporating 53 databases. In the present version, LiverAtlas covers data on liver-related genomics, transcriptomics, proteomics, metabolomics and hepatic diseases. Additionally, LiverAtlas provides a wealth of manually curated information, relevant literature citations and cross-references to other databases. Importantly, an expert-confirmed Human Liver Disease Ontology, including relevant information for 227 types of hepatic disease, has been constructed and is used to annotate LiverAtlas data. Furthermore, we have demonstrated two examples of applying LiverAtlas data to identify candidate markers for hepatocellular carcinoma (HCC) at the systems level and to develop a systems biology-based classifier by combining the differential gene expression with topological features of human protein interaction networks to enhance the ability of HCC differential diagnosis. LiverAtlas is the most comprehensive liver and hepatic disease resource, which helps biologists and clinicians to analyse their data at the systems level and will contribute much to the biomarker discovery and diagnostic performance enhancement for liver diseases. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

DBSecSys: a database of Burkholderia mallei secretion systems.

PubMed

Memišević, Vesna; Kumar, Kamal; Cheng, Li; Zavaljevski, Nela; DeShazer, David; Wallqvist, Anders; Reifman, Jaques

2014-07-16

Bacterial pathogenicity represents a major public health concern worldwide. Secretion systems are a key component of bacterial pathogenicity, as they provide the means for bacterial proteins to penetrate host-cell membranes and insert themselves directly into the host cells' cytosol. Burkholderia mallei is a Gram-negative bacterium that uses multiple secretion systems during its host infection life cycle. To date, the identities of secretion system proteins for B. mallei are not well known, and their pathogenic mechanisms of action and host factors are largely uncharacterized. We present the Database of Burkholderia malleiSecretion Systems (DBSecSys), a compilation of manually curated and computationally predicted bacterial secretion system proteins and their host factors. Currently, DBSecSys contains comprehensive experimentally and computationally derived information about B. mallei strain ATCC 23344. The database includes 143 B. mallei proteins associated with five secretion systems, their 1,635 human and murine interacting targets, and the corresponding 2,400 host-B. mallei interactions. The database also includes information about 10 pathogenic mechanisms of action for B. mallei secretion system proteins inferred from the available literature. Additionally, DBSecSys provides details about 42 virulence attenuation experiments for 27 B. mallei secretion system proteins. Users interact with DBSecSys through a Web interface that allows for data browsing, querying, visualizing, and downloading. DBSecSys provides a comprehensive, systematically organized resource of experimental and computational data associated with B. mallei secretion systems. It provides the unique ability to study secretion systems not only through characterization of their corresponding pathogen proteins, but also through characterization of their host-interacting partners.The database is available at https://applications.bhsai.org/dbsecsys.

Mutation extraction tools can be combined for robust recognition of genetic variants in the literature

PubMed Central

Jimeno Yepes, Antonio; Verspoor, Karin

2014-01-01

As the cost of genomic sequencing continues to fall, the amount of data being collected and studied for the purpose of understanding the genetic basis of disease is increasing dramatically. Much of the source information relevant to such efforts is available only from unstructured sources such as the scientific literature, and significant resources are expended in manually curating and structuring the information in the literature. As such, there have been a number of systems developed to target automatic extraction of mutations and other genetic variation from the literature using text mining tools. We have performed a broad survey of the existing publicly available tools for extraction of genetic variants from the scientific literature. We consider not just one tool but a number of different tools, individually and in combination, and apply the tools in two scenarios. First, they are compared in an intrinsic evaluation context, where the tools are tested for their ability to identify specific mentions of genetic variants in a corpus of manually annotated papers, the Variome corpus. Second, they are compared in an extrinsic evaluation context based on our previous study of text mining support for curation of the COSMIC and InSiGHT databases. Our results demonstrate that no single tool covers the full range of genetic variants mentioned in the literature. Rather, several tools have complementary coverage and can be used together effectively. In the intrinsic evaluation on the Variome corpus, the combined performance is above 0.95 in F-measure, while in the extrinsic evaluation the combined recall performance is above 0.71 for COSMIC and above 0.62 for InSiGHT, a substantial improvement over the performance of any individual tool. Based on the analysis of these results, we suggest several directions for the improvement of text mining tools for genetic variant extraction from the literature. PMID:25285203

NASA's Astromaterials Database: Enabling Research Through Increased Access to Sample Data, Metadata and Imagery

NASA Technical Reports Server (NTRS)

Evans, Cindy; Todd, Nancy

2014-01-01

The Astromaterials Acquisition & Curation Office at NASA's Johnson Space Center (JSC) is the designated facility for curating all of NASA's extraterrestrial samples. Today, the suite of collections includes the lunar samples from the Apollo missions, cosmic dust particles falling into the Earth's atmosphere, meteorites collected in Antarctica, comet and interstellar dust particles from the Stardust mission, asteroid particles from Japan's Hayabusa mission, solar wind atoms collected during the Genesis mission, and space-exposed hardware from several missions. To support planetary science research on these samples, JSC's Astromaterials Curation Office hosts NASA's Astromaterials Curation digital repository and data access portal [http://curator.jsc.nasa.gov/], providing descriptions of the missions and collections, and critical information about each individual sample. Our office is designing and implementing several informatics initiatives to better serve the planetary research community. First, we are re-hosting the basic database framework by consolidating legacy databases for individual collections and providing a uniform access point for information (descriptions, imagery, classification) on all of our samples. Second, we continue to upgrade and host digital compendia that summarize and highlight published findings on the samples (e.g., lunar samples, meteorites from Mars). We host high resolution imagery of samples as it becomes available, including newly scanned images of historical prints from the Apollo missions. Finally we are creating plans to collect and provide new data, including 3D imagery, point cloud data, micro CT data, and external links to other data sets on selected samples. Together, these individual efforts will provide unprecedented digital access to NASA's Astromaterials, enabling preservation of the samples through more specific and targeted requests, and supporting new planetary science research and collaborations on the samples.

CottonGen: a genomics, genetics and breeding database for cotton research

USDA-ARS?s Scientific Manuscript database

CottonGen (http://www.cottongen.org) is a curated and integrated web-based relational database providing access to publicly available genomic, genetic and breeding data for cotton. CottonGen supercedes CottonDB and the Cotton Marker Database, with enhanced tools for easier data sharing, mining, vis...

Development and applications of the EntomopathogenID MLSA database for use in agricultural systems

USDA-ARS?s Scientific Manuscript database

The current study reports the development and application of a publicly accessible, curated database of Hypocrealean entomopathogenic fungi sequence data. The goal was to provide a platform for users to easily access sequence data from reference strains. The database can be used to accurately identi...

A guide for building biological pathways along with two case studies: hair and breast development.

PubMed

Trindade, Daniel; Orsine, Lissur A; Barbosa-Silva, Adriano; Donnard, Elisa R; Ortega, J Miguel

2015-03-01

Genomic information is being underlined in the format of biological pathways. Building these biological pathways is an ongoing demand and benefits from methods for extracting information from biomedical literature with the aid of text-mining tools. Here we hopefully guide you in the attempt of building a customized pathway or chart representation of a system. Our manual is based on a group of software designed to look at biointeractions in a set of abstracts retrieved from PubMed. However, they aim to support the work of someone with biological background, who does not need to be an expert on the subject and will play the role of manual curator while designing the representation of the system, the pathway. We therefore illustrate with two challenging case studies: hair and breast development. They were chosen for focusing on recent acquisitions of human evolution. We produced sub-pathways for each study, representing different phases of development. Differently from most charts present in current databases, we present detailed descriptions, which will additionally guide PESCADOR users along the process. The implementation as a web interface makes PESCADOR a unique tool for guiding the user along the biointeractions, which will constitute a novel pathway. Copyright © 2014 Elsevier Inc. All rights reserved.

The Pfam protein families database: towards a more sustainable future.

PubMed

Finn, Robert D; Coggill, Penelope; Eberhardt, Ruth Y; Eddy, Sean R; Mistry, Jaina; Mitchell, Alex L; Potter, Simon C; Punta, Marco; Qureshi, Matloob; Sangrador-Vegas, Amaia; Salazar, Gustavo A; Tate, John; Bateman, Alex

2016-01-04

In the last two years the Pfam database (http://pfam.xfam.org) has undergone a substantial reorganisation to reduce the effort involved in making a release, thereby permitting more frequent releases. Arguably the most significant of these changes is that Pfam is now primarily based on the UniProtKB reference proteomes, with the counts of matched sequences and species reported on the website restricted to this smaller set. Building families on reference proteomes sequences brings greater stability, which decreases the amount of manual curation required to maintain them. It also reduces the number of sequences displayed on the website, whilst still providing access to many important model organisms. Matches to the full UniProtKB database are, however, still available and Pfam annotations for individual UniProtKB sequences can still be retrieved. Some Pfam entries (1.6%) which have no matches to reference proteomes remain; we are working with UniProt to see if sequences from them can be incorporated into reference proteomes. Pfam-B, the automatically-generated supplement to Pfam, has been removed. The current release (Pfam 29.0) includes 16 295 entries and 559 clans. The facility to view the relationship between families within a clan has been improved by the introduction of a new tool. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Models in Translational Oncology: A Public Resource Database for Preclinical Cancer Research.

PubMed

Galuschka, Claudia; Proynova, Rumyana; Roth, Benjamin; Augustin, Hellmut G; Müller-Decker, Karin

2017-05-15

The devastating diseases of human cancer are mimicked in basic and translational cancer research by a steadily increasing number of tumor models, a situation requiring a platform with standardized reports to share model data. Models in Translational Oncology (MiTO) database was developed as a unique Web platform aiming for a comprehensive overview of preclinical models covering genetically engineered organisms, models of transplantation, chemical/physical induction, or spontaneous development, reviewed here. MiTO serves data entry for metastasis profiles and interventions. Moreover, cell lines and animal lines including tool strains can be recorded. Hyperlinks for connection with other databases and file uploads as supplementary information are supported. Several communication tools are offered to facilitate exchange of information. Notably, intellectual property can be protected prior to publication by inventor-defined accessibility of any given model. Data recall is via a highly configurable keyword search. Genome editing is expected to result in changes of the spectrum of model organisms, a reason to open MiTO for species-independent data. Registered users may deposit own model fact sheets (FS). MiTO experts check them for plausibility. Independently, manually curated FS are provided to principle investigators for revision and publication. Importantly, noneditable versions of reviewed FS can be cited in peer-reviewed journals. Cancer Res; 77(10); 2557-63. ©2017 AACR . ©2017 American Association for Cancer Research.

Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems.

PubMed

Boué, Stéphanie; Talikka, Marja; Westra, Jurjen Willem; Hayes, William; Di Fabio, Anselmo; Park, Jennifer; Schlage, Walter K; Sewer, Alain; Fields, Brett; Ansari, Sam; Martin, Florian; Veljkovic, Emilija; Kenney, Renee; Peitsch, Manuel C; Hoeng, Julia

2015-01-01

With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com © The Author(s) 2015. Published by Oxford University Press.

RSAT matrix-clustering: dynamic exploration and redundancy reduction of transcription factor binding motif collections

PubMed Central

Jaeger, Sébastien; Thieffry, Denis

2017-01-01

Abstract Transcription factor (TF) databases contain multitudes of binding motifs (TFBMs) from various sources, from which non-redundant collections are derived by manual curation. The advent of high-throughput methods stimulated the production of novel collections with increasing numbers of motifs. Meta-databases, built by merging these collections, contain redundant versions, because available tools are not suited to automatically identify and explore biologically relevant clusters among thousands of motifs. Motif discovery from genome-scale data sets (e.g. ChIP-seq) also produces redundant motifs, hampering the interpretation of results. We present matrix-clustering, a versatile tool that clusters similar TFBMs into multiple trees, and automatically creates non-redundant TFBM collections. A feature unique to matrix-clustering is its dynamic visualisation of aligned TFBMs, and its capability to simultaneously treat multiple collections from various sources. We demonstrate that matrix-clustering considerably simplifies the interpretation of combined results from multiple motif discovery tools, and highlights biologically relevant variations of similar motifs. We also ran a large-scale application to cluster ∼11 000 motifs from 24 entire databases, showing that matrix-clustering correctly groups motifs belonging to the same TF families, and drastically reduced motif redundancy. matrix-clustering is integrated within the RSAT suite (http://rsat.eu/), accessible through a user-friendly web interface or command-line for its integration in pipelines. PMID:28591841

DDMGD: the database of text-mined associations between genes methylated in diseases from different species.

PubMed

Bin Raies, Arwa; Mansour, Hicham; Incitti, Roberto; Bajic, Vladimir B

2015-01-01

Gathering information about associations between methylated genes and diseases is important for diseases diagnosis and treatment decisions. Recent advancements in epigenetics research allow for large-scale discoveries of associations of genes methylated in diseases in different species. Searching manually for such information is not easy, as it is scattered across a large number of electronic publications and repositories. Therefore, we developed DDMGD database (http://www.cbrc.kaust.edu.sa/ddmgd/) to provide a comprehensive repository of information related to genes methylated in diseases that can be found through text mining. DDMGD's scope is not limited to a particular group of genes, diseases or species. Using the text mining system DEMGD we developed earlier and additional post-processing, we extracted associations of genes methylated in different diseases from PubMed Central articles and PubMed abstracts. The accuracy of extracted associations is 82% as estimated on 2500 hand-curated entries. DDMGD provides a user-friendly interface facilitating retrieval of these associations ranked according to confidence scores. Submission of new associations to DDMGD is provided. A comparison analysis of DDMGD with several other databases focused on genes methylated in diseases shows that DDMGD is comprehensive and includes most of the recent information on genes methylated in diseases. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

ECOTOX knowledgebase: New tools for data visualization and database interoperability

EPA Science Inventory

The ECOTOXicology knowledgebase (ECOTOX) is a comprehensive, curated database that summarizes toxicology data fromsingle chemical exposure studies to terrestrial and aquatic organisms. The ECOTOX Knowledgebase provides risk assessors and researchers consistent information on toxi...

InverPep: A database of invertebrate antimicrobial peptides.

PubMed

Gómez, Esteban A; Giraldo, Paula; Orduz, Sergio

2017-03-01

The aim of this work was to construct InverPep, a database specialised in experimentally validated antimicrobial peptides (AMPs) from invertebrates. AMP data contained in InverPep were manually curated from other databases and the scientific literature. MySQL was integrated with the development platform Laravel; this framework allows to integrate programming in PHP with HTML and was used to design the InverPep web page's interface. InverPep contains 18 separated fields, including InverPep code, phylum and species source, peptide name, sequence, peptide length, secondary structure, molar mass, charge, isoelectric point, hydrophobicity, Boman index, aliphatic index and percentage of hydrophobic amino acids. CALCAMPI, an algorithm to calculate the physicochemical properties of multiple peptides simultaneously, was programmed in PERL language. To date, InverPep contains 702 experimentally validated AMPs from invertebrate species. All of the peptides contain information associated with their source, physicochemical properties, secondary structure, biological activity and links to external literature. Most AMPs in InverPep have a length between 10 and 50 amino acids, a positive charge, a Boman index between 0 and 2 kcal/mol, and 30-50% hydrophobic amino acids. InverPep includes 33 AMPs not reported in other databases. Besides, CALCAMPI and statistical analysis of InverPep data is presented. The InverPep database is available in English and Spanish. InverPep is a useful database to study invertebrate AMPs and its information could be used for the design of new peptides. The user-friendly interface of InverPep and its information can be freely accessed via a web-based browser at http://ciencias.medellin.unal.edu.co/gruposdeinvestigacion/prospeccionydisenobiomoleculas/InverPep/public/home_en. Copyright © 2016 International Society for Chemotherapy of Infection and Cancer. Published by Elsevier Ltd. All rights reserved.

A curated database of cyanobacterial strains relevant for modern taxonomy and phylogenetic studies.

PubMed

Ramos, Vitor; Morais, João; Vasconcelos, Vitor M

2017-04-25

The dataset herein described lays the groundwork for an online database of relevant cyanobacterial strains, named CyanoType (http://lege.ciimar.up.pt/cyanotype). It is a database that includes categorized cyanobacterial strains useful for taxonomic, phylogenetic or genomic purposes, with associated information obtained by means of a literature-based curation. The dataset lists 371 strains and represents the first version of the database (CyanoType v.1). Information for each strain includes strain synonymy and/or co-identity, strain categorization, habitat, accession numbers for molecular data, taxonomy and nomenclature notes according to three different classification schemes, hierarchical automatic classification, phylogenetic placement according to a selection of relevant studies (including this), and important bibliographic references. The database will be updated periodically, namely by adding new strains meeting the criteria for inclusion and by revising and adding up-to-date metadata for strains already listed. A global 16S rDNA-based phylogeny is provided in order to assist users when choosing the appropriate strains for their studies.

A curated database of cyanobacterial strains relevant for modern taxonomy and phylogenetic studies

PubMed Central

Ramos, Vitor; Morais, João; Vasconcelos, Vitor M.

2017-01-01

The dataset herein described lays the groundwork for an online database of relevant cyanobacterial strains, named CyanoType (http://lege.ciimar.up.pt/cyanotype). It is a database that includes categorized cyanobacterial strains useful for taxonomic, phylogenetic or genomic purposes, with associated information obtained by means of a literature-based curation. The dataset lists 371 strains and represents the first version of the database (CyanoType v.1). Information for each strain includes strain synonymy and/or co-identity, strain categorization, habitat, accession numbers for molecular data, taxonomy and nomenclature notes according to three different classification schemes, hierarchical automatic classification, phylogenetic placement according to a selection of relevant studies (including this), and important bibliographic references. The database will be updated periodically, namely by adding new strains meeting the criteria for inclusion and by revising and adding up-to-date metadata for strains already listed. A global 16S rDNA-based phylogeny is provided in order to assist users when choosing the appropriate strains for their studies. PMID:28440791

Dynamic delivery of the National Transit Database Sampling Manual.

DOT National Transportation Integrated Search

2013-02-01

This project improves the National Transit Database (NTD) Sampling Manual and develops an Internet-based, WordPress-powered interactive Web tool to deliver the new NTD Sampling Manual dynamically. The new manual adds guidance and a tool for transit a...

Dynamic delivery of the National Transit Database sampling manual.

DOT National Transportation Integrated Search

2013-02-01

This project improves the National Transit Database (NTD) Sampling Manual and develops an Internet-based, WordPress-powered interactive Web tool to deliver the new NTD Sampling Manual dynamically. The new manual adds guidance and a tool for transit a...

Trust, but verify: On the importance of chemical structure curation in cheminformatics and QSAR modeling research

PubMed Central

Fourches, Denis; Muratov, Eugene; Tropsha, Alexander

2010-01-01

Molecular modelers and cheminformaticians typically analyze experimental data generated by other scientists. Consequently, when it comes to data accuracy, cheminformaticians are always at the mercy of data providers who may inadvertently publish (partially) erroneous data. Thus, dataset curation is crucial for any cheminformatics analysis such as similarity searching, clustering, QSAR modeling, virtual screening, etc., especially nowadays when the availability of chemical datasets in public domain has skyrocketed in recent years. Despite the obvious importance of this preliminary step in the computational analysis of any dataset, there appears to be no commonly accepted guidance or set of procedures for chemical data curation. The main objective of this paper is to emphasize the need for a standardized chemical data curation strategy that should be followed at the onset of any molecular modeling investigation. Herein, we discuss several simple but important steps for cleaning chemical records in a database including the removal of a fraction of the data that cannot be appropriately handled by conventional cheminformatics techniques. Such steps include the removal of inorganic and organometallic compounds, counterions, salts and mixtures; structure validation; ring aromatization; normalization of specific chemotypes; curation of tautomeric forms; and the deletion of duplicates. To emphasize the importance of data curation as a mandatory step in data analysis, we discuss several case studies where chemical curation of the original “raw” database enabled the successful modeling study (specifically, QSAR analysis) or resulted in a significant improvement of model's prediction accuracy. We also demonstrate that in some cases rigorously developed QSAR models could be even used to correct erroneous biological data associated with chemical compounds. We believe that good practices for curation of chemical records outlined in this paper will be of value to all scientists working in the fields of molecular modeling, cheminformatics, and QSAR studies. PMID:20572635

A knowledge base for tracking the impact of genomics on population health.

PubMed

Yu, Wei; Gwinn, Marta; Dotson, W David; Green, Ridgely Fisk; Clyne, Mindy; Wulf, Anja; Bowen, Scott; Kolor, Katherine; Khoury, Muin J

2016-12-01

We created an online knowledge base (the Public Health Genomics Knowledge Base (PHGKB)) to provide systematically curated and updated information that bridges population-based research on genomics with clinical and public health applications. Weekly horizon scanning of a wide variety of online resources is used to retrieve relevant scientific publications, guidelines, and commentaries. After curation by domain experts, links are deposited into Web-based databases. PHGKB currently consists of nine component databases. Users can search the entire knowledge base or search one or more component databases directly and choose options for customizing the display of their search results. PHGKB offers researchers, policy makers, practitioners, and the general public a way to find information they need to understand the complicated landscape of genomics and population health.Genet Med 18 12, 1312-1314.

ECOTOX Knowledgebase: New tools for data visualization and database interoperability -Poster

EPA Science Inventory

The ECOTOXicology knowledgebase (ECOTOX) is a comprehensive, curated database that summarizes toxicology data from single chemical exposure studies to terrestrial and aquatic organisms. The ECOTOX Knowledgebase provides risk assessors and researchers consistent information on tox...

ECOTOX Knowledgebase: New tools for data visualization and database interoperability (poster)

EPA Science Inventory

The ECOTOXicology knowledgebase (ECOTOX) is a comprehensive, curated database that summarizes toxicology data from single chemical exposure studies to terrestrial and aquatic organisms. The ECOTOX Knowledgebase provides risk assessors and researchers consistent information on tox...

DSSTox and Chemical Information Technologies in Support of PredictiveToxicology

EPA Science Inventory

The EPA NCCT Distributed Structure-Searchable Toxicity (DSSTox) Database project initially focused on the curation and publication of high-quality, standardized, chemical structure-annotated toxicity databases for use in structure-activity relationship (SAR) modeling. In recent y...

Reactome graph database: Efficient access to complex pathway data

PubMed Central

Korninger, Florian; Viteri, Guilherme; Marin-Garcia, Pablo; Ping, Peipei; Wu, Guanming; Stein, Lincoln; D’Eustachio, Peter

2018-01-01

Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. One of its main priorities is to provide easy and efficient access to its high quality curated data. At present, biological pathway databases typically store their contents in relational databases. This limits access efficiency because there are performance issues associated with queries traversing highly interconnected data. The same data in a graph database can be queried more efficiently. Here we present the rationale behind the adoption of a graph database (Neo4j) as well as the new ContentService (REST API) that provides access to these data. The Neo4j graph database and its query language, Cypher, provide efficient access to the complex Reactome data model, facilitating easy traversal and knowledge discovery. The adoption of this technology greatly improved query efficiency, reducing the average query time by 93%. The web service built on top of the graph database provides programmatic access to Reactome data by object oriented queries, but also supports more complex queries that take advantage of the new underlying graph-based data storage. By adopting graph database technology we are providing a high performance pathway data resource to the community. The Reactome graph database use case shows the power of NoSQL database engines for complex biological data types. PMID:29377902

Reactome graph database: Efficient access to complex pathway data.

PubMed

Fabregat, Antonio; Korninger, Florian; Viteri, Guilherme; Sidiropoulos, Konstantinos; Marin-Garcia, Pablo; Ping, Peipei; Wu, Guanming; Stein, Lincoln; D'Eustachio, Peter; Hermjakob, Henning

2018-01-01

Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. One of its main priorities is to provide easy and efficient access to its high quality curated data. At present, biological pathway databases typically store their contents in relational databases. This limits access efficiency because there are performance issues associated with queries traversing highly interconnected data. The same data in a graph database can be queried more efficiently. Here we present the rationale behind the adoption of a graph database (Neo4j) as well as the new ContentService (REST API) that provides access to these data. The Neo4j graph database and its query language, Cypher, provide efficient access to the complex Reactome data model, facilitating easy traversal and knowledge discovery. The adoption of this technology greatly improved query efficiency, reducing the average query time by 93%. The web service built on top of the graph database provides programmatic access to Reactome data by object oriented queries, but also supports more complex queries that take advantage of the new underlying graph-based data storage. By adopting graph database technology we are providing a high performance pathway data resource to the community. The Reactome graph database use case shows the power of NoSQL database engines for complex biological data types.

Imitating manual curation of text-mined facts in biomedicine.

PubMed

Rodriguez-Esteban, Raul; Iossifov, Ivan; Rzhetsky, Andrey

2006-09-08

Text-mining algorithms make mistakes in extracting facts from natural-language texts. In biomedical applications, which rely on use of text-mined data, it is critical to assess the quality (the probability that the message is correctly extracted) of individual facts--to resolve data conflicts and inconsistencies. Using a large set of almost 100,000 manually produced evaluations (most facts were independently reviewed more than once, producing independent evaluations), we implemented and tested a collection of algorithms that mimic human evaluation of facts provided by an automated information-extraction system. The performance of our best automated classifiers closely approached that of our human evaluators (ROC score close to 0.95). Our hypothesis is that, were we to use a larger number of human experts to evaluate any given sentence, we could implement an artificial-intelligence curator that would perform the classification job at least as accurately as an average individual human evaluator. We illustrated our analysis by visualizing the predicted accuracy of the text-mined relations involving the term cocaine.

Predicting Physical Interactions between Protein Complexes*

PubMed Central

Clancy, Trevor; Rødland, Einar Andreas; Nygard, Ståle; Hovig, Eivind

2013-01-01

Protein complexes enact most biochemical functions in the cell. Dynamic interactions between protein complexes are frequent in many cellular processes. As they are often of a transient nature, they may be difficult to detect using current genome-wide screens. Here, we describe a method to computationally predict physical interactions between protein complexes, applied to both humans and yeast. We integrated manually curated protein complexes and physical protein interaction networks, and we designed a statistical method to identify pairs of protein complexes where the number of protein interactions between a complex pair is due to an actual physical interaction between the complexes. An evaluation against manually curated physical complex-complex interactions in yeast revealed that 50% of these interactions could be predicted in this manner. A community network analysis of the highest scoring pairs revealed a biologically sensible organization of physical complex-complex interactions in the cell. Such analyses of proteomes may serve as a guide to the discovery of novel functional cellular relationships. PMID:23438732

Automated extraction and semantic analysis of mutation impacts from the biomedical literature

PubMed Central

2012-01-01

Background Mutations as sources of evolution have long been the focus of attention in the biomedical literature. Accessing the mutational information and their impacts on protein properties facilitates research in various domains, such as enzymology and pharmacology. However, manually curating the rich and fast growing repository of biomedical literature is expensive and time-consuming. As a solution, text mining approaches have increasingly been deployed in the biomedical domain. While the detection of single-point mutations is well covered by existing systems, challenges still exist in grounding impacts to their respective mutations and recognizing the affected protein properties, in particular kinetic and stability properties together with physical quantities. Results We present an ontology model for mutation impacts, together with a comprehensive text mining system for extracting and analysing mutation impact information from full-text articles. Organisms, as sources of proteins, are extracted to help disambiguation of genes and proteins. Our system then detects mutation series to correctly ground detected impacts using novel heuristics. It also extracts the affected protein properties, in particular kinetic and stability properties, as well as the magnitude of the effects and validates these relations against the domain ontology. The output of our system can be provided in various formats, in particular by populating an OWL-DL ontology, which can then be queried to provide structured information. The performance of the system is evaluated on our manually annotated corpora. In the impact detection task, our system achieves a precision of 70.4%-71.1%, a recall of 71.3%-71.5%, and grounds the detected impacts with an accuracy of 76.5%-77%. The developed system, including resources, evaluation data and end-user and developer documentation is freely available under an open source license at http://www.semanticsoftware.info/open-mutation-miner. Conclusion We present Open Mutation Miner (OMM), the first comprehensive, fully open-source approach to automatically extract impacts and related relevant information from the biomedical literature. We assessed the performance of our work on manually annotated corpora and the results show the reliability of our approach. The representation of the extracted information into a structured format facilitates knowledge management and aids in database curation and correction. Furthermore, access to the analysis results is provided through multiple interfaces, including web services for automated data integration and desktop-based solutions for end user interactions. PMID:22759648

Evaluating the quality of Marfan genotype-phenotype correlations in existing FBN1 databases.

PubMed

Groth, Kristian A; Von Kodolitsch, Yskert; Kutsche, Kerstin; Gaustadnes, Mette; Thorsen, Kasper; Andersen, Niels H; Gravholt, Claus H

2017-07-01

Genetic FBN1 testing is pivotal for confirming the clinical diagnosis of Marfan syndrome. In an effort to evaluate variant causality, FBN1 databases are often used. We evaluated the current databases regarding FBN1 variants and validated associated phenotype records with a new Marfan syndrome geno-phenotyping tool called the Marfan score. We evaluated four databases (UMD-FBN1, ClinVar, the Human Gene Mutation Database (HGMD), and Uniprot) containing 2,250 FBN1 variants supported by 4,904 records presented in 307 references. The Marfan score calculated for phenotype data from the records quantified variant associations with Marfan syndrome phenotype. We calculated a Marfan score for 1,283 variants, of which we confirmed the database diagnosis of Marfan syndrome in 77.1%. This represented only 35.8% of the total registered variants; 18.5-33.3% (UMD-FBN1 versus HGMD) of variants associated with Marfan syndrome in the databases could not be confirmed by the recorded phenotype. FBN1 databases can be imprecise and incomplete. Data should be used with caution when evaluating FBN1 variants. At present, the UMD-FBN1 database seems to be the biggest and best curated; therefore, it is the most comprehensive database. However, the need for better genotype-phenotype curated databases is evident, and we hereby present such a database.Genet Med advance online publication 01 December 2016.

Automated Database Mediation Using Ontological Metadata Mappings

PubMed Central

Marenco, Luis; Wang, Rixin; Nadkarni, Prakash

2009-01-01

Objective To devise an automated approach for integrating federated database information using database ontologies constructed from their extended metadata. Background One challenge of database federation is that the granularity of representation of equivalent data varies across systems. Dealing effectively with this problem is analogous to dealing with precoordinated vs. postcoordinated concepts in biomedical ontologies. Model Description The authors describe an approach based on ontological metadata mapping rules defined with elements of a global vocabulary, which allows a query specified at one granularity level to fetch data, where possible, from databases within the federation that use different granularities. This is implemented in OntoMediator, a newly developed production component of our previously described Query Integrator System. OntoMediator's operation is illustrated with a query that accesses three geographically separate, interoperating databases. An example based on SNOMED also illustrates the applicability of high-level rules to support the enforcement of constraints that can prevent inappropriate curator or power-user actions. Summary A rule-based framework simplifies the design and maintenance of systems where categories of data must be mapped to each other, for the purpose of either cross-database query or for curation of the contents of compositional controlled vocabularies. PMID:19567801

Rapid development of entity-based data models for bioinformatics with persistence object-oriented design and structured interfaces.

PubMed

Ezra Tsur, Elishai

2017-01-01

Databases are imperative for research in bioinformatics and computational biology. Current challenges in database design include data heterogeneity and context-dependent interconnections between data entities. These challenges drove the development of unified data interfaces and specialized databases. The curation of specialized databases is an ever-growing challenge due to the introduction of new data sources and the emergence of new relational connections between established datasets. Here, an open-source framework for the curation of specialized databases is proposed. The framework supports user-designed models of data encapsulation, objects persistency and structured interfaces to local and external data sources such as MalaCards, Biomodels and the National Centre for Biotechnology Information (NCBI) databases. The proposed framework was implemented using Java as the development environment, EclipseLink as the data persistency agent and Apache Derby as the database manager. Syntactic analysis was based on J3D, jsoup, Apache Commons and w3c.dom open libraries. Finally, a construction of a specialized database for aneurysms associated vascular diseases is demonstrated. This database contains 3-dimensional geometries of aneurysms, patient's clinical information, articles, biological models, related diseases and our recently published model of aneurysms' risk of rapture. Framework is available in: http://nbel-lab.com.

ElemeNT: a computational tool for detecting core promoter elements.

PubMed

Sloutskin, Anna; Danino, Yehuda M; Orenstein, Yaron; Zehavi, Yonathan; Doniger, Tirza; Shamir, Ron; Juven-Gershon, Tamar

2015-01-01

Core promoter elements play a pivotal role in the transcriptional output, yet they are often detected manually within sequences of interest. Here, we present 2 contributions to the detection and curation of core promoter elements within given sequences. First, the Elements Navigation Tool (ElemeNT) is a user-friendly web-based, interactive tool for prediction and display of putative core promoter elements and their biologically-relevant combinations. Second, the CORE database summarizes ElemeNT-predicted core promoter elements near CAGE and RNA-seq-defined Drosophila melanogaster transcription start sites (TSSs). ElemeNT's predictions are based on biologically-functional core promoter elements, and can be used to infer core promoter compositions. ElemeNT does not assume prior knowledge of the actual TSS position, and can therefore assist in annotation of any given sequence. These resources, freely accessible at http://lifefaculty.biu.ac.il/gershon-tamar/index.php/resources, facilitate the identification of core promoter elements as active contributors to gene expression.

Plant Omics Data Center: an integrated web repository for interspecies gene expression networks with NLP-based curation.

PubMed

Ohyanagi, Hajime; Takano, Tomoyuki; Terashima, Shin; Kobayashi, Masaaki; Kanno, Maasa; Morimoto, Kyoko; Kanegae, Hiromi; Sasaki, Yohei; Saito, Misa; Asano, Satomi; Ozaki, Soichi; Kudo, Toru; Yokoyama, Koji; Aya, Koichiro; Suwabe, Keita; Suzuki, Go; Aoki, Koh; Kubo, Yasutaka; Watanabe, Masao; Matsuoka, Makoto; Yano, Kentaro

2015-01-01

Comprehensive integration of large-scale omics resources such as genomes, transcriptomes and metabolomes will provide deeper insights into broader aspects of molecular biology. For better understanding of plant biology, we aim to construct a next-generation sequencing (NGS)-derived gene expression network (GEN) repository for a broad range of plant species. So far we have incorporated information about 745 high-quality mRNA sequencing (mRNA-Seq) samples from eight plant species (Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, Sorghum bicolor, Vitis vinifera, Solanum tuberosum, Medicago truncatula and Glycine max) from the public short read archive, digitally profiled the entire set of gene expression profiles, and drawn GENs by using correspondence analysis (CA) to take advantage of gene expression similarities. In order to understand the evolutionary significance of the GENs from multiple species, they were linked according to the orthology of each node (gene) among species. In addition to other gene expression information, functional annotation of the genes will facilitate biological comprehension. Currently we are improving the given gene annotations with natural language processing (NLP) techniques and manual curation. Here we introduce the current status of our analyses and the web database, PODC (Plant Omics Data Center; http://bioinf.mind.meiji.ac.jp/podc/), now open to the public, providing GENs, functional annotations and additional comprehensive omics resources. © The Author 2014. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists.

MaizeGDB update: New tools, data, and interface for the maize model organism database

USDA-ARS?s Scientific Manuscript database

MaizeGDB is a highly curated, community-oriented database and informatics service to researchers focused on the crop plant and model organism Zea mays ssp. mays. Although some form of the maize community database has existed over the last 25 years, there have only been two major releases. In 1991, ...

dbPTM 2016: 10-year anniversary of a resource for post-translational modification of proteins.

PubMed

Huang, Kai-Yao; Su, Min-Gang; Kao, Hui-Ju; Hsieh, Yun-Chung; Jhong, Jhih-Hua; Cheng, Kuang-Hao; Huang, Hsien-Da; Lee, Tzong-Yi

2016-01-04

Owing to the importance of the post-translational modifications (PTMs) of proteins in regulating biological processes, the dbPTM (http://dbPTM.mbc.nctu.edu.tw/) was developed as a comprehensive database of experimentally verified PTMs from several databases with annotations of potential PTMs for all UniProtKB protein entries. For this 10th anniversary of dbPTM, the updated resource provides not only a comprehensive dataset of experimentally verified PTMs, supported by the literature, but also an integrative interface for accessing all available databases and tools that are associated with PTM analysis. As well as collecting experimental PTM data from 14 public databases, this update manually curates over 12 000 modified peptides, including the emerging S-nitrosylation, S-glutathionylation and succinylation, from approximately 500 research articles, which were retrieved by text mining. As the number of available PTM prediction methods increases, this work compiles a non-homologous benchmark dataset to evaluate the predictive power of online PTM prediction tools. An increasing interest in the structural investigation of PTM substrate sites motivated the mapping of all experimental PTM peptides to protein entries of Protein Data Bank (PDB) based on database identifier and sequence identity, which enables users to examine spatially neighboring amino acids, solvent-accessible surface area and side-chain orientations for PTM substrate sites on tertiary structures. Since drug binding in PDB is annotated, this update identified over 1100 PTM sites that are associated with drug binding. The update also integrates metabolic pathways and protein-protein interactions to support the PTM network analysis for a group of proteins. Finally, the web interface is redesigned and enhanced to facilitate access to this resource. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Improving taxonomic accuracy for fungi in public sequence databases: applying ‘one name one species’ in well-defined genera with Trichoderma/Hypocrea as a test case

PubMed Central

Strope, Pooja K; Chaverri, Priscila; Gazis, Romina; Ciufo, Stacy; Domrachev, Michael; Schoch, Conrad L

2017-01-01

Abstract The ITS (nuclear ribosomal internal transcribed spacer) RefSeq database at the National Center for Biotechnology Information (NCBI) is dedicated to the clear association between name, specimen and sequence data. This database is focused on sequences obtained from type material stored in public collections. While the initial ITS sequence curation effort together with numerous fungal taxonomy experts attempted to cover as many orders as possible, we extended our latest focus to the family and genus ranks. We focused on Trichoderma for several reasons, mainly because the asexual and sexual synonyms were well documented, and a list of proposed names and type material were recently proposed and published. In this case study the recent taxonomic information was applied to do a complete taxonomic audit for the genus Trichoderma in the NCBI Taxonomy database. A name status report is available here: https://www.ncbi.nlm.nih.gov/Taxonomy/TaxIdentifier/tax_identifier.cgi. As a result, the ITS RefSeq Targeted Loci database at NCBI has been augmented with more sequences from type and verified material from Trichoderma species. Additionally, to aid in the cross referencing of data from single loci and genomes we have collected a list of quality records of the RPB2 gene obtained from type material in GenBank that could help validate future submissions. During the process of curation misidentified genomes were discovered, and sequence records from type material were found hidden under previous classifications. Source metadata curation, although more cumbersome, proved to be useful as confirmation of the type material designation. Database URL: http://www.ncbi.nlm.nih.gov/bioproject/PRJNA177353 PMID:29220466

CDD/SPARCLE: functional classification of proteins via subfamily domain architectures.

PubMed

Marchler-Bauer, Aron; Bo, Yu; Han, Lianyi; He, Jane; Lanczycki, Christopher J; Lu, Shennan; Chitsaz, Farideh; Derbyshire, Myra K; Geer, Renata C; Gonzales, Noreen R; Gwadz, Marc; Hurwitz, David I; Lu, Fu; Marchler, Gabriele H; Song, James S; Thanki, Narmada; Wang, Zhouxi; Yamashita, Roxanne A; Zhang, Dachuan; Zheng, Chanjuan; Geer, Lewis Y; Bryant, Stephen H

2017-01-04

NCBI's Conserved Domain Database (CDD) aims at annotating biomolecular sequences with the location of evolutionarily conserved protein domain footprints, and functional sites inferred from such footprints. An archive of pre-computed domain annotation is maintained for proteins tracked by NCBI's Entrez database, and live search services are offered as well. CDD curation staff supplements a comprehensive collection of protein domain and protein family models, which have been imported from external providers, with representations of selected domain families that are curated in-house and organized into hierarchical classifications of functionally distinct families and sub-families. CDD also supports comparative analyses of protein families via conserved domain architectures, and a recent curation effort focuses on providing functional characterizations of distinct subfamily architectures using SPARCLE: Subfamily Protein Architecture Labeling Engine. CDD can be accessed at https://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml. Published by Oxford University Press on behalf of Nucleic Acids Research 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database

PubMed Central

Davis, Allan Peter; Wiegers, Thomas C.; King, Benjamin L.; Wiegers, Jolene; Grondin, Cynthia J.; Sciaky, Daniela; Johnson, Robin J.; Mattingly, Carolyn J.

2016-01-01

Strategies for discovering common molecular events among disparate diseases hold promise for improving understanding of disease etiology and expanding treatment options. One technique is to leverage curated datasets found in the public domain. The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) manually curates chemical-gene, chemical-disease, and gene-disease interactions from the scientific literature. The use of official gene symbols in CTD interactions enables this information to be combined with the Gene Ontology (GO) file from NCBI Gene. By integrating these GO-gene annotations with CTD’s gene-disease dataset, we produce 753,000 inferences between 15,700 GO terms and 4,200 diseases, providing opportunities to explore presumptive molecular underpinnings of diseases and identify biological similarities. Through a variety of applications, we demonstrate the utility of this novel resource. As a proof-of-concept, we first analyze known repositioned drugs (e.g., raloxifene and sildenafil) and see that their target diseases have a greater degree of similarity when comparing GO terms vs. genes. Next, a computational analysis predicts seemingly non-intuitive diseases (e.g., stomach ulcers and atherosclerosis) as being similar to bipolar disorder, and these are validated in the literature as reported co-diseases. Additionally, we leverage other CTD content to develop testable hypotheses about thalidomide-gene networks to treat seemingly disparate diseases. Finally, we illustrate how CTD tools can rank a series of drugs as potential candidates for repositioning against B-cell chronic lymphocytic leukemia and predict cisplatin and the small molecule inhibitor JQ1 as lead compounds. The CTD dataset is freely available for users to navigate pathologies within the context of extensive biological processes, molecular functions, and cellular components conferred by GO. This inference set should aid researchers, bioinformaticists, and pharmaceutical drug makers in finding commonalities in disease mechanisms, which in turn could help identify new therapeutics, new indications for existing pharmaceuticals, potential disease comorbidities, and alerts for side effects. PMID:27171405

Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database.

PubMed

Davis, Allan Peter; Wiegers, Thomas C; King, Benjamin L; Wiegers, Jolene; Grondin, Cynthia J; Sciaky, Daniela; Johnson, Robin J; Mattingly, Carolyn J

2016-01-01

Strategies for discovering common molecular events among disparate diseases hold promise for improving understanding of disease etiology and expanding treatment options. One technique is to leverage curated datasets found in the public domain. The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) manually curates chemical-gene, chemical-disease, and gene-disease interactions from the scientific literature. The use of official gene symbols in CTD interactions enables this information to be combined with the Gene Ontology (GO) file from NCBI Gene. By integrating these GO-gene annotations with CTD's gene-disease dataset, we produce 753,000 inferences between 15,700 GO terms and 4,200 diseases, providing opportunities to explore presumptive molecular underpinnings of diseases and identify biological similarities. Through a variety of applications, we demonstrate the utility of this novel resource. As a proof-of-concept, we first analyze known repositioned drugs (e.g., raloxifene and sildenafil) and see that their target diseases have a greater degree of similarity when comparing GO terms vs. genes. Next, a computational analysis predicts seemingly non-intuitive diseases (e.g., stomach ulcers and atherosclerosis) as being similar to bipolar disorder, and these are validated in the literature as reported co-diseases. Additionally, we leverage other CTD content to develop testable hypotheses about thalidomide-gene networks to treat seemingly disparate diseases. Finally, we illustrate how CTD tools can rank a series of drugs as potential candidates for repositioning against B-cell chronic lymphocytic leukemia and predict cisplatin and the small molecule inhibitor JQ1 as lead compounds. The CTD dataset is freely available for users to navigate pathologies within the context of extensive biological processes, molecular functions, and cellular components conferred by GO. This inference set should aid researchers, bioinformaticists, and pharmaceutical drug makers in finding commonalities in disease mechanisms, which in turn could help identify new therapeutics, new indications for existing pharmaceuticals, potential disease comorbidities, and alerts for side effects.

Data Albums: An Event Driven Search, Aggregation and Curation Tool for Earth Science

NASA Technical Reports Server (NTRS)

Ramachandran, Rahul; Kulkarni, Ajinkya; Maskey, Manil; Bakare, Rohan; Basyal, Sabin; Li, Xiang; Flynn, Shannon

2014-01-01

Approaches used in Earth science research such as case study analysis and climatology studies involve discovering and gathering diverse data sets and information to support the research goals. To gather relevant data and information for case studies and climatology analysis is both tedious and time consuming. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. In cases where researchers are interested in studying a significant event, they have to manually assemble a variety of datasets relevant to it by searching the different distributed data systems. This paper presents a specialized search, aggregation and curation tool for Earth science to address these challenges. The search rool automatically creates curated 'Data Albums', aggregated collections of information related to a specific event, containing links to relevant data files [granules] from different instruments, tools and services for visualization and analysis, and information about the event contained in news reports, images or videos to supplement research analysis. Curation in the tool is driven via an ontology based relevancy ranking algorithm to filter out non relevant information and data.

WormBase 2014: new views of curated biology

PubMed Central

Harris, Todd W.; Baran, Joachim; Bieri, Tamberlyn; Cabunoc, Abigail; Chan, Juancarlos; Chen, Wen J.; Davis, Paul; Done, James; Grove, Christian; Howe, Kevin; Kishore, Ranjana; Lee, Raymond; Li, Yuling; Muller, Hans-Michael; Nakamura, Cecilia; Ozersky, Philip; Paulini, Michael; Raciti, Daniela; Schindelman, Gary; Tuli, Mary Ann; Auken, Kimberly Van; Wang, Daniel; Wang, Xiaodong; Williams, Gary; Wong, J. D.; Yook, Karen; Schedl, Tim; Hodgkin, Jonathan; Berriman, Matthew; Kersey, Paul; Spieth, John; Stein, Lincoln; Sternberg, Paul W.

2014-01-01

WormBase (http://www.wormbase.org/) is a highly curated resource dedicated to supporting research using the model organism Caenorhabditis elegans. With an electronic history predating the World Wide Web, WormBase contains information ranging from the sequence and phenotype of individual alleles to genome-wide studies generated using next-generation sequencing technologies. In recent years, we have expanded the contents to include data on additional nematodes of agricultural and medical significance, bringing the knowledge of C. elegans to bear on these systems and providing support for underserved research communities. Manual curation of the primary literature remains a central focus of the WormBase project, providing users with reliable, up-to-date and highly cross-linked information. In this update, we describe efforts to organize the original atomized and highly contextualized curated data into integrated syntheses of discrete biological topics. Next, we discuss our experiences coping with the vast increase in available genome sequences made possible through next-generation sequencing platforms. Finally, we describe some of the features and tools of the new WormBase Web site that help users better find and explore data of interest. PMID:24194605

MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics.

PubMed

Jeffryes, James G; Colastani, Ricardo L; Elbadawi-Sidhu, Mona; Kind, Tobias; Niehaus, Thomas D; Broadbelt, Linda J; Hanson, Andrew D; Fiehn, Oliver; Tyo, Keith E J; Henry, Christopher S

2015-01-01

In spite of its great promise, metabolomics has proven difficult to execute in an untargeted and generalizable manner. Liquid chromatography-mass spectrometry (LC-MS) has made it possible to gather data on thousands of cellular metabolites. However, matching metabolites to their spectral features continues to be a bottleneck, meaning that much of the collected information remains uninterpreted and that new metabolites are seldom discovered in untargeted studies. These challenges require new approaches that consider compounds beyond those available in curated biochemistry databases. Here we present Metabolic In silico Network Expansions (MINEs), an extension of known metabolite databases to include molecules that have not been observed, but are likely to occur based on known metabolites and common biochemical reactions. We utilize an algorithm called the Biochemical Network Integrated Computational Explorer (BNICE) and expert-curated reaction rules based on the Enzyme Commission classification system to propose the novel chemical structures and reactions that comprise MINE databases. Starting from the Kyoto Encyclopedia of Genes and Genomes (KEGG) COMPOUND database, the MINE contains over 571,000 compounds, of which 93% are not present in the PubChem database. However, these MINE compounds have on average higher structural similarity to natural products than compounds from KEGG or PubChem. MINE databases were able to propose annotations for 98.6% of a set of 667 MassBank spectra, 14% more than KEGG alone and equivalent to PubChem while returning far fewer candidates per spectra than PubChem (46 vs. 1715 median candidates). Application of MINEs to LC-MS accurate mass data enabled the identity of an unknown peak to be confidently predicted. MINE databases are freely accessible for non-commercial use via user-friendly web-tools at http://minedatabase.mcs.anl.gov and developer-friendly APIs. MINEs improve metabolomics peak identification as compared to general chemical databases whose results include irrelevant synthetic compounds. Furthermore, MINEs complement and expand on previous in silico generated compound databases that focus on human metabolism. We are actively developing the database; future versions of this resource will incorporate transformation rules for spontaneous chemical reactions and more advanced filtering and prioritization of candidate structures. Graphical abstractMINE database construction and access methods. The process of constructing a MINE database from the curated source databases is depicted on the left. The methods for accessing the database are shown on the right.

Text mining and expert curation to develop a database on psychiatric diseases and their genes

PubMed Central

Gutiérrez-Sacristán, Alba; Bravo, Àlex; Portero-Tresserra, Marta; Valverde, Olga; Armario, Antonio; Blanco-Gandía, M.C.; Farré, Adriana; Fernández-Ibarrondo, Lierni; Fonseca, Francina; Giraldo, Jesús; Leis, Angela; Mané, Anna; Mayer, M.A.; Montagud-Romero, Sandra; Nadal, Roser; Ortiz, Jordi; Pavon, Francisco Javier; Perez, Ezequiel Jesús; Rodríguez-Arias, Marta; Serrano, Antonia; Torrens, Marta; Warnault, Vincent; Sanz, Ferran

2017-01-01

Abstract Psychiatric disorders constitute one of the main causes of disability worldwide. During the past years, considerable research has been conducted on the genetic architecture of such diseases, although little understanding of their etiology has been achieved. The difficulty to access up-to-date, relevant genotype-phenotype information has hampered the application of this wealth of knowledge to translational research and clinical practice in order to improve diagnosis and treatment of psychiatric patients. PsyGeNET (http://www.psygenet.org/) has been developed with the aim of supporting research on the genetic architecture of psychiatric diseases, by providing integrated and structured accessibility to their genotype–phenotype association data, together with analysis and visualization tools. In this article, we describe the protocol developed for the sustainable update of this knowledge resource. It includes the recruitment of a team of domain experts in order to perform the curation of the data extracted by text mining. Annotation guidelines and a web-based annotation tool were developed to support the curators’ tasks. A curation workflow was designed including a pilot phase and two rounds of curation and analysis phases. Negative evidence from the literature on gene–disease associations (GDAs) was taken into account in the curation process. We report the results of the application of this workflow to the curation of GDAs for PsyGeNET, including the analysis of the inter-annotator agreement and suggest this model as a suitable approach for the sustainable development and update of knowledge resources. Database URL: http://www.psygenet.org PsyGeNET corpus: http://www.psygenet.org/ds/PsyGeNET/results/psygenetCorpus.tar PMID:29220439

SoyBase, The USDA-ARS Soybean Genetics and Genomics Database

USDA-ARS?s Scientific Manuscript database

SoyBase, the USDA-ARS soybean genetic database, is a comprehensive repository for professionally curated genetics, genomics and related data resources for soybean. SoyBase contains the most current genetic, physical and genomic sequence maps integrated with qualitative and quantitative traits. The...

The Biomolecular Interaction Network Database and related tools 2005 update

PubMed Central

Alfarano, C.; Andrade, C. E.; Anthony, K.; Bahroos, N.; Bajec, M.; Bantoft, K.; Betel, D.; Bobechko, B.; Boutilier, K.; Burgess, E.; Buzadzija, K.; Cavero, R.; D'Abreo, C.; Donaldson, I.; Dorairajoo, D.; Dumontier, M. J.; Dumontier, M. R.; Earles, V.; Farrall, R.; Feldman, H.; Garderman, E.; Gong, Y.; Gonzaga, R.; Grytsan, V.; Gryz, E.; Gu, V.; Haldorsen, E.; Halupa, A.; Haw, R.; Hrvojic, A.; Hurrell, L.; Isserlin, R.; Jack, F.; Juma, F.; Khan, A.; Kon, T.; Konopinsky, S.; Le, V.; Lee, E.; Ling, S.; Magidin, M.; Moniakis, J.; Montojo, J.; Moore, S.; Muskat, B.; Ng, I.; Paraiso, J. P.; Parker, B.; Pintilie, G.; Pirone, R.; Salama, J. J.; Sgro, S.; Shan, T.; Shu, Y.; Siew, J.; Skinner, D.; Snyder, K.; Stasiuk, R.; Strumpf, D.; Tuekam, B.; Tao, S.; Wang, Z.; White, M.; Willis, R.; Wolting, C.; Wong, S.; Wrong, A.; Xin, C.; Yao, R.; Yates, B.; Zhang, S.; Zheng, K.; Pawson, T.; Ouellette, B. F. F.; Hogue, C. W. V.

2005-01-01

The Biomolecular Interaction Network Database (BIND) (http://bind.ca) archives biomolecular interaction, reaction, complex and pathway information. Our aim is to curate the details about molecular interactions that arise from published experimental research and to provide this information, as well as tools to enable data analysis, freely to researchers worldwide. BIND data are curated into a comprehensive machine-readable archive of computable information and provides users with methods to discover interactions and molecular mechanisms. BIND has worked to develop new methods for visualization that amplify the underlying annotation of genes and proteins to facilitate the study of molecular interaction networks. BIND has maintained an open database policy since its inception in 1999. Data growth has proceeded at a tremendous rate, approaching over 100 000 records. New services provided include a new BIND Query and Submission interface, a Standard Object Access Protocol service and the Small Molecule Interaction Database (http://smid.blueprint.org) that allows users to determine probable small molecule binding sites of new sequences and examine conserved binding residues. PMID:15608229

MiDAS: the field guide to the microbes of activated sludge

PubMed Central

McIlroy, Simon Jon; Saunders, Aaron Marc; Albertsen, Mads; Nierychlo, Marta; McIlroy, Bianca; Hansen, Aviaja Anna; Karst, Søren Michael; Nielsen, Jeppe Lund; Nielsen, Per Halkjær

2015-01-01

The Microbial Database for Activated Sludge (MiDAS) field guide is a freely available online resource linking the identity of abundant and process critical microorganisms in activated sludge wastewater treatment systems to available data related to their functional importance. Phenotypic properties of some of these genera are described, but most are known only from sequence data. The MiDAS taxonomy is a manual curation of the SILVA taxonomy that proposes a name for all genus-level taxa observed to be abundant by large-scale 16 S rRNA gene amplicon sequencing of full-scale activated sludge communities. The taxonomy can be used to classify unknown sequences, and the online MiDAS field guide links the identity to the available information about their morphology, diversity, physiology and distribution. The use of a common taxonomy across the field will provide a solid foundation for the study of microbial ecology of the activated sludge process and related treatment processes. The online MiDAS field guide is a collaborative workspace intended to facilitate a better understanding of the ecology of activated sludge and related treatment processes—knowledge that will be an invaluable resource for the optimal design and operation of these systems. Database URL: http://www.midasfieldguide.org PMID:26120139

Vaxar: A Web-Based Database of Laboratory Animal Responses to Vaccinations and Its Application in the Meta-Analysis of Different Animal Responses to Tuberculosis Vaccinations

PubMed Central

Todd, Thomas; Dunn, Natalie; Xiang, Zuoshuang; He, Yongqun

2016-01-01

Animal models are indispensable for vaccine research and development. However, choosing which species to use and designing a vaccine study that is optimized for that species is often challenging. Vaxar (http://www.violinet.org/vaxar/) is a web-based database and analysis system that stores manually curated data regarding vaccine-induced responses in animals. To date, Vaxar encompasses models from 35 animal species including rodents, rabbits, ferrets, primates, and birds. These 35 species have been used to study more than 1300 experimentally tested vaccines for 164 pathogens and diseases significant to humans and domestic animals. The responses to vaccines by animals in more than 1500 experimental studies are recorded in Vaxar; these data can be used for systematic meta-analysis of various animal responses to a particular vaccine. For example, several variables, including animal strain, animal age, and the dose or route of either vaccination or challenge, might affect host response outcomes. Vaxar can also be used to identify variables that affect responses to different vaccines in a specific animal model. All data stored in Vaxar are publically available for web-based queries and analyses. Overall Vaxar provides a unique systematic approach for understanding vaccine-induced host immunity. PMID:27053566

Designing a User Manual to Support an In-House Database.

ERIC Educational Resources Information Center

Kraft, Melissa A.; Pugh, W. Jean

1988-01-01

Describes the steps involved in designing a user manual for an in-house database. Topics covered include goal definition, target audience identification, production scheduling, design and production choices, testing and review, and updating of the manual. (CLB)

Text-mining-assisted biocuration workflows in Argo

PubMed Central

Rak, Rafal; Batista-Navarro, Riza Theresa; Rowley, Andrew; Carter, Jacob; Ananiadou, Sophia

2014-01-01

Biocuration activities have been broadly categorized into the selection of relevant documents, the annotation of biological concepts of interest and identification of interactions between the concepts. Text mining has been shown to have a potential to significantly reduce the effort of biocurators in all the three activities, and various semi-automatic methodologies have been integrated into curation pipelines to support them. We investigate the suitability of Argo, a workbench for building text-mining solutions with the use of a rich graphical user interface, for the process of biocuration. Central to Argo are customizable workflows that users compose by arranging available elementary analytics to form task-specific processing units. A built-in manual annotation editor is the single most used biocuration tool of the workbench, as it allows users to create annotations directly in text, as well as modify or delete annotations created by automatic processing components. Apart from syntactic and semantic analytics, the ever-growing library of components includes several data readers and consumers that support well-established as well as emerging data interchange formats such as XMI, RDF and BioC, which facilitate the interoperability of Argo with other platforms or resources. To validate the suitability of Argo for curation activities, we participated in the BioCreative IV challenge whose purpose was to evaluate Web-based systems addressing user-defined biocuration tasks. Argo proved to have the edge over other systems in terms of flexibility of defining biocuration tasks. As expected, the versatility of the workbench inevitably lengthened the time the curators spent on learning the system before taking on the task, which may have affected the usability of Argo. The participation in the challenge gave us an opportunity to gather valuable feedback and identify areas of improvement, some of which have already been introduced. Database URL: http://argo.nactem.ac.uk PMID:25037308

If we build it, will they come? Curation and use of the ESO telescope bibliography

NASA Astrophysics Data System (ADS)

Grothkopf, Uta; Meakins, Silvia; Bordelon, Dominic

2015-12-01

The ESO Telescope Bibliography (telbib) is a database of refereed papers published by the ESO users community. It links data in the ESO Science Archive with the published literature, and vice versa. Developed and maintained by the ESO library, telbib also provides insights into the organization's research output and impact as measured through bibliometric studies. Curating telbib is a multi-step process that involves extensive tagging of the database records. Based on selected use cases, this talk will explain how the rich metadata provide parameters for reports and statistics in order to investigate the performance of ESO's facilities and to understand trends and developments in the publishing behaviour of the user community.

Natural Language Processing in aid of FlyBase curators

PubMed Central

Karamanis, Nikiforos; Seal, Ruth; Lewin, Ian; McQuilton, Peter; Vlachos, Andreas; Gasperin, Caroline; Drysdale, Rachel; Briscoe, Ted

2008-01-01

Background Despite increasing interest in applying Natural Language Processing (NLP) to biomedical text, whether this technology can facilitate tasks such as database curation remains unclear. Results PaperBrowser is the first NLP-powered interface that was developed under a user-centered approach to improve the way in which FlyBase curators navigate an article. In this paper, we first discuss how observing curators at work informed the design and evaluation of PaperBrowser. Then, we present how we appraise PaperBrowser's navigational functionalities in a user-based study using a text highlighting task and evaluation criteria of Human-Computer Interaction. Our results show that PaperBrowser reduces the amount of interactions between two highlighting events and therefore improves navigational efficiency by about 58% compared to the navigational mechanism that was previously available to the curators. Moreover, PaperBrowser is shown to provide curators with enhanced navigational utility by over 74% irrespective of the different ways in which they highlight text in the article. Conclusion We show that state-of-the-art performance in certain NLP tasks such as Named Entity Recognition and Anaphora Resolution can be combined with the navigational functionalities of PaperBrowser to support curation quite successfully. PMID:18410678

Public variant databases: liability?

PubMed

Thorogood, Adrian; Cook-Deegan, Robert; Knoppers, Bartha Maria

2017-07-01

Public variant databases support the curation, clinical interpretation, and sharing of genomic data, thus reducing harmful errors or delays in diagnosis. As variant databases are increasingly relied on in the clinical context, there is concern that negligent variant interpretation will harm patients and attract liability. This article explores the evolving legal duties of laboratories, public variant databases, and physicians in clinical genomics and recommends a governance framework for databases to promote responsible data sharing.Genet Med advance online publication 15 December 2016.

Textpresso Central: a customizable platform for searching, text mining, viewing, and curating biomedical literature.

PubMed

Müller, H-M; Van Auken, K M; Li, Y; Sternberg, P W

2018-03-09

The biomedical literature continues to grow at a rapid pace, making the challenge of knowledge retrieval and extraction ever greater. Tools that provide a means to search and mine the full text of literature thus represent an important way by which the efficiency of these processes can be improved. We describe the next generation of the Textpresso information retrieval system, Textpresso Central (TPC). TPC builds on the strengths of the original system by expanding the full text corpus to include the PubMed Central Open Access Subset (PMC OA), as well as the WormBase C. elegans bibliography. In addition, TPC allows users to create a customized corpus by uploading and processing documents of their choosing. TPC is UIMA compliant, to facilitate compatibility with external processing modules, and takes advantage of Lucene indexing and search technology for efficient handling of millions of full text documents. Like Textpresso, TPC searches can be performed using keywords and/or categories (semantically related groups of terms), but to provide better context for interpreting and validating queries, search results may now be viewed as highlighted passages in the context of full text. To facilitate biocuration efforts, TPC also allows users to select text spans from the full text and annotate them, create customized curation forms for any data type, and send resulting annotations to external curation databases. As an example of such a curation form, we describe integration of TPC with the Noctua curation tool developed by the Gene Ontology (GO) Consortium. Textpresso Central is an online literature search and curation platform that enables biocurators and biomedical researchers to search and mine the full text of literature by integrating keyword and category searches with viewing search results in the context of the full text. It also allows users to create customized curation interfaces, use those interfaces to make annotations linked to supporting evidence statements, and then send those annotations to any database in the world. Textpresso Central URL: http://www.textpresso.org/tpc.

TRENDS: A flight test relational database user's guide and reference manual

NASA Technical Reports Server (NTRS)

Bondi, M. J.; Bjorkman, W. S.; Cross, J. L.

1994-01-01

This report is designed to be a user's guide and reference manual for users intending to access rotocraft test data via TRENDS, the relational database system which was developed as a tool for the aeronautical engineer with no programming background. This report has been written to assist novice and experienced TRENDS users. TRENDS is a complete system for retrieving, searching, and analyzing both numerical and narrative data, and for displaying time history and statistical data in graphical and numerical formats. This manual provides a 'guided tour' and a 'user's guide' for the new and intermediate-skilled users. Examples for the use of each menu item within TRENDS is provided in the Menu Reference section of the manual, including full coverage for TIMEHIST, one of the key tools. This manual is written around the XV-15 Tilt Rotor database, but does include an appendix on the UH-60 Blackhawk database. This user's guide and reference manual establishes a referrable source for the research community and augments NASA TM-101025, TRENDS: The Aeronautical Post-Test, Database Management System, Jan. 1990, written by the same authors.

Pubcast and Genecast: Browsing and Exploring Publications and Associated Curated Content in Biology Through Mobile Devices.

PubMed

Goldweber, Scott; Theodore, Jamal; Torcivia-Rodriguez, John; Simonyan, Vahan; Mazumder, Raja

2017-01-01

Services such as Facebook, Amazon, and eBay were once solely accessed from stationary computers. These web services are now being used increasingly on mobile devices. We acknowledge this new reality by providing users a way to access publications and a curated cancer mutation database on their mobile device with daily automated updates. http://hive. biochemistry.gwu.edu/tools/HivePubcast.

From data point timelines to a well curated data set, data mining of experimental data and chemical structure data from scientific articles, problems and possible solutions.

PubMed

Ruusmann, Villu; Maran, Uko

2013-07-01

The scientific literature is important source of experimental and chemical structure data. Very often this data has been harvested into smaller or bigger data collections leaving the data quality and curation issues on shoulders of users. The current research presents a systematic and reproducible workflow for collecting series of data points from scientific literature and assembling a database that is suitable for the purposes of high quality modelling and decision support. The quality assurance aspect of the workflow is concerned with the curation of both chemical structures and associated toxicity values at (1) single data point level and (2) collection of data points level. The assembly of a database employs a novel "timeline" approach. The workflow is implemented as a software solution and its applicability is demonstrated on the example of the Tetrahymena pyriformis acute aquatic toxicity endpoint. A literature collection of 86 primary publications for T. pyriformis was found to contain 2,072 chemical compounds and 2,498 unique toxicity values, which divide into 2,440 numerical and 58 textual values. Every chemical compound was assigned to a preferred toxicity value. Examples for most common chemical and toxicological data curation scenarios are discussed.

The needs for chemistry standards, database tools and data curation at the chemical-biology interface (SLAS meeting)

EPA Science Inventory

This presentation will highlight known challenges with the production of high quality chemical databases and outline recent efforts made to address these challenges. Specific examples will be provided illustrating these challenges within the U.S. Environmental Protection Agency ...

DIANA-LncBase v2: indexing microRNA targets on non-coding transcripts

PubMed Central

Paraskevopoulou, Maria D.; Vlachos, Ioannis S.; Karagkouni, Dimitra; Georgakilas, Georgios; Kanellos, Ilias; Vergoulis, Thanasis; Zagganas, Konstantinos; Tsanakas, Panayiotis; Floros, Evangelos; Dalamagas, Theodore; Hatzigeorgiou, Artemis G.

2016-01-01

microRNAs (miRNAs) are short non-coding RNAs (ncRNAs) that act as post-transcriptional regulators of coding gene expression. Long non-coding RNAs (lncRNAs) have been recently reported to interact with miRNAs. The sponge-like function of lncRNAs introduces an extra layer of complexity in the miRNA interactome. DIANA-LncBase v1 provided a database of experimentally supported and in silico predicted miRNA Recognition Elements (MREs) on lncRNAs. The second version of LncBase (www.microrna.gr/LncBase) presents an extensive collection of miRNA:lncRNA interactions. The significantly enhanced database includes more than 70 000 low and high-throughput, (in)direct miRNA:lncRNA experimentally supported interactions, derived from manually curated publications and the analysis of 153 AGO CLIP-Seq libraries. The new experimental module presents a 14-fold increase compared to the previous release. LncBase v2 hosts in silico predicted miRNA targets on lncRNAs, identified with the DIANA-microT algorithm. The relevant module provides millions of predicted miRNA binding sites, accompanied with detailed metadata and MRE conservation metrics. LncBase v2 caters information regarding cell type specific miRNA:lncRNA regulation and enables users to easily identify interactions in 66 different cell types, spanning 36 tissues for human and mouse. Database entries are also supported by accurate lncRNA expression information, derived from the analysis of more than 6 billion RNA-Seq reads. PMID:26612864

RSAT matrix-clustering: dynamic exploration and redundancy reduction of transcription factor binding motif collections.

PubMed

Castro-Mondragon, Jaime Abraham; Jaeger, Sébastien; Thieffry, Denis; Thomas-Chollier, Morgane; van Helden, Jacques

2017-07-27

Transcription factor (TF) databases contain multitudes of binding motifs (TFBMs) from various sources, from which non-redundant collections are derived by manual curation. The advent of high-throughput methods stimulated the production of novel collections with increasing numbers of motifs. Meta-databases, built by merging these collections, contain redundant versions, because available tools are not suited to automatically identify and explore biologically relevant clusters among thousands of motifs. Motif discovery from genome-scale data sets (e.g. ChIP-seq) also produces redundant motifs, hampering the interpretation of results. We present matrix-clustering, a versatile tool that clusters similar TFBMs into multiple trees, and automatically creates non-redundant TFBM collections. A feature unique to matrix-clustering is its dynamic visualisation of aligned TFBMs, and its capability to simultaneously treat multiple collections from various sources. We demonstrate that matrix-clustering considerably simplifies the interpretation of combined results from multiple motif discovery tools, and highlights biologically relevant variations of similar motifs. We also ran a large-scale application to cluster ∼11 000 motifs from 24 entire databases, showing that matrix-clustering correctly groups motifs belonging to the same TF families, and drastically reduced motif redundancy. matrix-clustering is integrated within the RSAT suite (http://rsat.eu/), accessible through a user-friendly web interface or command-line for its integration in pipelines. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

A statistical approach to identify, monitor, and manage incomplete curated data sets.

PubMed

Howe, Douglas G

2018-04-02

Many biological knowledge bases gather data through expert curation of published literature. High data volume, selective partial curation, delays in access, and publication of data prior to the ability to curate it can result in incomplete curation of published data. Knowing which data sets are incomplete and how incomplete they are remains a challenge. Awareness that a data set may be incomplete is important for proper interpretation, to avoiding flawed hypothesis generation, and can justify further exploration of published literature for additional relevant data. Computational methods to assess data set completeness are needed. One such method is presented here. In this work, a multivariate linear regression model was used to identify genes in the Zebrafish Information Network (ZFIN) Database having incomplete curated gene expression data sets. Starting with 36,655 gene records from ZFIN, data aggregation, cleansing, and filtering reduced the set to 9870 gene records suitable for training and testing the model to predict the number of expression experiments per gene. Feature engineering and selection identified the following predictive variables: the number of journal publications; the number of journal publications already attributed for gene expression annotation; the percent of journal publications already attributed for expression data; the gene symbol; and the number of transgenic constructs associated with each gene. Twenty-five percent of the gene records (2483 genes) were used to train the model. The remaining 7387 genes were used to test the model. One hundred and twenty-two and 165 of the 7387 tested genes were identified as missing expression annotations based on their residuals being outside the model lower or upper 95% confidence interval respectively. The model had precision of 0.97 and recall of 0.71 at the negative 95% confidence interval and precision of 0.76 and recall of 0.73 at the positive 95% confidence interval. This method can be used to identify data sets that are incompletely curated, as demonstrated using the gene expression data set from ZFIN. This information can help both database resources and data consumers gauge when it may be useful to look further for published data to augment the existing expertly curated information.

Vaxjo: a web-based vaccine adjuvant database and its application for analysis of vaccine adjuvants and their uses in vaccine development.

PubMed

Sayers, Samantha; Ulysse, Guerlain; Xiang, Zuoshuang; He, Yongqun

2012-01-01

Vaccine adjuvants are compounds that enhance host immune responses to co-administered antigens in vaccines. Vaxjo is a web-based central database and analysis system that curates, stores, and analyzes vaccine adjuvants and their usages in vaccine development. Basic information of a vaccine adjuvant stored in Vaxjo includes adjuvant name, components, structure, appearance, storage, preparation, function, safety, and vaccines that use this adjuvant. Reliable references are curated and cited. Bioinformatics scripts are developed and used to link vaccine adjuvants to different adjuvanted vaccines stored in the general VIOLIN vaccine database. Presently, 103 vaccine adjuvants have been curated in Vaxjo. Among these adjuvants, 98 have been used in 384 vaccines stored in VIOLIN against over 81 pathogens, cancers, or allergies. All these vaccine adjuvants are categorized and analyzed based on adjuvant types, pathogens used, and vaccine types. As a use case study of vaccine adjuvants in infectious disease vaccines, the adjuvants used in Brucella vaccines are specifically analyzed. A user-friendly web query and visualization interface is developed for interactive vaccine adjuvant search. To support data exchange, the information of vaccine adjuvants is stored in the Vaccine Ontology (VO) in the Web Ontology Language (OWL) format.

Vaxjo: A Web-Based Vaccine Adjuvant Database and Its Application for Analysis of Vaccine Adjuvants and Their Uses in Vaccine Development

PubMed Central

Sayers, Samantha; Ulysse, Guerlain; Xiang, Zuoshuang; He, Yongqun

2012-01-01

Vaccine adjuvants are compounds that enhance host immune responses to co-administered antigens in vaccines. Vaxjo is a web-based central database and analysis system that curates, stores, and analyzes vaccine adjuvants and their usages in vaccine development. Basic information of a vaccine adjuvant stored in Vaxjo includes adjuvant name, components, structure, appearance, storage, preparation, function, safety, and vaccines that use this adjuvant. Reliable references are curated and cited. Bioinformatics scripts are developed and used to link vaccine adjuvants to different adjuvanted vaccines stored in the general VIOLIN vaccine database. Presently, 103 vaccine adjuvants have been curated in Vaxjo. Among these adjuvants, 98 have been used in 384 vaccines stored in VIOLIN against over 81 pathogens, cancers, or allergies. All these vaccine adjuvants are categorized and analyzed based on adjuvant types, pathogens used, and vaccine types. As a use case study of vaccine adjuvants in infectious disease vaccines, the adjuvants used in Brucella vaccines are specifically analyzed. A user-friendly web query and visualization interface is developed for interactive vaccine adjuvant search. To support data exchange, the information of vaccine adjuvants is stored in the Vaccine Ontology (VO) in the Web Ontology Language (OWL) format. PMID:22505817

The Listeria monocytogenes strain 10403S BioCyc database

PubMed Central

Orsi, Renato H.; Bergholz, Teresa M.; Wiedmann, Martin; Boor, Kathryn J.

2015-01-01

Listeria monocytogenes is a food-borne pathogen of humans and other animals. The striking ability to survive several stresses usually used for food preservation makes L. monocytogenes one of the biggest concerns to the food industry, while the high mortality of listeriosis in specific groups of humans makes it a great concern for public health. Previous studies have shown that a regulatory network involving alternative sigma (σ) factors and transcription factors is pivotal to stress survival. However, few studies have evaluated at the metabolic networks controlled by these regulatory mechanisms. The L. monocytogenes BioCyc database uses the strain 10403S as a model. Computer-generated initial annotation for all genes also allowed for identification, annotation and display of predicted reactions and pathways carried out by a single cell. Further ongoing manual curation based on published data as well as database mining for selected genes allowed the more refined annotation of functions, which, in turn, allowed for annotation of new pathways and fine-tuning of previously defined pathways to more L. monocytogenes-specific pathways. Using RNA-Seq data, several transcription start sites and promoter regions were mapped to the 10403S genome and annotated within the database. Additionally, the identification of promoter regions and a comprehensive review of available literature allowed the annotation of several regulatory interactions involving σ factors and transcription factors. The L. monocytogenes 10403S BioCyc database is a new resource for researchers studying Listeria and related organisms. It allows users to (i) have a comprehensive view of all reactions and pathways predicted to take place within the cell in the cellular overview, as well as to (ii) upload their own data, such as differential expression data, to visualize the data in the scope of predicted pathways and regulatory networks and to carry on enrichment analyses using several different annotations available within the database. Database URL: http://biocyc.org/organism-summary?object=10403S_RAST PMID:25819074

Curating and Preserving the Big Canopy Database System: an Active Curation Approach using SEAD

NASA Astrophysics Data System (ADS)

Myers, J.; Cushing, J. B.; Lynn, P.; Weiner, N.; Ovchinnikova, A.; Nadkarni, N.; McIntosh, A.

2015-12-01

Modern research is increasingly dependent upon highly heterogeneous data and on the associated cyberinfrastructure developed to organize, analyze, and visualize that data. However, due to the complexity and custom nature of such combined data-software systems, it can be very challenging to curate and preserve them for the long term at reasonable cost and in a way that retains their scientific value. In this presentation, we describe how this challenge was met in preserving the Big Canopy Database (CanopyDB) system using an agile approach and leveraging the Sustainable Environment - Actionable Data (SEAD) DataNet project's hosted data services. The CanopyDB system was developed over more than a decade at Evergreen State College to address the needs of forest canopy researchers. It is an early yet sophisticated exemplar of the type of system that has become common in biological research and science in general, including multiple relational databases for different experiments, a custom database generation tool used to create them, an image repository, and desktop and web tools to access, analyze, and visualize this data. SEAD provides secure project spaces with a semantic content abstraction (typed content with arbitrary RDF metadata statements and relationships to other content), combined with a standards-based curation and publication pipeline resulting in packaged research objects with Digital Object Identifiers. Using SEAD, our cross-project team was able to incrementally ingest CanopyDB components (images, datasets, software source code, documentation, executables, and virtualized services) and to iteratively define and extend the metadata and relationships needed to document them. We believe that both the process, and the richness of the resultant standards-based (OAI-ORE) preservation object, hold lessons for the development of best-practice solutions for preserving scientific data in association with the tools and services needed to derive value from it.

Standards for Clinical Grade Genomic Databases.

PubMed

Yohe, Sophia L; Carter, Alexis B; Pfeifer, John D; Crawford, James M; Cushman-Vokoun, Allison; Caughron, Samuel; Leonard, Debra G B

2015-11-01

Next-generation sequencing performed in a clinical environment must meet clinical standards, which requires reproducibility of all aspects of the testing. Clinical-grade genomic databases (CGGDs) are required to classify a variant and to assist in the professional interpretation of clinical next-generation sequencing. Applying quality laboratory standards to the reference databases used for sequence-variant interpretation presents a new challenge for validation and curation. To define CGGD and the categories of information contained in CGGDs and to frame recommendations for the structure and use of these databases in clinical patient care. Members of the College of American Pathologists Personalized Health Care Committee reviewed the literature and existing state of genomic databases and developed a framework for guiding CGGD development in the future. Clinical-grade genomic databases may provide different types of information. This work group defined 3 layers of information in CGGDs: clinical genomic variant repositories, genomic medical data repositories, and genomic medicine evidence databases. The layers are differentiated by the types of genomic and medical information contained and the utility in assisting with clinical interpretation of genomic variants. Clinical-grade genomic databases must meet specific standards regarding submission, curation, and retrieval of data, as well as the maintenance of privacy and security. These organizing principles for CGGDs should serve as a foundation for future development of specific standards that support the use of such databases for patient care.

FINDbase: a relational database recording frequencies of genetic defects leading to inherited disorders worldwide.

PubMed

van Baal, Sjozef; Kaimakis, Polynikis; Phommarinh, Manyphong; Koumbi, Daphne; Cuppens, Harry; Riccardino, Francesca; Macek, Milan; Scriver, Charles R; Patrinos, George P

2007-01-01

Frequency of INherited Disorders database (FINDbase) (http://www.findbase.org) is a relational database, derived from the ETHNOS software, recording frequencies of causative mutations leading to inherited disorders worldwide. Database records include the population and ethnic group, the disorder name and the related gene, accompanied by links to any corresponding locus-specific mutation database, to the respective Online Mendelian Inheritance in Man entries and the mutation together with its frequency in that population. The initial information is derived from the published literature, locus-specific databases and genetic disease consortia. FINDbase offers a user-friendly query interface, providing instant access to the list and frequencies of the different mutations. Query outputs can be either in a table or graphical format, accompanied by reference(s) on the data source. Registered users from three different groups, namely administrator, national coordinator and curator, are responsible for database curation and/or data entry/correction online via a password-protected interface. Databaseaccess is free of charge and there are no registration requirements for data querying. FINDbase provides a simple, web-based system for population-based mutation data collection and retrieval and can serve not only as a valuable online tool for molecular genetic testing of inherited disorders but also as a non-profit model for sustainable database funding, in the form of a 'database-journal'.

Alaska IPASS database preparation manual.

Treesearch

P. McHugh; D. Olson; C. Schallau

1989-01-01

Describes the data, their sources, and the calibration procedures used in compiling a database for the Alaska IPASS (interactive policy analysis simulation system) model. Although this manual is for Alaska, it provides generic instructions for analysts preparing databases for other geographical areas.

Public variant databases: liability?

PubMed Central

Thorogood, Adrian; Cook-Deegan, Robert; Knoppers, Bartha Maria

2017-01-01

Public variant databases support the curation, clinical interpretation, and sharing of genomic data, thus reducing harmful errors or delays in diagnosis. As variant databases are increasingly relied on in the clinical context, there is concern that negligent variant interpretation will harm patients and attract liability. This article explores the evolving legal duties of laboratories, public variant databases, and physicians in clinical genomics and recommends a governance framework for databases to promote responsible data sharing. Genet Med advance online publication 15 December 2016 PMID:27977006

IPD-MHC 2.0: an improved inter-species database for the study of the major histocompatibility complex

PubMed Central

Maccari, Giuseppe; Robinson, James; Ballingall, Keith; Guethlein, Lisbeth A.; Grimholt, Unni; Kaufman, Jim; Ho, Chak-Sum; de Groot, Natasja G.; Flicek, Paul; Bontrop, Ronald E.; Hammond, John A.; Marsh, Steven G. E.

2017-01-01

The IPD-MHC Database project (http://www.ebi.ac.uk/ipd/mhc/) collects and expertly curates sequences of the major histocompatibility complex from non-human species and provides the infrastructure and tools to enable accurate analysis. Since the first release of the database in 2003, IPD-MHC has grown and currently hosts a number of specific sections, with more than 7000 alleles from 70 species, including non-human primates, canines, felines, equids, ovids, suids, bovins, salmonids and murids. These sequences are expertly curated and made publicly available through an open access website. The IPD-MHC Database is a key resource in its field, and this has led to an average of 1500 unique visitors and more than 5000 viewed pages per month. As the database has grown in size and complexity, it has created a number of challenges in maintaining and organizing information, particularly the need to standardize nomenclature and taxonomic classification, while incorporating new allele submissions. Here, we describe the latest database release, the IPD-MHC 2.0 and discuss planned developments. This release incorporates sequence updates and new tools that enhance database queries and improve the submission procedure by utilizing common tools that are able to handle the varied requirements of each MHC-group. PMID:27899604

How should the completeness and quality of curated nanomaterial data be evaluated?

NASA Astrophysics Data System (ADS)

Marchese Robinson, Richard L.; Lynch, Iseult; Peijnenburg, Willie; Rumble, John; Klaessig, Fred; Marquardt, Clarissa; Rauscher, Hubert; Puzyn, Tomasz; Purian, Ronit; Åberg, Christoffer; Karcher, Sandra; Vriens, Hanne; Hoet, Peter; Hoover, Mark D.; Hendren, Christine Ogilvie; Harper, Stacey L.

2016-05-01

Nanotechnology is of increasing significance. Curation of nanomaterial data into electronic databases offers opportunities to better understand and predict nanomaterials' behaviour. This supports innovation in, and regulation of, nanotechnology. It is commonly understood that curated data need to be sufficiently complete and of sufficient quality to serve their intended purpose. However, assessing data completeness and quality is non-trivial in general and is arguably especially difficult in the nanoscience area, given its highly multidisciplinary nature. The current article, part of the Nanomaterial Data Curation Initiative series, addresses how to assess the completeness and quality of (curated) nanomaterial data. In order to address this key challenge, a variety of related issues are discussed: the meaning and importance of data completeness and quality, existing approaches to their assessment and the key challenges associated with evaluating the completeness and quality of curated nanomaterial data. Considerations which are specific to the nanoscience area and lessons which can be learned from other relevant scientific disciplines are considered. Hence, the scope of this discussion ranges from physicochemical characterisation requirements for nanomaterials and interference of nanomaterials with nanotoxicology assays to broader issues such as minimum information checklists, toxicology data quality schemes and computational approaches that facilitate evaluation of the completeness and quality of (curated) data. This discussion is informed by a literature review and a survey of key nanomaterial data curation stakeholders. Finally, drawing upon this discussion, recommendations are presented concerning the central question: how should the completeness and quality of curated nanomaterial data be evaluated?Nanotechnology is of increasing significance. Curation of nanomaterial data into electronic databases offers opportunities to better understand and predict nanomaterials' behaviour. This supports innovation in, and regulation of, nanotechnology. It is commonly understood that curated data need to be sufficiently complete and of sufficient quality to serve their intended purpose. However, assessing data completeness and quality is non-trivial in general and is arguably especially difficult in the nanoscience area, given its highly multidisciplinary nature. The current article, part of the Nanomaterial Data Curation Initiative series, addresses how to assess the completeness and quality of (curated) nanomaterial data. In order to address this key challenge, a variety of related issues are discussed: the meaning and importance of data completeness and quality, existing approaches to their assessment and the key challenges associated with evaluating the completeness and quality of curated nanomaterial data. Considerations which are specific to the nanoscience area and lessons which can be learned from other relevant scientific disciplines are considered. Hence, the scope of this discussion ranges from physicochemical characterisation requirements for nanomaterials and interference of nanomaterials with nanotoxicology assays to broader issues such as minimum information checklists, toxicology data quality schemes and computational approaches that facilitate evaluation of the completeness and quality of (curated) data. This discussion is informed by a literature review and a survey of key nanomaterial data curation stakeholders. Finally, drawing upon this discussion, recommendations are presented concerning the central question: how should the completeness and quality of curated nanomaterial data be evaluated? Electronic supplementary information (ESI) available: (1) Detailed information regarding issues raised in the main text; (2) original survey responses. See DOI: 10.1039/c5nr08944a

[The randomized controlled trial of the treatment for clavicular fracture by rotatory manual reduction with forceps holder and retrograde percutaneous pinning transfixation].

PubMed

Bi, Hong-zheng; Yang, Mao-qing; Tan, Yuan-chao; Fu, Song

2008-07-01

To study the curative effect and safety of rotatory manual reduction with forceps holder and retrograde percutaneous pinning transfixation in treating clavicular fracture. All 201 cases of clavicular fractures were randomly divided into treatment group (101 cases) and control group (100 cases). The treatment group was treated by rotatory manual reduction with forceps holder and retrograde percutaneous pinning transfixation. The control group was treated by open reduction and internal fixation with Kirschner pin. All cases were followed up for 4 to 21 months (mean 10.6 months). SPSS was used to analyze clinic healing time of fracture and shoulder-joint function in both two groups. After operation, 101 cases of treatment group achieved union of fracture and the clinical healing time was 28 to 49 days (mean 34.5+/-2.7 days). In control group,there were 4 cases with nonunion of fracture,the other 96 cases were union,the clinical healing time was 36 to 92 days (mean 55.3+/-4.8 days). The excellent and good rate of shoulder-joint function was 100% in treatment group and 83% in control group. By t-test and chi2-test, there was significant difference between the two groups in curative effect (P<0.05). Rotatory manual reduction with forceps holder and retrograde pinning transfixation can be used in various kinds of clavicular shaft fracture, with many virtues such as easy operation, reliable fixation, short union time of fracture, good functional recovery of shoulder-joint and no incision scar affecting appearance.

The prescribable drugs with efficacy in experimental epilepsies (PDE3) database for drug repurposing research in epilepsy.

PubMed

Sivapalarajah, Shayeeshan; Krishnakumar, Mathangi; Bickerstaffe, Harry; Chan, YikYing; Clarkson, Joseph; Hampden-Martin, Alistair; Mirza, Ahmad; Tanti, Matthew; Marson, Anthony; Pirmohamed, Munir; Mirza, Nasir

2018-02-01

Current antiepileptic drugs (AEDs) have several shortcomings. For example, they fail to control seizures in 30% of patients. Hence, there is a need to identify new AEDs. Drug repurposing is the discovery of new indications for approved drugs. This drug "recycling" offers the potential of significant savings in the time and cost of drug development. Many drugs licensed for other indications exhibit antiepileptic efficacy in animal models. Our aim was to create a database of "prescribable" drugs, approved for other conditions, with published evidence of efficacy in animal models of epilepsy, and to collate data that would assist in choosing the most promising candidates for drug repurposing. The database was created by the following: (1) computational literature-mining using novel software that identifies Medline abstracts containing the name of a prescribable drug, a rodent model of epilepsy, and a phrase indicating seizure reduction; then (2) crowdsourced manual curation of the identified abstracts. The final database includes 173 drugs and 500 abstracts. It is made freely available at www.liverpool.ac.uk/D3RE/PDE3. The database is reliable: 94% of the included drugs have corroborative evidence of efficacy in animal models (for example, evidence from multiple independent studies). The database includes many drugs that are appealing candidates for repurposing, as they are widely accepted by prescribers and patients-the database includes half of the 20 most commonly prescribed drugs in England-and they target many proteins involved in epilepsy but not targeted by current AEDs. It is important to note that the drugs are of potential relevance to human epilepsy-the database is highly enriched with drugs that target proteins of known causal human epilepsy genes (Fisher's exact test P-value < 3 × 10 -5 ). We present data to help prioritize the most promising candidates for repurposing from the database. The PDE3 database is an important new resource for drug repurposing research in epilepsy. Wiley Periodicals, Inc. © 2018 International League Against Epilepsy.

MET network in PubMed: a text-mined network visualization and curation system.

PubMed

Dai, Hong-Jie; Su, Chu-Hsien; Lai, Po-Ting; Huang, Ming-Siang; Jonnagaddala, Jitendra; Rose Jue, Toni; Rao, Shruti; Chou, Hui-Jou; Milacic, Marija; Singh, Onkar; Syed-Abdul, Shabbir; Hsu, Wen-Lian

2016-01-01

Metastasis is the dissemination of a cancer/tumor from one organ to another, and it is the most dangerous stage during cancer progression, causing more than 90% of cancer deaths. Improving the understanding of the complicated cellular mechanisms underlying metastasis requires investigations of the signaling pathways. To this end, we developed a METastasis (MET) network visualization and curation tool to assist metastasis researchers retrieve network information of interest while browsing through the large volume of studies in PubMed. MET can recognize relations among genes, cancers, tissues and organs of metastasis mentioned in the literature through text-mining techniques, and then produce a visualization of all mined relations in a metastasis network. To facilitate the curation process, MET is developed as a browser extension that allows curators to review and edit concepts and relations related to metastasis directly in PubMed. PubMed users can also view the metastatic networks integrated from the large collection of research papers directly through MET. For the BioCreative 2015 interactive track (IAT), a curation task was proposed to curate metastatic networks among PubMed abstracts. Six curators participated in the proposed task and a post-IAT task, curating 963 unique metastatic relations from 174 PubMed abstracts using MET.Database URL: http://btm.tmu.edu.tw/metastasisway. © The Author(s) 2016. Published by Oxford University Press.

Irinotecan and Oxaliplatin Might Provide Equal Benefit as Adjuvant Chemotherapy for Patients with Resectable Synchronous Colon Cancer and Liver-confined Metastases: A Nationwide Database Study.

PubMed

Liang, Yi-Hsin; Shao, Yu-Yun; Chen, Ho-Min; Cheng, Ann-Lii; Lai, Mei-Shu; Yeh, Kun-Huei

2017-12-01

Although irinotecan and oxaliplatin are both standard treatments for advanced colon cancer, it remains unknown whether either is effective for patients with resectable synchronous colon cancer and liver-confined metastasis (SCCLM) after curative surgery. A population-based cohort of patients diagnosed with de novo SCCLM between 2004 and 2009 was established by searching the database of the Taiwan Cancer Registry and the National Health Insurance Research Database of Taiwan. Patients who underwent curative surgery as their first therapy followed by chemotherapy doublets were classified into the irinotecan group or oxaliplatin group accordingly. Patients who received radiotherapy or did not receive chemotherapy doublets were excluded. We included 6,533 patients with de novo stage IV colon cancer. Three hundred and nine of them received chemotherapy doublets after surgery; 77 patients received irinotecan and 232 patients received oxaliplatin as adjuvant chemotherapy. The patients in both groups exhibited similar overall survival (median: not reached vs. 40.8 months, p=0.151) and time to the next line of treatment (median: 16.5 vs. 14.3 months, p=0.349) in both univariate and multivariate analyses. Additionally, patients with resectable SCCLM had significantly shorter median overall survival than patients with stage III colon cancer who underwent curative surgery and subsequent adjuvant chemotherapy, but longer median overall survival than patients with de novo stage IV colon cancer who underwent surgery only at the primary site followed by standard systemic chemotherapy (p<0.001). Irinotecan and oxaliplatin exhibited similar efficacy in patients who underwent curative surgery for resectable SCCLM. Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Population-specific documentation of pharmacogenomic markers and their allelic frequencies in FINDbase.

PubMed

Georgitsi, Marianthi; Viennas, Emmanouil; Gkantouna, Vassiliki; Christodoulopoulou, Elena; Zagoriti, Zoi; Tafrali, Christina; Ntellos, Fotios; Giannakopoulou, Olga; Boulakou, Athanassia; Vlahopoulou, Panagiota; Kyriacou, Eva; Tsaknakis, John; Tsakalidis, Athanassios; Poulas, Konstantinos; Tzimas, Giannis; Patrinos, George P

2011-01-01

Population and ethnic group-specific allele frequencies of pharmacogenomic markers are poorly documented and not systematically collected in structured data repositories. We developed the Frequency of Inherited Disorders Pharmacogenomics database (FINDbase-PGx), a separate module of the FINDbase, aiming to systematically document pharmacogenomic allele frequencies in various populations and ethnic groups worldwide. We critically collected and curated 214 scientific articles reporting pharmacogenomic markers allele frequencies in various populations and ethnic groups worldwide. Subsequently, in order to host the curated data, support data visualization and data mining, we developed a website application, utilizing Microsoft™ PivotViewer software. Curated allelic frequency data pertaining to 144 pharmacogenomic markers across 14 genes, representing approximately 87,000 individuals from 150 populations worldwide, are currently included in FINDbase-PGx. A user-friendly query interface allows for easy data querying, based on numerous content criteria, such as population, ethnic group, geographical region, gene, drug and rare allele frequency. FINDbase-PGx is a comprehensive database, which, unlike other pharmacogenomic knowledgebases, fulfills the much needed requirement to systematically document pharmacogenomic allelic frequencies in various populations and ethnic groups worldwide.

Practical guidelines addressing ethical issues pertaining to the curation of human locus-specific variation databases (LSDBs)

PubMed Central

Povey, Sue; Al Aqeel, Aida I; Cambon-Thomsen, Anne; Dalgleish, Raymond; den Dunnen, Johan T; Firth, Helen V; Greenblatt, Marc S; Barash, Carol Isaacson; Parker, Michael; Patrinos, George P; Savige, Judith; Sobrido, Maria-Jesus; Winship, Ingrid; Cotton, Richard GH

2010-01-01

More than 1,000 Web-based locus-specific variation databases (LSDBs) are listed on the Website of the Human Genetic Variation Society (HGVS). These individual efforts, which often relate phenotype to genotype, are a valuable source of information for clinicians, patients, and their families, as well as for basic research. The initiators of the Human Variome Project recently recognized that having access to some of the immense resources of unpublished information already present in diagnostic laboratories would provide critical data to help manage genetic disorders. However, there are significant ethical issues involved in sharing these data worldwide. An international working group presents second-generation guidelines addressing ethical issues relating to the curation of human LSDBs that provide information via a Web-based interface. It is intended that these should help current and future curators and may also inform the future decisions of ethics committees and legislators. These guidelines have been reviewed by the Ethics Committee of the Human Genome Organization (HUGO). Hum Mutat 31:–6, 2010. © 2010 Wiley-Liss, Inc. PMID:20683926

Smiles2Monomers: a link between chemical and biological structures for polymers.

PubMed

Dufresne, Yoann; Noé, Laurent; Leclère, Valérie; Pupin, Maude

2015-01-01

The monomeric composition of polymers is powerful for structure comparison and synthetic biology, among others. Many databases give access to the atomic structure of compounds but the monomeric structure of polymers is often lacking. We have designed a smart algorithm, implemented in the tool Smiles2Monomers (s2m), to infer efficiently and accurately the monomeric structure of a polymer from its chemical structure. Our strategy is divided into two steps: first, monomers are mapped on the atomic structure by an efficient subgraph-isomorphism algorithm ; second, the best tiling is computed so that non-overlapping monomers cover all the structure of the target polymer. The mapping is based on a Markovian index built by a dynamic programming algorithm. The index enables s2m to search quickly all the given monomers on a target polymer. After, a greedy algorithm combines the mapped monomers into a consistent monomeric structure. Finally, a local branch and cut algorithm refines the structure. We tested this method on two manually annotated databases of polymers and reconstructed the structures de novo with a sensitivity over 90 %. The average computation time per polymer is 2 s. s2m automatically creates de novo monomeric annotations for polymers, efficiently in terms of time computation and sensitivity. s2m allowed us to detect annotation errors in the tested databases and to easily find the accurate structures. So, s2m could be integrated into the curation process of databases of small compounds to verify the current entries and accelerate the annotation of new polymers. The full method can be downloaded or accessed via a website for peptide-like polymers at http://bioinfo.lifl.fr/norine/smiles2monomers.jsp.Graphical abstract:.

GeneAnalytics: An Integrative Gene Set Analysis Tool for Next Generation Sequencing, RNAseq and Microarray Data.

PubMed

Ben-Ari Fuchs, Shani; Lieder, Iris; Stelzer, Gil; Mazor, Yaron; Buzhor, Ella; Kaplan, Sergey; Bogoch, Yoel; Plaschkes, Inbar; Shitrit, Alina; Rappaport, Noa; Kohn, Asher; Edgar, Ron; Shenhav, Liraz; Safran, Marilyn; Lancet, Doron; Guan-Golan, Yaron; Warshawsky, David; Shtrichman, Ronit

2016-03-01

Postgenomics data are produced in large volumes by life sciences and clinical applications of novel omics diagnostics and therapeutics for precision medicine. To move from "data-to-knowledge-to-innovation," a crucial missing step in the current era is, however, our limited understanding of biological and clinical contexts associated with data. Prominent among the emerging remedies to this challenge are the gene set enrichment tools. This study reports on GeneAnalytics™ ( geneanalytics.genecards.org ), a comprehensive and easy-to-apply gene set analysis tool for rapid contextualization of expression patterns and functional signatures embedded in the postgenomics Big Data domains, such as Next Generation Sequencing (NGS), RNAseq, and microarray experiments. GeneAnalytics' differentiating features include in-depth evidence-based scoring algorithms, an intuitive user interface and proprietary unified data. GeneAnalytics employs the LifeMap Science's GeneCards suite, including the GeneCards®--the human gene database; the MalaCards-the human diseases database; and the PathCards--the biological pathways database. Expression-based analysis in GeneAnalytics relies on the LifeMap Discovery®--the embryonic development and stem cells database, which includes manually curated expression data for normal and diseased tissues, enabling advanced matching algorithm for gene-tissue association. This assists in evaluating differentiation protocols and discovering biomarkers for tissues and cells. Results are directly linked to gene, disease, or cell "cards" in the GeneCards suite. Future developments aim to enhance the GeneAnalytics algorithm as well as visualizations, employing varied graphical display items. Such attributes make GeneAnalytics a broadly applicable postgenomics data analyses and interpretation tool for translation of data to knowledge-based innovation in various Big Data fields such as precision medicine, ecogenomics, nutrigenomics, pharmacogenomics, vaccinomics, and others yet to emerge on the postgenomics horizon.

Consistency Analysis of Genome-Scale Models of Bacterial Metabolism: A Metamodel Approach

PubMed Central

Ponce-de-Leon, Miguel; Calle-Espinosa, Jorge; Peretó, Juli; Montero, Francisco

2015-01-01

Genome-scale metabolic models usually contain inconsistencies that manifest as blocked reactions and gap metabolites. With the purpose to detect recurrent inconsistencies in metabolic models, a large-scale analysis was performed using a previously published dataset of 130 genome-scale models. The results showed that a large number of reactions (~22%) are blocked in all the models where they are present. To unravel the nature of such inconsistencies a metamodel was construed by joining the 130 models in a single network. This metamodel was manually curated using the unconnected modules approach, and then, it was used as a reference network to perform a gap-filling on each individual genome-scale model. Finally, a set of 36 models that had not been considered during the construction of the metamodel was used, as a proof of concept, to extend the metamodel with new biochemical information, and to assess its impact on gap-filling results. The analysis performed on the metamodel allowed to conclude: 1) the recurrent inconsistencies found in the models were already present in the metabolic database used during the reconstructions process; 2) the presence of inconsistencies in a metabolic database can be propagated to the reconstructed models; 3) there are reactions not manifested as blocked which are active as a consequence of some classes of artifacts, and; 4) the results of an automatic gap-filling are highly dependent on the consistency and completeness of the metamodel or metabolic database used as the reference network. In conclusion the consistency analysis should be applied to metabolic databases in order to detect and fill gaps as well as to detect and remove artifacts and redundant information. PMID:26629901

The Listeria monocytogenes strain 10403S BioCyc database.

PubMed

Orsi, Renato H; Bergholz, Teresa M; Wiedmann, Martin; Boor, Kathryn J

2015-01-01

Listeria monocytogenes is a food-borne pathogen of humans and other animals. The striking ability to survive several stresses usually used for food preservation makes L. monocytogenes one of the biggest concerns to the food industry, while the high mortality of listeriosis in specific groups of humans makes it a great concern for public health. Previous studies have shown that a regulatory network involving alternative sigma (σ) factors and transcription factors is pivotal to stress survival. However, few studies have evaluated at the metabolic networks controlled by these regulatory mechanisms. The L. monocytogenes BioCyc database uses the strain 10403S as a model. Computer-generated initial annotation for all genes also allowed for identification, annotation and display of predicted reactions and pathways carried out by a single cell. Further ongoing manual curation based on published data as well as database mining for selected genes allowed the more refined annotation of functions, which, in turn, allowed for annotation of new pathways and fine-tuning of previously defined pathways to more L. monocytogenes-specific pathways. Using RNA-Seq data, several transcription start sites and promoter regions were mapped to the 10403S genome and annotated within the database. Additionally, the identification of promoter regions and a comprehensive review of available literature allowed the annotation of several regulatory interactions involving σ factors and transcription factors. The L. monocytogenes 10403S BioCyc database is a new resource for researchers studying Listeria and related organisms. It allows users to (i) have a comprehensive view of all reactions and pathways predicted to take place within the cell in the cellular overview, as well as to (ii) upload their own data, such as differential expression data, to visualize the data in the scope of predicted pathways and regulatory networks and to carry on enrichment analyses using several different annotations available within the database. © The Author(s) 2015. Published by Oxford University Press.

PrionHome: a database of prions and other sequences relevant to prion phenomena.

PubMed

Harbi, Djamel; Parthiban, Marimuthu; Gendoo, Deena M A; Ehsani, Sepehr; Kumar, Manish; Schmitt-Ulms, Gerold; Sowdhamini, Ramanathan; Harrison, Paul M

2012-01-01

Prions are units of propagation of an altered state of a protein or proteins; prions can propagate from organism to organism, through cooption of other protein copies. Prions contain no necessary nucleic acids, and are important both as both pathogenic agents, and as a potential force in epigenetic phenomena. The original prions were derived from a misfolded form of the mammalian Prion Protein PrP. Infection by these prions causes neurodegenerative diseases. Other prions cause non-Mendelian inheritance in budding yeast, and sometimes act as diseases of yeast. We report the bioinformatic construction of the PrionHome, a database of >2000 prion-related sequences. The data was collated from various public and private resources and filtered for redundancy. The data was then processed according to a transparent classification system of prionogenic sequences (i.e., sequences that can make prions), prionoids (i.e., proteins that propagate like prions between individual cells), and other prion-related phenomena. There are eight PrionHome classifications for sequences. The first four classifications are derived from experimental observations: prionogenic sequences, prionoids, other prion-related phenomena, and prion interactors. The second four classifications are derived from sequence analysis: orthologs, paralogs, pseudogenes, and candidate-prionogenic sequences. Database entries list: supporting information for PrionHome classifications, prion-determinant areas (where relevant), and disordered and compositionally-biased regions. Also included are literature references for the PrionHome classifications, transcripts and genomic coordinates, and structural data (including comparative models made for the PrionHome from manually curated alignments). We provide database usage examples for both vertebrate and fungal prion contexts. Using the database data, we have performed a detailed analysis of the compositional biases in known budding-yeast prionogenic sequences, showing that the only abundant bias pattern is for asparagine bias with subsidiary serine bias. We anticipate that this database will be a useful experimental aid and reference resource. It is freely available at: http://libaio.biol.mcgill.ca/prion.

PrionHome: A Database of Prions and Other Sequences Relevant to Prion Phenomena

PubMed Central

Harbi, Djamel; Parthiban, Marimuthu; Gendoo, Deena M. A.; Ehsani, Sepehr; Kumar, Manish; Schmitt-Ulms, Gerold; Sowdhamini, Ramanathan; Harrison, Paul M.

2012-01-01

Prions are units of propagation of an altered state of a protein or proteins; prions can propagate from organism to organism, through cooption of other protein copies. Prions contain no necessary nucleic acids, and are important both as both pathogenic agents, and as a potential force in epigenetic phenomena. The original prions were derived from a misfolded form of the mammalian Prion Protein PrP. Infection by these prions causes neurodegenerative diseases. Other prions cause non-Mendelian inheritance in budding yeast, and sometimes act as diseases of yeast. We report the bioinformatic construction of the PrionHome, a database of >2000 prion-related sequences. The data was collated from various public and private resources and filtered for redundancy. The data was then processed according to a transparent classification system of prionogenic sequences (i.e., sequences that can make prions), prionoids (i.e., proteins that propagate like prions between individual cells), and other prion-related phenomena. There are eight PrionHome classifications for sequences. The first four classifications are derived from experimental observations: prionogenic sequences, prionoids, other prion-related phenomena, and prion interactors. The second four classifications are derived from sequence analysis: orthologs, paralogs, pseudogenes, and candidate-prionogenic sequences. Database entries list: supporting information for PrionHome classifications, prion-determinant areas (where relevant), and disordered and compositionally-biased regions. Also included are literature references for the PrionHome classifications, transcripts and genomic coordinates, and structural data (including comparative models made for the PrionHome from manually curated alignments). We provide database usage examples for both vertebrate and fungal prion contexts. Using the database data, we have performed a detailed analysis of the compositional biases in known budding-yeast prionogenic sequences, showing that the only abundant bias pattern is for asparagine bias with subsidiary serine bias. We anticipate that this database will be a useful experimental aid and reference resource. It is freely available at: http://libaio.biol.mcgill.ca/prion. PMID:22363733

Literature-Based Gene Curation and Proposed Genetic Nomenclature for Cryptococcus

PubMed Central

Inglis, Diane O.; Skrzypek, Marek S.; Liaw, Edward; Moktali, Venkatesh; Sherlock, Gavin

2014-01-01

Cryptococcus, a major cause of disseminated infections in immunocompromised patients, kills over 600,000 people per year worldwide. Genes involved in the virulence of the meningitis-causing fungus are being characterized at an increasing rate, and to date, at least 648 Cryptococcus gene names have been published. However, these data are scattered throughout the literature and are challenging to find. Furthermore, conflicts in locus identification exist, so that named genes have been subsequently published under new names or names associated with one locus have been used for another locus. To avoid these conflicts and to provide a central source of Cryptococcus gene information, we have collected all published Cryptococcus gene names from the scientific literature and associated them with standard Cryptococcus locus identifiers and have incorporated them into FungiDB (www.fungidb.org). FungiDB is a panfungal genome database that collects gene information and functional data and provides search tools for 61 species of fungi and oomycetes. We applied these published names to a manually curated ortholog set of all Cryptococcus species currently in FungiDB, including Cryptococcus neoformans var. neoformans strains JEC21 and B-3501A, C. neoformans var. grubii strain H99, and Cryptococcus gattii strains R265 and WM276, and have written brief descriptions of their functions. We also compiled a protocol for gene naming that summarizes guidelines proposed by members of the Cryptococcus research community. The centralization of genomic and literature-based information for Cryptococcus at FungiDB will help researchers communicate about genes of interest, such as those related to virulence, and will further facilitate research on the pathogen. PMID:24813190

JASPAR 2010: the greatly expanded open-access database of transcription factor binding profiles

PubMed Central

Portales-Casamar, Elodie; Thongjuea, Supat; Kwon, Andrew T.; Arenillas, David; Zhao, Xiaobei; Valen, Eivind; Yusuf, Dimas; Lenhard, Boris; Wasserman, Wyeth W.; Sandelin, Albin

2010-01-01

JASPAR (http://jaspar.genereg.net) is the leading open-access database of matrix profiles describing the DNA-binding patterns of transcription factors (TFs) and other proteins interacting with DNA in a sequence-specific manner. Its fourth major release is the largest expansion of the core database to date: the database now holds 457 non-redundant, curated profiles. The new entries include the first batch of profiles derived from ChIP-seq and ChIP-chip whole-genome binding experiments, and 177 yeast TF binding profiles. The introduction of a yeast division brings the convenience of JASPAR to an active research community. As binding models are refined by newer data, the JASPAR database now uses versioning of matrices: in this release, 12% of the older models were updated to improved versions. Classification of TF families has been improved by adopting a new DNA-binding domain nomenclature. A curated catalog of mammalian TFs is provided, extending the use of the JASPAR profiles to additional TFs belonging to the same structural family. The changes in the database set the system ready for more rapid acquisition of new high-throughput data sources. Additionally, three new special collections provide matrix profile data produced by recent alternative high-throughput approaches. PMID:19906716

National Solar Radiation Database 1991-2010 Update: User's Manual

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wilcox, S. M.

This user's manual provides information on the updated 1991-2010 National Solar Radiation Database. Included are data format descriptions, data sources, production processes, and information about data uncertainty.

Overview of the gene ontology task at BioCreative IV.

PubMed

Mao, Yuqing; Van Auken, Kimberly; Li, Donghui; Arighi, Cecilia N; McQuilton, Peter; Hayman, G Thomas; Tweedie, Susan; Schaeffer, Mary L; Laulederkind, Stanley J F; Wang, Shur-Jen; Gobeill, Julien; Ruch, Patrick; Luu, Anh Tuan; Kim, Jung-Jae; Chiang, Jung-Hsien; Chen, Yu-De; Yang, Chia-Jung; Liu, Hongfang; Zhu, Dongqing; Li, Yanpeng; Yu, Hong; Emadzadeh, Ehsan; Gonzalez, Graciela; Chen, Jian-Ming; Dai, Hong-Jie; Lu, Zhiyong

2014-01-01

Gene ontology (GO) annotation is a common task among model organism databases (MODs) for capturing gene function data from journal articles. It is a time-consuming and labor-intensive task, and is thus often considered as one of the bottlenecks in literature curation. There is a growing need for semiautomated or fully automated GO curation techniques that will help database curators to rapidly and accurately identify gene function information in full-length articles. Despite multiple attempts in the past, few studies have proven to be useful with regard to assisting real-world GO curation. The shortage of sentence-level training data and opportunities for interaction between text-mining developers and GO curators has limited the advances in algorithm development and corresponding use in practical circumstances. To this end, we organized a text-mining challenge task for literature-based GO annotation in BioCreative IV. More specifically, we developed two subtasks: (i) to automatically locate text passages that contain GO-relevant information (a text retrieval task) and (ii) to automatically identify relevant GO terms for the genes in a given article (a concept-recognition task). With the support from five MODs, we provided teams with >4000 unique text passages that served as the basis for each GO annotation in our task data. Such evidence text information has long been recognized as critical for text-mining algorithm development but was never made available because of the high cost of curation. In total, seven teams participated in the challenge task. From the team results, we conclude that the state of the art in automatically mining GO terms from literature has improved over the past decade while much progress is still needed for computer-assisted GO curation. Future work should focus on addressing remaining technical challenges for improved performance of automatic GO concept recognition and incorporating practical benefits of text-mining tools into real-world GO annotation. http://www.biocreative.org/tasks/biocreative-iv/track-4-GO/. Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.

Integrating text mining into the MGI biocuration workflow

PubMed Central

Dowell, K.G.; McAndrews-Hill, M.S.; Hill, D.P.; Drabkin, H.J.; Blake, J.A.

2009-01-01

A major challenge for functional and comparative genomics resource development is the extraction of data from the biomedical literature. Although text mining for biological data is an active research field, few applications have been integrated into production literature curation systems such as those of the model organism databases (MODs). Not only are most available biological natural language (bioNLP) and information retrieval and extraction solutions difficult to adapt to existing MOD curation workflows, but many also have high error rates or are unable to process documents available in those formats preferred by scientific journals. In September 2008, Mouse Genome Informatics (MGI) at The Jackson Laboratory initiated a search for dictionary-based text mining tools that we could integrate into our biocuration workflow. MGI has rigorous document triage and annotation procedures designed to identify appropriate articles about mouse genetics and genome biology. We currently screen ∼1000 journal articles a month for Gene Ontology terms, gene mapping, gene expression, phenotype data and other key biological information. Although we do not foresee that curation tasks will ever be fully automated, we are eager to implement named entity recognition (NER) tools for gene tagging that can help streamline our curation workflow and simplify gene indexing tasks within the MGI system. Gene indexing is an MGI-specific curation function that involves identifying which mouse genes are being studied in an article, then associating the appropriate gene symbols with the article reference number in the MGI database. Here, we discuss our search process, performance metrics and success criteria, and how we identified a short list of potential text mining tools for further evaluation. We provide an overview of our pilot projects with NCBO's Open Biomedical Annotator and Fraunhofer SCAI's ProMiner. In doing so, we prove the potential for the further incorporation of semi-automated processes into the curation of the biomedical literature. PMID:20157492

Integrating text mining into the MGI biocuration workflow.

PubMed

Dowell, K G; McAndrews-Hill, M S; Hill, D P; Drabkin, H J; Blake, J A

2009-01-01

A major challenge for functional and comparative genomics resource development is the extraction of data from the biomedical literature. Although text mining for biological data is an active research field, few applications have been integrated into production literature curation systems such as those of the model organism databases (MODs). Not only are most available biological natural language (bioNLP) and information retrieval and extraction solutions difficult to adapt to existing MOD curation workflows, but many also have high error rates or are unable to process documents available in those formats preferred by scientific journals.In September 2008, Mouse Genome Informatics (MGI) at The Jackson Laboratory initiated a search for dictionary-based text mining tools that we could integrate into our biocuration workflow. MGI has rigorous document triage and annotation procedures designed to identify appropriate articles about mouse genetics and genome biology. We currently screen approximately 1000 journal articles a month for Gene Ontology terms, gene mapping, gene expression, phenotype data and other key biological information. Although we do not foresee that curation tasks will ever be fully automated, we are eager to implement named entity recognition (NER) tools for gene tagging that can help streamline our curation workflow and simplify gene indexing tasks within the MGI system. Gene indexing is an MGI-specific curation function that involves identifying which mouse genes are being studied in an article, then associating the appropriate gene symbols with the article reference number in the MGI database.Here, we discuss our search process, performance metrics and success criteria, and how we identified a short list of potential text mining tools for further evaluation. We provide an overview of our pilot projects with NCBO's Open Biomedical Annotator and Fraunhofer SCAI's ProMiner. In doing so, we prove the potential for the further incorporation of semi-automated processes into the curation of the biomedical literature.

IPD-MHC 2.0: an improved inter-species database for the study of the major histocompatibility complex.

PubMed

Maccari, Giuseppe; Robinson, James; Ballingall, Keith; Guethlein, Lisbeth A; Grimholt, Unni; Kaufman, Jim; Ho, Chak-Sum; de Groot, Natasja G; Flicek, Paul; Bontrop, Ronald E; Hammond, John A; Marsh, Steven G E

2017-01-04

The IPD-MHC Database project (http://www.ebi.ac.uk/ipd/mhc/) collects and expertly curates sequences of the major histocompatibility complex from non-human species and provides the infrastructure and tools to enable accurate analysis. Since the first release of the database in 2003, IPD-MHC has grown and currently hosts a number of specific sections, with more than 7000 alleles from 70 species, including non-human primates, canines, felines, equids, ovids, suids, bovins, salmonids and murids. These sequences are expertly curated and made publicly available through an open access website. The IPD-MHC Database is a key resource in its field, and this has led to an average of 1500 unique visitors and more than 5000 viewed pages per month. As the database has grown in size and complexity, it has created a number of challenges in maintaining and organizing information, particularly the need to standardize nomenclature and taxonomic classification, while incorporating new allele submissions. Here, we describe the latest database release, the IPD-MHC 2.0 and discuss planned developments. This release incorporates sequence updates and new tools that enhance database queries and improve the submission procedure by utilizing common tools that are able to handle the varied requirements of each MHC-group. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

How should the completeness and quality of curated nanomaterial data be evaluated?†

PubMed Central

Marchese Robinson, Richard L.; Lynch, Iseult; Peijnenburg, Willie; Rumble, John; Klaessig, Fred; Marquardt, Clarissa; Rauscher, Hubert; Puzyn, Tomasz; Purian, Ronit; Åberg, Christoffer; Karcher, Sandra; Vriens, Hanne; Hoet, Peter; Hoover, Mark D.; Hendren, Christine Ogilvie; Harper, Stacey L.

2016-01-01

Nanotechnology is of increasing significance. Curation of nanomaterial data into electronic databases offers opportunities to better understand and predict nanomaterials’ behaviour. This supports innovation in, and regulation of, nanotechnology. It is commonly understood that curated data need to be sufficiently complete and of sufficient quality to serve their intended purpose. However, assessing data completeness and quality is non-trivial in general and is arguably especially difficult in the nanoscience area, given its highly multidisciplinary nature. The current article, part of the Nanomaterial Data Curation Initiative series, addresses how to assess the completeness and quality of (curated) nanomaterial data. In order to address this key challenge, a variety of related issues are discussed: the meaning and importance of data completeness and quality, existing approaches to their assessment and the key challenges associated with evaluating the completeness and quality of curated nanomaterial data. Considerations which are specific to the nanoscience area and lessons which can be learned from other relevant scientific disciplines are considered. Hence, the scope of this discussion ranges from physicochemical characterisation requirements for nanomaterials and interference of nanomaterials with nanotoxicology assays to broader issues such as minimum information checklists, toxicology data quality schemes and computational approaches that facilitate evaluation of the completeness and quality of (curated) data. This discussion is informed by a literature review and a survey of key nanomaterial data curation stakeholders. Finally, drawing upon this discussion, recommendations are presented concerning the central question: how should the completeness and quality of curated nanomaterial data be evaluated? PMID:27143028

A crystallographic perspective on sharing data and knowledge

NASA Astrophysics Data System (ADS)

Bruno, Ian J.; Groom, Colin R.

2014-10-01

The crystallographic community is in many ways an exemplar of the benefits and practices of sharing data. Since the inception of the technique, virtually every published crystal structure has been made available to others. This has been achieved through the establishment of several specialist data centres, including the Cambridge Crystallographic Data Centre, which produces the Cambridge Structural Database. Containing curated structures of small organic molecules, some containing a metal, the database has been produced for almost 50 years. This has required the development of complex informatics tools and an environment allowing expert human curation. As importantly, a financial model has evolved which has, to date, ensured the sustainability of the resource. However, the opportunities afforded by technological changes and changing attitudes to sharing data make it an opportune moment to review current practices.

Mouse Genome Database: From sequence to phenotypes and disease models

PubMed Central

Richardson, Joel E.; Kadin, James A.; Smith, Cynthia L.; Blake, Judith A.; Bult, Carol J.

2015-01-01

Summary The Mouse Genome Database (MGD, www.informatics.jax.org) is the international scientific database for genetic, genomic, and biological data on the laboratory mouse to support the research requirements of the biomedical community. To accomplish this goal, MGD provides broad data coverage, serves as the authoritative standard for mouse nomenclature for genes, mutants, and strains, and curates and integrates many types of data from literature and electronic sources. Among the key data sets MGD supports are: the complete catalog of mouse genes and genome features, comparative homology data for mouse and vertebrate genes, the authoritative set of Gene Ontology (GO) annotations for mouse gene functions, a comprehensive catalog of mouse mutations and their phenotypes, and a curated compendium of mouse models of human diseases. Here, we describe the data acquisition process, specifics about MGD's key data areas, methods to access and query MGD data, and outreach and user help facilities. genesis 53:458–473, 2015. © 2015 The Authors. Genesis Published by Wiley Periodicals, Inc. PMID:26150326

mTHPC mediated photodynamic therapy (PDT) of squamous cell carcinoma in the head and neck: a systematic review.

PubMed

de Visscher, S A H J; Dijkstra, P U; Tan, I B; Roodenburg, J L N; Witjes, M J H

2013-03-01

Photodynamic therapy (PDT) is used in curative and palliative treatment of head and neck squamous cell carcinoma (HNSCC). To evaluate available evidence on the use of mTHPC (Foscan®) mediated PDT, we conducted a review of the literature. A systematic review was performed by searching seven bibliographic databases on database specific mesh terms and free text words in the categories; "head and neck neoplasms", "Photodynamic Therapy" and "Foscan". Papers identified were assessed on several criteria by two independent reviewers. The search identified 566 unique papers. Twelve studies were included for our review. Six studies reported PDT with curative intent and six studies reported PDT with palliative intent, of which three studies used interstitial PDT. The studies did not compare PDT to other treatments and none exceeded level 3 using the Oxford levels of evidence. Pooling of data (n=301) was possible for four of the six studies with curative intent. T1 tumors showed higher complete response rates compared to T2 (86% vs 63%). PDT with palliative intent was predominantly used in patients unsuitable for further conventional treatment. After PDT, substantial tumor response and increase in quality of life was observed. Complications of PDT were mostly related to non-compliance to light restriction guidelines. The studies on mTHPC mediated PDT for HNSCC are not sufficient for adequate assessment of the efficacy for curative intent. To assess efficacy of PDT with curative intent, high quality comparative, randomized studies are needed. Palliative treatment with PDT seems to increase the quality of life in otherwise untreatable patients. Copyright © 2012 Elsevier Ltd. All rights reserved.

Information extraction from Italian medical reports: An ontology-driven approach.

PubMed

Viani, Natalia; Larizza, Cristiana; Tibollo, Valentina; Napolitano, Carlo; Priori, Silvia G; Bellazzi, Riccardo; Sacchi, Lucia

2018-03-01

In this work, we propose an ontology-driven approach to identify events and their attributes from episodes of care included in medical reports written in Italian. For this language, shared resources for clinical information extraction are not easily accessible. The corpus considered in this work includes 5432 non-annotated medical reports belonging to patients with rare arrhythmias. To guide the information extraction process, we built a domain-specific ontology that includes the events and the attributes to be extracted, with related regular expressions. The ontology and the annotation system were constructed on a development set, while the performance was evaluated on an independent test set. As a gold standard, we considered a manually curated hospital database named TRIAD, which stores most of the information written in reports. The proposed approach performs well on the considered Italian medical corpus, with a percentage of correct annotations above 90% for most considered clinical events. We also assessed the possibility to adapt the system to the analysis of another language (i.e., English), with promising results. Our annotation system relies on a domain ontology to extract and link information in clinical text. We developed an ontology that can be easily enriched and translated, and the system performs well on the considered task. In the future, it could be successfully used to automatically populate the TRIAD database. Copyright © 2017 Elsevier B.V. All rights reserved.

Updates to the Cool Season Food Legume Genome Database: Resources for pea, lentil, faba bean and chickpea genetics, genomics and breeding

USDA-ARS?s Scientific Manuscript database

The Cool Season Food Legume Genome database (CSFL, www.coolseasonfoodlegume.org) is an online resource for genomics, genetics, and breeding research for chickpea, lentil,pea, and faba bean. The user-friendly and curated website allows for all publicly available map,marker,trait, gene,transcript, ger...

Enriching public descriptions of marine phages using the Genomic Standards Consortium MIGS standard

PubMed Central

Duhaime, Melissa Beth; Kottmann, Renzo; Field, Dawn; Glöckner, Frank Oliver

2011-01-01

In any sequencing project, the possible depth of comparative analysis is determined largely by the amount and quality of the accompanying contextual data. The structure, content, and storage of this contextual data should be standardized to ensure consistent coverage of all sequenced entities and facilitate comparisons. The Genomic Standards Consortium (GSC) has developed the “Minimum Information about Genome/Metagenome Sequences (MIGS/MIMS)” checklist for the description of genomes and here we annotate all 30 publicly available marine bacteriophage sequences to the MIGS standard. These annotations build on existing International Nucleotide Sequence Database Collaboration (INSDC) records, and confirm, as expected that current submissions lack most MIGS fields. MIGS fields were manually curated from the literature and placed in XML format as specified by the Genomic Contextual Data Markup Language (GCDML). These “machine-readable” reports were then analyzed to highlight patterns describing this collection of genomes. Completed reports are provided in GCDML. This work represents one step towards the annotation of our complete collection of genome sequences and shows the utility of capturing richer metadata along with raw sequences. PMID:21677864

OralCard: a bioinformatic tool for the study of oral proteome.

PubMed

Arrais, Joel P; Rosa, Nuno; Melo, José; Coelho, Edgar D; Amaral, Diana; Correia, Maria José; Barros, Marlene; Oliveira, José Luís

2013-07-01

The molecular complexity of the human oral cavity can only be clarified through identification of components that participate within it. However current proteomic techniques produce high volumes of information that are dispersed over several online databases. Collecting all of this data and using an integrative approach capable of identifying unknown associations is still an unsolved problem. This is the main motivation for this work. We present the online bioinformatic tool OralCard, which comprises results from 55 manually curated articles reflecting the oral molecular ecosystem (OralPhysiOme). It comprises experimental information available from the oral proteome both of human (OralOme) and microbial origin (MicroOralOme) structured in protein, disease and organism. This tool is a key resource for researchers to understand the molecular foundations implicated in biology and disease mechanisms of the oral cavity. The usefulness of this tool is illustrated with the analysis of the oral proteome associated with diabetes melitus type 2. OralCard is available at http://bioinformatics.ua.pt/oralcard. Copyright © 2013 Elsevier Ltd. All rights reserved.

VenomKB, a new knowledge base for facilitating the validation of putative venom therapies

PubMed Central

Romano, Joseph D.; Tatonetti, Nicholas P.

2015-01-01

Animal venoms have been used for therapeutic purposes since the dawn of recorded history. Only a small fraction, however, have been tested for pharmaceutical utility. Modern computational methods enable the systematic exploration of novel therapeutic uses for venom compounds. Unfortunately, there is currently no comprehensive resource describing the clinical effects of venoms to support this computational analysis. We present VenomKB, a new publicly accessible knowledge base and website that aims to act as a repository for emerging and putative venom therapies. Presently, it consists of three database tables: (1) Manually curated records of putative venom therapies supported by scientific literature, (2) automatically parsed MEDLINE articles describing compounds that may be venom derived, and their effects on the human body, and (3) automatically retrieved records from the new Semantic Medline resource that describe the effects of venom compounds on mammalian anatomy. Data from VenomKB may be selectively retrieved in a variety of popular data formats, are open-source, and will be continually updated as venom therapies become better understood. PMID:26601758

AlloRep: A Repository of Sequence, Structural and Mutagenesis Data for the LacI/GalR Transcription Regulators.

PubMed

Sousa, Filipa L; Parente, Daniel J; Shis, David L; Hessman, Jacob A; Chazelle, Allen; Bennett, Matthew R; Teichmann, Sarah A; Swint-Kruse, Liskin

2016-02-22

Protein families evolve functional variation by accumulating point mutations at functionally important amino acid positions. Homologs in the LacI/GalR family of transcription regulators have evolved to bind diverse DNA sequences and allosteric regulatory molecules. In addition to playing key roles in bacterial metabolism, these proteins have been widely used as a model family for benchmarking structural and functional prediction algorithms. We have collected manually curated sequence alignments for >3000 sequences, in vivo phenotypic and biochemical data for >5750 LacI/GalR mutational variants, and noncovalent residue contact networks for 65 LacI/GalR homolog structures. Using this rich data resource, we compared the noncovalent residue contact networks of the LacI/GalR subfamilies to design and experimentally validate an allosteric mutant of a synthetic LacI/GalR repressor for use in biotechnology. The AlloRep database (freely available at www.AlloRep.org) is a key resource for future evolutionary studies of LacI/GalR homologs and for benchmarking computational predictions of functional change. Copyright © 2015 Elsevier Ltd. All rights reserved.

FindPrimaryPairs: An efficient algorithm for predicting element-transferring reactant/product pairs in metabolic networks.

PubMed

Steffensen, Jon Lund; Dufault-Thompson, Keith; Zhang, Ying

2018-01-01

The metabolism of individual organisms and biological communities can be viewed as a network of metabolites connected to each other through chemical reactions. In metabolic networks, chemical reactions transform reactants into products, thereby transferring elements between these metabolites. Knowledge of how elements are transferred through reactant/product pairs allows for the identification of primary compound connections through a metabolic network. However, such information is not readily available and is often challenging to obtain for large reaction databases or genome-scale metabolic models. In this study, a new algorithm was developed for automatically predicting the element-transferring reactant/product pairs using the limited information available in the standard representation of metabolic networks. The algorithm demonstrated high efficiency in analyzing large datasets and provided accurate predictions when benchmarked with manually curated data. Applying the algorithm to the visualization of metabolic networks highlighted pathways of primary reactant/product connections and provided an organized view of element-transferring biochemical transformations. The algorithm was implemented as a new function in the open source software package PSAMM in the release v0.30 (https://zhanglab.github.io/psamm/).

DIANA-LncBase v2: indexing microRNA targets on non-coding transcripts.

PubMed

Paraskevopoulou, Maria D; Vlachos, Ioannis S; Karagkouni, Dimitra; Georgakilas, Georgios; Kanellos, Ilias; Vergoulis, Thanasis; Zagganas, Konstantinos; Tsanakas, Panayiotis; Floros, Evangelos; Dalamagas, Theodore; Hatzigeorgiou, Artemis G

2016-01-04

microRNAs (miRNAs) are short non-coding RNAs (ncRNAs) that act as post-transcriptional regulators of coding gene expression. Long non-coding RNAs (lncRNAs) have been recently reported to interact with miRNAs. The sponge-like function of lncRNAs introduces an extra layer of complexity in the miRNA interactome. DIANA-LncBase v1 provided a database of experimentally supported and in silico predicted miRNA Recognition Elements (MREs) on lncRNAs. The second version of LncBase (www.microrna.gr/LncBase) presents an extensive collection of miRNA:lncRNA interactions. The significantly enhanced database includes more than 70 000 low and high-throughput, (in)direct miRNA:lncRNA experimentally supported interactions, derived from manually curated publications and the analysis of 153 AGO CLIP-Seq libraries. The new experimental module presents a 14-fold increase compared to the previous release. LncBase v2 hosts in silico predicted miRNA targets on lncRNAs, identified with the DIANA-microT algorithm. The relevant module provides millions of predicted miRNA binding sites, accompanied with detailed metadata and MRE conservation metrics. LncBase v2 caters information regarding cell type specific miRNA:lncRNA regulation and enables users to easily identify interactions in 66 different cell types, spanning 36 tissues for human and mouse. Database entries are also supported by accurate lncRNA expression information, derived from the analysis of more than 6 billion RNA-Seq reads. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

National Solar Radiation Database 1991-2005 Update: User's Manual

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wilcox, S.

2007-04-01

This manual describes how to obtain and interpret the data products from the updated 1991-2005 National Solar Radiation Database (NSRDB). This is an update of the original 1961-1990 NSRDB released in 1992.

Revising the Representation of Fatty Acid, Glycerolipid, and Glycerophospholipid Metabolism in the Consensus Model of Yeast Metabolism

PubMed Central

Aung, Hnin W.; Henry, Susan A.

2013-01-01

Abstract Genome-scale metabolic models are built using information from an organism's annotated genome and, correspondingly, information on reactions catalyzed by the set of metabolic enzymes encoded by the genome. These models have been successfully applied to guide metabolic engineering to increase production of metabolites of industrial interest. Congruity between simulated and experimental metabolic behavior is influenced by the accuracy of the representation of the metabolic network in the model. In the interest of applying the consensus model of Saccharomyces cerevisiae metabolism for increased productivity of triglycerides, we manually evaluated the representation of fatty acid, glycerophospholipid, and glycerolipid metabolism in the consensus model (Yeast v6.0). These areas of metabolism were chosen due to their tightly interconnected nature to triglyceride synthesis. Manual curation was facilitated by custom MATLAB functions that return information contained in the model for reactions associated with genes and metabolites within the stated areas of metabolism. Through manual curation, we have identified inconsistencies between information contained in the model and literature knowledge. These inconsistencies include incorrect gene-reaction associations, improper definition of substrates/products in reactions, inappropriate assignments of reaction directionality, nonfunctional β-oxidation pathways, and missing reactions relevant to the synthesis and degradation of triglycerides. Suggestions to amend these inconsistencies in the Yeast v6.0 model can be implemented through a MATLAB script provided in the Supplementary Materials, Supplementary Data S1 (Supplementary Data are available online at www.liebertpub.com/ind). PMID:24678285

BμG@Sbase—a microbial gene expression and comparative genomic database

PubMed Central

Witney, Adam A.; Waldron, Denise E.; Brooks, Lucy A.; Tyler, Richard H.; Withers, Michael; Stoker, Neil G.; Wren, Brendan W.; Butcher, Philip D.; Hinds, Jason

2012-01-01

The reducing cost of high-throughput functional genomic technologies is creating a deluge of high volume, complex data, placing the burden on bioinformatics resources and tool development. The Bacterial Microarray Group at St George's (BμG@S) has been at the forefront of bacterial microarray design and analysis for over a decade and while serving as a hub of a global network of microbial research groups has developed BμG@Sbase, a microbial gene expression and comparative genomic database. BμG@Sbase (http://bugs.sgul.ac.uk/bugsbase/) is a web-browsable, expertly curated, MIAME-compliant database that stores comprehensive experimental annotation and multiple raw and analysed data formats. Consistent annotation is enabled through a structured set of web forms, which guide the user through the process following a set of best practices and controlled vocabulary. The database currently contains 86 expertly curated publicly available data sets (with a further 124 not yet published) and full annotation information for 59 bacterial microarray designs. The data can be browsed and queried using an explorer-like interface; integrating intuitive tree diagrams to present complex experimental details clearly and concisely. Furthermore the modular design of the database will provide a robust platform for integrating other data types beyond microarrays into a more Systems analysis based future. PMID:21948792

BμG@Sbase--a microbial gene expression and comparative genomic database.

PubMed

Witney, Adam A; Waldron, Denise E; Brooks, Lucy A; Tyler, Richard H; Withers, Michael; Stoker, Neil G; Wren, Brendan W; Butcher, Philip D; Hinds, Jason

2012-01-01

The reducing cost of high-throughput functional genomic technologies is creating a deluge of high volume, complex data, placing the burden on bioinformatics resources and tool development. The Bacterial Microarray Group at St George's (BμG@S) has been at the forefront of bacterial microarray design and analysis for over a decade and while serving as a hub of a global network of microbial research groups has developed BμG@Sbase, a microbial gene expression and comparative genomic database. BμG@Sbase (http://bugs.sgul.ac.uk/bugsbase/) is a web-browsable, expertly curated, MIAME-compliant database that stores comprehensive experimental annotation and multiple raw and analysed data formats. Consistent annotation is enabled through a structured set of web forms, which guide the user through the process following a set of best practices and controlled vocabulary. The database currently contains 86 expertly curated publicly available data sets (with a further 124 not yet published) and full annotation information for 59 bacterial microarray designs. The data can be browsed and queried using an explorer-like interface; integrating intuitive tree diagrams to present complex experimental details clearly and concisely. Furthermore the modular design of the database will provide a robust platform for integrating other data types beyond microarrays into a more Systems analysis based future.

Hematopoietic Project - SEER Registrars

Cancer.gov

Use this manual and corresponding database for coding cases diagnosed January 1, 2010 and forward. The changes do not require recoding of old cases. Contains data collection rules for hematopoietic and lymphoid neoplasms (2010+). Access a database and coding manual.

Biological Databases for Behavioral Neurobiology

PubMed Central

Baker, Erich J.

2014-01-01

Databases are, at their core, abstractions of data and their intentionally derived relationships. They serve as a central organizing metaphor and repository, supporting or augmenting nearly all bioinformatics. Behavioral domains provide a unique stage for contemporary databases, as research in this area spans diverse data types, locations, and data relationships. This chapter provides foundational information on the diversity and prevalence of databases, how data structures support the various needs of behavioral neuroscience analysis and interpretation. The focus is on the classes of databases, data curation, and advanced applications in bioinformatics using examples largely drawn from research efforts in behavioral neuroscience. PMID:23195119

PFR²: a curated database of planktonic foraminifera 18S ribosomal DNA as a resource for studies of plankton ecology, biogeography and evolution.

PubMed

Morard, Raphaël; Darling, Kate F; Mahé, Frédéric; Audic, Stéphane; Ujiié, Yurika; Weiner, Agnes K M; André, Aurore; Seears, Heidi A; Wade, Christopher M; Quillévéré, Frédéric; Douady, Christophe J; Escarguel, Gilles; de Garidel-Thoron, Thibault; Siccha, Michael; Kucera, Michal; de Vargas, Colomban

2015-11-01

Planktonic foraminifera (Rhizaria) are ubiquitous marine pelagic protists producing calcareous shells with conspicuous morphology. They play an important role in the marine carbon cycle, and their exceptional fossil record serves as the basis for biochronostratigraphy and past climate reconstructions. A major worldwide sampling effort over the last two decades has resulted in the establishment of multiple large collections of cryopreserved individual planktonic foraminifera samples. Thousands of 18S rDNA partial sequences have been generated, representing all major known morphological taxa across their worldwide oceanic range. This comprehensive data coverage provides an opportunity to assess patterns of molecular ecology and evolution in a holistic way for an entire group of planktonic protists. We combined all available published and unpublished genetic data to build PFR(2), the Planktonic foraminifera Ribosomal Reference database. The first version of the database includes 3322 reference 18S rDNA sequences belonging to 32 of the 47 known morphospecies of extant planktonic foraminifera, collected from 460 oceanic stations. All sequences have been rigorously taxonomically curated using a six-rank annotation system fully resolved to the morphological species level and linked to a series of metadata. The PFR(2) website, available at http://pfr2.sb-roscoff.fr, allows downloading the entire database or specific sections, as well as the identification of new planktonic foraminiferal sequences. Its novel, fully documented curation process integrates advances in morphological and molecular taxonomy. It allows for an increase in its taxonomic resolution and assures that integrity is maintained by including a complete contingency tracking of annotations and assuring that the annotations remain internally consistent. © 2015 John Wiley & Sons Ltd.

RCDB: Renal Cancer Gene Database.

PubMed

Ramana, Jayashree

2012-05-18

Renal cell carcinoma or RCC is one of the common and most lethal urological cancers, with 40% of the patients succumbing to death because of metastatic progression of the disease. Treatment of metastatic RCC remains highly challenging because of its resistance to chemotherapy as well as radiotherapy, besides surgical resection. Whereas RCC comprises tumors with differing histological types, clear cell RCC remains the most common. A major problem in the clinical management of patients presenting with localized ccRCC is the inability to determine tumor aggressiveness and accurately predict the risk of metastasis following surgery. As a measure to improve the diagnosis and prognosis of RCC, researchers have identified several molecular markers through a number of techniques. However the wealth of information available is scattered in literature and not easily amenable to data-mining. To reduce this gap, this work describes a comprehensive repository called Renal Cancer Gene Database, as an integrated gateway to study renal cancer related data. Renal Cancer Gene Database is a manually curated compendium of 240 protein-coding and 269 miRNA genes contributing to the etiology and pathogenesis of various forms of renal cell carcinomas. The protein coding genes have been classified according to the kind of gene alteration observed in RCC. RCDB also includes the miRNAsdysregulated in RCC, along with the corresponding information regarding the type of RCC and/or metastatic or prognostic significance. While some of the miRNA genes showed an association with other types of cancers few were unique to RCC. Users can query the database using keywords, category and chromosomal location of the genes. The knowledgebase can be freely accessed via a user-friendly web interface at http://www.juit.ac.in/attachments/jsr/rcdb/homenew.html. It is hoped that this database would serve as a useful complement to the existing public resources and as a good starting point for researchers and physicians interested in RCC genetics.

The Comprehensive Phytopathogen Genomics Resource: a web-based resource for data-mining plant pathogen genomes.

PubMed

Hamilton, John P; Neeno-Eckwall, Eric C; Adhikari, Bishwo N; Perna, Nicole T; Tisserat, Ned; Leach, Jan E; Lévesque, C André; Buell, C Robin

2011-01-01

The Comprehensive Phytopathogen Genomics Resource (CPGR) provides a web-based portal for plant pathologists and diagnosticians to view the genome and trancriptome sequence status of 806 bacterial, fungal, oomycete, nematode, viral and viroid plant pathogens. Tools are available to search and analyze annotated genome sequences of 74 bacterial, fungal and oomycete pathogens. Oomycete and fungal genomes are obtained directly from GenBank, whereas bacterial genome sequences are downloaded from the A Systematic Annotation Package (ASAP) database that provides curation of genomes using comparative approaches. Curated lists of bacterial genes relevant to pathogenicity and avirulence are also provided. The Plant Pathogen Transcript Assemblies Database provides annotated assemblies of the transcribed regions of 82 eukaryotic genomes from publicly available single pass Expressed Sequence Tags. Data-mining tools are provided along with tools to create candidate diagnostic markers, an emerging use for genomic sequence data in plant pathology. The Plant Pathogen Ribosomal DNA (rDNA) database is a resource for pathogens that lack genome or transcriptome data sets and contains 131 755 rDNA sequences from GenBank for 17 613 species identified as plant pathogens and related genera. Database URL: http://cpgr.plantbiology.msu.edu.

Identifying relevant data for a biological database: handcrafted rules versus machine learning.

PubMed

Sehgal, Aditya Kumar; Das, Sanmay; Noto, Keith; Saier, Milton H; Elkan, Charles

2011-01-01

With well over 1,000 specialized biological databases in use today, the task of automatically identifying novel, relevant data for such databases is increasingly important. In this paper, we describe practical machine learning approaches for identifying MEDLINE documents and Swiss-Prot/TrEMBL protein records, for incorporation into a specialized biological database of transport proteins named TCDB. We show that both learning approaches outperform rules created by hand by a human expert. As one of the first case studies involving two different approaches to updating a deployed database, both the methods compared and the results will be of interest to curators of many specialized databases.

Digital Management and Curation of the National Rock and Ore Collections at NMNH, Smithsonian

NASA Astrophysics Data System (ADS)

Cottrell, E.; Andrews, B.; Sorensen, S. S.; Hale, L. J.

2011-12-01

The National Museum of Natural History, Smithsonian Institution, is home to the world's largest curated rock collection. The collection houses 160,680 physical rock and ore specimen lots ("samples"), all of which already have a digital record that can be accessed by the public through a searchable web interface (http://collections.mnh.si.edu/search/ms/). In addition, there are 66 accessions pending that when catalogued will add approximately 60,000 specimen lots. NMNH's collections are digitally managed on the KE EMu° platform which has emerged as the premier system for managing collections in natural history museums worldwide. In 2010 the Smithsonian released an ambitious 5 year Digitization Strategic Plan. In Mineral Sciences, new digitization efforts in the next five years will focus on integrating various digital resources for volcanic specimens. EMu sample records will link to the corresponding records for physical eruption information housed within the database of Smithsonian's Global Volcanism Program (GVP). Linkages are also planned between our digital records and geochemical databases (like EarthChem or PetDB) maintained by third parties. We anticipate that these linkages will increase the use of NMNH collections as well as engender new scholarly directions for research. Another large project the museum is currently undertaking involves the integration of the functionality of in-house designed Transaction Management software with the EMu database. This will allow access to the details (borrower, quantity, date, and purpose) of all loans of a given specimen through its catalogue record. We hope this will enable cross-referencing and fertilization of research ideas while avoiding duplicate efforts. While these digitization efforts are critical, we propose that the greatest challenge to sample curation is not posed by digitization and that a global sample registry alone will not ensure that samples are available for reuse. We suggest instead that the ability of the Earth science community to identify and preserve important collections and make them available for future study is limited by personnel and space resources from the level of the individual PI to the level of national facilities. Moreover, when it comes to specimen "estate planning," the cultural attitudes of scientists, institutions, and funding agencies are often inadequate to provide for long-term specimen curation - even if specimen discovery is enabled by digital registry. Timely access to curated samples requires that adequate resources be devoted to the physical care of specimens (facilities) and to the personnel costs associated with curation - from the conservation, storage, and inventory management of specimens, to the dispersal of samples for research, education, and exhibition.

Xenbase: Core features, data acquisition, and data processing.

PubMed

James-Zorn, Christina; Ponferrada, Virgillio G; Burns, Kevin A; Fortriede, Joshua D; Lotay, Vaneet S; Liu, Yu; Brad Karpinka, J; Karimi, Kamran; Zorn, Aaron M; Vize, Peter D

2015-08-01

Xenbase, the Xenopus model organism database (www.xenbase.org), is a cloud-based, web-accessible resource that integrates the diverse genomic and biological data from Xenopus research. Xenopus frogs are one of the major vertebrate animal models used for biomedical research, and Xenbase is the central repository for the enormous amount of data generated using this model tetrapod. The goal of Xenbase is to accelerate discovery by enabling investigators to make novel connections between molecular pathways in Xenopus and human disease. Our relational database and user-friendly interface make these data easy to query and allows investigators to quickly interrogate and link different data types in ways that would otherwise be difficult, time consuming, or impossible. Xenbase also enhances the value of these data through high-quality gene expression curation and data integration, by providing bioinformatics tools optimized for Xenopus experiments, and by linking Xenopus data to other model organisms and to human data. Xenbase draws in data via pipelines that download data, parse the content, and save them into appropriate files and database tables. Furthermore, Xenbase makes these data accessible to the broader biomedical community by continually providing annotated data updates to organizations such as NCBI, UniProtKB, and Ensembl. Here, we describe our bioinformatics, genome-browsing tools, data acquisition and sharing, our community submitted and literature curation pipelines, text-mining support, gene page features, and the curation of gene nomenclature and gene models. © 2015 Wiley Periodicals, Inc.

The Neotoma Paleoecology Database: An International Community-Curated Resource for Paleoecological and Paleoenvironmental Data

NASA Astrophysics Data System (ADS)

Williams, J. W.; Grimm, E. C.; Ashworth, A. C.; Blois, J.; Charles, D. F.; Crawford, S.; Davis, E.; Goring, S. J.; Graham, R. W.; Miller, D. A.; Smith, A. J.; Stryker, M.; Uhen, M. D.

2017-12-01

The Neotoma Paleoecology Database supports global change research at the intersection of geology and ecology by providing a high-quality, community-curated data repository for paleoecological data. These data are widely used to study biological responses and feedbacks to past environmental change at local to global scales. The Neotoma data model is flexible and can store multiple kinds of fossil, biogeochemical, or physical variables measured from sedimentary archives. Data additions to Neotoma are growing and include >3.5 million observations, >16,000 datasets, and >8,500 sites. Dataset types include fossil pollen, vertebrates, diatoms, ostracodes, macroinvertebrates, plant macrofossils, insects, testate amoebae, geochronological data, and the recently added organic biomarkers, stable isotopes, and specimen-level data. Neotoma data can be found and retrieved in multiple ways, including the Explorer map-based interface, a RESTful Application Programming Interface, the neotoma R package, and digital object identifiers. Neotoma has partnered with the Paleobiology Database to produce a common data portal for paleobiological data, called the Earth Life Consortium. A new embargo management is designed to allow investigators to put their data into Neotoma and then make use of Neotoma's value-added services. Neotoma's distributed scientific governance model is flexible and scalable, with many open pathways for welcoming new members, data contributors, stewards, and research communities. As the volume and variety of scientific data grow, community-curated data resources such as Neotoma have become foundational infrastructure for big data science.

Web services-based text-mining demonstrates broad impacts for interoperability and process simplification.

PubMed

Wiegers, Thomas C; Davis, Allan Peter; Mattingly, Carolyn J

2014-01-01

The Critical Assessment of Information Extraction systems in Biology (BioCreAtIvE) challenge evaluation tasks collectively represent a community-wide effort to evaluate a variety of text-mining and information extraction systems applied to the biological domain. The BioCreative IV Workshop included five independent subject areas, including Track 3, which focused on named-entity recognition (NER) for the Comparative Toxicogenomics Database (CTD; http://ctdbase.org). Previously, CTD had organized document ranking and NER-related tasks for the BioCreative Workshop 2012; a key finding of that effort was that interoperability and integration complexity were major impediments to the direct application of the systems to CTD's text-mining pipeline. This underscored a prevailing problem with software integration efforts. Major interoperability-related issues included lack of process modularity, operating system incompatibility, tool configuration complexity and lack of standardization of high-level inter-process communications. One approach to potentially mitigate interoperability and general integration issues is the use of Web services to abstract implementation details; rather than integrating NER tools directly, HTTP-based calls from CTD's asynchronous, batch-oriented text-mining pipeline could be made to remote NER Web services for recognition of specific biological terms using BioC (an emerging family of XML formats) for inter-process communications. To test this concept, participating groups developed Representational State Transfer /BioC-compliant Web services tailored to CTD's NER requirements. Participants were provided with a comprehensive set of training materials. CTD evaluated results obtained from the remote Web service-based URLs against a test data set of 510 manually curated scientific articles. Twelve groups participated in the challenge. Recall, precision, balanced F-scores and response times were calculated. Top balanced F-scores for gene, chemical and disease NER were 61, 74 and 51%, respectively. Response times ranged from fractions-of-a-second to over a minute per article. We present a description of the challenge and summary of results, demonstrating how curation groups can effectively use interoperable NER technologies to simplify text-mining pipeline implementation. Database URL: http://ctdbase.org/ © The Author(s) 2014. Published by Oxford University Press.

Web services-based text-mining demonstrates broad impacts for interoperability and process simplification

PubMed Central

Wiegers, Thomas C.; Davis, Allan Peter; Mattingly, Carolyn J.

2014-01-01

The Critical Assessment of Information Extraction systems in Biology (BioCreAtIvE) challenge evaluation tasks collectively represent a community-wide effort to evaluate a variety of text-mining and information extraction systems applied to the biological domain. The BioCreative IV Workshop included five independent subject areas, including Track 3, which focused on named-entity recognition (NER) for the Comparative Toxicogenomics Database (CTD; http://ctdbase.org). Previously, CTD had organized document ranking and NER-related tasks for the BioCreative Workshop 2012; a key finding of that effort was that interoperability and integration complexity were major impediments to the direct application of the systems to CTD's text-mining pipeline. This underscored a prevailing problem with software integration efforts. Major interoperability-related issues included lack of process modularity, operating system incompatibility, tool configuration complexity and lack of standardization of high-level inter-process communications. One approach to potentially mitigate interoperability and general integration issues is the use of Web services to abstract implementation details; rather than integrating NER tools directly, HTTP-based calls from CTD's asynchronous, batch-oriented text-mining pipeline could be made to remote NER Web services for recognition of specific biological terms using BioC (an emerging family of XML formats) for inter-process communications. To test this concept, participating groups developed Representational State Transfer /BioC-compliant Web services tailored to CTD's NER requirements. Participants were provided with a comprehensive set of training materials. CTD evaluated results obtained from the remote Web service-based URLs against a test data set of 510 manually curated scientific articles. Twelve groups participated in the challenge. Recall, precision, balanced F-scores and response times were calculated. Top balanced F-scores for gene, chemical and disease NER were 61, 74 and 51%, respectively. Response times ranged from fractions-of-a-second to over a minute per article. We present a description of the challenge and summary of results, demonstrating how curation groups can effectively use interoperable NER technologies to simplify text-mining pipeline implementation. Database URL: http://ctdbase.org/ PMID:24919658

DBSecSys 2.0: a database of Burkholderia mallei and Burkholderia pseudomallei secretion systems.

PubMed

Memišević, Vesna; Kumar, Kamal; Zavaljevski, Nela; DeShazer, David; Wallqvist, Anders; Reifman, Jaques

2016-09-20

Burkholderia mallei and B. pseudomallei are the causative agents of glanders and melioidosis, respectively, diseases with high morbidity and mortality rates. B. mallei and B. pseudomallei are closely related genetically; B. mallei evolved from an ancestral strain of B. pseudomallei by genome reduction and adaptation to an obligate intracellular lifestyle. Although these two bacteria cause different diseases, they share multiple virulence factors, including bacterial secretion systems, which represent key components of bacterial pathogenicity. Despite recent progress, the secretion system proteins for B. mallei and B. pseudomallei, their pathogenic mechanisms of action, and host factors are not well characterized. We previously developed a manually curated database, DBSecSys, of bacterial secretion system proteins for B. mallei. Here, we report an expansion of the database with corresponding information about B. pseudomallei. DBSecSys 2.0 contains comprehensive literature-based and computationally derived information about B. mallei ATCC 23344 and literature-based and computationally derived information about B. pseudomallei K96243. The database contains updated information for 163 B. mallei proteins from the previous database and 61 additional B. mallei proteins, and new information for 281 B. pseudomallei proteins associated with 5 secretion systems, their 1,633 human- and murine-interacting targets, and 2,400 host-B. mallei interactions and 2,286 host-B. pseudomallei interactions. The database also includes information about 13 pathogenic mechanisms of action for B. mallei and B. pseudomallei secretion system proteins inferred from the available literature or computationally. Additionally, DBSecSys 2.0 provides details about 82 virulence attenuation experiments for 52 B. mallei secretion system proteins and 98 virulence attenuation experiments for 61 B. pseudomallei secretion system proteins. We updated the Web interface and data access layer to speed-up users' search of detailed information for orthologous proteins related to secretion systems of the two pathogens. The updates of DBSecSys 2.0 provide unique capabilities to access comprehensive information about secretion systems of B. mallei and B. pseudomallei. They enable studies and comparisons of corresponding proteins of these two closely related pathogens and their host-interacting partners. The database is available at http://dbsecsys.bhsai.org .

T3SEdb: data warehousing of virulence effectors secreted by the bacterial Type III Secretion System.

PubMed

Tay, Daniel Ming Ming; Govindarajan, Kunde Ramamoorthy; Khan, Asif M; Ong, Terenze Yao Rui; Samad, Hanif M; Soh, Wei Wei; Tong, Minyan; Zhang, Fan; Tan, Tin Wee

2010-10-15

Effectors of Type III Secretion System (T3SS) play a pivotal role in establishing and maintaining pathogenicity in the host and therefore the identification of these effectors is important in understanding virulence. However, the effectors display high level of sequence diversity, therefore making the identification a difficult process. There is a need to collate and annotate existing effector sequences in public databases to enable systematic analyses of these sequences for development of models for screening and selection of putative novel effectors from bacterial genomes that can be validated by a smaller number of key experiments. Herein, we present T3SEdb http://effectors.bic.nus.edu.sg/T3SEdb, a specialized database of annotated T3SS effector (T3SE) sequences containing 1089 records from 46 bacterial species compiled from the literature and public protein databases. Procedures have been defined for i) comprehensive annotation of experimental status of effectors, ii) submission and curation review of records by users of the database, and iii) the regular update of T3SEdb existing and new records. Keyword fielded and sequence searches (BLAST, regular expression) are supported for both experimentally verified and hypothetical T3SEs. More than 171 clusters of T3SEs were detected based on sequence identity comparisons (intra-cluster difference up to ~60%). Owing to this high level of sequence diversity of T3SEs, the T3SEdb provides a large number of experimentally known effector sequences with wide species representation for creation of effector predictors. We created a reliable effector prediction tool, integrated into the database, to demonstrate the application of the database for such endeavours. T3SEdb is the first specialised database reported for T3SS effectors, enriched with manual annotations that facilitated systematic construction of a reliable prediction model for identification of novel effectors. The T3SEdb represents a platform for inclusion of additional annotations of metadata for future developments of sophisticated effector prediction models for screening and selection of putative novel effectors from bacterial genomes/proteomes that can be validated by a small number of key experiments.

UNSODA UNSATURATED SOIL HYDRAULIC DATABASE USER'S MANUAL VERSION 1.0

EPA Science Inventory

This report contains general documentation and serves as a user manual of the UNSODA program. UNSODA is a database of unsaturated soil hydraulic properties (water retention, hydraulic conductivity, and soil water diffusivity), basic soil properties (particle-size distribution, b...

A comprehensive map of the influenza A virus replication cycle

PubMed Central

2013-01-01

Background Influenza is a common infectious disease caused by influenza viruses. Annual epidemics cause severe illnesses, deaths, and economic loss around the world. To better defend against influenza viral infection, it is essential to understand its mechanisms and associated host responses. Many studies have been conducted to elucidate these mechanisms, however, the overall picture remains incompletely understood. A systematic understanding of influenza viral infection in host cells is needed to facilitate the identification of influential host response mechanisms and potential drug targets. Description We constructed a comprehensive map of the influenza A virus (‘IAV’) life cycle (‘FluMap’) by undertaking a literature-based, manual curation approach. Based on information obtained from publicly available pathway databases, updated with literature-based information and input from expert virologists and immunologists, FluMap is currently composed of 960 factors (i.e., proteins, mRNAs etc.) and 456 reactions, and is annotated with ~500 papers and curation comments. In addition to detailing the type of molecular interactions, isolate/strain specific data are also available. The FluMap was built with the pathway editor CellDesigner in standard SBML (Systems Biology Markup Language) format and visualized as an SBGN (Systems Biology Graphical Notation) diagram. It is also available as a web service (online map) based on the iPathways+ system to enable community discussion by influenza researchers. We also demonstrate computational network analyses to identify targets using the FluMap. Conclusion The FluMap is a comprehensive pathway map that can serve as a graphically presented knowledge-base and as a platform to analyze functional interactions between IAV and host factors. Publicly available webtools will allow continuous updating to ensure the most reliable representation of the host-virus interaction network. The FluMap is available at http://www.influenza-x.org/flumap/. PMID:24088197

Literature-based gene curation and proposed genetic nomenclature for cryptococcus.

PubMed

Inglis, Diane O; Skrzypek, Marek S; Liaw, Edward; Moktali, Venkatesh; Sherlock, Gavin; Stajich, Jason E

2014-07-01

Cryptococcus, a major cause of disseminated infections in immunocompromised patients, kills over 600,000 people per year worldwide. Genes involved in the virulence of the meningitis-causing fungus are being characterized at an increasing rate, and to date, at least 648 Cryptococcus gene names have been published. However, these data are scattered throughout the literature and are challenging to find. Furthermore, conflicts in locus identification exist, so that named genes have been subsequently published under new names or names associated with one locus have been used for another locus. To avoid these conflicts and to provide a central source of Cryptococcus gene information, we have collected all published Cryptococcus gene names from the scientific literature and associated them with standard Cryptococcus locus identifiers and have incorporated them into FungiDB (www.fungidb.org). FungiDB is a panfungal genome database that collects gene information and functional data and provides search tools for 61 species of fungi and oomycetes. We applied these published names to a manually curated ortholog set of all Cryptococcus species currently in FungiDB, including Cryptococcus neoformans var. neoformans strains JEC21 and B-3501A, C. neoformans var. grubii strain H99, and Cryptococcus gattii strains R265 and WM276, and have written brief descriptions of their functions. We also compiled a protocol for gene naming that summarizes guidelines proposed by members of the Cryptococcus research community. The centralization of genomic and literature-based information for Cryptococcus at FungiDB will help researchers communicate about genes of interest, such as those related to virulence, and will further facilitate research on the pathogen. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

Concentrations of indoor pollutants (CIP) database user's manual (Version 4. 0)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Apte, M.G.; Brown, S.R.; Corradi, C.A.

1990-10-01

This is the latest release of the database and the user manual. The user manual is a tutorial and reference for utilizing the CIP Database system. An installation guide is included to cover various hardware configurations. Numerous examples and explanations of the dialogue between the user and the database program are provided. It is hoped that this resource will, along with on-line help and the menu-driven software, make for a quick and easy learning curve. For the purposes of this manual, it is assumed that the user is acquainted with the goals of the CIP Database, which are: (1) tomore » collect existing measurements of concentrations of indoor air pollutants in a user-oriented database and (2) to provide a repository of references citing measured field results openly accessible to a wide audience of researchers, policy makers, and others interested in the issues of indoor air quality. The database software, as distinct from the data, is contained in two files, CIP. EXE and PFIL.COM. CIP.EXE is made up of a number of programs written in dBase III command code and compiled using Clipper into a single, executable file. PFIL.COM is a program written in Turbo Pascal that handles the output of summary text files and is called from CIP.EXE. Version 4.0 of the CIP Database is current through March 1990.« less

GMODWeb: a web framework for the generic model organism database

PubMed Central

O'Connor, Brian D; Day, Allen; Cain, Scott; Arnaiz, Olivier; Sperling, Linda; Stein, Lincoln D

2008-01-01

The Generic Model Organism Database (GMOD) initiative provides species-agnostic data models and software tools for representing curated model organism data. Here we describe GMODWeb, a GMOD project designed to speed the development of model organism database (MOD) websites. Sites created with GMODWeb provide integration with other GMOD tools and allow users to browse and search through a variety of data types. GMODWeb was built using the open source Turnkey web framework and is available from . PMID:18570664

Systems and methods for automatically identifying and linking names in digital resources

DOEpatents

Parker, Charles T.; Lyons, Catherine M.; Roston, Gerald P.; Garrity, George M.

2017-06-06

The present invention provides systems and methods for automatically identifying name-like-strings in digital resources, matching these name-like-string against a set of names held in an expertly curated database, and for those name-like-strings found in said database, enhancing the content by associating additional matter with the name, wherein said matter includes information about the names that is held within said database and pointers to other digital resources which include the same name and it synonyms.

Comparative Analysis of Transcription Factors Families across Fungal Tree of Life

DOE Office of Scientific and Technical Information (OSTI.GOV)

Salamov, Asaf; Grigoriev, Igor

2015-03-19

Transcription factors (TFs) are proteins that regulate the transcription of genes, by binding to specific DNA sequences. Based on literature (Shelest, 2008; Weirauch and Hughes,2011) collected and manually curated list of DBD Pfam domains (in total 62 DBD domains) We looked for distribution of TFs in 395 fungal genomes plus additionally in plant genomes (Phytozome), prokaryotes(IMG), some animals/metazoans and protists genomes

NPS (Naval Postgraduate School) Supply Requisition Database - Interactive Software as an Alternative to Written Instructions.

DTIC Science & Technology

1986-03-01

SRdb ... .......... .35 APPENDIX A: ABBREVIATIONS AND ACRONYMS ......... 37 " APPENDIX B: USER’S MANUAL ..... ............... 38 APPENDIX C: DATABASE...percentage of situations. The purpose of this paper is to examine and propose a software-oriented alternative to the current manual , instruction-driven...Department Customer Service Manual (Ref. 1] and the applicable NPS Comptroller instruction [Ref. 2]. Several modifications to these written quidelines

Examination of the Involvement of Cholinergic-Associated Genes in Nicotine Behaviors in European and African Americans.

PubMed

Melroy-Greif, Whitney E; Simonson, Matthew A; Corley, Robin P; Lutz, Sharon M; Hokanson, John E; Ehringer, Marissa A

2017-04-01

Cigarette smoking is a physiologically harmful habit. Nicotinic acetylcholine receptors (nAChRs) are bound by nicotine and upregulated in response to chronic exposure to nicotine. It is known that upregulation of these receptors is not due to a change in mRNA of these genes, however, more precise details on the process are still uncertain, with several plausible hypotheses describing how nAChRs are upregulated. We have manually curated a set of genes believed to play a role in nicotine-induced nAChR upregulation. Here, we test the hypothesis that these genes are associated with and contribute risk for nicotine dependence (ND) and the number of cigarettes smoked per day (CPD). Studies with genotypic data on European and African Americans (EAs and AAs, respectively) were collected and a gene-based test was run to test for an association between each gene and ND and CPD. Although several novel genes were associated with CPD and ND at P < 0.05 in EAs and AAs, these associations did not survive correction for multiple testing. Previous associations between CHRNA3, CHRNA5, CHRNB4 and CPD in EAs were replicated. Our hypothesis-driven approach avoided many of the limitations inherent in pathway analyses and provided nominal evidence for association between cholinergic-related genes and nicotine behaviors. We evaluated the evidence for association between a manually curated set of genes and nicotine behaviors in European and African Americans. Although no genes were associated after multiple testing correction, this study has several strengths: by manually curating a set of genes we circumvented the limitations inherent in many pathway analyses and tested several genes that had not yet been examined in a human genetic study; gene-based tests are a useful way to test for association with a set of genes; and these genes were collected based on literature review and conversations with experts, highlighting the importance of scientific collaboration. © The Author 2016. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Triage by ranking to support the curation of protein interactions

PubMed Central

Pasche, Emilie; Gobeill, Julien; Rech de Laval, Valentine; Gleizes, Anne; Michel, Pierre-André; Bairoch, Amos

2017-01-01

Abstract Today, molecular biology databases are the cornerstone of knowledge sharing for life and health sciences. The curation and maintenance of these resources are labour intensive. Although text mining is gaining impetus among curators, its integration in curation workflow has not yet been widely adopted. The Swiss Institute of Bioinformatics Text Mining and CALIPHO groups joined forces to design a new curation support system named nextA5. In this report, we explore the integration of novel triage services to support the curation of two types of biological data: protein–protein interactions (PPIs) and post-translational modifications (PTMs). The recognition of PPIs and PTMs poses a special challenge, as it not only requires the identification of biological entities (proteins or residues), but also that of particular relationships (e.g. binding or position). These relationships cannot be described with onto-terminological descriptors such as the Gene Ontology for molecular functions, which makes the triage task more challenging. Prioritizing papers for these tasks thus requires the development of different approaches. In this report, we propose a new method to prioritize articles containing information specific to PPIs and PTMs. The new resources (RESTful APIs, semantically annotated MEDLINE library) enrich the neXtA5 platform. We tuned the article prioritization model on a set of 100 proteins previously annotated by the CALIPHO group. The effectiveness of the triage service was tested with a dataset of 200 annotated proteins. We defined two sets of descriptors to support automatic triage: the first set to enrich for papers with PPI data, and the second for PTMs. All occurrences of these descriptors were marked-up in MEDLINE and indexed, thus constituting a semantically annotated version of MEDLINE. These annotations were then used to estimate the relevance of a particular article with respect to the chosen annotation type. This relevance score was combined with a local vector-space search engine to generate a ranked list of PMIDs. We also evaluated a query refinement strategy, which adds specific keywords (such as ‘binds’ or ‘interacts’) to the original query. Compared to PubMed, the search effectiveness of the nextA5 triage service is improved by 190% for the prioritization of papers with PPIs information and by 260% for papers with PTMs information. Combining advanced retrieval and query refinement strategies with automatically enriched MEDLINE contents is effective to improve triage in complex curation tasks such as the curation of protein PPIs and PTMs. Database URL: http://candy.hesge.ch/nextA5 PMID:29220432

Solving gap metabolites and blocked reactions in genome-scale models: application to the metabolic network of Blattabacterium cuenoti.

PubMed

Ponce-de-León, Miguel; Montero, Francisco; Peretó, Juli

2013-10-31

Metabolic reconstruction is the computational-based process that aims to elucidate the network of metabolites interconnected through reactions catalyzed by activities assigned to one or more genes. Reconstructed models may contain inconsistencies that appear as gap metabolites and blocked reactions. Although automatic methods for solving this problem have been previously developed, there are many situations where manual curation is still needed. We introduce a general definition of gap metabolite that allows its detection in a straightforward manner. Moreover, a method for the detection of Unconnected Modules, defined as isolated sets of blocked reactions connected through gap metabolites, is proposed. The method has been successfully applied to the curation of iCG238, the genome-scale metabolic model for the bacterium Blattabacterium cuenoti, obligate endosymbiont of cockroaches. We found the proposed approach to be a valuable tool for the curation of genome-scale metabolic models. The outcome of its application to the genome-scale model B. cuenoti iCG238 is a more accurate model version named as B. cuenoti iMP240.

R-Syst::diatom: an open-access and curated barcode database for diatoms and freshwater monitoring.

PubMed

Rimet, Frédéric; Chaumeil, Philippe; Keck, François; Kermarrec, Lenaïg; Vasselon, Valentin; Kahlert, Maria; Franc, Alain; Bouchez, Agnès

2016-01-01

Diatoms are micro-algal indicators of freshwater pollution. Current standardized methodologies are based on microscopic determinations, which is time consuming and prone to identification uncertainties. The use of DNA-barcoding has been proposed as a way to avoid these flaws. Combining barcoding with next-generation sequencing enables collection of a large quantity of barcodes from natural samples. These barcodes are identified as certain diatom taxa by comparing the sequences to a reference barcoding library using algorithms. Proof of concept was recently demonstrated for synthetic and natural communities and underlined the importance of the quality of this reference library. We present an open-access and curated reference barcoding database for diatoms, called R-Syst::diatom, developed in the framework of R-Syst, the network of systematic supported by INRA (French National Institute for Agricultural Research), see http://www.rsyst.inra.fr/en. R-Syst::diatom links DNA-barcodes to their taxonomical identifications, and is dedicated to identify barcodes from natural samples. The data come from two sources, a culture collection of freshwater algae maintained in INRA in which new strains are regularly deposited and barcoded and from the NCBI (National Center for Biotechnology Information) nucleotide database. Two kinds of barcodes were chosen to support the database: 18S (18S ribosomal RNA) and rbcL (Ribulose-1,5-bisphosphate carboxylase/oxygenase), because of their efficiency. Data are curated using innovative (Declic) and classical bioinformatic tools (Blast, classical phylogenies) and up-to-date taxonomy (Catalogues and peer reviewed papers). Every 6 months R-Syst::diatom is updated. The database is available through the R-Syst microalgae website (http://www.rsyst.inra.fr/) and a platform dedicated to next-generation sequencing data analysis, virtual_BiodiversityL@b (https://galaxy-pgtp.pierroton.inra.fr/). We present here the content of the library regarding the number of barcodes and diatom taxa. In addition to these information, morphological features (e.g. biovolumes, chloroplasts…), life-forms (mobility, colony-type) or ecological features (taxa preferenda to pollution) are indicated in R-Syst::diatom. Database URL: http://www.rsyst.inra.fr/. © The Author(s) 2016. Published by Oxford University Press.

An Information System for European culture collections: the way forward.

PubMed

Casaregola, Serge; Vasilenko, Alexander; Romano, Paolo; Robert, Vincent; Ozerskaya, Svetlana; Kopf, Anna; Glöckner, Frank O; Smith, David

2016-01-01

Culture collections contain indispensable information about the microorganisms preserved in their repositories, such as taxonomical descriptions, origins, physiological and biochemical characteristics, bibliographic references, etc. However, information currently accessible in databases rarely adheres to common standard protocols. The resultant heterogeneity between culture collections, in terms of both content and format, notably hampers microorganism-based research and development (R&D). The optimized exploitation of these resources thus requires standardized, and simplified, access to the associated information. To this end, and in the interest of supporting R&D in the fields of agriculture, health and biotechnology, a pan-European distributed research infrastructure, MIRRI, including over 40 public culture collections and research institutes from 19 European countries, was established. A prime objective of MIRRI is to unite and provide universal access to the fragmented, and untapped, resources, information and expertise available in European public collections of microorganisms; a key component of which is to develop a dynamic Information System. For the first time, both culture collection curators as well as their users have been consulted and their feedback, concerning the needs and requirements for collection databases and data accessibility, utilised. Users primarily noted that databases were not interoperable, thus rendering a global search of multiple databases impossible. Unreliable or out-of-date and, in particular, non-homogenous, taxonomic information was also considered to be a major obstacle to searching microbial data efficiently. Moreover, complex searches are rarely possible in online databases thus limiting the extent of search queries. Curators also consider that overall harmonization-including Standard Operating Procedures, data structure, and software tools-is necessary to facilitate their work and to make high-quality data easily accessible to their users. Clearly, the needs of culture collection curators coincide with those of users on the crucial point of database interoperability. In this regard, and in order to design an appropriate Information System, important aspects on which the culture collection community should focus include: the interoperability of data sets with the ontologies to be used; setting best practice in data management, and the definition of an appropriate data standard.

R-Syst::diatom: an open-access and curated barcode database for diatoms and freshwater monitoring

PubMed Central

Rimet, Frédéric; Chaumeil, Philippe; Keck, François; Kermarrec, Lenaïg; Vasselon, Valentin; Kahlert, Maria; Franc, Alain; Bouchez, Agnès

2016-01-01

Diatoms are micro-algal indicators of freshwater pollution. Current standardized methodologies are based on microscopic determinations, which is time consuming and prone to identification uncertainties. The use of DNA-barcoding has been proposed as a way to avoid these flaws. Combining barcoding with next-generation sequencing enables collection of a large quantity of barcodes from natural samples. These barcodes are identified as certain diatom taxa by comparing the sequences to a reference barcoding library using algorithms. Proof of concept was recently demonstrated for synthetic and natural communities and underlined the importance of the quality of this reference library. We present an open-access and curated reference barcoding database for diatoms, called R-Syst::diatom, developed in the framework of R-Syst, the network of systematic supported by INRA (French National Institute for Agricultural Research), see http://www.rsyst.inra.fr/en. R-Syst::diatom links DNA-barcodes to their taxonomical identifications, and is dedicated to identify barcodes from natural samples. The data come from two sources, a culture collection of freshwater algae maintained in INRA in which new strains are regularly deposited and barcoded and from the NCBI (National Center for Biotechnology Information) nucleotide database. Two kinds of barcodes were chosen to support the database: 18S (18S ribosomal RNA) and rbcL (Ribulose-1,5-bisphosphate carboxylase/oxygenase), because of their efficiency. Data are curated using innovative (Declic) and classical bioinformatic tools (Blast, classical phylogenies) and up-to-date taxonomy (Catalogues and peer reviewed papers). Every 6 months R-Syst::diatom is updated. The database is available through the R-Syst microalgae website (http://www.rsyst.inra.fr/) and a platform dedicated to next-generation sequencing data analysis, virtual_BiodiversityL@b (https://galaxy-pgtp.pierroton.inra.fr/). We present here the content of the library regarding the number of barcodes and diatom taxa. In addition to these information, morphological features (e.g. biovolumes, chloroplasts…), life-forms (mobility, colony-type) or ecological features (taxa preferenda to pollution) are indicated in R-Syst::diatom. Database URL: http://www.rsyst.inra.fr/ PMID:26989149

Reduce Manual Curation by Combining Gene Predictions from Multiple Annotation Engines, a Case Study of Start Codon Prediction

PubMed Central

Ederveen, Thomas H. A.; Overmars, Lex; van Hijum, Sacha A. F. T.

2013-01-01

Nowadays, prokaryotic genomes are sequenced faster than the capacity to manually curate gene annotations. Automated genome annotation engines provide users a straight-forward and complete solution for predicting ORF coordinates and function. For many labs, the use of AGEs is therefore essential to decrease the time necessary for annotating a given prokaryotic genome. However, it is not uncommon for AGEs to provide different and sometimes conflicting predictions. Combining multiple AGEs might allow for more accurate predictions. Here we analyzed the ab initio open reading frame (ORF) calling performance of different AGEs based on curated genome annotations of eight strains from different bacterial species with GC% ranging from 35–52%. We present a case study which demonstrates a novel way of comparative genome annotation, using combinations of AGEs in a pre-defined order (or path) to predict ORF start codons. The order of AGE combinations is from high to low specificity, where the specificity is based on the eight genome annotations. For each AGE combination we are able to derive a so-called projected confidence value, which is the average specificity of ORF start codon prediction based on the eight genomes. The projected confidence enables estimating likeliness of a correct prediction for a particular ORF start codon by a particular AGE combination, pinpointing ORFs notoriously difficult to predict start codons. We correctly predict start codons for 90.5±4.8% of the genes in a genome (based on the eight genomes) with an accuracy of 81.1±7.6%. Our consensus-path methodology allows a marked improvement over majority voting (9.7±4.4%) and with an optimal path ORF start prediction sensitivity is gained while maintaining a high specificity. PMID:23675487

LitPathExplorer: a confidence-based visual text analytics tool for exploring literature-enriched pathway models.

PubMed

Soto, Axel J; Zerva, Chrysoula; Batista-Navarro, Riza; Ananiadou, Sophia

2018-04-15

Pathway models are valuable resources that help us understand the various mechanisms underpinning complex biological processes. Their curation is typically carried out through manual inspection of published scientific literature to find information relevant to a model, which is a laborious and knowledge-intensive task. Furthermore, models curated manually cannot be easily updated and maintained with new evidence extracted from the literature without automated support. We have developed LitPathExplorer, a visual text analytics tool that integrates advanced text mining, semi-supervised learning and interactive visualization, to facilitate the exploration and analysis of pathway models using statements (i.e. events) extracted automatically from the literature and organized according to levels of confidence. LitPathExplorer supports pathway modellers and curators alike by: (i) extracting events from the literature that corroborate existing models with evidence; (ii) discovering new events which can update models; and (iii) providing a confidence value for each event that is automatically computed based on linguistic features and article metadata. Our evaluation of event extraction showed a precision of 89% and a recall of 71%. Evaluation of our confidence measure, when used for ranking sampled events, showed an average precision ranging between 61 and 73%, which can be improved to 95% when the user is involved in the semi-supervised learning process. Qualitative evaluation using pair analytics based on the feedback of three domain experts confirmed the utility of our tool within the context of pathway model exploration. LitPathExplorer is available at http://nactem.ac.uk/LitPathExplorer_BI/. sophia.ananiadou@manchester.ac.uk. Supplementary data are available at Bioinformatics online.

Curating NASA's Past, Present, and Future Astromaterial Sample Collections

NASA Technical Reports Server (NTRS)

Zeigler, R. A.; Allton, J. H.; Evans, C. A.; Fries, M. D.; McCubbin, F. M.; Nakamura-Messenger, K.; Righter, K.; Zolensky, M.; Stansbery, E. K.

2016-01-01

The Astromaterials Acquisition and Curation Office at NASA Johnson Space Center (hereafter JSC curation) is responsible for curating all of NASA's extraterrestrial samples. JSC presently curates 9 different astromaterials collections in seven different clean-room suites: (1) Apollo Samples (ISO (International Standards Organization) class 6 + 7); (2) Antarctic Meteorites (ISO 6 + 7); (3) Cosmic Dust Particles (ISO 5); (4) Microparticle Impact Collection (ISO 7; formerly called Space-Exposed Hardware); (5) Genesis Solar Wind Atoms (ISO 4); (6) Stardust Comet Particles (ISO 5); (7) Stardust Interstellar Particles (ISO 5); (8) Hayabusa Asteroid Particles (ISO 5); (9) OSIRIS-REx Spacecraft Coupons and Witness Plates (ISO 7). Additional cleanrooms are currently being planned to house samples from two new collections, Hayabusa 2 (2021) and OSIRIS-REx (2023). In addition to the labs that house the samples, we maintain a wide variety of infra-structure facilities required to support the clean rooms: HEPA-filtered air-handling systems, ultrapure dry gaseous nitrogen systems, an ultrapure water system, and cleaning facilities to provide clean tools and equipment for the labs. We also have sample preparation facilities for making thin sections, microtome sections, and even focused ion-beam sections. We routinely monitor the cleanliness of our clean rooms and infrastructure systems, including measurements of inorganic or organic contamination, weekly airborne particle counts, compositional and isotopic monitoring of liquid N2 deliveries, and daily UPW system monitoring. In addition to the physical maintenance of the samples, we track within our databases the current and ever changing characteristics (weight, location, etc.) of more than 250,000 individually numbered samples across our various collections, as well as more than 100,000 images, and countless "analog" records that record the sample processing records of each individual sample. JSC Curation is co-located with JSC's Astromaterials Research Office, which houses a world-class suite of analytical instrumentation and scientists. We leverage these labs and personnel to better curate the samples. Part of the cu-ration process is planning for the future, and we refer to these planning efforts as "advanced curation". Advanced Curation is tasked with developing procedures, technology, and data sets necessary for curating new types of collections as envi-sioned by NASA exploration goals. We are (and have been) planning for future cu-ration, including cold curation, extended curation of ices and volatiles, curation of samples with special chemical considerations such as perchlorate-rich samples, and curation of organically- and biologically-sensitive samples.

Evolutionary characters, phenotypes and ontologies: curating data from the systematic biology literature.

PubMed

Dahdul, Wasila M; Balhoff, James P; Engeman, Jeffrey; Grande, Terry; Hilton, Eric J; Kothari, Cartik; Lapp, Hilmar; Lundberg, John G; Midford, Peter E; Vision, Todd J; Westerfield, Monte; Mabee, Paula M

2010-05-20

The wealth of phenotypic descriptions documented in the published articles, monographs, and dissertations of phylogenetic systematics is traditionally reported in a free-text format, and it is therefore largely inaccessible for linkage to biological databases for genetics, development, and phenotypes, and difficult to manage for large-scale integrative work. The Phenoscape project aims to represent these complex and detailed descriptions with rich and formal semantics that are amenable to computation and integration with phenotype data from other fields of biology. This entails reconceptualizing the traditional free-text characters into the computable Entity-Quality (EQ) formalism using ontologies. We used ontologies and the EQ formalism to curate a collection of 47 phylogenetic studies on ostariophysan fishes (including catfishes, characins, minnows, knifefishes) and their relatives with the goal of integrating these complex phenotype descriptions with information from an existing model organism database (zebrafish, http://zfin.org). We developed a curation workflow for the collection of character, taxonomic and specimen data from these publications. A total of 4,617 phenotypic characters (10,512 states) for 3,449 taxa, primarily species, were curated into EQ formalism (for a total of 12,861 EQ statements) using anatomical and taxonomic terms from teleost-specific ontologies (Teleost Anatomy Ontology and Teleost Taxonomy Ontology) in combination with terms from a quality ontology (Phenotype and Trait Ontology). Standards and guidelines for consistently and accurately representing phenotypes were developed in response to the challenges that were evident from two annotation experiments and from feedback from curators. The challenges we encountered and many of the curation standards and methods for improving consistency that we developed are generally applicable to any effort to represent phenotypes using ontologies. This is because an ontological representation of the detailed variations in phenotype, whether between mutant or wildtype, among individual humans, or across the diversity of species, requires a process by which a precise combination of terms from domain ontologies are selected and organized according to logical relations. The efficiencies that we have developed in this process will be useful for any attempt to annotate complex phenotypic descriptions using ontologies. We also discuss some ramifications of EQ representation for the domain of systematics.

Biological Databases for Human Research

PubMed Central

Zou, Dong; Ma, Lina; Yu, Jun; Zhang, Zhang

2015-01-01

The completion of the Human Genome Project lays a foundation for systematically studying the human genome from evolutionary history to precision medicine against diseases. With the explosive growth of biological data, there is an increasing number of biological databases that have been developed in aid of human-related research. Here we present a collection of human-related biological databases and provide a mini-review by classifying them into different categories according to their data types. As human-related databases continue to grow not only in count but also in volume, challenges are ahead in big data storage, processing, exchange and curation. PMID:25712261

A comprehensive clinical research database based on CDISC ODM and i2b2.

PubMed

Meineke, Frank A; Stäubert, Sebastian; Löbe, Matthias; Winter, Alfred

2014-01-01

We present a working approach for a clinical research database as part of an archival information system. The CDISC ODM standard is target for clinical study and research relevant routine data, thus decoupling the data ingest process from the access layer. The presented research database is comprehensive as it covers annotating, mapping and curation of poorly annotated source data. Besides a conventional relational database the medical data warehouse i2b2 serves as main frontend for end-users. The system we developed is suitable to support patient recruitment, cohort identification and quality assurance in daily routine.

Accurate atom-mapping computation for biochemical reactions.

PubMed

Latendresse, Mario; Malerich, Jeremiah P; Travers, Mike; Karp, Peter D

2012-11-26

The complete atom mapping of a chemical reaction is a bijection of the reactant atoms to the product atoms that specifies the terminus of each reactant atom. Atom mapping of biochemical reactions is useful for many applications of systems biology, in particular for metabolic engineering where synthesizing new biochemical pathways has to take into account for the number of carbon atoms from a source compound that are conserved in the synthesis of a target compound. Rapid, accurate computation of the atom mapping(s) of a biochemical reaction remains elusive despite significant work on this topic. In particular, past researchers did not validate the accuracy of mapping algorithms. We introduce a new method for computing atom mappings called the minimum weighted edit-distance (MWED) metric. The metric is based on bond propensity to react and computes biochemically valid atom mappings for a large percentage of biochemical reactions. MWED models can be formulated efficiently as Mixed-Integer Linear Programs (MILPs). We have demonstrated this approach on 7501 reactions of the MetaCyc database for which 87% of the models could be solved in less than 10 s. For 2.1% of the reactions, we found multiple optimal atom mappings. We show that the error rate is 0.9% (22 reactions) by comparing these atom mappings to 2446 atom mappings of the manually curated Kyoto Encyclopedia of Genes and Genomes (KEGG) RPAIR database. To our knowledge, our computational atom-mapping approach is the most accurate and among the fastest published to date. The atom-mapping data will be available in the MetaCyc database later in 2012; the atom-mapping software will be available within the Pathway Tools software later in 2012.

Quantitative comparison of microarray experiments with published leukemia related gene expression signatures.

PubMed

Klein, Hans-Ulrich; Ruckert, Christian; Kohlmann, Alexander; Bullinger, Lars; Thiede, Christian; Haferlach, Torsten; Dugas, Martin

2009-12-15

Multiple gene expression signatures derived from microarray experiments have been published in the field of leukemia research. A comparison of these signatures with results from new experiments is useful for verification as well as for interpretation of the results obtained. Currently, the percentage of overlapping genes is frequently used to compare published gene signatures against a signature derived from a new experiment. However, it has been shown that the percentage of overlapping genes is of limited use for comparing two experiments due to the variability of gene signatures caused by different array platforms or assay-specific influencing parameters. Here, we present a robust approach for a systematic and quantitative comparison of published gene expression signatures with an exemplary query dataset. A database storing 138 leukemia-related published gene signatures was designed. Each gene signature was manually annotated with terms according to a leukemia-specific taxonomy. Two analysis steps are implemented to compare a new microarray dataset with the results from previous experiments stored and curated in the database. First, the global test method is applied to assess gene signatures and to constitute a ranking among them. In a subsequent analysis step, the focus is shifted from single gene signatures to chromosomal aberrations or molecular mutations as modeled in the taxonomy. Potentially interesting disease characteristics are detected based on the ranking of gene signatures associated with these aberrations stored in the database. Two example analyses are presented. An implementation of the approach is freely available as web-based application. The presented approach helps researchers to systematically integrate the knowledge derived from numerous microarray experiments into the analysis of a new dataset. By means of example leukemia datasets we demonstrate that this approach detects related experiments as well as related molecular mutations and may help to interpret new microarray data.

Concentrations of indoor pollutants database: User's manual

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1992-05-01

This manual describes the computer-based database on indoor air pollutants. This comprehensive database alloys helps utility personnel perform rapid searches on literature related to indoor air pollutants. Besides general information, it provides guidance for finding specific information on concentrations of indoor air pollutants. The manual includes information on installing and using the database as well as a tutorial to assist the user in becoming familiar with the procedures involved in doing bibliographic and summary section searches. The manual demonstrates how to search for information by going through a series of questions that provide search parameters such as pollutants type, year,more » building type, keywords (from a specific list), country, geographic region, author's last name, and title. As more and more parameters are specified, the list of references found in the data search becomes smaller and more specific to the user's needs. Appendixes list types of information that can be input into the database when making a request. The CIP database allows individual utilities to obtain information on indoor air quality based on building types and other factors in their own service territory. This information is useful for utilities with concerns about indoor air quality and the control of indoor air pollutants. The CIP database itself is distributed by the Electric Power Software Center and runs on IBM PC-compatible computers.« less

Concentrations of indoor pollutants database: User`s manual

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1992-05-01

This manual describes the computer-based database on indoor air pollutants. This comprehensive database alloys helps utility personnel perform rapid searches on literature related to indoor air pollutants. Besides general information, it provides guidance for finding specific information on concentrations of indoor air pollutants. The manual includes information on installing and using the database as well as a tutorial to assist the user in becoming familiar with the procedures involved in doing bibliographic and summary section searches. The manual demonstrates how to search for information by going through a series of questions that provide search parameters such as pollutants type, year,more » building type, keywords (from a specific list), country, geographic region, author`s last name, and title. As more and more parameters are specified, the list of references found in the data search becomes smaller and more specific to the user`s needs. Appendixes list types of information that can be input into the database when making a request. The CIP database allows individual utilities to obtain information on indoor air quality based on building types and other factors in their own service territory. This information is useful for utilities with concerns about indoor air quality and the control of indoor air pollutants. The CIP database itself is distributed by the Electric Power Software Center and runs on IBM PC-compatible computers.« less

NeMedPlant: a database of therapeutic applications and chemical constituents of medicinal plants from north-east region of India

PubMed Central

Meetei, Potshangbam Angamba; Singh, Pankaj; Nongdam, Potshangbam; Prabhu, N Prakash; Rathore, RS; Vindal, Vaibhav

2012-01-01

The North-East region of India is one of the twelve mega biodiversity region, containing many rare and endangered species. A curated database of medicinal and aromatic plants from the regions called NeMedPlant is developed. The database contains traditional, scientific and medicinal information about plants and their active constituents, obtained from scholarly literature and local sources. The database is cross-linked with major biochemical databases and analytical tools. The integrated database provides resource for investigations into hitherto unexplored medicinal plants and serves to speed up the discovery of natural productsbased drugs. Availability The database is available for free at http://bif.uohyd.ac.in/nemedplant/orhttp://202.41.85.11/nemedplant/ PMID:22419844

The BDNYC database of low-mass stars, brown dwarfs, and planetary mass companions

NASA Astrophysics Data System (ADS)

Cruz, Kelle; Rodriguez, David; Filippazzo, Joseph; Gonzales, Eileen; Faherty, Jacqueline K.; Rice, Emily; BDNYC

2018-01-01

We present a web-interface to a database of low-mass stars, brown dwarfs, and planetary mass companions. Users can send SELECT SQL queries to the database, perform searches by coordinates or name, check the database inventory on specified objects, and even plot spectra interactively. The initial version of this database contains information for 198 objects and version 2 will contain over 1000 objects. The database currently includes photometric data from 2MASS, WISE, and Spitzer and version 2 will include a significant portion of the publicly available optical and NIR spectra for brown dwarfs. The database is maintained and curated by the BDNYC research group and we welcome contributions from other researchers via GitHub.

ARACNe-based inference, using curated microarray data, of Arabidopsis thaliana root transcriptional regulatory networks

PubMed Central

2014-01-01

Background Uncovering the complex transcriptional regulatory networks (TRNs) that underlie plant and animal development remains a challenge. However, a vast amount of data from public microarray experiments is available, which can be subject to inference algorithms in order to recover reliable TRN architectures. Results In this study we present a simple bioinformatics methodology that uses public, carefully curated microarray data and the mutual information algorithm ARACNe in order to obtain a database of transcriptional interactions. We used data from Arabidopsis thaliana root samples to show that the transcriptional regulatory networks derived from this database successfully recover previously identified root transcriptional modules and to propose new transcription factors for the SHORT ROOT/SCARECROW and PLETHORA pathways. We further show that these networks are a powerful tool to integrate and analyze high-throughput expression data, as exemplified by our analysis of a SHORT ROOT induction time-course microarray dataset, and are a reliable source for the prediction of novel root gene functions. In particular, we used our database to predict novel genes involved in root secondary cell-wall synthesis and identified the MADS-box TF XAL1/AGL12 as an unexpected participant in this process. Conclusions This study demonstrates that network inference using carefully curated microarray data yields reliable TRN architectures. In contrast to previous efforts to obtain root TRNs, that have focused on particular functional modules or tissues, our root transcriptional interactions provide an overview of the transcriptional pathways present in Arabidopsis thaliana roots and will likely yield a plethora of novel hypotheses to be tested experimentally. PMID:24739361

Pathobiology and management of laboratory rodents administered CDC category A agents.

PubMed

He, Yongqun; Rush, Howard G; Liepman, Rachel S; Xiang, Zuoshuang; Colby, Lesley A

2007-02-01

The Centers for Disease Control and Prevention Category A infectious agents include Bacillus anthracis (anthrax), Clostridium botulinum toxin (botulism), Yersinia pestis (plague), variola major virus (smallpox), Francisella tularensis (tularemia), and the filoviruses and arenaviruses that induce viral hemorrhagic fevers. These agents are regarded as having the greatest potential for adverse impact on public health and therefore are a focus of renewed attention in infectious disease research. Frequently rodent models are used to study the pathobiology of these agents. Although much is known regarding naturally occurring infections in humans, less is documented on the sources of exposures and potential risks of infection to researchers and animal care personnel after the administration of these hazardous substances to laboratory animals. Failure to appropriately manage the animals can result both in the creation of workplace hazards if human exposures occur and in disruption of the research if unintended animal exposures occur. Here we review representative Category A agents, with a focus on comparing the biologic effects in naturally infected humans and rodent models and on considerations specific to the management of infected rodent subjects. The information reviewed for each agent has been curated manually and stored in a unique Internet-based database system called HazARD (Hazards in Animal Research Database, http://helab.bioinformatics.med.umich.edu/hazard/) that is designed to assist researchers, administrators, safety officials, Institutional Biosafety Committees, and veterinary personnel seeking information on the management of risks associated with animal studies involving hazardous substances.

Splice-mediated Variants of Proteins (SpliVaP) - data and characterization of changes in signatures among protein isoforms due to alternative splicing.

PubMed

Floris, Matteo; Orsini, Massimiliano; Thanaraj, Thangavel Alphonse

2008-10-02

It is often the case that mammalian genes are alternatively spliced; the resulting alternate transcripts often encode protein isoforms that differ in amino acid sequences. Changes among the protein isoforms can alter the cellular properties of proteins. The effect can range from a subtle modulation to a complete loss of function. (i) We examined human splice-mediated protein isoforms (as extracted from a manually curated data set, and from a computationally predicted data set) for differences in the annotation for protein signatures (Pfam domains and PRINTS fingerprints) and we characterized the differences & their effects on protein functionalities. An important question addressed relates to the extent of protein isoforms that may lack any known function in the cell. (ii) We present a database that reports differences in protein signatures among human splice-mediated protein isoform sequences. (i) Characterization: The work points to distinct sets of alternatively spliced genes with varying degrees of annotation for the splice-mediated protein isoforms. Protein molecular functions seen to be often affected are those that relate to: binding, catalytic, transcription regulation, structural molecule, transporter, motor, and antioxidant; and the processes that are often affected are nucleic acid binding, signal transduction, and protein-protein interactions. Signatures are often included/excluded and truncated in length among protein isoforms; truncation is seen as the predominant type of change. Analysis points to the following novel aspects: (a) Analysis using data from the manually curated Vega indicates that one in 8.9 genes can lead to a protein isoform of no "known" function; and one in 18 expressed protein isoforms can be such an "orphan" isoform; the corresponding numbers as seen with computationally predicted ASD data set are: one in 4.9 genes and one in 9.8 isoforms. (b) When swapping of signatures occurs, it is often between those of same functional classifications. (c) Pfam domains can occur in varying lengths, and PRINTS fingerprints can occur with varying number of constituent motifs among isoforms - since such a variation is seen in large number of genes, it could be a general mechanism to modulate protein function. (ii) The reported resource (at http://www.bioinformatica.crs4.org/tools/dbs/splivap/) provides the community ability to access data on splice-mediated protein isoforms (with value-added annotation such as association with diseases) through changes in protein signatures.

[Physiotherapy methods in the rehabilitation of patients with cerebral vascular diseases].

PubMed

Ezhov, V V

1996-01-01

The author has examined 650 patients aged 30-65 years with prestroke forms of cerebrovascular diseases. Basing on clinico-neurological, electrophysiological and biochemical data, five new variants of physiotherapy are proposed: transcerebral and segmental dalargin electrophoresis combined with magnetotherapy and manual therapy. Relevant practical recommendations are provided. The author's findings extend the knowledge of curative potential of physical methods and on their mechanism of action in cerebrovascular insufficiency.

Matching disease and phenotype ontologies in the ontology alignment evaluation initiative.

PubMed

Harrow, Ian; Jiménez-Ruiz, Ernesto; Splendiani, Andrea; Romacker, Martin; Woollard, Peter; Markel, Scott; Alam-Faruque, Yasmin; Koch, Martin; Malone, James; Waaler, Arild

2017-12-02

The disease and phenotype track was designed to evaluate the relative performance of ontology matching systems that generate mappings between source ontologies. Disease and phenotype ontologies are important for applications such as data mining, data integration and knowledge management to support translational science in drug discovery and understanding the genetics of disease. Eleven systems (out of 21 OAEI participating systems) were able to cope with at least one of the tasks in the Disease and Phenotype track. AML, FCA-Map, LogMap(Bio) and PhenoMF systems produced the top results for ontology matching in comparison to consensus alignments. The results against manually curated mappings proved to be more difficult most likely because these mapping sets comprised mostly subsumption relationships rather than equivalence. Manual assessment of unique equivalence mappings showed that AML, LogMap(Bio) and PhenoMF systems have the highest precision results. Four systems gave the highest performance for matching disease and phenotype ontologies. These systems coped well with the detection of equivalence matches, but struggled to detect semantic similarity. This deserves more attention in the future development of ontology matching systems. The findings of this evaluation show that such systems could help to automate equivalence matching in the workflow of curators, who maintain ontology mapping services in numerous domains such as disease and phenotype.

Stop the Bleeding: the Development of a Tool to Streamline NASA Earth Science Metadata Curation Efforts

NASA Astrophysics Data System (ADS)

le Roux, J.; Baker, A.; Caltagirone, S.; Bugbee, K.

2017-12-01

The Common Metadata Repository (CMR) is a high-performance, high-quality repository for Earth science metadata records, and serves as the primary way to search NASA's growing 17.5 petabytes of Earth science data holdings. Released in 2015, CMR has the capability to support several different metadata standards already being utilized by NASA's combined network of Earth science data providers, or Distributed Active Archive Centers (DAACs). The Analysis and Review of CMR (ARC) Team located at Marshall Space Flight Center is working to improve the quality of records already in CMR with the goal of making records optimal for search and discovery. This effort entails a combination of automated and manual review, where each NASA record in CMR is checked for completeness, accuracy, and consistency. This effort is highly collaborative in nature, requiring communication and transparency of findings amongst NASA personnel, DAACs, the CMR team and other metadata curation teams. Through the evolution of this project it has become apparent that there is a need to document and report findings, as well as track metadata improvements in a more efficient manner. The ARC team has collaborated with Element 84 in order to develop a metadata curation tool to meet these needs. In this presentation, we will provide an overview of this metadata curation tool and its current capabilities. Challenges and future plans for the tool will also be discussed.

A new approach for detecting adventitious viruses shows Sf-rhabdovirus-negative Sf-RVN cells are suitable for safe biologicals production.

PubMed

Geisler, Christoph

2018-02-07

Adventitious viral contamination in cell substrates used for biologicals production is a major safety concern. A powerful new approach that can be used to identify adventitious viruses is a combination of bioinformatics tools with massively parallel sequencing technology. Typically, this involves mapping or BLASTN searching individual reads against viral nucleotide databases. Although extremely sensitive for known viruses, this approach can easily miss viruses that are too dissimilar to viruses in the database. Moreover, it is computationally intensive and requires reference cell genome databases. To avoid these drawbacks, we set out to develop an alternative approach. We reasoned that searching genome and transcriptome assemblies for adventitious viral contaminants using TBLASTN with a compact viral protein database covering extant viral diversity as the query could be fast and sensitive without a requirement for high performance computing hardware. We tested our approach on Spodoptera frugiperda Sf-RVN, a recently isolated insect cell line, to determine if it was contaminated with one or more adventitious viruses. We used Illumina reads to assemble the Sf-RVN genome and transcriptome and searched them for adventitious viral contaminants using TBLASTN with our viral protein database. We found no evidence of viral contamination, which was substantiated by the fact that our searches otherwise identified diverse sequences encoding virus-like proteins. These sequences included Maverick, R1 LINE, and errantivirus transposons, all of which are common in insect genomes. We also identified previously described as well as novel endogenous viral elements similar to ORFs encoded by diverse insect viruses. Our results demonstrate TBLASTN searching massively parallel sequencing (MPS) assemblies with a compact, manually curated viral protein database is more sensitive for adventitious virus detection than BLASTN, as we identified various sequences that encoded virus-like proteins, but had no similarity to viral sequences at the nucleotide level. Moreover, searches were fast without requiring high performance computing hardware. Our study also documents the enhanced biosafety profile of Sf-RVN as compared to other Sf cell lines, and supports the notion that Sf-RVN is highly suitable for the production of safe biologicals.

Genotator: a disease-agnostic tool for genetic annotation of disease.

PubMed

Wall, Dennis P; Pivovarov, Rimma; Tong, Mark; Jung, Jae-Yoon; Fusaro, Vincent A; DeLuca, Todd F; Tonellato, Peter J

2010-10-29

Disease-specific genetic information has been increasing at rapid rates as a consequence of recent improvements and massive cost reductions in sequencing technologies. Numerous systems designed to capture and organize this mounting sea of genetic data have emerged, but these resources differ dramatically in their disease coverage and genetic depth. With few exceptions, researchers must manually search a variety of sites to assemble a complete set of genetic evidence for a particular disease of interest, a process that is both time-consuming and error-prone. We designed a real-time aggregation tool that provides both comprehensive coverage and reliable gene-to-disease rankings for any disease. Our tool, called Genotator, automatically integrates data from 11 externally accessible clinical genetics resources and uses these data in a straightforward formula to rank genes in order of disease relevance. We tested the accuracy of coverage of Genotator in three separate diseases for which there exist specialty curated databases, Autism Spectrum Disorder, Parkinson's Disease, and Alzheimer Disease. Genotator is freely available at http://genotator.hms.harvard.edu. Genotator demonstrated that most of the 11 selected databases contain unique information about the genetic composition of disease, with 2514 genes found in only one of the 11 databases. These findings confirm that the integration of these databases provides a more complete picture than would be possible from any one database alone. Genotator successfully identified at least 75% of the top ranked genes for all three of our use cases, including a 90% concordance with the top 40 ranked candidates for Alzheimer Disease. As a meta-query engine, Genotator provides high coverage of both historical genetic research as well as recent advances in the genetic understanding of specific diseases. As such, Genotator provides a real-time aggregation of ranked data that remains current with the pace of research in the disease fields. Genotator's algorithm appropriately transforms query terms to match the input requirements of each targeted databases and accurately resolves named synonyms to ensure full coverage of the genetic results with official nomenclature. Genotator generates an excel-style output that is consistent across disease queries and readily importable to other applications.

Gramene database: navigating plant comparative genomics resources

USDA-ARS?s Scientific Manuscript database

Gramene (http://www.gramene.org) is an online, open source, curated resource for plant comparative genomics and pathway analysis designed to support researchers working in plant genomics, breeding, evolutionary biology, system biology, and metabolic engineering. It exploits phylogenetic relationship...

The Disease Portals, disease-gene annotation and the RGD disease ontology at the Rat Genome Database.

PubMed

Hayman, G Thomas; Laulederkind, Stanley J F; Smith, Jennifer R; Wang, Shur-Jen; Petri, Victoria; Nigam, Rajni; Tutaj, Marek; De Pons, Jeff; Dwinell, Melinda R; Shimoyama, Mary

2016-01-01

The Rat Genome Database (RGD;http://rgd.mcw.edu/) provides critical datasets and software tools to a diverse community of rat and non-rat researchers worldwide. To meet the needs of the many users whose research is disease oriented, RGD has created a series of Disease Portals and has prioritized its curation efforts on the datasets important to understanding the mechanisms of various diseases. Gene-disease relationships for three species, rat, human and mouse, are annotated to capture biomarkers, genetic associations, molecular mechanisms and therapeutic targets. To generate gene-disease annotations more effectively and in greater detail, RGD initially adopted the MEDIC disease vocabulary from the Comparative Toxicogenomics Database and adapted it for use by expanding this framework with the addition of over 1000 terms to create the RGD Disease Ontology (RDO). The RDO provides the foundation for, at present, 10 comprehensive disease area-related dataset and analysis platforms at RGD, the Disease Portals. Two major disease areas are the focus of data acquisition and curation efforts each year, leading to the release of the related Disease Portals. Collaborative efforts to realize a more robust disease ontology are underway. Database URL:http://rgd.mcw.edu. © The Author(s) 2016. Published by Oxford University Press.

Scrubchem: Building Bioactivity Datasets from Pubchem ...

EPA Pesticide Factsheets

The PubChem Bioassay database is a non-curated public repository with data from 64 sources, including: ChEMBL, BindingDb, DrugBank, EPA Tox21, NIH Molecular Libraries Screening Program, and various other academic, government, and industrial contributors. Methods for extracting this public data into quality datasets, useable for analytical research, presents several big-data challenges for which we have designed manageable solutions. According to our preliminary work, there are approximately 549 million bioactivity values and related meta-data within PubChem that can be mapped to over 10,000 biological targets. However, this data is not ready for use in data-driven research, mainly due to lack of structured annotations.We used a pragmatic approach that provides increasing access to bioactivity values in the PubChem Bioassay database. This included restructuring of individual PubChem Bioassay files into a relational database (ScrubChem). ScrubChem contains all primary PubChem Bioassay data that was: reparsed; error-corrected (when applicable); enriched with additional data links from other NCBI databases; and improved by adding key biological and assay annotations derived from logic-based language processing rules. The utility of ScrubChem and the curation process were illustrated using an example bioactivity dataset for the androgen receptor protein. This initial work serves as a trial ground for establishing the technical framework for accessing, integrating, cu

EnzML: multi-label prediction of enzyme classes using InterPro signatures

PubMed Central

2012-01-01

Background Manual annotation of enzymatic functions cannot keep up with automatic genome sequencing. In this work we explore the capacity of InterPro sequence signatures to automatically predict enzymatic function. Results We present EnzML, a multi-label classification method that can efficiently account also for proteins with multiple enzymatic functions: 50,000 in UniProt. EnzML was evaluated using a standard set of 300,747 proteins for which the manually curated Swiss-Prot and KEGG databases have agreeing Enzyme Commission (EC) annotations. EnzML achieved more than 98% subset accuracy (exact match of all correct Enzyme Commission classes of a protein) for the entire dataset and between 87 and 97% subset accuracy in reannotating eight entire proteomes: human, mouse, rat, mouse-ear cress, fruit fly, the S. pombe yeast, the E. coli bacterium and the M. jannaschii archaebacterium. To understand the role played by the dataset size, we compared the cross-evaluation results of smaller datasets, either constructed at random or from specific taxonomic domains such as archaea, bacteria, fungi, invertebrates, plants and vertebrates. The results were confirmed even when the redundancy in the dataset was reduced using UniRef100, UniRef90 or UniRef50 clusters. Conclusions InterPro signatures are a compact and powerful attribute space for the prediction of enzymatic function. This representation makes multi-label machine learning feasible in reasonable time (30 minutes to train on 300,747 instances with 10,852 attributes and 2,201 class values) using the Mulan Binary Relevance Nearest Neighbours algorithm implementation (BR-kNN). PMID:22533924