Enabling Efficient and Confident Annotation of LC−MS Metabolomics Data through MS1 Spectrum and Time Prediction

DOE PAGES

Broeckling, Corey D.; Ganna, Andrea; Layer, Mark; ...

2016-09-08

Liquid chromatography coupled to electrospray ionization-mass spectrometry (LC–ESI-MS) is a versatile and robust platform for metabolomic analysis. However, while ESI is a soft ionization technique, in-source phenomena including multimerization, nonproton cation adduction, and in-source fragmentation complicate interpretation of MS data. Here, we report chromatographic and mass spectrometric behavior of 904 authentic standards collected under conditions identical to a typical nontargeted profiling experiment. The data illustrate that the often high level of complexity in MS spectra is likely to result in misinterpretation during the annotation phase of the experiment and a large overestimation of the number of compounds detected. However, ourmore » analysis of this MS spectral library data indicates that in-source phenomena are not random but depend at least in part on chemical structure. These nonrandom patterns enabled predictions to be made as to which in-source signals are likely to be observed for a given compound. Using the authentic standard spectra as a training set, we modeled the in-source phenomena for all compounds in the Human Metabolome Database to generate a theoretical in-source spectrum and retention time library. A novel spectral similarity matching platform was developed to facilitate efficient spectral searching for nontargeted profiling applications. Taken together, this collection of experimental spectral data, predictive modeling, and informatic tools enables more efficient, reliable, and transparent metabolite annotation.« less

Enabling Efficient and Confident Annotation of LC-MS Metabolomics Data through MS1 Spectrum and Time Prediction

DOE PAGES

Broeckling, Corey D.; Ganna, Andrea; Layer, Mark; ...

2016-08-25

Liquid chromatography coupled to electrospray ionization-mass spectrometry (LC–ESI-MS) is a versatile and robust platform for metabolomic analysis. However, while ESI is a soft ionization technique, in-source phenomena including multimerization, nonproton cation adduction, and in-source fragmentation complicate interpretation of MS data. Here, we report chromatographic and mass spectrometric behavior of 904 authentic standards collected under conditions identical to a typical nontargeted profiling experiment. The data illustrate that the often high level of complexity in MS spectra is likely to result in misinterpretation during the annotation phase of the experiment and a large overestimation of the number of compounds detected. However, ourmore » analysis of this MS spectral library data indicates that in-source phenomena are not random but depend at least in part on chemical structure. These nonrandom patterns enabled predictions to be made as to which in-source signals are likely to be observed for a given compound. Using the authentic standard spectra as a training set, we modeled the in-source phenomena for all compounds in the Human Metabolome Database to generate a theoretical in-source spectrum and retention time library. A novel spectral similarity matching platform was developed to facilitate efficient spectral searching for nontargeted profiling applications. Taken together, this collection of experimental spectral data, predictive modeling, and informatic tools enables more efficient, reliable, and transparent metabolite annotation.« less

Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics.

PubMed

Vaniya, Arpana; Fiehn, Oliver

2015-06-01

Identification of unknown metabolites is the bottleneck in advancing metabolomics, leaving interpretation of metabolomics results ambiguous. The chemical diversity of metabolism is vast, making structure identification arduous and time consuming. Currently, comprehensive analysis of mass spectra in metabolomics is limited to library matching, but tandem mass spectral libraries are small compared to the large number of compounds found in the biosphere, including xenobiotics. Resolving this bottleneck requires richer data acquisition and better computational tools. Multi-stage mass spectrometry (MSn) trees show promise to aid in this regard. Fragmentation trees explore the fragmentation process, generate fragmentation rules and aid in sub-structure identification, while mass spectral trees delineate the dependencies in multi-stage MS of collision-induced dissociations. This review covers advancements over the past 10 years as a tool for metabolite identification, including algorithms, software and databases used to build and to implement fragmentation trees and mass spectral annotations.

Updates in metabolomics tools and resources: 2014-2015.

PubMed

Misra, Biswapriya B; van der Hooft, Justin J J

2016-01-01

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources--in the form of tools, software, and databases--is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

HMDB 3.0--The Human Metabolome Database in 2013.

PubMed

Wishart, David S; Jewison, Timothy; Guo, An Chi; Wilson, Michael; Knox, Craig; Liu, Yifeng; Djoumbou, Yannick; Mandal, Rupasri; Aziat, Farid; Dong, Edison; Bouatra, Souhaila; Sinelnikov, Igor; Arndt, David; Xia, Jianguo; Liu, Philip; Yallou, Faizath; Bjorndahl, Trent; Perez-Pineiro, Rolando; Eisner, Roman; Allen, Felicity; Neveu, Vanessa; Greiner, Russ; Scalbert, Augustin

2013-01-01

The Human Metabolome Database (HMDB) (www.hmdb.ca) is a resource dedicated to providing scientists with the most current and comprehensive coverage of the human metabolome. Since its first release in 2007, the HMDB has been used to facilitate research for nearly 1000 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 3.0) has been significantly expanded and enhanced over the 2009 release (version 2.0). In particular, the number of annotated metabolite entries has grown from 6500 to more than 40,000 (a 600% increase). This enormous expansion is a result of the inclusion of both 'detected' metabolites (those with measured concentrations or experimental confirmation of their existence) and 'expected' metabolites (those for which biochemical pathways are known or human intake/exposure is frequent but the compound has yet to be detected in the body). The latest release also has greatly increased the number of metabolites with biofluid or tissue concentration data, the number of compounds with reference spectra and the number of data fields per entry. In addition to this expansion in data quantity, new database visualization tools and new data content have been added or enhanced. These include better spectral viewing tools, more powerful chemical substructure searches, an improved chemical taxonomy and better, more interactive pathway maps. This article describes these enhancements to the HMDB, which was previously featured in the 2009 NAR Database Issue. (Note to referees, HMDB 3.0 will go live on 18 September 2012.).

Automated LC-HRMS(/MS) Approach for the Annotation of Fragment Ions Derived from Stable Isotope Labeling-Assisted Untargeted Metabolomics

PubMed Central

2014-01-01

Structure elucidation of biological compounds is still a major bottleneck of untargeted LC-HRMS approaches in metabolomics research. The aim of the present study was to combine stable isotope labeling and tandem mass spectrometry for the automated interpretation of the elemental composition of fragment ions and thereby facilitate the structural characterization of metabolites. The software tool FragExtract was developed and evaluated with LC-HRMS/MS spectra of both native 12C- and uniformly 13C (U-13C)-labeled analytical standards of 10 fungal substances in pure solvent and spiked into fungal culture filtrate of Fusarium graminearum respectively. Furthermore, the developed approach is exemplified with nine unknown biochemical compounds contained in F. graminearum samples derived from an untargeted metabolomics experiment. The mass difference between the corresponding fragment ions present in the MS/MS spectra of the native and U-13C-labeled compound enabled the assignment of the number of carbon atoms to each fragment signal and allowed the generation of meaningful putative molecular formulas for each fragment ion, which in turn also helped determine the elemental composition of the precursor ion. Compared to laborious manual analysis of the MS/MS spectra, the presented algorithm marks an important step toward efficient fragment signal elucidation and structure annotation of metabolites in future untargeted metabolomics studies. Moreover, as demonstrated for a fungal culture sample, FragExtract also assists the characterization of unknown metabolites, which are not contained in databases, and thus exhibits a significant contribution to untargeted metabolomics research. PMID:24965664

A metabolomics guided exploration of marine natural product chemical space.

PubMed

Floros, Dimitrios J; Jensen, Paul R; Dorrestein, Pieter C; Koyama, Nobuhiro

2016-09-01

Natural products from culture collections have enormous impact in advancing discovery programs for metabolites of biotechnological importance. These discovery efforts rely on the metabolomic characterization of strain collections. Many emerging approaches compare metabolomic profiles of such collections, but few enable the analysis and prioritization of thousands of samples from diverse organisms while delivering chemistry specific read outs. In this work we utilize untargeted LC-MS/MS based metabolomics together with molecular networking to. This approach annotated 76 molecular families (a spectral match rate of 28 %), including clinically and biotechnologically important molecules such as valinomycin, actinomycin D, and desferrioxamine E. Targeting a molecular family produced primarily by one microorganism led to the isolation and structure elucidation of two new molecules designated maridric acids A and B. Molecular networking guided exploration of large culture collections allows for rapid dereplication of know molecules and can highlight producers of uniques metabolites. These methods, together with large culture collections and growing databases, allow for data driven strain prioritization with a focus on novel chemistries.

Metabolomics as a Hypothesis-Generating Functional Genomics Tool for the Annotation of Arabidopsis thaliana Genes of “Unknown Function”

PubMed Central

Quanbeck, Stephanie M.; Brachova, Libuse; Campbell, Alexis A.; Guan, Xin; Perera, Ann; He, Kun; Rhee, Seung Y.; Bais, Preeti; Dickerson, Julie A.; Dixon, Philip; Wohlgemuth, Gert; Fiehn, Oliver; Barkan, Lenore; Lange, Iris; Lange, B. Markus; Lee, Insuk; Cortes, Diego; Salazar, Carolina; Shuman, Joel; Shulaev, Vladimir; Huhman, David V.; Sumner, Lloyd W.; Roth, Mary R.; Welti, Ruth; Ilarslan, Hilal; Wurtele, Eve S.; Nikolau, Basil J.

2012-01-01

Metabolomics is the methodology that identifies and measures global pools of small molecules (of less than about 1,000 Da) of a biological sample, which are collectively called the metabolome. Metabolomics can therefore reveal the metabolic outcome of a genetic or environmental perturbation of a metabolic regulatory network, and thus provide insights into the structure and regulation of that network. Because of the chemical complexity of the metabolome and limitations associated with individual analytical platforms for determining the metabolome, it is currently difficult to capture the complete metabolome of an organism or tissue, which is in contrast to genomics and transcriptomics. This paper describes the analysis of Arabidopsis metabolomics data sets acquired by a consortium that includes five analytical laboratories, bioinformaticists, and biostatisticians, which aims to develop and validate metabolomics as a hypothesis-generating functional genomics tool. The consortium is determining the metabolomes of Arabidopsis T-DNA mutant stocks, grown in standardized controlled environment optimized to minimize environmental impacts on the metabolomes. Metabolomics data were generated with seven analytical platforms, and the combined data is being provided to the research community to formulate initial hypotheses about genes of unknown function (GUFs). A public database (www.PlantMetabolomics.org) has been developed to provide the scientific community with access to the data along with tools to allow for its interactive analysis. Exemplary datasets are discussed to validate the approach, which illustrate how initial hypotheses can be generated from the consortium-produced metabolomics data, integrated with prior knowledge to provide a testable hypothesis concerning the functionality of GUFs. PMID:22645570

MIDAS: a database-searching algorithm for metabolite identification in metabolomics.

PubMed

Wang, Yingfeng; Kora, Guruprasad; Bowen, Benjamin P; Pan, Chongle

2014-10-07

A database searching approach can be used for metabolite identification in metabolomics by matching measured tandem mass spectra (MS/MS) against the predicted fragments of metabolites in a database. Here, we present the open-source MIDAS algorithm (Metabolite Identification via Database Searching). To evaluate a metabolite-spectrum match (MSM), MIDAS first enumerates possible fragments from a metabolite by systematic bond dissociation, then calculates the plausibility of the fragments based on their fragmentation pathways, and finally scores the MSM to assess how well the experimental MS/MS spectrum from collision-induced dissociation (CID) is explained by the metabolite's predicted CID MS/MS spectrum. MIDAS was designed to search high-resolution tandem mass spectra acquired on time-of-flight or Orbitrap mass spectrometer against a metabolite database in an automated and high-throughput manner. The accuracy of metabolite identification by MIDAS was benchmarked using four sets of standard tandem mass spectra from MassBank. On average, for 77% of original spectra and 84% of composite spectra, MIDAS correctly ranked the true compounds as the first MSMs out of all MetaCyc metabolites as decoys. MIDAS correctly identified 46% more original spectra and 59% more composite spectra at the first MSMs than an existing database-searching algorithm, MetFrag. MIDAS was showcased by searching a published real-world measurement of a metabolome from Synechococcus sp. PCC 7002 against the MetaCyc metabolite database. MIDAS identified many metabolites missed in the previous study. MIDAS identifications should be considered only as candidate metabolites, which need to be confirmed using standard compounds. To facilitate manual validation, MIDAS provides annotated spectra for MSMs and labels observed mass spectral peaks with predicted fragments. The database searching and manual validation can be performed online at http://midas.omicsbio.org.

MetaMapR: pathway independent metabolomic network analysis incorporating unknowns.

PubMed

Grapov, Dmitry; Wanichthanarak, Kwanjeera; Fiehn, Oliver

2015-08-15

Metabolic network mapping is a widely used approach for integration of metabolomic experimental results with biological domain knowledge. However, current approaches can be limited by biochemical domain or pathway knowledge which results in sparse disconnected graphs for real world metabolomic experiments. MetaMapR integrates enzymatic transformations with metabolite structural similarity, mass spectral similarity and empirical associations to generate richly connected metabolic networks. This open source, web-based or desktop software, written in the R programming language, leverages KEGG and PubChem databases to derive associations between metabolites even in cases where biochemical domain or molecular annotations are unknown. Network calculation is enhanced through an interface to the Chemical Translation System, which allows metabolite identifier translation between >200 common biochemical databases. Analysis results are presented as interactive visualizations or can be exported as high-quality graphics and numerical tables which can be imported into common network analysis and visualization tools. Freely available at http://dgrapov.github.io/MetaMapR/. Requires R and a modern web browser. Installation instructions, tutorials and application examples are available at http://dgrapov.github.io/MetaMapR/. ofiehn@ucdavis.edu. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Digging into the low molecular weight peptidome with the OligoNet web server.

PubMed

Liu, Youzhong; Forcisi, Sara; Lucio, Marianna; Harir, Mourad; Bahut, Florian; Deleris-Bou, Magali; Krieger-Weber, Sibylle; Gougeon, Régis D; Alexandre, Hervé; Schmitt-Kopplin, Philippe

2017-09-15

Bioactive peptides play critical roles in regulating many biological processes. Recently, natural short peptides biomarkers are drawing significant attention and are considered as "hidden treasure" of drug candidates. High resolution and high mass accuracy provided by mass spectrometry (MS)-based untargeted metabolomics would enable the rapid detection and wide coverage of the low-molecular-weight peptidome. However, translating unknown masses (<1 500 Da) into putative peptides is often limited due to the lack of automatic data processing tools and to the limit of peptide databases. The web server OligoNet responds to this challenge by attempting to decompose each individual mass into a combination of amino acids out of metabolomics datasets. It provides an additional network-based data interpretation named "Peptide degradation network" (PDN), which unravels interesting relations between annotated peptides and generates potential functional patterns. The ab initio PDN built from yeast metabolic profiling data shows a great similarity with well-known metabolic networks, and could aid biological interpretation. OligoNet allows also an easy evaluation and interpretation of annotated peptides in systems biology, and is freely accessible at https://daniellyz200608105.shinyapps.io/OligoNet/ .

MINEs: Open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics

DOE PAGES

Jeffryes, James G.; Colastani, Ricardo L.; Elbadawi-Sidhu, Mona; ...

2015-08-28

Metabolomics have 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 likelymore » 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. 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.« less

Metabolic changes associated with papillary thyroid carcinoma: A nuclear magnetic resonance-based metabolomics study.

PubMed

Li, Yanyun; Chen, Minjian; Liu, Cuiping; Xia, Yankai; Xu, Bo; Hu, Yanhui; Chen, Ting; Shen, Meiping; Tang, Wei

2018-05-01

Papillary thyroid carcinoma (PTC) is the most common thyroid cancer. Nuclear magnetic resonance (NMR)‑based metabolomic technique is the gold standard in metabolite structural elucidation, and can provide different coverage of information compared with other metabolomic techniques. Here, we firstly conducted NMR based metabolomics study regarding detailed metabolic changes especially metabolic pathway changes related to PTC pathogenesis. 1H NMR-based metabolomic technique was adopted in conju-nction with multivariate analysis to analyze matched tumor and normal thyroid tissues obtained from 16 patients. The results were further annotated with Kyoto Encyclopedia of Genes and Genomes (KEGG), and Human Metabolome Database, and then were analyzed using modules of pathway analysis and enrichment analysis of MetaboAnalyst 3.0. Based on the analytical techniques, we established the models of principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal partial least-squares discriminant analysis (OPLS‑DA) which could discriminate PTC from normal thyroid tissue, and found 15 robust differentiated metabolites from two OPLS-DA models. We identified 8 KEGG pathways and 3 pathways of small molecular pathway database which were significantly related to PTC by using pathway analysis and enrichment analysis, respectively, through which we identified metabolisms related to PTC including branched chain amino acid metabolism (leucine and valine), other amino acid metabolism (glycine and taurine), glycolysis (lactate), tricarboxylic acid cycle (citrate), choline metabolism (choline, ethanolamine and glycerolphosphocholine) and lipid metabolism (very-low‑density lipoprotein and low-density lipoprotein). In conclusion, the PTC was characterized with increased glycolysis and inhibited tricarboxylic acid cycle, increased oncogenic amino acids as well as abnormal choline and lipid metabolism. The findings in this study provide new insights into detailed metabolic changes of PTC, and hold great potential in the treatment of PTC.

Analysis of metabolomics datasets with high-performance computing and metabolite atlases

DOE PAGES

Yao, Yushu; Sun, Terence; Wang, Tony; ...

2015-07-20

Even with the widespread use of liquid chromatography mass spectrometry (LC/MS) based metabolomics, there are still a number of challenges facing this promising technique. Many, diverse experimental workflows exist; yet there is a lack of infrastructure and systems for tracking and sharing of information. Here, we describe the Metabolite Atlas framework and interface that provides highly-efficient, web-based access to raw mass spectrometry data in concert with assertions about chemicals detected to help address some of these challenges. This integration, by design, enables experimentalists to explore their raw data, specify and refine features annotations such that they can be leveraged formore » future experiments. Fast queries of the data through the web using SciDB, a parallelized database for high performance computing, make this process operate quickly. Furthermore, by using scripting containers, such as IPython or Jupyter, to analyze the data, scientists can utilize a wide variety of freely available graphing, statistics, and information management resources. In addition, the interfaces facilitate integration with systems biology tools to ultimately link metabolomics data with biological models.« less

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.

CAMERA: An integrated strategy for compound spectra extraction and annotation of LC/MS data sets

PubMed Central

Kuhl, Carsten; Tautenhahn, Ralf; Böttcher, Christoph; Larson, Tony R.; Neumann, Steffen

2013-01-01

Liquid chromatography coupled to mass spectrometry is routinely used for metabolomics experiments. In contrast to the fairly routine and automated data acquisition steps, subsequent compound annotation and identification require extensive manual analysis and thus form a major bottle neck in data interpretation. Here we present CAMERA, a Bioconductor package integrating algorithms to extract compound spectra, annotate isotope and adduct peaks, and propose the accurate compound mass even in highly complex data. To evaluate the algorithms, we compared the annotation of CAMERA against a manually defined annotation for a mixture of known compounds spiked into a complex matrix at different concentrations. CAMERA successfully extracted accurate masses for 89.7% and 90.3% of the annotatable compounds in positive and negative ion mode, respectively. Furthermore, we present a novel annotation approach that combines spectral information of data acquired in opposite ion modes to further improve the annotation rate. We demonstrate the utility of CAMERA in two different, easily adoptable plant metabolomics experiments, where the application of CAMERA drastically reduced the amount of manual analysis. PMID:22111785

Annotation: a computational solution for streamlining metabolomics analysis

PubMed Central

Domingo-Almenara, Xavier; Montenegro-Burke, J. Rafael; Benton, H. Paul; Siuzdak, Gary

2017-01-01

Metabolite identification is still considered an imposing bottleneck in liquid chromatography mass spectrometry (LC/MS) untargeted metabolomics. The identification workflow usually begins with detecting relevant LC/MS peaks via peak-picking algorithms and retrieving putative identities based on accurate mass searching. However, accurate mass search alone provides poor evidence for metabolite identification. For this reason, computational annotation is used to reveal the underlying metabolites monoisotopic masses, improving putative identification in addition to confirmation with tandem mass spectrometry. This review examines LC/MS data from a computational and analytical perspective, focusing on the occurrence of neutral losses and in-source fragments, to understand the challenges in computational annotation methodologies. Herein, we examine the state-of-the-art strategies for computational annotation including: (i) peak grouping or full scan (MS1) pseudo-spectra extraction, i.e., clustering all mass spectral signals stemming from each metabolite; (ii) annotation using ion adduction and mass distance among ion peaks; (iii) incorporation of biological knowledge such as biotransformations or pathways; (iv) tandem MS data; and (v) metabolite retention time calibration, usually achieved by prediction from molecular descriptors. Advantages and pitfalls of each of these strategies are discussed, as well as expected future trends in computational annotation. PMID:29039932

Told through the wine: A liquid chromatography-mass spectrometry interplatform comparison reveals the influence of the global approach on the final annotated metabolites in non-targeted metabolomics.

PubMed

Díaz, Ramon; Gallart-Ayala, Hector; Sancho, Juan V; Nuñez, Oscar; Zamora, Tatiana; Martins, Claudia P B; Hernández, Félix; Hernández-Cassou, Santiago; Saurina, Javier; Checa, Antonio

2016-02-12

This work focuses on the influence of the selected LC-HRMS platform on the final annotated compounds in non-targeted metabolomics. Two platforms that differed in columns, mobile phases, gradients, chromatographs, mass spectrometers (Orbitrap [Platform#1] and Q-TOF [Platform#2]), data processing and marker selection protocols were compared. A total of 42 wines samples from three different protected denomination of origin (PDO) were analyzed. At the feature level, good (O)PLS-DA models were obtained for both platforms (Q(2)[Platform#1]=0.89, 0.83 and 0.72; Q(2)[Platform#2]=0.86, 0.86 and 0.77 for Penedes, Ribera del Duero and Rioja wines respectively) with 100% correctly classified samples in all cases. At the annotated metabolite level, platforms proposed 9 and 8 annotated metabolites respectively which were identified by matching standards or the MS/MS spectra of the compounds. At this stage, there was no coincidence among platforms regarding the suggested metabolites. When screened on the raw data, 6 and 5 of these compounds were detected on the other platform with a similar trend. Some of the detected metabolites showed complimentary information when integrated on biological pathways. Through the use of some examples at the annotated metabolite level, possible explanations of this initial divergence on the results are presented. This work shows the complications that may arise on the comparison of non-targeted metabolomics platforms even when metabolite focused approaches are used in the identification. Copyright © 2016 Elsevier B.V. All rights reserved.

Functional Analysis of Metabolomics Data.

PubMed

Chagoyen, Mónica; López-Ibáñez, Javier; Pazos, Florencio

2016-01-01

Metabolomics aims at characterizing the repertory of small chemical compounds in a biological sample. As it becomes more massive and larger sets of compounds are detected, a functional analysis is required to convert these raw lists of compounds into biological knowledge. The most common way of performing such analysis is "annotation enrichment analysis," also used in transcriptomics and proteomics. This approach extracts the annotations overrepresented in the set of chemical compounds arisen in a given experiment. Here, we describe the protocols for performing such analysis as well as for visualizing a set of compounds in different representations of the metabolic networks, in both cases using free accessible web tools.

Recent Progress in the Development of Metabolome Databases for Plant Systems Biology

PubMed Central

Fukushima, Atsushi; Kusano, Miyako

2013-01-01

Metabolomics has grown greatly as a functional genomics tool, and has become an invaluable diagnostic tool for biochemical phenotyping of biological systems. Over the past decades, a number of databases involving information related to mass spectra, compound names and structures, statistical/mathematical models and metabolic pathways, and metabolite profile data have been developed. Such databases complement each other and support efficient growth in this area, although the data resources remain scattered across the World Wide Web. Here, we review available metabolome databases and summarize the present status of development of related tools, particularly focusing on the plant metabolome. Data sharing discussed here will pave way for the robust interpretation of metabolomic data and advances in plant systems biology. PMID:23577015

MeRy-B, a metabolomic database and knowledge base for exploring plant primary metabolism.

PubMed

Deborde, Catherine; Jacob, Daniel

2014-01-01

Plant primary metabolites are organic compounds that are common to all or most plant species and are essential for plant growth, development, and reproduction. They are intermediates and products of metabolism involved in photosynthesis and other biosynthetic processes. Primary metabolites belong to different compound families, mainly carbohydrates, organic acids, amino acids, nucleotides, fatty acids, steroids, or lipids. Until recently, unlike the Human Metabolome Database ( http://www.hmdb.ca ) dedicated to human metabolism, there was no centralized database or repository dedicated exclusively to the plant kingdom that contained information on metabolites and their concentrations in a detailed experimental context. MeRy-B is the first platform for plant (1)H-NMR metabolomic profiles (MeRy-B, http://bit.ly/meryb ), designed to provide a knowledge base of curated plant profiles and metabolites obtained by NMR, together with the corresponding experimental and analytical metadata. MeRy-B contains lists of plant metabolites, mostly primary metabolites and unknown compounds, with information about experimental conditions, the factors studied, and metabolite concentrations for 19 different plant species (Arabidopsis, broccoli, daphne, grape, maize, barrel clover, melon, Ostreococcus tauri, palm date, palm tree, peach, pine tree, eucalyptus, plantain rice, strawberry, sugar beet, tomato, vanilla), compiled from more than 2,300 annotated NMR profiles for various organs or tissues deposited by 30 different private or public contributors in September 2013. Currently, about half of the data deposited in MeRy-B is publicly available. In this chapter, readers will be shown how to (1) navigate through and retrieve data of publicly available projects on MeRy-B website; (2) visualize lists of experimentally identified metabolites and their concentrations in all plant species present in MeRy-B; (3) get primary metabolite list for a particular plant species in MeRy-B; and for a particular tissue (4) find information on a primary metabolite regardless of the species.

Tools for the functional interpretation of metabolomic experiments.

PubMed

Chagoyen, Monica; Pazos, Florencio

2013-11-01

The so-called 'omics' approaches used in modern biology aim at massively characterizing the molecular repertories of living systems at different levels. Metabolomics is one of the last additions to the 'omics' family and it deals with the characterization of the set of metabolites in a given biological system. As metabolomic techniques become more massive and allow characterizing larger sets of metabolites, automatic methods for analyzing these sets in order to obtain meaningful biological information are required. Only recently the first tools specifically designed for this task in metabolomics appeared. They are based on approaches previously used in transcriptomics and other 'omics', such as annotation enrichment analysis. These, together with generic tools for metabolic analysis and visualization not specifically designed for metabolomics will for sure be in the toolbox of the researches doing metabolomic experiments in the near future.

MetaboLights: towards a new COSMOS of metabolomics data management.

PubMed

Steinbeck, Christoph; Conesa, Pablo; Haug, Kenneth; Mahendraker, Tejasvi; Williams, Mark; Maguire, Eamonn; Rocca-Serra, Philippe; Sansone, Susanna-Assunta; Salek, Reza M; Griffin, Julian L

2012-10-01

Exciting funding initiatives are emerging in Europe and the US for metabolomics data production, storage, dissemination and analysis. This is based on a rich ecosystem of resources around the world, which has been build during the past ten years, including but not limited to resources such as MassBank in Japan and the Human Metabolome Database in Canada. Now, the European Bioinformatics Institute has launched MetaboLights, a database for metabolomics experiments and the associated metadata (http://www.ebi.ac.uk/metabolights). It is the first comprehensive, cross-species, cross-platform metabolomics database maintained by one of the major open access data providers in molecular biology. In October, the European COSMOS consortium will start its work on Metabolomics data standardization, publication and dissemination workflows. The NIH in the US is establishing 6-8 metabolomics services cores as well as a national metabolomics repository. This communication reports about MetaboLights as a new resource for Metabolomics research, summarises the related developments and outlines how they may consolidate the knowledge management in this third large omics field next to proteomics and genomics.

Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics

PubMed Central

Giacomoni, Franck; Le Corguillé, Gildas; Monsoor, Misharl; Landi, Marion; Pericard, Pierre; Pétéra, Mélanie; Duperier, Christophe; Tremblay-Franco, Marie; Martin, Jean-François; Jacob, Daniel; Goulitquer, Sophie; Thévenot, Etienne A.; Caron, Christophe

2015-01-01

Summary: The complex, rapidly evolving field of computational metabolomics calls for collaborative infrastructures where the large volume of new algorithms for data pre-processing, statistical analysis and annotation can be readily integrated whatever the language, evaluated on reference datasets and chained to build ad hoc workflows for users. We have developed Workflow4Metabolomics (W4M), the first fully open-source and collaborative online platform for computational metabolomics. W4M is a virtual research environment built upon the Galaxy web-based platform technology. It enables ergonomic integration, exchange and running of individual modules and workflows. Alternatively, the whole W4M framework and computational tools can be downloaded as a virtual machine for local installation. Availability and implementation: http://workflow4metabolomics.org homepage enables users to open a private account and access the infrastructure. W4M is developed and maintained by the French Bioinformatics Institute (IFB) and the French Metabolomics and Fluxomics Infrastructure (MetaboHUB). Contact: contact@workflow4metabolomics.org PMID:25527831

Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics.

PubMed

Giacomoni, Franck; Le Corguillé, Gildas; Monsoor, Misharl; Landi, Marion; Pericard, Pierre; Pétéra, Mélanie; Duperier, Christophe; Tremblay-Franco, Marie; Martin, Jean-François; Jacob, Daniel; Goulitquer, Sophie; Thévenot, Etienne A; Caron, Christophe

2015-05-01

The complex, rapidly evolving field of computational metabolomics calls for collaborative infrastructures where the large volume of new algorithms for data pre-processing, statistical analysis and annotation can be readily integrated whatever the language, evaluated on reference datasets and chained to build ad hoc workflows for users. We have developed Workflow4Metabolomics (W4M), the first fully open-source and collaborative online platform for computational metabolomics. W4M is a virtual research environment built upon the Galaxy web-based platform technology. It enables ergonomic integration, exchange and running of individual modules and workflows. Alternatively, the whole W4M framework and computational tools can be downloaded as a virtual machine for local installation. http://workflow4metabolomics.org homepage enables users to open a private account and access the infrastructure. W4M is developed and maintained by the French Bioinformatics Institute (IFB) and the French Metabolomics and Fluxomics Infrastructure (MetaboHUB). contact@workflow4metabolomics.org. © The Author 2014. Published by Oxford University Press.

The Human Urine Metabolome

PubMed Central

Bouatra, Souhaila; Aziat, Farid; Mandal, Rupasri; Guo, An Chi; Wilson, Michael R.; Knox, Craig; Bjorndahl, Trent C.; Krishnamurthy, Ramanarayan; Saleem, Fozia; Liu, Philip; Dame, Zerihun T.; Poelzer, Jenna; Huynh, Jessica; Yallou, Faizath S.; Psychogios, Nick; Dong, Edison; Bogumil, Ralf; Roehring, Cornelia; Wishart, David S.

2013-01-01

Urine has long been a “favored” biofluid among metabolomics researchers. It is sterile, easy-to-obtain in large volumes, largely free from interfering proteins or lipids and chemically complex. However, this chemical complexity has also made urine a particularly difficult substrate to fully understand. As a biological waste material, urine typically contains metabolic breakdown products from a wide range of foods, drinks, drugs, environmental contaminants, endogenous waste metabolites and bacterial by-products. Many of these compounds are poorly characterized and poorly understood. In an effort to improve our understanding of this biofluid we have undertaken a comprehensive, quantitative, metabolome-wide characterization of human urine. This involved both computer-aided literature mining and comprehensive, quantitative experimental assessment/validation. The experimental portion employed NMR spectroscopy, gas chromatography mass spectrometry (GC-MS), direct flow injection mass spectrometry (DFI/LC-MS/MS), inductively coupled plasma mass spectrometry (ICP-MS) and high performance liquid chromatography (HPLC) experiments performed on multiple human urine samples. This multi-platform metabolomic analysis allowed us to identify 445 and quantify 378 unique urine metabolites or metabolite species. The different analytical platforms were able to identify (quantify) a total of: 209 (209) by NMR, 179 (85) by GC-MS, 127 (127) by DFI/LC-MS/MS, 40 (40) by ICP-MS and 10 (10) by HPLC. Our use of multiple metabolomics platforms and technologies allowed us to identify several previously unknown urine metabolites and to substantially enhance the level of metabolome coverage. It also allowed us to critically assess the relative strengths and weaknesses of different platforms or technologies. The literature review led to the identification and annotation of another 2206 urinary compounds and was used to help guide the subsequent experimental studies. An online database containing the complete set of 2651 confirmed human urine metabolite species, their structures (3079 in total), concentrations, related literature references and links to their known disease associations are freely available at http://www.urinemetabolome.ca. PMID:24023812

Changes of Metabolomic Profile in Helianthus annuus under Exposure to Chromium(VI) Studied by capHPLC-ESI-QTOF-MS and MS/MS

PubMed Central

Gonzalez Ibarra, Alan Alexander; Wrobel, Kazimierz; Yanez Barrientos, Eunice; Corrales Escobosa, Alma Rosa; Gutierrez Corona, J. Felix; Enciso Donis, Israel

2017-01-01

The application of capHPLC-ESI-QTOF-MS and MS/MS to study the impact of Cr(VI) on metabolites profile in Helianthus annuus is reported. Germinated seeds were grown hydroponically in the presence of Cr(VI) (25 mgCr/L) and root extracts of the exposed and control plants were analyzed by untargeted metabolomic approach. The main goal was to detect which metabolite groups were mostly affected by Cr(VI) stress; two data analysis tools (ProfileAnalysis, Bruker, and online XCMS) were used under criteria of intensity threshold 5 · 104 cps, fold change ≥ 5, and p ≤ 0.01, yielding precursor ions. Molecular formulas were assigned based on data processing with two computational tools (SIRIUS and MS-Finder); annotation of candidate structures was performed by database search using CSI:FingerID and MS-Finder. Even though ultimate identification has not been achieved, it was demonstrated that secondary metabolism became activated under Cr(VI) stress. Among 42 candidate compounds returned from database search for seven molecular formulas, ten structures corresponded to isocoumarin derivatives and eleven were sesquiterpenes or sesquiterpene lactones; three benzofurans and four glycoside or pyrane derivatives of phenolic compounds were also suggested. To gain further insight on the effect of Cr(VI) in sunflower, isocoumarins and sesquiterpenes were selected as the target compounds for future study. PMID:29359067

A Computational Solution to Automatically Map Metabolite Libraries in the Context of Genome Scale Metabolic Networks.

PubMed

Merlet, Benjamin; Paulhe, Nils; Vinson, Florence; Frainay, Clément; Chazalviel, Maxime; Poupin, Nathalie; Gloaguen, Yoann; Giacomoni, Franck; Jourdan, Fabien

2016-01-01

This article describes a generic programmatic method for mapping chemical compound libraries on organism-specific metabolic networks from various databases (KEGG, BioCyc) and flat file formats (SBML and Matlab files). We show how this pipeline was successfully applied to decipher the coverage of chemical libraries set up by two metabolomics facilities MetaboHub (French National infrastructure for metabolomics and fluxomics) and Glasgow Polyomics (GP) on the metabolic networks available in the MetExplore web server. The present generic protocol is designed to formalize and reduce the volume of information transfer between the library and the network database. Matching of metabolites between libraries and metabolic networks is based on InChIs or InChIKeys and therefore requires that these identifiers are specified in both libraries and networks. In addition to providing covering statistics, this pipeline also allows the visualization of mapping results in the context of metabolic networks. In order to achieve this goal, we tackled issues on programmatic interaction between two servers, improvement of metabolite annotation in metabolic networks and automatic loading of a mapping in genome scale metabolic network analysis tool MetExplore. It is important to note that this mapping can also be performed on a single or a selection of organisms of interest and is thus not limited to large facilities.

ChlamyCyc: an integrative systems biology database and web-portal for Chlamydomonas reinhardtii.

PubMed

May, Patrick; Christian, Jan-Ole; Kempa, Stefan; Walther, Dirk

2009-05-04

The unicellular green alga Chlamydomonas reinhardtii is an important eukaryotic model organism for the study of photosynthesis and plant growth. In the era of modern high-throughput technologies there is an imperative need to integrate large-scale data sets from high-throughput experimental techniques using computational methods and database resources to provide comprehensive information about the molecular and cellular organization of a single organism. In the framework of the German Systems Biology initiative GoFORSYS, a pathway database and web-portal for Chlamydomonas (ChlamyCyc) was established, which currently features about 250 metabolic pathways with associated genes, enzymes, and compound information. ChlamyCyc was assembled using an integrative approach combining the recently published genome sequence, bioinformatics methods, and experimental data from metabolomics and proteomics experiments. We analyzed and integrated a combination of primary and secondary database resources, such as existing genome annotations from JGI, EST collections, orthology information, and MapMan classification. ChlamyCyc provides a curated and integrated systems biology repository that will enable and assist in systematic studies of fundamental cellular processes in Chlamydomonas. The ChlamyCyc database and web-portal is freely available under http://chlamycyc.mpimp-golm.mpg.de.

High precision mass measurements for wine metabolomics

PubMed Central

Roullier-Gall, Chloé; Witting, Michael; Gougeon, Régis D.; Schmitt-Kopplin, Philippe

2014-01-01

An overview of the critical steps for the non-targeted Ultra-High Performance Liquid Chromatography coupled with Quadrupole Time-of-Flight Mass Spectrometry (UPLC-Q-ToF-MS) analysis of wine chemistry is given, ranging from the study design, data preprocessing and statistical analyses, to markers identification. UPLC-Q-ToF-MS data was enhanced by the alignment of exact mass data from FTICR-MS, and marker peaks were identified using UPLC-Q-ToF-MS2. In combination with multivariate statistical tools and the annotation of peaks with metabolites from relevant databases, this analytical process provides a fine description of the chemical complexity of wines, as exemplified in the case of red (Pinot noir) and white (Chardonnay) wines from various geographic origins in Burgundy. PMID:25431760

High precision mass measurements for wine metabolomics

NASA Astrophysics Data System (ADS)

Roullier-Gall, Chloé; Witting, Michael; Gougeon, Régis; Schmitt-Kopplin, Philippe

2014-11-01

An overview of the critical steps for the non-targeted Ultra-High Performance Liquid Chromatography coupled with Quadrupole Time-of-Flight Mass Spectrometry (UPLC-Q-ToF-MS) analysis of wine chemistry is given, ranging from the study design, data preprocessing and statistical analyses, to markers identification. UPLC-Q-ToF-MS data was enhanced by the alignment of exact mass data from FTICR-MS, and marker peaks were identified using UPLC-Q-ToF-MS². In combination with multivariate statistical tools and the annotation of peaks with metabolites from relevant databases, this analytical process provides a fine description of the chemical complexity of wines, as exemplified in the case of red (Pinot noir) and white (Chardonnay) wines from various geographic origins in Burgundy.

RaMP: A Comprehensive Relational Database of Metabolomics Pathways for Pathway Enrichment Analysis of Genes and Metabolites

PubMed Central

Zhang, Bofei; Hu, Senyang; Baskin, Elizabeth; Patt, Andrew; Siddiqui, Jalal K.

2018-01-01

The value of metabolomics in translational research is undeniable, and metabolomics data are increasingly generated in large cohorts. The functional interpretation of disease-associated metabolites though is difficult, and the biological mechanisms that underlie cell type or disease-specific metabolomics profiles are oftentimes unknown. To help fully exploit metabolomics data and to aid in its interpretation, analysis of metabolomics data with other complementary omics data, including transcriptomics, is helpful. To facilitate such analyses at a pathway level, we have developed RaMP (Relational database of Metabolomics Pathways), which combines biological pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, WikiPathways, and the Human Metabolome DataBase (HMDB). To the best of our knowledge, an off-the-shelf, public database that maps genes and metabolites to biochemical/disease pathways and can readily be integrated into other existing software is currently lacking. For consistent and comprehensive analysis, RaMP enables batch and complex queries (e.g., list all metabolites involved in glycolysis and lung cancer), can readily be integrated into pathway analysis tools, and supports pathway overrepresentation analysis given a list of genes and/or metabolites of interest. For usability, we have developed a RaMP R package (https://github.com/Mathelab/RaMP-DB), including a user-friendly RShiny web application, that supports basic simple and batch queries, pathway overrepresentation analysis given a list of genes or metabolites of interest, and network visualization of gene-metabolite relationships. The package also includes the raw database file (mysql dump), thereby providing a stand-alone downloadable framework for public use and integration with other tools. In addition, the Python code needed to recreate the database on another system is also publicly available (https://github.com/Mathelab/RaMP-BackEnd). Updates for databases in RaMP will be checked multiple times a year and RaMP will be updated accordingly. PMID:29470400

RaMP: A Comprehensive Relational Database of Metabolomics Pathways for Pathway Enrichment Analysis of Genes and Metabolites.

PubMed

Zhang, Bofei; Hu, Senyang; Baskin, Elizabeth; Patt, Andrew; Siddiqui, Jalal K; Mathé, Ewy A

2018-02-22

The value of metabolomics in translational research is undeniable, and metabolomics data are increasingly generated in large cohorts. The functional interpretation of disease-associated metabolites though is difficult, and the biological mechanisms that underlie cell type or disease-specific metabolomics profiles are oftentimes unknown. To help fully exploit metabolomics data and to aid in its interpretation, analysis of metabolomics data with other complementary omics data, including transcriptomics, is helpful. To facilitate such analyses at a pathway level, we have developed RaMP (Relational database of Metabolomics Pathways), which combines biological pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, WikiPathways, and the Human Metabolome DataBase (HMDB). To the best of our knowledge, an off-the-shelf, public database that maps genes and metabolites to biochemical/disease pathways and can readily be integrated into other existing software is currently lacking. For consistent and comprehensive analysis, RaMP enables batch and complex queries (e.g., list all metabolites involved in glycolysis and lung cancer), can readily be integrated into pathway analysis tools, and supports pathway overrepresentation analysis given a list of genes and/or metabolites of interest. For usability, we have developed a RaMP R package (https://github.com/Mathelab/RaMP-DB), including a user-friendly RShiny web application, that supports basic simple and batch queries, pathway overrepresentation analysis given a list of genes or metabolites of interest, and network visualization of gene-metabolite relationships. The package also includes the raw database file (mysql dump), thereby providing a stand-alone downloadable framework for public use and integration with other tools. In addition, the Python code needed to recreate the database on another system is also publicly available (https://github.com/Mathelab/RaMP-BackEnd). Updates for databases in RaMP will be checked multiple times a year and RaMP will be updated accordingly.

A Molecular Framework for Understanding DCIS

DTIC Science & Technology

2016-10-01

well. Pathologic and Clinical Annotation Database A clinical annotation database titled the Breast Oncology Database has been established to...complement the procured SPORE sample characteristics and annotated pathology data. This Breast Oncology Database is an offsite clinical annotation...database adheres to CSMC Enterprise Information Services (EIS) research database security standards. The Breast Oncology Database consists of: 9 Baseline

compMS2Miner: An Automatable Metabolite Identification, Visualization, and Data-Sharing R Package for High-Resolution LC-MS Data Sets.

PubMed

Edmands, William M B; Petrick, Lauren; Barupal, Dinesh K; Scalbert, Augustin; Wilson, Mark J; Wickliffe, Jeffrey K; Rappaport, Stephen M

2017-04-04

A long-standing challenge of untargeted metabolomic profiling by ultrahigh-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) is efficient transition from unknown mass spectral features to confident metabolite annotations. The compMS 2 Miner (Comprehensive MS 2 Miner) package was developed in the R language to facilitate rapid, comprehensive feature annotation using a peak-picker-output and MS 2 data files as inputs. The number of MS 2 spectra that can be collected during a metabolomic profiling experiment far outweigh the amount of time required for pain-staking manual interpretation; therefore, a degree of software workflow autonomy is required for broad-scale metabolite annotation. CompMS 2 Miner integrates many useful tools in a single workflow for metabolite annotation and also provides a means to overview the MS 2 data with a Web application GUI compMS 2 Explorer (Comprehensive MS 2 Explorer) that also facilitates data-sharing and transparency. The automatable compMS 2 Miner workflow consists of the following steps: (i) matching unknown MS 1 features to precursor MS 2 scans, (ii) filtration of spectral noise (dynamic noise filter), (iii) generation of composite mass spectra by multiple similar spectrum signal summation and redundant/contaminant spectra removal, (iv) interpretation of possible fragment ion substructure using an internal database, (v) annotation of unknowns with chemical and spectral databases with prediction of mammalian biotransformation metabolites, wrapper functions for in silico fragmentation software, nearest neighbor chemical similarity scoring, random forest based retention time prediction, text-mining based false positive removal/true positive ranking, chemical taxonomic prediction and differential evolution based global annotation score optimization, and (vi) network graph visualizations, data curation, and sharing are made possible via the compMS 2 Explorer application. Metabolite identities and comments can also be recorded using an interactive table within compMS 2 Explorer. The utility of the package is illustrated with a data set of blood serum samples from 7 diet induced obese (DIO) and 7 nonobese (NO) C57BL/6J mice, which were also treated with an antibiotic (streptomycin) to knockdown the gut microbiota. The results of fully autonomous and objective usage of compMS 2 Miner are presented here. All automatically annotated spectra output by the workflow are provided in the Supporting Information and can alternatively be explored as publically available compMS 2 Explorer applications for both positive and negative modes ( https://wmbedmands.shinyapps.io/compMS2_mouseSera_POS and https://wmbedmands.shinyapps.io/compMS2_mouseSera_NEG ). The workflow provided rapid annotation of a diversity of endogenous and gut microbially derived metabolites affected by both diet and antibiotic treatment, which conformed to previously published reports. Composite spectra (n = 173) were autonomously matched to entries of the Massbank of North America (MoNA) spectral repository. These experimental and virtual (lipidBlast) spectra corresponded to 29 common endogenous compound classes (e.g., 51 lysophosphatidylcholines spectra) and were then used to calculate the ranking capability of 7 individual scoring metrics. It was found that an average of the 7 individual scoring metrics provided the most effective weighted average ranking ability of 3 for the MoNA matched spectra in spite of potential risk of false positive annotations emerging from automation. Minor structural differences such as relative carbon-carbon double bond positions were found in several cases to affect the correct rank of the MoNA annotated metabolite. The latest release and an example workflow is available in the package vignette ( https://github.com/WMBEdmands/compMS2Miner ) and a version of the published application is available on the shinyapps.io site ( https://wmbedmands.shinyapps.io/compMS2Example ).

Profiling of Altered Metabolomic States in Nicotiana tabacum Cells Induced by Priming Agents

PubMed Central

Mhlongo, Msizi I.; Steenkamp, Paul A.; Piater, Lizelle A.; Madala, Ntakadzeni E.; Dubery, Ian A.

2016-01-01

Metabolomics has developed into a valuable tool for advancing our understanding of plant metabolism. Plant innate immune defenses can be activated and enhanced so that, subsequent to being pre-sensitized, plants are able to launch a stronger and faster defense response upon exposure to pathogenic microorganisms, a phenomenon known as priming. Here, three contrasting chemical activators, namely acibenzolar-S-methyl, azelaic acid and riboflavin, were used to induce a primed state in Nicotiana tabacum cells. Identified biomarkers were then compared to responses induced by three phytohormones—abscisic acid, methyljasmonate, and salicylic acid. Altered metabolomes were studied using a metabolite fingerprinting approach based on liquid chromatography and mass spectrometry. Multivariate data models indicated that these inducers cause time-dependent metabolic perturbations in the cultured cells and revealed biomarkers of which the levels are affected by these agents. A total of 34 metabolites were annotated from the mass spectral data and online databases. Venn diagrams were used to identify common biomarkers as well as those unique to a specific agent. Results implicate 20 cinnamic acid derivatives conjugated to (i) quinic acid (chlorogenic acids), (ii) tyramine, (iii) polyamines, or (iv) glucose as discriminatory biomarkers of priming in tobacco cells. Functional roles for most of these metabolites in plant defense responses could thus be proposed. Metabolites induced by the activators belong to the early phenylpropanoid pathway, which indicates that different stimuli can activate similar pathways but with different metabolite fingerprints. Possible linkages to phytohormone-dependent pathways at a metabolomic level were indicated in the case of cells treated with salicylic acid and methyljasmonate. The results contribute to a better understanding of the priming phenomenon and advance our knowledge of cinnamic acid derivatives as versatile defense metabolites. PMID:27803705

Siderophore biosynthesis coordinately modulated the virulence-associated interactive metabolome of uropathogenic Escherichia coli and human urine.

PubMed

Su, Qiao; Guan, Tianbing; Lv, Haitao

2016-04-14

Uropathogenic Escherichia coli (UPEC) growth in women's bladders during urinary tract infection (UTI) incurs substantial chemical exchange, termed the "interactive metabolome", which primarily accounts for the metabolic costs (utilized metabolome) and metabolic donations (excreted metabolome) between UPEC and human urine. Here, we attempted to identify the individualized interactive metabolome between UPEC and human urine. We were able to distinguish UPEC from non-UPEC by employing a combination of metabolomics and genetics. Our results revealed that the interactive metabolome between UPEC and human urine was markedly different from that between non-UPEC and human urine, and that UPEC triggered much stronger perturbations in the interactive metabolome in human urine. Furthermore, siderophore biosynthesis coordinately modulated the individualized interactive metabolome, which we found to be a critical component of UPEC virulence. The individualized virulence-associated interactive metabolome contained 31 different metabolites and 17 central metabolic pathways that were annotated to host these different metabolites, including energetic metabolism, amino acid metabolism, and gut microbe metabolism. Changes in the activities of these pathways mechanistically pinpointed the virulent capability of siderophore biosynthesis. Together, our findings provide novel insights into UPEC virulence, and we propose that siderophores are potential targets for further discovery of drugs to treat UPEC-induced UTI.

Investigation of the Hepatoprotective Effect of Prunus mume Sieb. et Zucc Extract in a Mouse Model of Alcoholic Liver Injury Through High-Resolution Metabolomics.

PubMed

Khan, Adnan; Pan, Jeong Hoon; Cho, Seongha; Lee, Sojung; Kim, Young Jun; Park, Youngja H

2017-08-01

This study aimed to identify the changes in the metabolomics profile of liver damage caused by alcohol consumption and verify the beneficial effect of Prunus mume Sieb. et Zucc extract (PME) in protection of alcohol-induced injury by attenuating the level of identified metabolites. Mice were treated with PME and saline or untreated once daily for 5 days, followed by alcohol injection. The plasma samples were analyzed using liquid chromatography-mass spectrometry-based high-resolution metabolomics followed by a multivariate statistical analysis using MetaboAnalyst 3.0 to obtain significantly expressed metabolites, using a false discovery rate threshold of q = 0.05. Metabolites were annotated using Metlin database and mapped through Kyoto Encyclopedia of Genes and Genomes (KEGG). Among 4999 total features, 101 features were significant among alcohol- and PME-treated mice groups. All the samples cluster showed a clear separation in the heat map, and the scores plot of orthogonal partial least squares-discriminant analysis (OPLS-DA) model discriminated the three groups. Phosphatidylcholine, Saikosaponin BK1, Ganoderiol I, and N-2-[4-(3,3-dimethylallyloxy) phenyl] ethylcinnamide were among the significant compounds with a low intensity in alcohol group compared to PME group, suggesting that these compounds have a relation in the development of PME's protective effect. The study confirms the hepatoprotective, antioxidant, and anti-inflammatory effects of PME against alcohol-induced liver steatosis, inflammation, and apoptosis.

BioSpider: a web server for automating metabolome annotations.

PubMed

Knox, Craig; Shrivastava, Savita; Stothard, Paul; Eisner, Roman; Wishart, David S

2007-01-01

One of the growing challenges in life science research lies in finding useful, descriptive or quantitative data about newly reported biomolecules (genes, proteins, metabolites and drugs). An even greater challenge is finding information that connects these genes, proteins, drugs or metabolites to each other. Much of this information is scattered through hundreds of different databases, abstracts or books and almost none of it is particularly well integrated. While some efforts are being undertaken at the NCBI and EBI to integrate many different databases together, this still falls short of the goal of having some kind of human-readable synopsis that summarizes the state of knowledge about a given biomolecule - especially small molecules. To address this shortfall, we have developed BioSpider. BioSpider is essentially an automated report generator designed specifically to tabulate and summarize data on biomolecules - both large and small. Specifically, BioSpider allows users to type in almost any kind of biological or chemical identifier (protein/gene name, sequence, accession number, chemical name, brand name, SMILES string, InCHI string, CAS number, etc.) and it returns an in-depth synoptic report (approximately 3-30 pages in length) about that biomolecule and any other biomolecule it may target. This summary includes physico-chemical parameters, images, models, data files, descriptions and predictions concerning the query molecule. BioSpider uses a web-crawler to scan through dozens of public databases and employs a variety of specially developed text mining tools and locally developed prediction tools to find, extract and assemble data for its reports. Because of its breadth, depth and comprehensiveness, we believe BioSpider will prove to be a particularly valuable tool for researchers in metabolomics. BioSpider is available at: www.biospider.ca

xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data.

PubMed

Uppal, Karan; Soltow, Quinlyn A; Strobel, Frederick H; Pittard, W Stephen; Gernert, Kim M; Yu, Tianwei; Jones, Dean P

2013-01-16

Detection of low abundance metabolites is important for de novo mapping of metabolic pathways related to diet, microbiome or environmental exposures. Multiple algorithms are available to extract m/z features from liquid chromatography-mass spectral data in a conservative manner, which tends to preclude detection of low abundance chemicals and chemicals found in small subsets of samples. The present study provides software to enhance such algorithms for feature detection, quality assessment, and annotation. xMSanalyzer is a set of utilities for automated processing of metabolomics data. The utilites can be classified into four main modules to: 1) improve feature detection for replicate analyses by systematic re-extraction with multiple parameter settings and data merger to optimize the balance between sensitivity and reliability, 2) evaluate sample quality and feature consistency, 3) detect feature overlap between datasets, and 4) characterize high-resolution m/z matches to small molecule metabolites and biological pathways using multiple chemical databases. The package was tested with plasma samples and shown to more than double the number of features extracted while improving quantitative reliability of detection. MS/MS analysis of a random subset of peaks that were exclusively detected using xMSanalyzer confirmed that the optimization scheme improves detection of real metabolites. xMSanalyzer is a package of utilities for data extraction, quality control assessment, detection of overlapping and unique metabolites in multiple datasets, and batch annotation of metabolites. The program was designed to integrate with existing packages such as apLCMS and XCMS, but the framework can also be used to enhance data extraction for other LC/MS data software.

Siderophore biosynthesis coordinately modulated the virulence-associated interactive metabolome of uropathogenic Escherichia coli and human urine

PubMed Central

Su, Qiao; Guan, Tianbing; Lv, Haitao

2016-01-01

Uropathogenic Escherichia coli (UPEC) growth in women’s bladders during urinary tract infection (UTI) incurs substantial chemical exchange, termed the “interactive metabolome”, which primarily accounts for the metabolic costs (utilized metabolome) and metabolic donations (excreted metabolome) between UPEC and human urine. Here, we attempted to identify the individualized interactive metabolome between UPEC and human urine. We were able to distinguish UPEC from non-UPEC by employing a combination of metabolomics and genetics. Our results revealed that the interactive metabolome between UPEC and human urine was markedly different from that between non-UPEC and human urine, and that UPEC triggered much stronger perturbations in the interactive metabolome in human urine. Furthermore, siderophore biosynthesis coordinately modulated the individualized interactive metabolome, which we found to be a critical component of UPEC virulence. The individualized virulence-associated interactive metabolome contained 31 different metabolites and 17 central metabolic pathways that were annotated to host these different metabolites, including energetic metabolism, amino acid metabolism, and gut microbe metabolism. Changes in the activities of these pathways mechanistically pinpointed the virulent capability of siderophore biosynthesis. Together, our findings provide novel insights into UPEC virulence, and we propose that siderophores are potential targets for further discovery of drugs to treat UPEC-induced UTI. PMID:27076285

Informatics for Metabolomics.

PubMed

Kusonmano, Kanthida; Vongsangnak, Wanwipa; Chumnanpuen, Pramote

2016-01-01

Metabolome profiling of biological systems has the powerful ability to provide the biological understanding of their metabolic functional states responding to the environmental factors or other perturbations. Tons of accumulative metabolomics data have thus been established since pre-metabolomics era. This is directly influenced by the high-throughput analytical techniques, especially mass spectrometry (MS)- and nuclear magnetic resonance (NMR)-based techniques. Continuously, the significant numbers of informatics techniques for data processing, statistical analysis, and data mining have been developed. The following tools and databases are advanced for the metabolomics society which provide the useful metabolomics information, e.g., the chemical structures, mass spectrum patterns for peak identification, metabolite profiles, biological functions, dynamic metabolite changes, and biochemical transformations of thousands of small molecules. In this chapter, we aim to introduce overall metabolomics studies from pre- to post-metabolomics era and their impact on society. Directing on post-metabolomics era, we provide a conceptual framework of informatics techniques for metabolomics and show useful examples of techniques, tools, and databases for metabolomics data analysis starting from preprocessing toward functional interpretation. Throughout the framework of informatics techniques for metabolomics provided, it can be further used as a scaffold for translational biomedical research which can thus lead to reveal new metabolite biomarkers, potential metabolic targets, or key metabolic pathways for future disease therapy.

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

CycADS: an annotation database system to ease the development and update of BioCyc databases

PubMed Central

Vellozo, Augusto F.; Véron, Amélie S.; Baa-Puyoulet, Patrice; Huerta-Cepas, Jaime; Cottret, Ludovic; Febvay, Gérard; Calevro, Federica; Rahbé, Yvan; Douglas, Angela E.; Gabaldón, Toni; Sagot, Marie-France; Charles, Hubert; Colella, Stefano

2011-01-01

In recent years, genomes from an increasing number of organisms have been sequenced, but their annotation remains a time-consuming process. The BioCyc databases offer a framework for the integrated analysis of metabolic networks. The Pathway tool software suite allows the automated construction of a database starting from an annotated genome, but it requires prior integration of all annotations into a specific summary file or into a GenBank file. To allow the easy creation and update of a BioCyc database starting from the multiple genome annotation resources available over time, we have developed an ad hoc data management system that we called Cyc Annotation Database System (CycADS). CycADS is centred on a specific database model and on a set of Java programs to import, filter and export relevant information. Data from GenBank and other annotation sources (including for example: KAAS, PRIAM, Blast2GO and PhylomeDB) are collected into a database to be subsequently filtered and extracted to generate a complete annotation file. This file is then used to build an enriched BioCyc database using the PathoLogic program of Pathway Tools. The CycADS pipeline for annotation management was used to build the AcypiCyc database for the pea aphid (Acyrthosiphon pisum) whose genome was recently sequenced. The AcypiCyc database webpage includes also, for comparative analyses, two other metabolic reconstruction BioCyc databases generated using CycADS: TricaCyc for Tribolium castaneum and DromeCyc for Drosophila melanogaster. Linked to its flexible design, CycADS offers a powerful software tool for the generation and regular updating of enriched BioCyc databases. The CycADS system is particularly suited for metabolic gene annotation and network reconstruction in newly sequenced genomes. Because of the uniform annotation used for metabolic network reconstruction, CycADS is particularly useful for comparative analysis of the metabolism of different organisms. Database URL: http://www.cycadsys.org PMID:21474551

Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools

PubMed Central

Sud, Manish; Fahy, Eoin; Cotter, Dawn; Azam, Kenan; Vadivelu, Ilango; Burant, Charles; Edison, Arthur; Fiehn, Oliver; Higashi, Richard; Nair, K. Sreekumaran; Sumner, Susan; Subramaniam, Shankar

2016-01-01

The Metabolomics Workbench, available at www.metabolomicsworkbench.org, is a public repository for metabolomics metadata and experimental data spanning various species and experimental platforms, metabolite standards, metabolite structures, protocols, tutorials, and training material and other educational resources. It provides a computational platform to integrate, analyze, track, deposit and disseminate large volumes of heterogeneous data from a wide variety of metabolomics studies including mass spectrometry (MS) and nuclear magnetic resonance spectrometry (NMR) data spanning over 20 different species covering all the major taxonomic categories including humans and other mammals, plants, insects, invertebrates and microorganisms. Additionally, a number of protocols are provided for a range of metabolite classes, sample types, and both MS and NMR-based studies, along with a metabolite structure database. The metabolites characterized in the studies available on the Metabolomics Workbench are linked to chemical structures in the metabolite structure database to facilitate comparative analysis across studies. The Metabolomics Workbench, part of the data coordinating effort of the National Institute of Health (NIH) Common Fund's Metabolomics Program, provides data from the Common Fund's Metabolomics Resource Cores, metabolite standards, and analysis tools to the wider metabolomics community and seeks data depositions from metabolomics researchers across the world. PMID:26467476

Assessing Public Metabolomics Metadata, Towards Improving Quality.

PubMed

Ferreira, João D; Inácio, Bruno; Salek, Reza M; Couto, Francisco M

2017-12-13

Public resources need to be appropriately annotated with metadata in order to make them discoverable, reproducible and traceable, further enabling them to be interoperable or integrated with other datasets. While data-sharing policies exist to promote the annotation process by data owners, these guidelines are still largely ignored. In this manuscript, we analyse automatic measures of metadata quality, and suggest their application as a mean to encourage data owners to increase the metadata quality of their resources and submissions, thereby contributing to higher quality data, improved data sharing, and the overall accountability of scientific publications. We analyse these metadata quality measures in the context of a real-world repository of metabolomics data (i.e. MetaboLights), including a manual validation of the measures, and an analysis of their evolution over time. Our findings suggest that the proposed measures can be used to mimic a manual assessment of metadata quality.

Plant metabolomics: from holistic hope, to hype, to hot topic.

PubMed

Hall, Robert D

2006-01-01

In a short time, plant metabolomics has gone from being just an ambitious concept to being a rapidly growing, valuable technology applied in the stride to gain a more global picture of the molecular organization of multicellular organisms. The combination of improved analytical capabilities with newly designed, dedicated statistical, bioinformatics and data mining strategies, is beginning to broaden the horizons of our understanding of how plants are organized and how metabolism is both controlled but highly flexible. Metabolomics is predicted to play a significant, if not indispensable role in bridging the phenotype-genotype gap and thus in assisting us in our desire for full genome sequence annotation as part of the quest to link gene to function. Plants are a fabulously rich source of diverse functional biochemicals and metabolomics is also already proving valuable in an applied context. By creating unique opportunities for us to interrogate plant systems and characterize their biochemical composition, metabolomics will greatly assist in identifying and defining much of the still unexploited biodiversity available today.

Characterization of proteomic and metabolomic responses to dietary factors and supplements.

PubMed

Astle, John; Ferguson, Jonathan T; German, J Bruce; Harrigan, George G; Kelleher, Neil L; Kodadek, Thomas; Parks, Bryan A; Roth, Michael J; Singletary, Keith W; Wenger, Craig D; Mahady, Gail B

2007-12-01

Over the past decade there has been a renewed interest in research and development of both dietary and nutritional supplements. Significant advancements have been made in the scientific assessment of the quality, safety, and efficacy of these products because of the strong interest in and financial support of these projects. As research in both fields continues to advance, opportunities to use new and innovative research technologies and methodologies, such as proteomics and metabolomics, are critical for the future progress of the science. The purpose of the symposium was to begin the process of communicating new innovative proteomic and metabolomic methodologies that may be applied by researchers in both the nutrition and the natural product communities. This symposium highlighted 2 proteomic approaches, protein fingerprinting in complex mixtures with peptoid microarrays and top-down mass spectrometry for annotation of gene products. Likewise, an overview of the methodologies used in metabolomic profiling of natural products was presented, and an illustration of an integrated metabolomics approach in nutrition research was highlighted.

A global approach to analysis and interpretation of metabolic data for plant natural product discovery.

PubMed

Hur, Manhoi; Campbell, Alexis Ann; Almeida-de-Macedo, Marcia; Li, Ling; Ransom, Nick; Jose, Adarsh; Crispin, Matt; Nikolau, Basil J; Wurtele, Eve Syrkin

2013-04-01

Discovering molecular components and their functionality is key to the development of hypotheses concerning the organization and regulation of metabolic networks. The iterative experimental testing of such hypotheses is the trajectory that can ultimately enable accurate computational modelling and prediction of metabolic outcomes. This information can be particularly important for understanding the biology of natural products, whose metabolism itself is often only poorly defined. Here, we describe factors that must be in place to optimize the use of metabolomics in predictive biology. A key to achieving this vision is a collection of accurate time-resolved and spatially defined metabolite abundance data and associated metadata. One formidable challenge associated with metabolite profiling is the complexity and analytical limits associated with comprehensively determining the metabolome of an organism. Further, for metabolomics data to be efficiently used by the research community, it must be curated in publicly available metabolomics databases. Such databases require clear, consistent formats, easy access to data and metadata, data download, and accessible computational tools to integrate genome system-scale datasets. Although transcriptomics and proteomics integrate the linear predictive power of the genome, the metabolome represents the nonlinear, final biochemical products of the genome, which results from the intricate system(s) that regulate genome expression. For example, the relationship of metabolomics data to the metabolic network is confounded by redundant connections between metabolites and gene-products. However, connections among metabolites are predictable through the rules of chemistry. Therefore, enhancing the ability to integrate the metabolome with anchor-points in the transcriptome and proteome will enhance the predictive power of genomics data. We detail a public database repository for metabolomics, tools and approaches for statistical analysis of metabolomics data, and methods for integrating these datasets with transcriptomic data to create hypotheses concerning specialized metabolisms that generate the diversity in natural product chemistry. We discuss the importance of close collaborations among biologists, chemists, computer scientists and statisticians throughout the development of such integrated metabolism-centric databases and software.

A global approach to analysis and interpretation of metabolic data for plant natural product discovery†

PubMed Central

Hur, Manhoi; Campbell, Alexis Ann; Almeida-de-Macedo, Marcia; Li, Ling; Ransom, Nick; Jose, Adarsh; Crispin, Matt; Nikolau, Basil J.

2013-01-01

Discovering molecular components and their functionality is key to the development of hypotheses concerning the organization and regulation of metabolic networks. The iterative experimental testing of such hypotheses is the trajectory that can ultimately enable accurate computational modelling and prediction of metabolic outcomes. This information can be particularly important for understanding the biology of natural products, whose metabolism itself is often only poorly defined. Here, we describe factors that must be in place to optimize the use of metabolomics in predictive biology. A key to achieving this vision is a collection of accurate time-resolved and spatially defined metabolite abundance data and associated metadata. One formidable challenge associated with metabolite profiling is the complexity and analytical limits associated with comprehensively determining the metabolome of an organism. Further, for metabolomics data to be efficiently used by the research community, it must be curated in publically available metabolomics databases. Such databases require clear, consistent formats, easy access to data and metadata, data download, and accessible computational tools to integrate genome system-scale datasets. Although transcriptomics and proteomics integrate the linear predictive power of the genome, the metabolome represents the nonlinear, final biochemical products of the genome, which results from the intricate system(s) that regulate genome expression. For example, the relationship of metabolomics data to the metabolic network is confounded by redundant connections between metabolites and gene-products. However, connections among metabolites are predictable through the rules of chemistry. Therefore, enhancing the ability to integrate the metabolome with anchor-points in the transcriptome and proteome will enhance the predictive power of genomics data. We detail a public database repository for metabolomics, tools and approaches for statistical analysis of metabolomics data, and methods for integrating these dataset with transcriptomic data to create hypotheses concerning specialized metabolism that generates the diversity in natural product chemistry. We discuss the importance of close collaborations among biologists, chemists, computer scientists and statisticians throughout the development of such integrated metabolism-centric databases and software. PMID:23447050

Supporting metabolomics with adaptable software: design architectures for the end-user.

PubMed

Sarpe, Vladimir; Schriemer, David C

2017-02-01

Large and disparate sets of LC-MS data are generated by modern metabolomics profiling initiatives, and while useful software tools are available to annotate and quantify compounds, the field requires continued software development in order to sustain methodological innovation. Advances in software development practices allow for a new paradigm in tool development for metabolomics, where increasingly the end-user can develop or redeploy utilities ranging from simple algorithms to complex workflows. Resources that provide an organized framework for development are described and illustrated with LC-MS processing packages that have leveraged their design tools. Full access to these resources depends in part on coding experience, but the emergence of workflow builders and pluggable frameworks strongly reduces the skill level required. Developers in the metabolomics community are encouraged to use these resources and design content for uptake and reuse. Copyright © 2016 Elsevier Ltd. All rights reserved.

Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools.

PubMed

Sud, Manish; Fahy, Eoin; Cotter, Dawn; Azam, Kenan; Vadivelu, Ilango; Burant, Charles; Edison, Arthur; Fiehn, Oliver; Higashi, Richard; Nair, K Sreekumaran; Sumner, Susan; Subramaniam, Shankar

2016-01-04

The Metabolomics Workbench, available at www.metabolomicsworkbench.org, is a public repository for metabolomics metadata and experimental data spanning various species and experimental platforms, metabolite standards, metabolite structures, protocols, tutorials, and training material and other educational resources. It provides a computational platform to integrate, analyze, track, deposit and disseminate large volumes of heterogeneous data from a wide variety of metabolomics studies including mass spectrometry (MS) and nuclear magnetic resonance spectrometry (NMR) data spanning over 20 different species covering all the major taxonomic categories including humans and other mammals, plants, insects, invertebrates and microorganisms. Additionally, a number of protocols are provided for a range of metabolite classes, sample types, and both MS and NMR-based studies, along with a metabolite structure database. The metabolites characterized in the studies available on the Metabolomics Workbench are linked to chemical structures in the metabolite structure database to facilitate comparative analysis across studies. The Metabolomics Workbench, part of the data coordinating effort of the National Institute of Health (NIH) Common Fund's Metabolomics Program, provides data from the Common Fund's Metabolomics Resource Cores, metabolite standards, and analysis tools to the wider metabolomics community and seeks data depositions from metabolomics researchers across the world. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Advances in metabolome information retrieval: turning chemistry into biology. Part II: biological information recovery.

PubMed

Tebani, Abdellah; Afonso, Carlos; Bekri, Soumeya

2018-05-01

This work reports the second part of a review intending to give the state of the art of major metabolic phenotyping strategies. It particularly deals with inherent advantages and limits regarding data analysis issues and biological information retrieval tools along with translational challenges. This Part starts with introducing the main data preprocessing strategies of the different metabolomics data. Then, it describes the main data analysis techniques including univariate and multivariate aspects. It also addresses the challenges related to metabolite annotation and characterization. Finally, functional analysis including pathway and network strategies are discussed. The last section of this review is devoted to practical considerations and current challenges and pathways to bring metabolomics into clinical environments.

Advancing the large-scale CCS database for metabolomics and lipidomics at the machine-learning era.

PubMed

Zhou, Zhiwei; Tu, Jia; Zhu, Zheng-Jiang

2018-02-01

Metabolomics and lipidomics aim to comprehensively measure the dynamic changes of all metabolites and lipids that are present in biological systems. The use of ion mobility-mass spectrometry (IM-MS) for metabolomics and lipidomics has facilitated the separation and the identification of metabolites and lipids in complex biological samples. The collision cross-section (CCS) value derived from IM-MS is a valuable physiochemical property for the unambiguous identification of metabolites and lipids. However, CCS values obtained from experimental measurement and computational modeling are limited available, which significantly restricts the application of IM-MS. In this review, we will discuss the recently developed machine-learning based prediction approach, which could efficiently generate precise CCS databases in a large scale. We will also highlight the applications of CCS databases to support metabolomics and lipidomics. Copyright © 2017 Elsevier Ltd. All rights reserved.

Caenorhabditis elegans chemical biology: lessons from small molecules

USDA-ARS?s Scientific Manuscript database

How can we complement Caenorhabditis elegans genomics and proteomics with a comprehensive structural and functional annotation of its metabolome? Several lines of evidence indicate that small molecules of largely undetermined structure play important roles in C. elegans biology, including key pathw...

A computational platform to maintain and migrate manual functional annotations for BioCyc databases.

PubMed

Walsh, Jesse R; Sen, Taner Z; Dickerson, Julie A

2014-10-12

BioCyc databases are an important resource for information on biological pathways and genomic data. Such databases represent the accumulation of biological data, some of which has been manually curated from literature. An essential feature of these databases is the continuing data integration as new knowledge is discovered. As functional annotations are improved, scalable methods are needed for curators to manage annotations without detailed knowledge of the specific design of the BioCyc database. We have developed CycTools, a software tool which allows curators to maintain functional annotations in a model organism database. This tool builds on existing software to improve and simplify annotation data imports of user provided data into BioCyc databases. Additionally, CycTools automatically resolves synonyms and alternate identifiers contained within the database into the appropriate internal identifiers. Automating steps in the manual data entry process can improve curation efforts for major biological databases. The functionality of CycTools is demonstrated by transferring GO term annotations from MaizeCyc to matching proteins in CornCyc, both maize metabolic pathway databases available at MaizeGDB, and by creating strain specific databases for metabolic engineering.

Metabolomic profiling of the nectars of Aquilegia pubescens and A. Canadensis

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

Noutsos, Christos; Perera, Ann M.; Nikolau, Basil J.

To date, variation in nectar chemistry of flowering plants has not been studied in detail. Such variation exerts considerable influence on pollinator–plant interactions, as well as on flower traits that play important roles in the selection of a plant for visitation by specific pollinators. Over the past 60 years the Aquilegia genus has been used as a key model for speciation studies. In this study, we defined the metabolomic profiles of flower samples of two Aquilegia species, A. Canadensis and A. pubescens. We identified a total of 75 metabolites that were classified into six main categories: organic acids, fattymore » acids, amino acids, esters, sugars, and unknowns. The mean abundances of 25 of these metabolites were significantly different between the two species, providing insights into interspecies variation in floral chemistry. Using the PlantSEED biochemistry database, we found that the majority of these metabolites are involved in biosynthetic pathways. Finally, we explored the annotated genome of A. coerulea, using the PlantSEED pipeline and reconstructed the metabolic network of Aquilegia. As a result, this network, which contains the metabolic pathways involved in generating the observed chemical variation, is now publicly available from the DOE Systems Biology Knowledge Base (KBase; http://kbase.us).« less

Metabolomic profiling of the nectars of Aquilegia pubescens and A. Canadensis

DOE PAGES

Noutsos, Christos; Perera, Ann M.; Nikolau, Basil J.; ...

2015-05-01

To date, variation in nectar chemistry of flowering plants has not been studied in detail. Such variation exerts considerable influence on pollinator–plant interactions, as well as on flower traits that play important roles in the selection of a plant for visitation by specific pollinators. Over the past 60 years the Aquilegia genus has been used as a key model for speciation studies. In this study, we defined the metabolomic profiles of flower samples of two Aquilegia species, A. Canadensis and A. pubescens. We identified a total of 75 metabolites that were classified into six main categories: organic acids, fattymore » acids, amino acids, esters, sugars, and unknowns. The mean abundances of 25 of these metabolites were significantly different between the two species, providing insights into interspecies variation in floral chemistry. Using the PlantSEED biochemistry database, we found that the majority of these metabolites are involved in biosynthetic pathways. Finally, we explored the annotated genome of A. coerulea, using the PlantSEED pipeline and reconstructed the metabolic network of Aquilegia. As a result, this network, which contains the metabolic pathways involved in generating the observed chemical variation, is now publicly available from the DOE Systems Biology Knowledge Base (KBase; http://kbase.us).« less

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.

An editor for pathway drawing and data visualization in the Biopathways Workbench.

PubMed

Byrnes, Robert W; Cotter, Dawn; Maer, Andreia; Li, Joshua; Nadeau, David; Subramaniam, Shankar

2009-10-02

Pathway models serve as the basis for much of systems biology. They are often built using programs designed for the purpose. Constructing new models generally requires simultaneous access to experimental data of diverse types, to databases of well-characterized biological compounds and molecular intermediates, and to reference model pathways. However, few if any software applications provide all such capabilities within a single user interface. The Pathway Editor is a program written in the Java programming language that allows de-novo pathway creation and downloading of LIPID MAPS (Lipid Metabolites and Pathways Strategy) and KEGG lipid metabolic pathways, and of measured time-dependent changes to lipid components of metabolism. Accessed through Java Web Start, the program downloads pathways from the LIPID MAPS Pathway database (Pathway) as well as from the LIPID MAPS web server http://www.lipidmaps.org. Data arises from metabolomic (lipidomic), microarray, and protein array experiments performed by the LIPID MAPS consortium of laboratories and is arranged by experiment. Facility is provided to create, connect, and annotate nodes and processes on a drawing panel with reference to database objects and time course data. Node and interaction layout as well as data display may be configured in pathway diagrams as desired. Users may extend diagrams, and may also read and write data and non-lipidomic KEGG pathways to and from files. Pathway diagrams in XML format, containing database identifiers referencing specific compounds and experiments, can be saved to a local file for subsequent use. The program is built upon a library of classes, referred to as the Biopathways Workbench, that convert between different file formats and database objects. An example of this feature is provided in the form of read/construct/write access to models in SBML (Systems Biology Markup Language) contained in the local file system. Inclusion of access to multiple experimental data types and of pathway diagrams within a single interface, automatic updating through connectivity to an online database, and a focus on annotation, including reference to standardized lipid nomenclature as well as common lipid names, supports the view that the Pathway Editor represents a significant, practicable contribution to current pathway modeling tools.

An application of a relational database system for high-throughput prediction of elemental compositions from accurate mass values.

PubMed

Sakurai, Nozomu; Ara, Takeshi; Kanaya, Shigehiko; Nakamura, Yukiko; Iijima, Yoko; Enomoto, Mitsuo; Motegi, Takeshi; Aoki, Koh; Suzuki, Hideyuki; Shibata, Daisuke

2013-01-15

High-accuracy mass values detected by high-resolution mass spectrometry analysis enable prediction of elemental compositions, and thus are used for metabolite annotations in metabolomic studies. Here, we report an application of a relational database to significantly improve the rate of elemental composition predictions. By searching a database of pre-calculated elemental compositions with fixed kinds and numbers of atoms, the approach eliminates redundant evaluations of the same formula that occur in repeated calculations with other tools. When our approach is compared with HR2, which is one of the fastest tools available, our database search times were at least 109 times shorter than those of HR2. When a solid-state drive (SSD) was applied, the search time was 488 times shorter at 5 ppm mass tolerance and 1833 times at 0.1 ppm. Even if the search by HR2 was performed with 8 threads in a high-spec Windows 7 PC, the database search times were at least 26 and 115 times shorter without and with the SSD. These improvements were enhanced in a low spec Windows XP PC. We constructed a web service 'MFSearcher' to query the database in a RESTful manner. Available for free at http://webs2.kazusa.or.jp/mfsearcher. The web service is implemented in Java, MySQL, Apache and Tomcat, with all major browsers supported. sakurai@kazusa.or.jp Supplementary data are available at Bioinformatics online.

Create, run, share, publish, and reference your LC-MS, FIA-MS, GC-MS, and NMR data analysis workflows with the Workflow4Metabolomics 3.0 Galaxy online infrastructure for metabolomics.

PubMed

Guitton, Yann; Tremblay-Franco, Marie; Le Corguillé, Gildas; Martin, Jean-François; Pétéra, Mélanie; Roger-Mele, Pierrick; Delabrière, Alexis; Goulitquer, Sophie; Monsoor, Misharl; Duperier, Christophe; Canlet, Cécile; Servien, Rémi; Tardivel, Patrick; Caron, Christophe; Giacomoni, Franck; Thévenot, Etienne A

2017-12-01

Metabolomics is a key approach in modern functional genomics and systems biology. Due to the complexity of metabolomics data, the variety of experimental designs, and the multiplicity of bioinformatics tools, providing experimenters with a simple and efficient resource to conduct comprehensive and rigorous analysis of their data is of utmost importance. In 2014, we launched the Workflow4Metabolomics (W4M; http://workflow4metabolomics.org) online infrastructure for metabolomics built on the Galaxy environment, which offers user-friendly features to build and run data analysis workflows including preprocessing, statistical analysis, and annotation steps. Here we present the new W4M 3.0 release, which contains twice as many tools as the first version, and provides two features which are, to our knowledge, unique among online resources. First, data from the four major metabolomics technologies (i.e., LC-MS, FIA-MS, GC-MS, and NMR) can be analyzed on a single platform. By using three studies in human physiology, alga evolution, and animal toxicology, we demonstrate how the 40 available tools can be easily combined to address biological issues. Second, the full analysis (including the workflow, the parameter values, the input data and output results) can be referenced with a permanent digital object identifier (DOI). Publication of data analyses is of major importance for robust and reproducible science. Furthermore, the publicly shared workflows are of high-value for e-learning and training. The Workflow4Metabolomics 3.0 e-infrastructure thus not only offers a unique online environment for analysis of data from the main metabolomics technologies, but it is also the first reference repository for metabolomics workflows. Copyright © 2017 Elsevier Ltd. All rights reserved.

New Strategies and Challenges in Lung Proteomics and Metabolomics. An Official American Thoracic Society Workshop Report.

PubMed

Bowler, Russell P; Wendt, Chris H; Fessler, Michael B; Foster, Matthew W; Kelly, Rachel S; Lasky-Su, Jessica; Rogers, Angela J; Stringer, Kathleen A; Winston, Brent W

2017-12-01

This document presents the proceedings from the workshop entitled, "New Strategies and Challenges in Lung Proteomics and Metabolomics" held February 4th-5th, 2016, in Denver, Colorado. It was sponsored by the National Heart Lung Blood Institute, the American Thoracic Society, the Colorado Biological Mass Spectrometry Society, and National Jewish Health. The goal of this workshop was to convene, for the first time, relevant experts in lung proteomics and metabolomics to discuss and overcome specific challenges in these fields that are unique to the lung. The main objectives of this workshop were to identify, review, and/or understand: (1) emerging technologies in metabolomics and proteomics as applied to the study of the lung; (2) the unique composition and challenges of lung-specific biological specimens for metabolomic and proteomic analysis; (3) the diverse informatics approaches and databases unique to metabolomics and proteomics, with special emphasis on the lung; (4) integrative platforms across genetic and genomic databases that can be applied to lung-related metabolomic and proteomic studies; and (5) the clinical applications of proteomics and metabolomics. The major findings and conclusions of this workshop are summarized at the end of the report, and outline the progress and challenges that face these rapidly advancing fields.

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

VitisCyc: a metabolic pathway knowledgebase for grapevine (Vitis vinifera)

PubMed Central

Naithani, Sushma; Raja, Rajani; Waddell, Elijah N.; Elser, Justin; Gouthu, Satyanarayana; Deluc, Laurent G.; Jaiswal, Pankaj

2014-01-01

We have developed VitisCyc, a grapevine-specific metabolic pathway database that allows researchers to (i) search and browse the database for its various components such as metabolic pathways, reactions, compounds, genes and proteins, (ii) compare grapevine metabolic networks with other publicly available plant metabolic networks, and (iii) upload, visualize and analyze high-throughput data such as transcriptomes, proteomes, metabolomes etc. using OMICs-Viewer tool. VitisCyc is based on the genome sequence of the nearly homozygous genotype PN40024 of Vitis vinifera “Pinot Noir” cultivar with 12X v1 annotations and was built on BioCyc platform using Pathway Tools software and MetaCyc reference database. Furthermore, VitisCyc was enriched for plant-specific pathways and grape-specific metabolites, reactions and pathways. Currently VitisCyc harbors 68 super pathways, 362 biosynthesis pathways, 118 catabolic pathways, 5 detoxification pathways, 36 energy related pathways and 6 transport pathways, 10,908 enzymes, 2912 enzymatic reactions, 31 transport reactions and 2024 compounds. VitisCyc, as a community resource, can aid in the discovery of candidate genes and pathways that are regulated during plant growth and development, and in response to biotic and abiotic stress signals generated from a plant's immediate environment. VitisCyc version 3.18 is available online at http://pathways.cgrb.oregonstate.edu. PMID:25538713

FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry.

PubMed

Palmer, Andrew; Phapale, Prasad; Chernyavsky, Ilya; Lavigne, Regis; Fay, Dominik; Tarasov, Artem; Kovalev, Vitaly; Fuchser, Jens; Nikolenko, Sergey; Pineau, Charles; Becker, Michael; Alexandrov, Theodore

2017-01-01

High-mass-resolution imaging mass spectrometry promises to localize hundreds of metabolites in tissues, cell cultures, and agar plates with cellular resolution, but it is hampered by the lack of bioinformatics tools for automated metabolite identification. We report pySM, a framework for false discovery rate (FDR)-controlled metabolite annotation at the level of the molecular sum formula, for high-mass-resolution imaging mass spectrometry (https://github.com/alexandrovteam/pySM). We introduce a metabolite-signal match score and a target-decoy FDR estimate for spatial metabolomics.

An overview of plant volatile metabolomics, sample treatment and reporting considerations with emphasis on mechanical damage and biological control of weeds.

PubMed

Beck, John J; Smith, Lincoln; Baig, Nausheena

2014-01-01

The technology for the collection and analysis of plant-emitted volatiles for understanding chemical cues of plant-plant, plant-insect or plant-microbe interactions has increased over the years. Consequently, the in situ collection, analysis and identification of volatiles are considered integral to elucidation of complex plant communications. Due to the complexity and range of emissions the conditions for consistent emission of volatiles are difficult to standardise. To discuss: evaluation of emitted volatile metabolites as a means of screening potential target- and non-target weeds/plants for insect biological control agents; plant volatile metabolomics to analyse resultant data; importance of considering volatiles from damaged plants; and use of a database for reporting experimental conditions and results. Recent literature relating to plant volatiles and plant volatile metabolomics are summarised to provide a basic understanding of how metabolomics can be applied to the study of plant volatiles. An overview of plant secondary metabolites, plant volatile metabolomics, analysis of plant volatile metabolomics data and the subsequent input into a database, the roles of plant volatiles, volatile emission as a function of treatment, and the application of plant volatile metabolomics to biological control of invasive weeds. It is recommended that in addition to a non-damaged treatment, plants be damaged prior to collecting volatiles to provide the greatest diversity of odours. For the model system provided, optimal volatile emission occurred when the leaf was punctured with a needle. Results stored in a database should include basic environmental conditions or treatments. Copyright © 2013 John Wiley & Sons, Ltd.

ASGARD: an open-access database of annotated transcriptomes for emerging model arthropod species.

PubMed

Zeng, Victor; Extavour, Cassandra G

2012-01-01

The increased throughput and decreased cost of next-generation sequencing (NGS) have shifted the bottleneck genomic research from sequencing to annotation, analysis and accessibility. This is particularly challenging for research communities working on organisms that lack the basic infrastructure of a sequenced genome, or an efficient way to utilize whatever sequence data may be available. Here we present a new database, the Assembled Searchable Giant Arthropod Read Database (ASGARD). This database is a repository and search engine for transcriptomic data from arthropods that are of high interest to multiple research communities but currently lack sequenced genomes. We demonstrate the functionality and utility of ASGARD using de novo assembled transcriptomes from the milkweed bug Oncopeltus fasciatus, the cricket Gryllus bimaculatus and the amphipod crustacean Parhyale hawaiensis. We have annotated these transcriptomes to assign putative orthology, coding region determination, protein domain identification and Gene Ontology (GO) term annotation to all possible assembly products. ASGARD allows users to search all assemblies by orthology annotation, GO term annotation or Basic Local Alignment Search Tool. User-friendly features of ASGARD include search term auto-completion suggestions based on database content, the ability to download assembly product sequences in FASTA format, direct links to NCBI data for predicted orthologs and graphical representation of the location of protein domains and matches to similar sequences from the NCBI non-redundant database. ASGARD will be a useful repository for transcriptome data from future NGS studies on these and other emerging model arthropods, regardless of sequencing platform, assembly or annotation status. This database thus provides easy, one-stop access to multi-species annotated transcriptome information. We anticipate that this database will be useful for members of multiple research communities, including developmental biology, physiology, evolutionary biology, ecology, comparative genomics and phylogenomics. Database URL: asgard.rc.fas.harvard.edu.

Apollo2Go: a web service adapter for the Apollo genome viewer to enable distributed genome annotation.

PubMed

Klee, Kathrin; Ernst, Rebecca; Spannagl, Manuel; Mayer, Klaus F X

2007-08-30

Apollo, a genome annotation viewer and editor, has become a widely used genome annotation and visualization tool for distributed genome annotation projects. When using Apollo for annotation, database updates are carried out by uploading intermediate annotation files into the respective database. This non-direct database upload is laborious and evokes problems of data synchronicity. To overcome these limitations we extended the Apollo data adapter with a generic, configurable web service client that is able to retrieve annotation data in a GAME-XML-formatted string and pass it on to Apollo's internal input routine. This Apollo web service adapter, Apollo2Go, simplifies the data exchange in distributed projects and aims to render the annotation process more comfortable. The Apollo2Go software is freely available from ftp://ftpmips.gsf.de/plants/apollo_webservice.

Apollo2Go: a web service adapter for the Apollo genome viewer to enable distributed genome annotation

PubMed Central

Klee, Kathrin; Ernst, Rebecca; Spannagl, Manuel; Mayer, Klaus FX

2007-01-01

Background Apollo, a genome annotation viewer and editor, has become a widely used genome annotation and visualization tool for distributed genome annotation projects. When using Apollo for annotation, database updates are carried out by uploading intermediate annotation files into the respective database. This non-direct database upload is laborious and evokes problems of data synchronicity. Results To overcome these limitations we extended the Apollo data adapter with a generic, configurable web service client that is able to retrieve annotation data in a GAME-XML-formatted string and pass it on to Apollo's internal input routine. Conclusion This Apollo web service adapter, Apollo2Go, simplifies the data exchange in distributed projects and aims to render the annotation process more comfortable. The Apollo2Go software is freely available from . PMID:17760972

Rapid storage and retrieval of genomic intervals from a relational database system using nested containment lists

PubMed Central

Wiley, Laura K.; Sivley, R. Michael; Bush, William S.

2013-01-01

Efficient storage and retrieval of genomic annotations based on range intervals is necessary, given the amount of data produced by next-generation sequencing studies. The indexing strategies of relational database systems (such as MySQL) greatly inhibit their use in genomic annotation tasks. This has led to the development of stand-alone applications that are dependent on flat-file libraries. In this work, we introduce MyNCList, an implementation of the NCList data structure within a MySQL database. MyNCList enables the storage, update and rapid retrieval of genomic annotations from the convenience of a relational database system. Range-based annotations of 1 million variants are retrieved in under a minute, making this approach feasible for whole-genome annotation tasks. Database URL: https://github.com/bushlab/mynclist PMID:23894185

Rapid storage and retrieval of genomic intervals from a relational database system using nested containment lists.

PubMed

Wiley, Laura K; Sivley, R Michael; Bush, William S

2013-01-01

Efficient storage and retrieval of genomic annotations based on range intervals is necessary, given the amount of data produced by next-generation sequencing studies. The indexing strategies of relational database systems (such as MySQL) greatly inhibit their use in genomic annotation tasks. This has led to the development of stand-alone applications that are dependent on flat-file libraries. In this work, we introduce MyNCList, an implementation of the NCList data structure within a MySQL database. MyNCList enables the storage, update and rapid retrieval of genomic annotations from the convenience of a relational database system. Range-based annotations of 1 million variants are retrieved in under a minute, making this approach feasible for whole-genome annotation tasks. Database URL: https://github.com/bushlab/mynclist.

dbWFA: a web-based database for functional annotation of Triticum aestivum transcripts

PubMed Central

Vincent, Jonathan; Dai, Zhanwu; Ravel, Catherine; Choulet, Frédéric; Mouzeyar, Said; Bouzidi, M. Fouad; Agier, Marie; Martre, Pierre

2013-01-01

The functional annotation of genes based on sequence homology with genes from model species genomes is time-consuming because it is necessary to mine several unrelated databases. The aim of the present work was to develop a functional annotation database for common wheat Triticum aestivum (L.). The database, named dbWFA, is based on the reference NCBI UniGene set, an expressed gene catalogue built by expressed sequence tag clustering, and on full-length coding sequences retrieved from the TriFLDB database. Information from good-quality heterogeneous sources, including annotations for model plant species Arabidopsis thaliana (L.) Heynh. and Oryza sativa L., was gathered and linked to T. aestivum sequences through BLAST-based homology searches. Even though the complexity of the transcriptome cannot yet be fully appreciated, we developed a tool to easily and promptly obtain information from multiple functional annotation systems (Gene Ontology, MapMan bin codes, MIPS Functional Categories, PlantCyc pathway reactions and TAIR gene families). The use of dbWFA is illustrated here with several query examples. We were able to assign a putative function to 45% of the UniGenes and 81% of the full-length coding sequences from TriFLDB. Moreover, comparison of the annotation of the whole T. aestivum UniGene set along with curated annotations of the two model species assessed the accuracy of the annotation provided by dbWFA. To further illustrate the use of dbWFA, genes specifically expressed during the early cell division or late storage polymer accumulation phases of T. aestivum grain development were identified using a clustering analysis and then annotated using dbWFA. The annotation of these two sets of genes was consistent with previous analyses of T. aestivum grain transcriptomes and proteomes. Database URL: urgi.versailles.inra.fr/dbWFA/ PMID:23660284

Resolving the problem of multiple accessions of the same transcript deposited across various public databases.

PubMed

Weirick, Tyler; John, David; Uchida, Shizuka

2017-03-01

Maintaining the consistency of genomic annotations is an increasingly complex task because of the iterative and dynamic nature of assembly and annotation, growing numbers of biological databases and insufficient integration of annotations across databases. As information exchange among databases is poor, a 'novel' sequence from one reference annotation could be annotated in another. Furthermore, relationships to nearby or overlapping annotated transcripts are even more complicated when using different genome assemblies. To better understand these problems, we surveyed current and previous versions of genomic assemblies and annotations across a number of public databases containing long noncoding RNA. We identified numerous discrepancies of transcripts regarding their genomic locations, transcript lengths and identifiers. Further investigation showed that the positional differences between reference annotations of essentially the same transcript could lead to differences in its measured expression at the RNA level. To aid in resolving these problems, we present the algorithm 'Universal Genomic Accession Hash (UGAHash)' and created an open source web tool to encourage the usage of the UGAHash algorithm. The UGAHash web tool (http://ugahash.uni-frankfurt.de) can be accessed freely without registration. The web tool allows researchers to generate Universal Genomic Accessions for genomic features or to explore annotations deposited in the public databases of the past and present versions. We anticipate that the UGAHash web tool will be a valuable tool to check for the existence of transcripts before judging the newly discovered transcripts as novel. © The Author 2016. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

TogoTable: cross-database annotation system using the Resource Description Framework (RDF) data model.

PubMed

Kawano, Shin; Watanabe, Tsutomu; Mizuguchi, Sohei; Araki, Norie; Katayama, Toshiaki; Yamaguchi, Atsuko

2014-07-01

TogoTable (http://togotable.dbcls.jp/) is a web tool that adds user-specified annotations to a table that a user uploads. Annotations are drawn from several biological databases that use the Resource Description Framework (RDF) data model. TogoTable uses database identifiers (IDs) in the table as a query key for searching. RDF data, which form a network called Linked Open Data (LOD), can be searched from SPARQL endpoints using a SPARQL query language. Because TogoTable uses RDF, it can integrate annotations from not only the reference database to which the IDs originally belong, but also externally linked databases via the LOD network. For example, annotations in the Protein Data Bank can be retrieved using GeneID through links provided by the UniProt RDF. Because RDF has been standardized by the World Wide Web Consortium, any database with annotations based on the RDF data model can be easily incorporated into this tool. We believe that TogoTable is a valuable Web tool, particularly for experimental biologists who need to process huge amounts of data such as high-throughput experimental output. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Experimental Strategies for Functional Annotation and Metabolism Discovery: Targeted Screening of Solute Binding Proteins and Unbiased Panning of Metabolomes

DOE PAGES

Vetting, Matthew W.; Al-Obaidi, Nawar; Zhao, Suwen; ...

2014-12-25

The rate at which genome sequencing data is accruing demands enhanced methods for functional annotation and metabolism discovery. Solute binding proteins (SBPs) facilitate the transport of the first reactant in a metabolic pathway, thereby constraining the regions of chemical space and the chemistries that must be considered for pathway reconstruction. Here in this paper, we describe high-throughput protein production and differential scanning fluorimetry platforms, which enabled the screening of 158 SBPs against a 189 component library specifically tailored for this class of proteins. Like all screening efforts, this approach is limited by the practical constraints imposed by construction of themore » library, i.e., we can study only those metabolites that are known to exist and which can be made in sufficient quantities for experimentation. To move beyond these inherent limitations, we illustrate the promise of crystallographic- and mass spectrometric-based approaches for the unbiased use of entire metabolomes as screening libraries. Together, our approaches identified 40 new SBP ligands, generated experiment-based annotations for 2084 SBPs in 71 isofunctional clusters, and defined numerous metabolic pathways, including novel catabolic pathways for the utilization of ethanolamine as sole nitrogen source and the use of D-Ala-D-Ala as sole carbon source. These efforts begin to define an integrated strategy for realizing the full value of amassing genome sequence data.« less

Large-scale annotation of small-molecule libraries using public databases.

PubMed

Zhou, Yingyao; Zhou, Bin; Chen, Kaisheng; Yan, S Frank; King, Frederick J; Jiang, Shumei; Winzeler, Elizabeth A

2007-01-01

While many large publicly accessible databases provide excellent annotation for biological macromolecules, the same is not true for small chemical compounds. Commercial data sources also fail to encompass an annotation interface for large numbers of compounds and tend to be cost prohibitive to be widely available to biomedical researchers. Therefore, using annotation information for the selection of lead compounds from a modern day high-throughput screening (HTS) campaign presently occurs only under a very limited scale. The recent rapid expansion of the NIH PubChem database provides an opportunity to link existing biological databases with compound catalogs and provides relevant information that potentially could improve the information garnered from large-scale screening efforts. Using the 2.5 million compound collection at the Genomics Institute of the Novartis Research Foundation (GNF) as a model, we determined that approximately 4% of the library contained compounds with potential annotation in such databases as PubChem and the World Drug Index (WDI) as well as related databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) and ChemIDplus. Furthermore, the exact structure match analysis showed 32% of GNF compounds can be linked to third party databases via PubChem. We also showed annotations such as MeSH (medical subject headings) terms can be applied to in-house HTS databases in identifying signature biological inhibition profiles of interest as well as expediting the assay validation process. The automated annotation of thousands of screening hits in batch is becoming feasible and has the potential to play an essential role in the hit-to-lead decision making process.

COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access.

PubMed

Salek, Reza M; Neumann, Steffen; Schober, Daniel; Hummel, Jan; Billiau, Kenny; Kopka, Joachim; Correa, Elon; Reijmers, Theo; Rosato, Antonio; Tenori, Leonardo; Turano, Paola; Marin, Silvia; Deborde, Catherine; Jacob, Daniel; Rolin, Dominique; Dartigues, Benjamin; Conesa, Pablo; Haug, Kenneth; Rocca-Serra, Philippe; O'Hagan, Steve; Hao, Jie; van Vliet, Michael; Sysi-Aho, Marko; Ludwig, Christian; Bouwman, Jildau; Cascante, Marta; Ebbels, Timothy; Griffin, Julian L; Moing, Annick; Nikolski, Macha; Oresic, Matej; Sansone, Susanna-Assunta; Viant, Mark R; Goodacre, Royston; Günther, Ulrich L; Hankemeier, Thomas; Luchinat, Claudio; Walther, Dirk; Steinbeck, Christoph

Metabolomics has become a crucial phenotyping technique in a range of research fields including medicine, the life sciences, biotechnology and the environmental sciences. This necessitates the transfer of experimental information between research groups, as well as potentially to publishers and funders. After the initial efforts of the metabolomics standards initiative, minimum reporting standards were proposed which included the concepts for metabolomics databases. Built by the community, standards and infrastructure for metabolomics are still needed to allow storage, exchange, comparison and re-utilization of metabolomics data. The Framework Programme 7 EU Initiative 'coordination of standards in metabolomics' (COSMOS) is developing a robust data infrastructure and exchange standards for metabolomics data and metadata. This is to support workflows for a broad range of metabolomics applications within the European metabolomics community and the wider metabolomics and biomedical communities' participation. Here we announce our concepts and efforts asking for re-engagement of the metabolomics community, academics and industry, journal publishers, software and hardware vendors, as well as those interested in standardisation worldwide (addressing missing metabolomics ontologies, complex-metadata capturing and XML based open source data exchange format), to join and work towards updating and implementing metabolomics standards.

The food metabolome: a window over dietary exposure.

PubMed

Scalbert, Augustin; Brennan, Lorraine; Manach, Claudine; Andres-Lacueva, Cristina; Dragsted, Lars O; Draper, John; Rappaport, Stephen M; van der Hooft, Justin J J; Wishart, David S

2014-06-01

The food metabolome is defined as the part of the human metabolome directly derived from the digestion and biotransformation of foods and their constituents. With >25,000 compounds known in various foods, the food metabolome is extremely complex, with a composition varying widely according to the diet. By its very nature it represents a considerable and still largely unexploited source of novel dietary biomarkers that could be used to measure dietary exposures with a high level of detail and precision. Most dietary biomarkers currently have been identified on the basis of our knowledge of food compositions by using hypothesis-driven approaches. However, the rapid development of metabolomics resulting from the development of highly sensitive modern analytic instruments, the availability of metabolite databases, and progress in (bio)informatics has made agnostic approaches more attractive as shown by the recent identification of novel biomarkers of intakes for fruit, vegetables, beverages, meats, or complex diets. Moreover, examples also show how the scrutiny of the food metabolome can lead to the discovery of bioactive molecules and dietary factors associated with diseases. However, researchers still face hurdles, which slow progress and need to be resolved to bring this emerging field of research to maturity. These limits were discussed during the First International Workshop on the Food Metabolome held in Glasgow. Key recommendations made during the workshop included more coordination of efforts; development of new databases, software tools, and chemical libraries for the food metabolome; and shared repositories of metabolomic data. Once achieved, major progress can be expected toward a better understanding of the complex interactions between diet and human health. © 2014 American Society for Nutrition.

ODG: Omics database generator - a tool for generating, querying, and analyzing multi-omics comparative databases to facilitate biological understanding.

PubMed

Guhlin, Joseph; Silverstein, Kevin A T; Zhou, Peng; Tiffin, Peter; Young, Nevin D

2017-08-10

Rapid generation of omics data in recent years have resulted in vast amounts of disconnected datasets without systemic integration and knowledge building, while individual groups have made customized, annotated datasets available on the web with few ways to link them to in-lab datasets. With so many research groups generating their own data, the ability to relate it to the larger genomic and comparative genomic context is becoming increasingly crucial to make full use of the data. The Omics Database Generator (ODG) allows users to create customized databases that utilize published genomics data integrated with experimental data which can be queried using a flexible graph database. When provided with omics and experimental data, ODG will create a comparative, multi-dimensional graph database. ODG can import definitions and annotations from other sources such as InterProScan, the Gene Ontology, ENZYME, UniPathway, and others. This annotation data can be especially useful for studying new or understudied species for which transcripts have only been predicted, and rapidly give additional layers of annotation to predicted genes. In better studied species, ODG can perform syntenic annotation translations or rapidly identify characteristics of a set of genes or nucleotide locations, such as hits from an association study. ODG provides a web-based user-interface for configuring the data import and for querying the database. Queries can also be run from the command-line and the database can be queried directly through programming language hooks available for most languages. ODG supports most common genomic formats as well as generic, easy to use tab-separated value format for user-provided annotations. ODG is a user-friendly database generation and query tool that adapts to the supplied data to produce a comparative genomic database or multi-layered annotation database. ODG provides rapid comparative genomic annotation and is therefore particularly useful for non-model or understudied species. For species for which more data are available, ODG can be used to conduct complex multi-omics, pattern-matching queries.

Recent Advances in Targeted and Untargeted Metabolomics by NMR and MS/NMR Methods

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

Bingol, Kerem

Metabolomics has made significant progress in multiple fronts in the last 18 months. This minireview aimed to give an overview of these advancements in the light of their contribution to targeted and untargeted metabolomics. New computational approaches have emerged to overcome manual absolute quantitation step of metabolites in 1D 1H NMR spectra. This provides more consistency between inter-laboratory comparisons. Integration of 2D NMR metabolomics databases under a unified web server allowed very accurate identification of the metabolites that have been catalogued in these databases. For the remaining uncatalogued and unknown metabolites, new cheminformatics approaches have been developed by combining NMRmore » and mass spectrometry. These hybrid NMR/MS approaches accelerated the identification of unknowns in untargeted studies, and now they are allowing to profile ever larger number of metabolites in application studies.« less

Comparative Metabolomics of Mycoplasma bovis and Mycoplasma gallisepticum Reveals Fundamental Differences in Active Metabolic Pathways and Suggests Novel Gene Annotations.

PubMed

Masukagami, Y; De Souza, D P; Dayalan, S; Bowen, C; O'Callaghan, S; Kouremenos, K; Nijagal, B; Tull, D; Tivendale, K A; Markham, P F; McConville, M J; Browning, G F; Sansom, F M

2017-01-01

Mycoplasmas are simple, but successful parasites that have the smallest genome of any free-living cell and are thought to have a highly streamlined cellular metabolism. Here, we have undertaken a detailed metabolomic analysis of two species, Mycoplasma bovis and Mycoplasma gallisepticum , which cause economically important diseases in cattle and poultry, respectively. Untargeted gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry analyses of mycoplasma metabolite extracts revealed significant differences in the steady-state levels of many metabolites in central carbon metabolism, while 13 C stable isotope labeling studies revealed marked differences in carbon source utilization. These data were mapped onto in silico metabolic networks predicted from genome wide annotations. The analyses elucidated distinct differences, including a clear difference in glucose utilization, with a marked decrease in glucose uptake and glycolysis in M. bovis compared to M. gallisepticum , which may reflect differing host nutrient availabilities. The 13 C-labeling patterns also revealed several functional metabolic pathways that were previously unannotated in these species, allowing us to assign putative enzyme functions to the products of a number of genes of unknown function, especially in M. bovis . This study demonstrates the considerable potential of metabolomic analyses to assist in characterizing significant differences in the metabolism of different bacterial species and in improving genome annotation. IMPORTANCE Mycoplasmas are pathogenic bacteria that cause serious chronic infections in production animals, resulting in considerable losses worldwide, as well as causing disease in humans. These bacteria have extremely reduced genomes and are thought to have limited metabolic flexibility, even though they are highly successful persistent parasites in a diverse number of species. The extent to which different Mycoplasma species are capable of catabolizing host carbon sources and nutrients, or synthesizing essential metabolites, remains poorly defined. We have used advanced metabolomic techniques to identify metabolic pathways that are active in two species of Mycoplasma that infect distinct hosts (poultry and cattle). We show that these species exhibit marked differences in metabolite steady-state levels and carbon source utilization. This information has been used to functionally characterize previously unknown genes in the genomes of these pathogens. These species-specific differences are likely to reflect important differences in host nutrient levels and pathogenic mechanisms.

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.

Compound annotation in liquid chromatography/high-resolution mass spectrometry based metabolomics: robust adduct ion determination as a prerequisite to structure prediction in electrospray ionization mass spectra.

PubMed

Jaeger, Carsten; Méret, Michaël; Schmitt, Clemens A; Lisec, Jan

2017-08-15

A bottleneck in metabolic profiling of complex biological extracts is confident, non-supervised annotation of ideally all contained, chemically highly diverse small molecules. Recent computational strategies combining sum formula prediction with in silico fragmentation achieve confident de novo annotation, once the correct neutral mass of a compound is known. Current software solutions for automated adduct ion assignment, however, are either publicly unavailable or have been validated against only few experimental electrospray ionization (ESI) mass spectra. We here present findMAIN (find Main Adduct IoN), a new heuristic approach for interpreting ESI mass spectra. findMAIN scores MS 1 spectra based on explained intensity, mass accuracy and isotope charge agreement of adducts and related ionization products and annotates peaks of the (de)protonated molecule and adduct ions. The approach was validated against 1141 ESI positive mode spectra of chemically diverse standard compounds acquired on different high-resolution mass spectrometric instruments (Orbitrap and time-of-flight). Robustness against impure spectra was evaluated. Correct adduct ion assignment was achieved for up to 83% of the spectra. Performance was independent of compound class and mass spectrometric platform. The algorithm proved highly tolerant against spectral contamination as demonstrated exemplarily for co-eluting compounds as well as systematically by pairwise mixing of spectra. When used in conjunction with MS-FINDER, a state-of-the-art sum formula tool, correct sum formulas were obtained for 77% of spectra. It outperformed both 'brute force' approaches and current state-of-the-art annotation packages tested as potential alternatives. Limitations of the heuristic pertained to poorly ionizing compounds and cationic compounds forming [M] + ions. A new, validated approach for interpreting ESI mass spectra is presented, filling a gap in the nontargeted metabolomics workflow. It is freely available in the latest version of R package InterpretMSSpectrum. Copyright © 2017 John Wiley & Sons, Ltd.

MitoFish and MitoAnnotator: A Mitochondrial Genome Database of Fish with an Accurate and Automatic Annotation Pipeline

PubMed Central

Iwasaki, Wataru; Fukunaga, Tsukasa; Isagozawa, Ryota; Yamada, Koichiro; Maeda, Yasunobu; Satoh, Takashi P.; Sado, Tetsuya; Mabuchi, Kohji; Takeshima, Hirohiko; Miya, Masaki; Nishida, Mutsumi

2013-01-01

Mitofish is a database of fish mitochondrial genomes (mitogenomes) that includes powerful and precise de novo annotations for mitogenome sequences. Fish occupy an important position in the evolution of vertebrates and the ecology of the hydrosphere, and mitogenomic sequence data have served as a rich source of information for resolving fish phylogenies and identifying new fish species. The importance of a mitogenomic database continues to grow at a rapid pace as massive amounts of mitogenomic data are generated with the advent of new sequencing technologies. A severe bottleneck seems likely to occur with regard to mitogenome annotation because of the overwhelming pace of data accumulation and the intrinsic difficulties in annotating sequences with degenerating transfer RNA structures, divergent start/stop codons of the coding elements, and the overlapping of adjacent elements. To ease this data backlog, we developed an annotation pipeline named MitoAnnotator. MitoAnnotator automatically annotates a fish mitogenome with a high degree of accuracy in approximately 5 min; thus, it is readily applicable to data sets of dozens of sequences. MitoFish also contains re-annotations of previously sequenced fish mitogenomes, enabling researchers to refer to them when they find annotations that are likely to be erroneous or while conducting comparative mitogenomic analyses. For users who need more information on the taxonomy, habitats, phenotypes, or life cycles of fish, MitoFish provides links to related databases. MitoFish and MitoAnnotator are freely available at http://mitofish.aori.u-tokyo.ac.jp/ (last accessed August 28, 2013); all of the data can be batch downloaded, and the annotation pipeline can be used via a web interface. PMID:23955518

PolySearch2: a significantly improved text-mining system for discovering associations between human diseases, genes, drugs, metabolites, toxins and more.

PubMed

Liu, Yifeng; Liang, Yongjie; Wishart, David

2015-07-01

PolySearch2 (http://polysearch.ca) is an online text-mining system for identifying relationships between biomedical entities such as human diseases, genes, SNPs, proteins, drugs, metabolites, toxins, metabolic pathways, organs, tissues, subcellular organelles, positive health effects, negative health effects, drug actions, Gene Ontology terms, MeSH terms, ICD-10 medical codes, biological taxonomies and chemical taxonomies. PolySearch2 supports a generalized 'Given X, find all associated Ys' query, where X and Y can be selected from the aforementioned biomedical entities. An example query might be: 'Find all diseases associated with Bisphenol A'. To find its answers, PolySearch2 searches for associations against comprehensive collections of free-text collections, including local versions of MEDLINE abstracts, PubMed Central full-text articles, Wikipedia full-text articles and US Patent application abstracts. PolySearch2 also searches 14 widely used, text-rich biological databases such as UniProt, DrugBank and Human Metabolome Database to improve its accuracy and coverage. PolySearch2 maintains an extensive thesaurus of biological terms and exploits the latest search engine technology to rapidly retrieve relevant articles and databases records. PolySearch2 also generates, ranks and annotates associative candidates and present results with relevancy statistics and highlighted key sentences to facilitate user interpretation. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

PolySearch2: a significantly improved text-mining system for discovering associations between human diseases, genes, drugs, metabolites, toxins and more

PubMed Central

Liu, Yifeng; Liang, Yongjie; Wishart, David

2015-01-01

PolySearch2 (http://polysearch.ca) is an online text-mining system for identifying relationships between biomedical entities such as human diseases, genes, SNPs, proteins, drugs, metabolites, toxins, metabolic pathways, organs, tissues, subcellular organelles, positive health effects, negative health effects, drug actions, Gene Ontology terms, MeSH terms, ICD-10 medical codes, biological taxonomies and chemical taxonomies. PolySearch2 supports a generalized ‘Given X, find all associated Ys’ query, where X and Y can be selected from the aforementioned biomedical entities. An example query might be: ‘Find all diseases associated with Bisphenol A’. To find its answers, PolySearch2 searches for associations against comprehensive collections of free-text collections, including local versions of MEDLINE abstracts, PubMed Central full-text articles, Wikipedia full-text articles and US Patent application abstracts. PolySearch2 also searches 14 widely used, text-rich biological databases such as UniProt, DrugBank and Human Metabolome Database to improve its accuracy and coverage. PolySearch2 maintains an extensive thesaurus of biological terms and exploits the latest search engine technology to rapidly retrieve relevant articles and databases records. PolySearch2 also generates, ranks and annotates associative candidates and present results with relevancy statistics and highlighted key sentences to facilitate user interpretation. PMID:25925572

PlantCAZyme: a database for plant carbohydrate-active enzymes

PubMed Central

Ekstrom, Alexander; Taujale, Rahil; McGinn, Nathan; Yin, Yanbin

2014-01-01

PlantCAZyme is a database built upon dbCAN (database for automated carbohydrate active enzyme annotation), aiming to provide pre-computed sequence and annotation data of carbohydrate active enzymes (CAZymes) to plant carbohydrate and bioenergy research communities. The current version contains data of 43 790 CAZymes of 159 protein families from 35 plants (including angiosperms, gymnosperms, lycophyte and bryophyte mosses) and chlorophyte algae with fully sequenced genomes. Useful features of the database include: (i) a BLAST server and a HMMER server that allow users to search against our pre-computed sequence data for annotation purpose, (ii) a download page to allow batch downloading data of a specific CAZyme family or species and (iii) protein browse pages to provide an easy access to the most comprehensive sequence and annotation data. Database URL: http://cys.bios.niu.edu/plantcazyme/ PMID:25125445

An approach to describing and analysing bulk biological annotation quality: a case study using UniProtKB.

PubMed

Bell, Michael J; Gillespie, Colin S; Swan, Daniel; Lord, Phillip

2012-09-15

Annotations are a key feature of many biological databases, used to convey our knowledge of a sequence to the reader. Ideally, annotations are curated manually, however manual curation is costly, time consuming and requires expert knowledge and training. Given these issues and the exponential increase of data, many databases implement automated annotation pipelines in an attempt to avoid un-annotated entries. Both manual and automated annotations vary in quality between databases and annotators, making assessment of annotation reliability problematic for users. The community lacks a generic measure for determining annotation quality and correctness, which we look at addressing within this article. Specifically we investigate word reuse within bulk textual annotations and relate this to Zipf's Principle of Least Effort. We use the UniProt Knowledgebase (UniProtKB) as a case study to demonstrate this approach since it allows us to compare annotation change, both over time and between automated and manually curated annotations. By applying power-law distributions to word reuse in annotation, we show clear trends in UniProtKB over time, which are consistent with existing studies of quality on free text English. Further, we show a clear distinction between manual and automated analysis and investigate cohorts of protein records as they mature. These results suggest that this approach holds distinct promise as a mechanism for judging annotation quality. Source code is available at the authors website: http://homepages.cs.ncl.ac.uk/m.j.bell1/annotation. phillip.lord@newcastle.ac.uk.

Comparison of peak-picking workflows for untargeted liquid chromatography/high-resolution mass spectrometry metabolomics data analysis.

PubMed

Rafiei, Atefeh; Sleno, Lekha

2015-01-15

Data analysis is a key step in mass spectrometry based untargeted metabolomics, starting with the generation of generic peak lists from raw liquid chromatography/mass spectrometry (LC/MS) data. Due to the use of various algorithms by different workflows, the results of different peak-picking strategies often differ widely. Raw LC/HRMS data from two types of biological samples (bile and urine), as well as a standard mixture of 84 metabolites, were processed with four peak-picking softwares: Peakview®, Markerview™, MetabolitePilot™ and XCMS Online. The overlaps between the results of each peak-generating method were then investigated. To gauge the relevance of peak lists, a database search using the METLIN online database was performed to determine which features had accurate masses matching known metabolites as well as a secondary filtering based on MS/MS spectral matching. In this study, only a small proportion of all peaks (less than 10%) were common to all four software programs. Comparison of database searching results showed peaks found uniquely by one workflow have less chance of being found in the METLIN metabolomics database and are even less likely to be confirmed by MS/MS. It was shown that the performance of peak-generating workflows has a direct impact on untargeted metabolomics results. As it was demonstrated that the peaks found in more than one peak detection workflow have higher potential to be identified by accurate mass as well as MS/MS spectrum matching, it is suggested to use the overlap of different peak-picking workflows as preliminary peak lists for more rugged statistical analysis in global metabolomics investigations. Copyright © 2014 John Wiley & Sons, Ltd.

A Comprehensive Strategy to Construct In-house Database for Accurate and Batch Identification of Small Molecular Metabolites.

PubMed

Zhao, Xinjie; Zeng, Zhongda; Chen, Aiming; Lu, Xin; Zhao, Chunxia; Hu, Chunxiu; Zhou, Lina; Liu, Xinyu; Wang, Xiaolin; Hou, Xiaoli; Ye, Yaorui; Xu, Guowang

2018-05-29

Identification of the metabolites is an essential step in metabolomics study to interpret regulatory mechanism of pathological and physiological processes. However, it is still a big headache in LC-MSn-based studies because of the complexity of mass spectrometry, chemical diversity of metabolites, and deficiency of standards database. In this work, a comprehensive strategy is developed for accurate and batch metabolite identification in non-targeted metabolomics studies. First, a well defined procedure was applied to generate reliable and standard LC-MS2 data including tR, MS1 and MS2 information at a standard operational procedure (SOP). An in-house database including about 2000 metabolites was constructed and used to identify the metabolites in non-targeted metabolic profiling by retention time calibration using internal standards, precursor ion alignment and ion fusion, auto-MS2 information extraction and selection, and database batch searching and scoring. As an application example, a pooled serum sample was analyzed to deliver the strategy, 202 metabolites were identified in the positive ion mode. It shows our strategy is useful for LC-MSn-based non-targeted metabolomics study.

Metabolonote: A Wiki-Based Database for Managing Hierarchical Metadata of Metabolome Analyses

PubMed Central

Ara, Takeshi; Enomoto, Mitsuo; Arita, Masanori; Ikeda, Chiaki; Kera, Kota; Yamada, Manabu; Nishioka, Takaaki; Ikeda, Tasuku; Nihei, Yoshito; Shibata, Daisuke; Kanaya, Shigehiko; Sakurai, Nozomu

2015-01-01

Metabolomics – technology for comprehensive detection of small molecules in an organism – lags behind the other “omics” in terms of publication and dissemination of experimental data. Among the reasons for this are difficulty precisely recording information about complicated analytical experiments (metadata), existence of various databases with their own metadata descriptions, and low reusability of the published data, resulting in submitters (the researchers who generate the data) being insufficiently motivated. To tackle these issues, we developed Metabolonote, a Semantic MediaWiki-based database designed specifically for managing metabolomic metadata. We also defined a metadata and data description format, called “Togo Metabolome Data” (TogoMD), with an ID system that is required for unique access to each level of the tree-structured metadata such as study purpose, sample, analytical method, and data analysis. Separation of the management of metadata from that of data and permission to attach related information to the metadata provide advantages for submitters, readers, and database developers. The metadata are enriched with information such as links to comparable data, thereby functioning as a hub of related data resources. They also enhance not only readers’ understanding and use of data but also submitters’ motivation to publish the data. The metadata are computationally shared among other systems via APIs, which facilitate the construction of novel databases by database developers. A permission system that allows publication of immature metadata and feedback from readers also helps submitters to improve their metadata. Hence, this aspect of Metabolonote, as a metadata preparation tool, is complementary to high-quality and persistent data repositories such as MetaboLights. A total of 808 metadata for analyzed data obtained from 35 biological species are published currently. Metabolonote and related tools are available free of cost at http://metabolonote.kazusa.or.jp/. PMID:25905099

Metabolonote: a wiki-based database for managing hierarchical metadata of metabolome analyses.

PubMed

Ara, Takeshi; Enomoto, Mitsuo; Arita, Masanori; Ikeda, Chiaki; Kera, Kota; Yamada, Manabu; Nishioka, Takaaki; Ikeda, Tasuku; Nihei, Yoshito; Shibata, Daisuke; Kanaya, Shigehiko; Sakurai, Nozomu

2015-01-01

Metabolomics - technology for comprehensive detection of small molecules in an organism - lags behind the other "omics" in terms of publication and dissemination of experimental data. Among the reasons for this are difficulty precisely recording information about complicated analytical experiments (metadata), existence of various databases with their own metadata descriptions, and low reusability of the published data, resulting in submitters (the researchers who generate the data) being insufficiently motivated. To tackle these issues, we developed Metabolonote, a Semantic MediaWiki-based database designed specifically for managing metabolomic metadata. We also defined a metadata and data description format, called "Togo Metabolome Data" (TogoMD), with an ID system that is required for unique access to each level of the tree-structured metadata such as study purpose, sample, analytical method, and data analysis. Separation of the management of metadata from that of data and permission to attach related information to the metadata provide advantages for submitters, readers, and database developers. The metadata are enriched with information such as links to comparable data, thereby functioning as a hub of related data resources. They also enhance not only readers' understanding and use of data but also submitters' motivation to publish the data. The metadata are computationally shared among other systems via APIs, which facilitate the construction of novel databases by database developers. A permission system that allows publication of immature metadata and feedback from readers also helps submitters to improve their metadata. Hence, this aspect of Metabolonote, as a metadata preparation tool, is complementary to high-quality and persistent data repositories such as MetaboLights. A total of 808 metadata for analyzed data obtained from 35 biological species are published currently. Metabolonote and related tools are available free of cost at http://metabolonote.kazusa.or.jp/.

Biomarkers of Fatigue: Metabolomics Profiles Predictive of Cognitive Performance

DTIC Science & Technology

2013-05-01

metabolites. The latest version of the Human Metabolome Database (v. 2.5; released August , 2009) includes approximately 8,000 identified mammalian...monoamine oxidase; COMT , catechol-O-methyl transferase. (Modiefied from Rubí and Maechler, 2010). Ovals indicate metabolites found to be significantly

Compliance with minimum information guidelines in public metabolomics repositories

PubMed Central

Spicer, Rachel A.; Salek, Reza; Steinbeck, Christoph

2017-01-01

The Metabolomics Standards Initiative (MSI) guidelines were first published in 2007. These guidelines provided reporting standards for all stages of metabolomics analysis: experimental design, biological context, chemical analysis and data processing. Since 2012, a series of public metabolomics databases and repositories, which accept the deposition of metabolomic datasets, have arisen. In this study, the compliance of 399 public data sets, from four major metabolomics data repositories, to the biological context MSI reporting standards was evaluated. None of the reporting standards were complied with in every publicly available study, although adherence rates varied greatly, from 0 to 97%. The plant minimum reporting standards were the most complied with and the microbial and in vitro were the least. Our results indicate the need for reassessment and revision of the existing MSI reporting standards. PMID:28949328

Compliance with minimum information guidelines in public metabolomics repositories.

PubMed

Spicer, Rachel A; Salek, Reza; Steinbeck, Christoph

2017-09-26

The Metabolomics Standards Initiative (MSI) guidelines were first published in 2007. These guidelines provided reporting standards for all stages of metabolomics analysis: experimental design, biological context, chemical analysis and data processing. Since 2012, a series of public metabolomics databases and repositories, which accept the deposition of metabolomic datasets, have arisen. In this study, the compliance of 399 public data sets, from four major metabolomics data repositories, to the biological context MSI reporting standards was evaluated. None of the reporting standards were complied with in every publicly available study, although adherence rates varied greatly, from 0 to 97%. The plant minimum reporting standards were the most complied with and the microbial and in vitro were the least. Our results indicate the need for reassessment and revision of the existing MSI reporting standards.

The Protein Information Resource: an integrated public resource of functional annotation of proteins

PubMed Central

Wu, Cathy H.; Huang, Hongzhan; Arminski, Leslie; Castro-Alvear, Jorge; Chen, Yongxing; Hu, Zhang-Zhi; Ledley, Robert S.; Lewis, Kali C.; Mewes, Hans-Werner; Orcutt, Bruce C.; Suzek, Baris E.; Tsugita, Akira; Vinayaka, C. R.; Yeh, Lai-Su L.; Zhang, Jian; Barker, Winona C.

2002-01-01

The Protein Information Resource (PIR) serves as an integrated public resource of functional annotation of protein data to support genomic/proteomic research and scientific discovery. The PIR, in collaboration with the Munich Information Center for Protein Sequences (MIPS) and the Japan International Protein Information Database (JIPID), produces the PIR-International Protein Sequence Database (PSD), the major annotated protein sequence database in the public domain, containing about 250 000 proteins. To improve protein annotation and the coverage of experimentally validated data, a bibliography submission system is developed for scientists to submit, categorize and retrieve literature information. Comprehensive protein information is available from iProClass, which includes family classification at the superfamily, domain and motif levels, structural and functional features of proteins, as well as cross-references to over 40 biological databases. To provide timely and comprehensive protein data with source attribution, we have introduced a non-redundant reference protein database, PIR-NREF. The database consists of about 800 000 proteins collected from PIR-PSD, SWISS-PROT, TrEMBL, GenPept, RefSeq and PDB, with composite protein names and literature data. To promote database interoperability, we provide XML data distribution and open database schema, and adopt common ontologies. The PIR web site (http://pir.georgetown.edu/) features data mining and sequence analysis tools for information retrieval and functional identification of proteins based on both sequence and annotation information. The PIR databases and other files are also available by FTP (ftp://nbrfa.georgetown.edu/pir_databases). PMID:11752247

Metabolite fingerprinting of urine suggests breed-specific dietary metabolism differences in domestic dogs.

PubMed

Beckmann, Manfred; Enot, David P; Overy, David P; Scott, Ian M; Jones, Paul G; Allaway, David; Draper, John

2010-04-01

Selective breeding of dogs has culminated in a large number of modern breeds distinctive in terms of size, shape and behaviour. Inadvertently, a range of breed-specific genetic disorders have become fixed in some pure-bred populations. Several inherited conditions confer chronic metabolic defects that are influenced strongly by diet, but it is likely that many less obvious breed-specific differences in physiology exist. Using Labrador retrievers and miniature Schnauzers maintained in a simulated domestic setting on a controlled diet, an experimental design was validated in relation to husbandry, sampling and sample processing for metabolomics. Metabolite fingerprints were generated from 'spot' urine samples using flow injection electrospray MS (FIE-MS). With class based on breed, urine chemical fingerprints were modelled using Random Forest (a supervised data classification technique), and metabolite features (m/z) explanatory of breed-specific differences were putatively annotated using the ARMeC database (http://www.armec.org). GC-MS profiling to confirm FIE-MS predictions indicated major breed-specific differences centred on the metabolism of diet-related polyphenols. Metabolism of further diet components, including potentially prebiotic oligosaccharides, animal-derived fats and glycerol, appeared significantly different between the two breeds. Analysis of the urinary metabolome of young male dogs representative of a wider range of breeds from animals maintained under domestic conditions on unknown diets provided preliminary evidence that many breeds may indeed have distinctive metabolic differences, with significant differences particularly apparent in comparisons between large and smaller breeds.

Systematic analysis of snake neurotoxins' functional classification using a data warehousing approach.

PubMed

Siew, Joyce Phui Yee; Khan, Asif M; Tan, Paul T J; Koh, Judice L Y; Seah, Seng Hong; Koo, Chuay Yeng; Chai, Siaw Ching; Armugam, Arunmozhiarasi; Brusic, Vladimir; Jeyaseelan, Kandiah

2004-12-12

Sequence annotations, functional and structural data on snake venom neurotoxins (svNTXs) are scattered across multiple databases and literature sources. Sequence annotations and structural data are available in the public molecular databases, while functional data are almost exclusively available in the published articles. There is a need for a specialized svNTXs database that contains NTX entries, which are organized, well annotated and classified in a systematic manner. We have systematically analyzed svNTXs and classified them using structure-function groups based on their structural, functional and phylogenetic properties. Using conserved motifs in each phylogenetic group, we built an intelligent module for the prediction of structural and functional properties of unknown NTXs. We also developed an annotation tool to aid the functional prediction of newly identified NTXs as an additional resource for the venom research community. We created a searchable online database of NTX proteins sequences (http://research.i2r.a-star.edu.sg/Templar/DB/snake_neurotoxin). This database can also be found under Swiss-Prot Toxin Annotation Project website (http://www.expasy.org/sprot/).

Solving the Problem: Genome Annotation Standards before the Data Deluge.

PubMed

Klimke, William; O'Donovan, Claire; White, Owen; Brister, J Rodney; Clark, Karen; Fedorov, Boris; Mizrachi, Ilene; Pruitt, Kim D; Tatusova, Tatiana

2011-10-15

The promise of genome sequencing was that the vast undiscovered country would be mapped out by comparison of the multitude of sequences available and would aid researchers in deciphering the role of each gene in every organism. Researchers recognize that there is a need for high quality data. However, different annotation procedures, numerous databases, and a diminishing percentage of experimentally determined gene functions have resulted in a spectrum of annotation quality. NCBI in collaboration with sequencing centers, archival databases, and researchers, has developed the first international annotation standards, a fundamental step in ensuring that high quality complete prokaryotic genomes are available as gold standard references. Highlights include the development of annotation assessment tools, community acceptance of protein naming standards, comparison of annotation resources to provide consistent annotation, and improved tracking of the evidence used to generate a particular annotation. The development of a set of minimal standards, including the requirement for annotated complete prokaryotic genomes to contain a full set of ribosomal RNAs, transfer RNAs, and proteins encoding core conserved functions, is an historic milestone. The use of these standards in existing genomes and future submissions will increase the quality of databases, enabling researchers to make accurate biological discoveries.

Solving the Problem: Genome Annotation Standards before the Data Deluge

PubMed Central

Klimke, William; O'Donovan, Claire; White, Owen; Brister, J. Rodney; Clark, Karen; Fedorov, Boris; Mizrachi, Ilene; Pruitt, Kim D.; Tatusova, Tatiana

2011-01-01

The promise of genome sequencing was that the vast undiscovered country would be mapped out by comparison of the multitude of sequences available and would aid researchers in deciphering the role of each gene in every organism. Researchers recognize that there is a need for high quality data. However, different annotation procedures, numerous databases, and a diminishing percentage of experimentally determined gene functions have resulted in a spectrum of annotation quality. NCBI in collaboration with sequencing centers, archival databases, and researchers, has developed the first international annotation standards, a fundamental step in ensuring that high quality complete prokaryotic genomes are available as gold standard references. Highlights include the development of annotation assessment tools, community acceptance of protein naming standards, comparison of annotation resources to provide consistent annotation, and improved tracking of the evidence used to generate a particular annotation. The development of a set of minimal standards, including the requirement for annotated complete prokaryotic genomes to contain a full set of ribosomal RNAs, transfer RNAs, and proteins encoding core conserved functions, is an historic milestone. The use of these standards in existing genomes and future submissions will increase the quality of databases, enabling researchers to make accurate biological discoveries. PMID:22180819

The Génolevures database.

PubMed

Martin, Tiphaine; Sherman, David J; Durrens, Pascal

2011-01-01

The Génolevures online database (URL: http://www.genolevures.org) stores and provides the data and results obtained by the Génolevures Consortium through several campaigns of genome annotation of the yeasts in the Saccharomycotina subphylum (hemiascomycetes). This database is dedicated to large-scale comparison of these genomes, storing not only the different chromosomal elements detected in the sequences, but also the logical relations between them. The database is divided into a public part, accessible to anyone through Internet, and a private part where the Consortium members make genome annotations with our Magus annotation system; this system is used to annotate several related genomes in parallel. The public database is widely consulted and offers structured data, organized using a REST web site architecture that allows for automated requests. The implementation of the database, as well as its associated tools and methods, is evolving to cope with the influx of genome sequences produced by Next Generation Sequencing (NGS). Copyright © 2011 Académie des sciences. Published by Elsevier SAS. All rights reserved.

Protein Information Resource: a community resource for expert annotation of protein data

PubMed Central

Barker, Winona C.; Garavelli, John S.; Hou, Zhenglin; Huang, Hongzhan; Ledley, Robert S.; McGarvey, Peter B.; Mewes, Hans-Werner; Orcutt, Bruce C.; Pfeiffer, Friedhelm; Tsugita, Akira; Vinayaka, C. R.; Xiao, Chunlin; Yeh, Lai-Su L.; Wu, Cathy

2001-01-01

The Protein Information Resource, in collaboration with the Munich Information Center for Protein Sequences (MIPS) and the Japan International Protein Information Database (JIPID), produces the most comprehensive and expertly annotated protein sequence database in the public domain, the PIR-International Protein Sequence Database. To provide timely and high quality annotation and promote database interoperability, the PIR-International employs rule-based and classification-driven procedures based on controlled vocabulary and standard nomenclature and includes status tags to distinguish experimentally determined from predicted protein features. The database contains about 200 000 non-redundant protein sequences, which are classified into families and superfamilies and their domains and motifs identified. Entries are extensively cross-referenced to other sequence, classification, genome, structure and activity databases. The PIR web site features search engines that use sequence similarity and database annotation to facilitate the analysis and functional identification of proteins. The PIR-Inter­national databases and search tools are accessible on the PIR web site at http://pir.georgetown.edu/ and at the MIPS web site at http://www.mips.biochem.mpg.de. The PIR-International Protein Sequence Database and other files are also available by FTP. PMID:11125041

Accessing the SEED genome databases via Web services API: tools for programmers.

PubMed

Disz, Terry; Akhter, Sajia; Cuevas, Daniel; Olson, Robert; Overbeek, Ross; Vonstein, Veronika; Stevens, Rick; Edwards, Robert A

2010-06-14

The SEED integrates many publicly available genome sequences into a single resource. The database contains accurate and up-to-date annotations based on the subsystems concept that leverages clustering between genomes and other clues to accurately and efficiently annotate microbial genomes. The backend is used as the foundation for many genome annotation tools, such as the Rapid Annotation using Subsystems Technology (RAST) server for whole genome annotation, the metagenomics RAST server for random community genome annotations, and the annotation clearinghouse for exchanging annotations from different resources. In addition to a web user interface, the SEED also provides Web services based API for programmatic access to the data in the SEED, allowing the development of third-party tools and mash-ups. The currently exposed Web services encompass over forty different methods for accessing data related to microbial genome annotations. The Web services provide comprehensive access to the database back end, allowing any programmer access to the most consistent and accurate genome annotations available. The Web services are deployed using a platform independent service-oriented approach that allows the user to choose the most suitable programming platform for their application. Example code demonstrate that Web services can be used to access the SEED using common bioinformatics programming languages such as Perl, Python, and Java. We present a novel approach to access the SEED database. Using Web services, a robust API for access to genomics data is provided, without requiring large volume downloads all at once. The API ensures timely access to the most current datasets available, including the new genomes as soon as they come online.

Bovine Genome Database: supporting community annotation and analysis of the Bos taurus genome

PubMed Central

2010-01-01

Background A goal of the Bovine Genome Database (BGD; http://BovineGenome.org) has been to support the Bovine Genome Sequencing and Analysis Consortium (BGSAC) in the annotation and analysis of the bovine genome. We were faced with several challenges, including the need to maintain consistent quality despite diversity in annotation expertise in the research community, the need to maintain consistent data formats, and the need to minimize the potential duplication of annotation effort. With new sequencing technologies allowing many more eukaryotic genomes to be sequenced, the demand for collaborative annotation is likely to increase. Here we present our approach, challenges and solutions facilitating a large distributed annotation project. Results and Discussion BGD has provided annotation tools that supported 147 members of the BGSAC in contributing 3,871 gene models over a fifteen-week period, and these annotations have been integrated into the bovine Official Gene Set. Our approach has been to provide an annotation system, which includes a BLAST site, multiple genome browsers, an annotation portal, and the Apollo Annotation Editor configured to connect directly to our Chado database. In addition to implementing and integrating components of the annotation system, we have performed computational analyses to create gene evidence tracks and a consensus gene set, which can be viewed on individual gene pages at BGD. Conclusions We have provided annotation tools that alleviate challenges associated with distributed annotation. Our system provides a consistent set of data to all annotators and eliminates the need for annotators to format data. Involving the bovine research community in genome annotation has allowed us to leverage expertise in various areas of bovine biology to provide biological insight into the genome sequence. PMID:21092105

Identifying developmental toxicity pathways for a subset of ToxCast chemicals using human embryonic stem cells and metabolomics

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

Kleinstreuer, N.C., E-mail: kleinstreuer.nicole@epa.gov; Smith, A.M.; West, P.R.

2011-11-15

Metabolomics analysis was performed on the supernatant of human embryonic stem (hES) cell cultures exposed to a blinded subset of 11 chemicals selected from the chemical library of EPA's ToxCast Trade-Mark-Sign chemical screening and prioritization research project. Metabolites from hES cultures were evaluated for known and novel signatures that may be indicative of developmental toxicity. Significant fold changes in endogenous metabolites were detected for 83 putatively annotated mass features in response to the subset of ToxCast chemicals. The annotations were mapped to specific human metabolic pathways. This revealed strong effects on pathways for nicotinate and nicotinamide metabolism, pantothenate and CoAmore » biosynthesis, glutathione metabolism, and arginine and proline metabolism pathways. Predictivity for adverse outcomes in mammalian prenatal developmental toxicity studies used ToxRefDB and other sources of information, including Stemina Biomarker Discovery's predictive DevTox Registered-Sign model trained on 23 pharmaceutical agents of known developmental toxicity and differing potency. The model initially predicted developmental toxicity from the blinded ToxCast compounds in concordance with animal data with 73% accuracy. Retraining the model with data from the unblinded test compounds at one concentration level increased the predictive accuracy for the remaining concentrations to 83%. These preliminary results on a 11-chemical subset of the ToxCast chemical library indicate that metabolomics analysis of the hES secretome provides information valuable for predictive modeling and mechanistic understanding of mammalian developmental toxicity. -- Highlights: Black-Right-Pointing-Pointer We tested 11 environmental compounds in a hESC metabolomics platform. Black-Right-Pointing-Pointer Significant changes in secreted small molecule metabolites were observed. Black-Right-Pointing-Pointer Perturbed mass features map to pathways critical for normal development and pregnancy. Black-Right-Pointing-Pointer Arginine, proline, nicotinate, nicotinamide and glutathione pathways were affected.« less

The Chemistry of Plant–Microbe Interactions in the Rhizosphere and the Potential for Metabolomics to Reveal Signaling Related to Defense Priming and Induced Systemic Resistance

PubMed Central

Mhlongo, Msizi I.; Piater, Lizelle A.; Madala, Ntakadzeni E.; Labuschagne, Nico; Dubery, Ian A.

2018-01-01

Plant roots communicate with microbes in a sophisticated manner through chemical communication within the rhizosphere, thereby leading to biofilm formation of beneficial microbes and, in the case of plant growth-promoting rhizomicrobes/-bacteria (PGPR), resulting in priming of defense, or induced resistance in the plant host. The knowledge of plant–plant and plant–microbe interactions have been greatly extended over recent years; however, the chemical communication leading to priming is far from being well understood. Furthermore, linkage between below- and above-ground plant physiological processes adds to the complexity. In metabolomics studies, the main aim is to profile and annotate all exo- and endo-metabolites in a biological system that drive and participate in physiological processes. Recent advances in this field has enabled researchers to analyze 100s of compounds in one sample over a short time period. Here, from a metabolomics viewpoint, we review the interactions within the rhizosphere and subsequent above-ground ‘signalomics’, and emphasize the contributions that mass spectrometric-based metabolomic approaches can bring to the study of plant-beneficial – and priming events. PMID:29479360

High-Resolution Metabolomics Assessment of Military Personnel: Evaluating Analytical Strategies for Chemical Detection.

PubMed

Liu, Ken H; Walker, Douglas I; Uppal, Karan; Tran, ViLinh; Rohrbeck, Patricia; Mallon, Timothy M; Jones, Dean P

2016-08-01

The aim of this study was to maximize detection of serum metabolites with high-resolution metabolomics (HRM). Department of Defense Serum Repository (DoDSR) samples were analyzed using ultrahigh resolution mass spectrometry with three complementary chromatographic phases and four ionization modes. Chemical coverage was evaluated by number of ions detected and accurate mass matches to a human metabolomics database. Individual HRM platforms provided accurate mass matches for up to 58% of the KEGG metabolite database. Combining two analytical methods increased matches to 72% and included metabolites in most major human metabolic pathways and chemical classes. Detection and feature quality varied by analytical configuration. Dual chromatography HRM with positive and negative electrospray ionization provides an effective generalized method for metabolic assessment of military personnel.

High-resolution metabolomics assessment of military personnel: Evaluating analytical strategies for chemical detection

PubMed Central

Liu, Ken H.; Walker, Douglas I.; Uppal, Karan; Tran, ViLinh; Rohrbeck, Patricia; Mallon, Timothy M.; Jones, Dean P.

2016-01-01

Objective To maximize detection of serum metabolites with high-resolution metabolomics (HRM). Methods Department of Defense Serum Repository (DoDSR) samples were analyzed using ultra-high resolution mass spectrometry with three complementary chromatographic phases and four ionization modes. Chemical coverage was evaluated by number of ions detected and accurate mass matches to a human metabolomics database. Results Individual HRM platforms provided accurate mass matches for up to 58% of the KEGG metabolite database. Combining two analytical methods increased matches to 72%, and included metabolites in most major human metabolic pathways and chemical classes. Detection and feature quality varied by analytical configuration. Conclusions Dual chromatography HRM with positive and negative electrospray ionization provides an effective generalized method for metabolic assessment of military personnel. PMID:27501105

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.

New Biomarkers of Coffee Consumption Identified by the Non-Targeted Metabolomic Profiling of Cohort Study Subjects

PubMed Central

Martin, Jean-François; Lyan, Bernard; Pujos-Guillot, Estelle; Fezeu, Leopold; Hercberg, Serge; Comte, Blandine; Galan, Pilar; Touvier, Mathilde; Manach, Claudine

2014-01-01

Coffee contains various bioactives implicated with human health and disease risk. To accurately assess the effects of overall consumption upon health and disease, individual intake must be measured in large epidemiological studies. Metabolomics has emerged as a powerful approach to discover biomarkers of intake for a large range of foods. Here we report the profiling of the urinary metabolome of cohort study subjects to search for new biomarkers of coffee intake. Using repeated 24-hour dietary records and a food frequency questionnaire, 20 high coffee consumers (183–540 mL/d) and 19 low consumers were selected from the French SU.VI.MAX2 cohort. Morning spot urine samples from each subject were profiled by high-resolution mass spectrometry. Partial least-square discriminant analysis of multidimensional liquid chromatography-mass spectrometry data clearly distinguished high consumers from low via 132 significant (p-value<0.05) discriminating features. Ion clusters whose intensities were most elevated in the high consumers were annotated using online and in-house databases and their identities checked using commercial standards and MS-MS fragmentation. The best discriminants, and thus potential markers of coffee consumption, were the glucuronide of the diterpenoid atractyligenin, the diketopiperazine cyclo(isoleucyl-prolyl), and the alkaloid trigonelline. Some caffeine metabolites, such as 1-methylxanthine, were also among the discriminants, however caffeine may be consumed from other sources and its metabolism is subject to inter-individual variation. Receiver operating characteristics curve analysis showed that the biomarkers identified could be used effectively in combination for increased sensitivity and specificity. Once validated in other cohorts or intervention studies, these specific single or combined biomarkers will become a valuable alternative to assessment of coffee intake by dietary survey and finally lead to a better understanding of the health implications of coffee consumption. PMID:24713823

CEBS object model for systems biology data, SysBio-OM.

PubMed

Xirasagar, Sandhya; Gustafson, Scott; Merrick, B Alex; Tomer, Kenneth B; Stasiewicz, Stanley; Chan, Denny D; Yost, Kenneth J; Yates, John R; Sumner, Susan; Xiao, Nianqing; Waters, Michael D

2004-09-01

To promote a systems biology approach to understanding the biological effects of environmental stressors, the Chemical Effects in Biological Systems (CEBS) knowledge base is being developed to house data from multiple complex data streams in a systems friendly manner that will accommodate extensive querying from users. Unified data representation via a single object model will greatly aid in integrating data storage and management, and facilitate reuse of software to analyze and display data resulting from diverse differential expression or differential profile technologies. Data streams include, but are not limited to, gene expression analysis (transcriptomics), protein expression and protein-protein interaction analysis (proteomics) and changes in low molecular weight metabolite levels (metabolomics). To enable the integration of microarray gene expression, proteomics and metabolomics data in the CEBS system, we designed an object model, Systems Biology Object Model (SysBio-OM). The model is comprehensive and leverages other open source efforts, namely the MicroArray Gene Expression Object Model (MAGE-OM) and the Proteomics Experiment Data Repository (PEDRo) object model. SysBio-OM is designed by extending MAGE-OM to represent protein expression data elements (including those from PEDRo), protein-protein interaction and metabolomics data. SysBio-OM promotes the standardization of data representation and data quality by facilitating the capture of the minimum annotation required for an experiment. Such standardization refines the accuracy of data mining and interpretation. The open source SysBio-OM model, which can be implemented on varied computing platforms is presented here. A universal modeling language depiction of the entire SysBio-OM is available at http://cebs.niehs.nih.gov/SysBioOM/. The Rational Rose object model package is distributed under an open source license that permits unrestricted academic and commercial use and is available at http://cebs.niehs.nih.gov/cebsdownloads. The database and interface are being built to implement the model and will be available for public use at http://cebs.niehs.nih.gov.

The Universal Protein Resource (UniProt): an expanding universe of protein information.

PubMed

Wu, Cathy H; Apweiler, Rolf; Bairoch, Amos; Natale, Darren A; Barker, Winona C; Boeckmann, Brigitte; Ferro, Serenella; Gasteiger, Elisabeth; Huang, Hongzhan; Lopez, Rodrigo; Magrane, Michele; Martin, Maria J; Mazumder, Raja; O'Donovan, Claire; Redaschi, Nicole; Suzek, Baris

2006-01-01

The Universal Protein Resource (UniProt) provides a central resource on protein sequences and functional annotation with three database components, each addressing a key need in protein bioinformatics. The UniProt Knowledgebase (UniProtKB), comprising the manually annotated UniProtKB/Swiss-Prot section and the automatically annotated UniProtKB/TrEMBL section, is the preeminent storehouse of protein annotation. The extensive cross-references, functional and feature annotations and literature-based evidence attribution enable scientists to analyse proteins and query across databases. The UniProt Reference Clusters (UniRef) speed similarity searches via sequence space compression by merging sequences that are 100% (UniRef100), 90% (UniRef90) or 50% (UniRef50) identical. Finally, the UniProt Archive (UniParc) stores all publicly available protein sequences, containing the history of sequence data with links to the source databases. UniProt databases continue to grow in size and in availability of information. Recent and upcoming changes to database contents, formats, controlled vocabularies and services are described. New download availability includes all major releases of UniProtKB, sequence collections by taxonomic division and complete proteomes. A bibliography mapping service has been added, and an ID mapping service will be available soon. UniProt databases can be accessed online at http://www.uniprot.org or downloaded at ftp://ftp.uniprot.org/pub/databases/.

TabSQL: a MySQL tool to facilitate mapping user data to public databases.

PubMed

Xia, Xiao-Qin; McClelland, Michael; Wang, Yipeng

2010-06-23

With advances in high-throughput genomics and proteomics, it is challenging for biologists to deal with large data files and to map their data to annotations in public databases. We developed TabSQL, a MySQL-based application tool, for viewing, filtering and querying data files with large numbers of rows. TabSQL provides functions for downloading and installing table files from public databases including the Gene Ontology database (GO), the Ensembl databases, and genome databases from the UCSC genome bioinformatics site. Any other database that provides tab-delimited flat files can also be imported. The downloaded gene annotation tables can be queried together with users' data in TabSQL using either a graphic interface or command line. TabSQL allows queries across the user's data and public databases without programming. It is a convenient tool for biologists to annotate and enrich their data.

TabSQL: a MySQL tool to facilitate mapping user data to public databases

PubMed Central

2010-01-01

Background With advances in high-throughput genomics and proteomics, it is challenging for biologists to deal with large data files and to map their data to annotations in public databases. Results We developed TabSQL, a MySQL-based application tool, for viewing, filtering and querying data files with large numbers of rows. TabSQL provides functions for downloading and installing table files from public databases including the Gene Ontology database (GO), the Ensembl databases, and genome databases from the UCSC genome bioinformatics site. Any other database that provides tab-delimited flat files can also be imported. The downloaded gene annotation tables can be queried together with users' data in TabSQL using either a graphic interface or command line. Conclusions TabSQL allows queries across the user's data and public databases without programming. It is a convenient tool for biologists to annotate and enrich their data. PMID:20573251

Protein sequence annotation in the genome era: the annotation concept of SWISS-PROT+TREMBL.

PubMed

Apweiler, R; Gateau, A; Contrino, S; Martin, M J; Junker, V; O'Donovan, C; Lang, F; Mitaritonna, N; Kappus, S; Bairoch, A

1997-01-01

SWISS-PROT is a curated protein sequence database which strives to provide a high level of annotation, a minimal level of redundancy and high level of integration with other databases. Ongoing genome sequencing projects have dramatically increased the number of protein sequences to be incorporated into SWISS-PROT. Since we do not want to dilute the quality standards of SWISS-PROT by incorporating sequences without proper sequence analysis and annotation, we cannot speed up the incorporation of new incoming data indefinitely. However, as we also want to make the sequences available as fast as possible, we introduced TREMBL (TRanslation of EMBL nucleotide sequence database), a supplement to SWISS-PROT. TREMBL consists of computer-annotated entries in SWISS-PROT format derived from the translation of all coding sequences (CDS) in the EMBL nucleotide sequence database, except for CDS already included in SWISS-PROT. While TREMBL is already of immense value, its computer-generated annotation does not match the quality of SWISS-PROTs. The main difference is in the protein functional information attached to sequences. With this in mind, we are dedicating substantial effort to develop and apply computer methods to enhance the functional information attached to TREMBL entries.

The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST)

PubMed Central

Overbeek, Ross; Olson, Robert; Pusch, Gordon D.; Olsen, Gary J.; Davis, James J.; Disz, Terry; Edwards, Robert A.; Gerdes, Svetlana; Parrello, Bruce; Shukla, Maulik; Vonstein, Veronika; Wattam, Alice R.; Xia, Fangfang; Stevens, Rick

2014-01-01

In 2004, the SEED (http://pubseed.theseed.org/) was created to provide consistent and accurate genome annotations across thousands of genomes and as a platform for discovering and developing de novo annotations. The SEED is a constantly updated integration of genomic data with a genome database, web front end, API and server scripts. It is used by many scientists for predicting gene functions and discovering new pathways. In addition to being a powerful database for bioinformatics research, the SEED also houses subsystems (collections of functionally related protein families) and their derived FIGfams (protein families), which represent the core of the RAST annotation engine (http://rast.nmpdr.org/). When a new genome is submitted to RAST, genes are called and their annotations are made by comparison to the FIGfam collection. If the genome is made public, it is then housed within the SEED and its proteins populate the FIGfam collection. This annotation cycle has proven to be a robust and scalable solution to the problem of annotating the exponentially increasing number of genomes. To date, >12 000 users worldwide have annotated >60 000 distinct genomes using RAST. Here we describe the interconnectedness of the SEED database and RAST, the RAST annotation pipeline and updates to both resources. PMID:24293654

The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST).

PubMed

Overbeek, Ross; Olson, Robert; Pusch, Gordon D; Olsen, Gary J; Davis, James J; Disz, Terry; Edwards, Robert A; Gerdes, Svetlana; Parrello, Bruce; Shukla, Maulik; Vonstein, Veronika; Wattam, Alice R; Xia, Fangfang; Stevens, Rick

2014-01-01

In 2004, the SEED (http://pubseed.theseed.org/) was created to provide consistent and accurate genome annotations across thousands of genomes and as a platform for discovering and developing de novo annotations. The SEED is a constantly updated integration of genomic data with a genome database, web front end, API and server scripts. It is used by many scientists for predicting gene functions and discovering new pathways. In addition to being a powerful database for bioinformatics research, the SEED also houses subsystems (collections of functionally related protein families) and their derived FIGfams (protein families), which represent the core of the RAST annotation engine (http://rast.nmpdr.org/). When a new genome is submitted to RAST, genes are called and their annotations are made by comparison to the FIGfam collection. If the genome is made public, it is then housed within the SEED and its proteins populate the FIGfam collection. This annotation cycle has proven to be a robust and scalable solution to the problem of annotating the exponentially increasing number of genomes. To date, >12 000 users worldwide have annotated >60 000 distinct genomes using RAST. Here we describe the interconnectedness of the SEED database and RAST, the RAST annotation pipeline and updates to both resources.

Brassica ASTRA: an integrated database for Brassica genomic research.

PubMed

Love, Christopher G; Robinson, Andrew J; Lim, Geraldine A C; Hopkins, Clare J; Batley, Jacqueline; Barker, Gary; Spangenberg, German C; Edwards, David

2005-01-01

Brassica ASTRA is a public database for genomic information on Brassica species. The database incorporates expressed sequences with Swiss-Prot and GenBank comparative sequence annotation as well as secondary Gene Ontology (GO) annotation derived from the comparison with Arabidopsis TAIR GO annotations. Simple sequence repeat molecular markers are identified within resident sequences and mapped onto the closely related Arabidopsis genome sequence. Bacterial artificial chromosome (BAC) end sequences derived from the Multinational Brassica Genome Project are also mapped onto the Arabidopsis genome sequence enabling users to identify candidate Brassica BACs corresponding to syntenic regions of Arabidopsis. This information is maintained in a MySQL database with a web interface providing the primary means of interrogation. The database is accessible at http://hornbill.cspp.latrobe.edu.au.

The annotation-enriched non-redundant patent sequence databases.

PubMed

Li, Weizhong; Kondratowicz, Bartosz; McWilliam, Hamish; Nauche, Stephane; Lopez, Rodrigo

2013-01-01

The EMBL-European Bioinformatics Institute (EMBL-EBI) offers public access to patent sequence data, providing a valuable service to the intellectual property and scientific communities. The non-redundant (NR) patent sequence databases comprise two-level nucleotide and protein sequence clusters (NRNL1, NRNL2, NRPL1 and NRPL2) based on sequence identity (level-1) and patent family (level-2). Annotation from the source entries in these databases is merged and enhanced with additional information from the patent literature and biological context. Corrections in patent publication numbers, kind-codes and patent equivalents significantly improve the data quality. Data are available through various user interfaces including web browser, downloads via FTP, SRS, Dbfetch and EBI-Search. Sequence similarity/homology searches against the databases are available using BLAST, FASTA and PSI-Search. In this article, we describe the data collection and annotation and also outline major changes and improvements introduced since 2009. Apart from data growth, these changes include additional annotation for singleton clusters, the identifier versioning for tracking entry change and the entry mappings between the two-level databases. Database URL: http://www.ebi.ac.uk/patentdata/nr/

The Annotation-enriched non-redundant patent sequence databases

PubMed Central

Li, Weizhong; Kondratowicz, Bartosz; McWilliam, Hamish; Nauche, Stephane; Lopez, Rodrigo

2013-01-01

The EMBL-European Bioinformatics Institute (EMBL-EBI) offers public access to patent sequence data, providing a valuable service to the intellectual property and scientific communities. The non-redundant (NR) patent sequence databases comprise two-level nucleotide and protein sequence clusters (NRNL1, NRNL2, NRPL1 and NRPL2) based on sequence identity (level-1) and patent family (level-2). Annotation from the source entries in these databases is merged and enhanced with additional information from the patent literature and biological context. Corrections in patent publication numbers, kind-codes and patent equivalents significantly improve the data quality. Data are available through various user interfaces including web browser, downloads via FTP, SRS, Dbfetch and EBI-Search. Sequence similarity/homology searches against the databases are available using BLAST, FASTA and PSI-Search. In this article, we describe the data collection and annotation and also outline major changes and improvements introduced since 2009. Apart from data growth, these changes include additional annotation for singleton clusters, the identifier versioning for tracking entry change and the entry mappings between the two-level databases. Database URL: http://www.ebi.ac.uk/patentdata/nr/ PMID:23396323

MASS SPECTROMETRY-BASED METABOLOMICS

PubMed Central

Dettmer, Katja; Aronov, Pavel A.; Hammock, Bruce D.

2007-01-01

This review presents an overview of the dynamically developing field of mass spectrometry-based metabolomics. Metabolomics aims at the comprehensive and quantitative analysis of wide arrays of metabolites in biological samples. These numerous analytes have very diverse physico-chemical properties and occur at different abundance levels. Consequently, comprehensive metabolomics investigations are primarily a challenge for analytical chemistry and specifically mass spectrometry has vast potential as a tool for this type of investigation. Metabolomics require special approaches for sample preparation, separation, and mass spectrometric analysis. Current examples of those approaches are described in this review. It primarily focuses on metabolic fingerprinting, a technique that analyzes all detectable analytes in a given sample with subsequent classification of samples and identification of differentially expressed metabolites, which define the sample classes. To perform this complex task, data analysis tools, metabolite libraries, and databases are required. Therefore, recent advances in metabolomics bioinformatics are also discussed. PMID:16921475

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

Hymenoptera Genome Database: integrating genome annotations in HymenopteraMine

PubMed Central

Elsik, Christine G.; Tayal, Aditi; Diesh, Colin M.; Unni, Deepak R.; Emery, Marianne L.; Nguyen, Hung N.; Hagen, Darren E.

2016-01-01

We report an update of the Hymenoptera Genome Database (HGD) (http://HymenopteraGenome.org), a model organism database for insect species of the order Hymenoptera (ants, bees and wasps). HGD maintains genomic data for 9 bee species, 10 ant species and 1 wasp, including the versions of genome and annotation data sets published by the genome sequencing consortiums and those provided by NCBI. A new data-mining warehouse, HymenopteraMine, based on the InterMine data warehousing system, integrates the genome data with data from external sources and facilitates cross-species analyses based on orthology. New genome browsers and annotation tools based on JBrowse/WebApollo provide easy genome navigation, and viewing of high throughput sequence data sets and can be used for collaborative genome annotation. All of the genomes and annotation data sets are combined into a single BLAST server that allows users to select and combine sequence data sets to search. PMID:26578564

Increasing rigor in NMR-based metabolomics through validated and open source tools

PubMed Central

Eghbalnia, Hamid R; Romero, Pedro R; Westler, William M; Baskaran, Kumaran; Ulrich, Eldon L; Markley, John L

2016-01-01

The metabolome, the collection of small molecules associated with an organism, is a growing subject of inquiry, with the data utilized for data-intensive systems biology, disease diagnostics, biomarker discovery, and the broader characterization of small molecules in mixtures. Owing to their close proximity to the functional endpoints that govern an organism’s phenotype, metabolites are highly informative about functional states. The field of metabolomics identifies and quantifies endogenous and exogenous metabolites in biological samples. Information acquired from nuclear magnetic spectroscopy (NMR), mass spectrometry (MS), and the published literature, as processed by statistical approaches, are driving increasingly wider applications of metabolomics. This review focuses on the role of databases and software tools in advancing the rigor, robustness, reproducibility, and validation of metabolomics studies. PMID:27643760

Increasing rigor in NMR-based metabolomics through validated and open source tools.

PubMed

Eghbalnia, Hamid R; Romero, Pedro R; Westler, William M; Baskaran, Kumaran; Ulrich, Eldon L; Markley, John L

2017-02-01

The metabolome, the collection of small molecules associated with an organism, is a growing subject of inquiry, with the data utilized for data-intensive systems biology, disease diagnostics, biomarker discovery, and the broader characterization of small molecules in mixtures. Owing to their close proximity to the functional endpoints that govern an organism's phenotype, metabolites are highly informative about functional states. The field of metabolomics identifies and quantifies endogenous and exogenous metabolites in biological samples. Information acquired from nuclear magnetic spectroscopy (NMR), mass spectrometry (MS), and the published literature, as processed by statistical approaches, are driving increasingly wider applications of metabolomics. This review focuses on the role of databases and software tools in advancing the rigor, robustness, reproducibility, and validation of metabolomics studies. Copyright © 2016. Published by Elsevier Ltd.

Metabolome searcher: a high throughput tool for metabolite identification and metabolic pathway mapping directly from mass spectrometry and using genome restriction.

PubMed

Dhanasekaran, A Ranjitha; Pearson, Jon L; Ganesan, Balasubramanian; Weimer, Bart C

2015-02-25

Mass spectrometric analysis of microbial metabolism provides a long list of possible compounds. Restricting the identification of the possible compounds to those produced by the specific organism would benefit the identification process. Currently, identification of mass spectrometry (MS) data is commonly done using empirically derived compound databases. Unfortunately, most databases contain relatively few compounds, leaving long lists of unidentified molecules. Incorporating genome-encoded metabolism enables MS output identification that may not be included in databases. Using an organism's genome as a database restricts metabolite identification to only those compounds that the organism can produce. To address the challenge of metabolomic analysis from MS data, a web-based application to directly search genome-constructed metabolic databases was developed. The user query returns a genome-restricted list of possible compound identifications along with the putative metabolic pathways based on the name, formula, SMILES structure, and the compound mass as defined by the user. Multiple queries can be done simultaneously by submitting a text file created by the user or obtained from the MS analysis software. The user can also provide parameters specific to the experiment's MS analysis conditions, such as mass deviation, adducts, and detection mode during the query so as to provide additional levels of evidence to produce the tentative identification. The query results are provided as an HTML page and downloadable text file of possible compounds that are restricted to a specific genome. Hyperlinks provided in the HTML file connect the user to the curated metabolic databases housed in ProCyc, a Pathway Tools platform, as well as the KEGG Pathway database for visualization and metabolic pathway analysis. Metabolome Searcher, a web-based tool, facilitates putative compound identification of MS output based on genome-restricted metabolic capability. This enables researchers to rapidly extend the possible identifications of large data sets for metabolites that are not in compound databases. Putative compound names with their associated metabolic pathways from metabolomics data sets are returned to the user for additional biological interpretation and visualization. This novel approach enables compound identification by restricting the possible masses to those encoded in the genome.

Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data.

PubMed

Marco-Ramell, Anna; Palau-Rodriguez, Magali; Alay, Ania; Tulipani, Sara; Urpi-Sarda, Mireia; Sanchez-Pla, Alex; Andres-Lacueva, Cristina

2018-01-02

Bioinformatic tools for the enrichment of 'omics' datasets facilitate interpretation and understanding of data. To date few are suitable for metabolomics datasets. The main objective of this work is to give a critical overview, for the first time, of the performance of these tools. To that aim, datasets from metabolomic repositories were selected and enriched data were created. Both types of data were analysed with these tools and outputs were thoroughly examined. An exploratory multivariate analysis of the most used tools for the enrichment of metabolite sets, based on a non-metric multidimensional scaling (NMDS) of Jaccard's distances, was performed and mirrored their diversity. Codes (identifiers) of the metabolites of the datasets were searched in different metabolite databases (HMDB, KEGG, PubChem, ChEBI, BioCyc/HumanCyc, LipidMAPS, ChemSpider, METLIN and Recon2). The databases that presented more identifiers of the metabolites of the dataset were PubChem, followed by METLIN and ChEBI. However, these databases had duplicated entries and might present false positives. The performance of over-representation analysis (ORA) tools, including BioCyc/HumanCyc, ConsensusPathDB, IMPaLA, MBRole, MetaboAnalyst, Metabox, MetExplore, MPEA, PathVisio and Reactome and the mapping tool KEGGREST, was examined. Results were mostly consistent among tools and between real and enriched data despite the variability of the tools. Nevertheless, a few controversial results such as differences in the total number of metabolites were also found. Disease-based enrichment analyses were also assessed, but they were not found to be accurate probably due to the fact that metabolite disease sets are not up-to-date and the difficulty of predicting diseases from a list of metabolites. We have extensively reviewed the state-of-the-art of the available range of tools for metabolomic datasets, the completeness of metabolite databases, the performance of ORA methods and disease-based analyses. Despite the variability of the tools, they provided consistent results independent of their analytic approach. However, more work on the completeness of metabolite and pathway databases is required, which strongly affects the accuracy of enrichment analyses. Improvements will be translated into more accurate and global insights of the metabolome.

Ontological interpretation of biomedical database content.

PubMed

Santana da Silva, Filipe; Jansen, Ludger; Freitas, Fred; Schulz, Stefan

2017-06-26

Biological databases store data about laboratory experiments, together with semantic annotations, in order to support data aggregation and retrieval. The exact meaning of such annotations in the context of a database record is often ambiguous. We address this problem by grounding implicit and explicit database content in a formal-ontological framework. By using a typical extract from the databases UniProt and Ensembl, annotated with content from GO, PR, ChEBI and NCBI Taxonomy, we created four ontological models (in OWL), which generate explicit, distinct interpretations under the BioTopLite2 (BTL2) upper-level ontology. The first three models interpret database entries as individuals (IND), defined classes (SUBC), and classes with dispositions (DISP), respectively; the fourth model (HYBR) is a combination of SUBC and DISP. For the evaluation of these four models, we consider (i) database content retrieval, using ontologies as query vocabulary; (ii) information completeness; and, (iii) DL complexity and decidability. The models were tested under these criteria against four competency questions (CQs). IND does not raise any ontological claim, besides asserting the existence of sample individuals and relations among them. Modelling patterns have to be created for each type of annotation referent. SUBC is interpreted regarding maximally fine-grained defined subclasses under the classes referred to by the data. DISP attempts to extract truly ontological statements from the database records, claiming the existence of dispositions. HYBR is a hybrid of SUBC and DISP and is more parsimonious regarding expressiveness and query answering complexity. For each of the four models, the four CQs were submitted as DL queries. This shows the ability to retrieve individuals with IND, and classes in SUBC and HYBR. DISP does not retrieve anything because the axioms with disposition are embedded in General Class Inclusion (GCI) statements. Ambiguity of biological database content is addressed by a method that identifies implicit knowledge behind semantic annotations in biological databases and grounds it in an expressive upper-level ontology. The result is a seamless representation of database structure, content and annotations as OWL models.

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.

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).

Can Inferred Provenance and Its Visualisation Be Used to Detect Erroneous Annotation? A Case Study Using UniProtKB

PubMed Central

Bell, Michael J.; Collison, Matthew; Lord, Phillip

2013-01-01

A constant influx of new data poses a challenge in keeping the annotation in biological databases current. Most biological databases contain significant quantities of textual annotation, which often contains the richest source of knowledge. Many databases reuse existing knowledge; during the curation process annotations are often propagated between entries. However, this is often not made explicit. Therefore, it can be hard, potentially impossible, for a reader to identify where an annotation originated from. Within this work we attempt to identify annotation provenance and track its subsequent propagation. Specifically, we exploit annotation reuse within the UniProt Knowledgebase (UniProtKB), at the level of individual sentences. We describe a visualisation approach for the provenance and propagation of sentences in UniProtKB which enables a large-scale statistical analysis. Initially levels of sentence reuse within UniProtKB were analysed, showing that reuse is heavily prevalent, which enables the tracking of provenance and propagation. By analysing sentences throughout UniProtKB, a number of interesting propagation patterns were identified, covering over sentences. Over sentences remain in the database after they have been removed from the entries where they originally occurred. Analysing a subset of these sentences suggest that approximately are erroneous, whilst appear to be inconsistent. These results suggest that being able to visualise sentence propagation and provenance can aid in the determination of the accuracy and quality of textual annotation. Source code and supplementary data are available from the authors website at http://homepages.cs.ncl.ac.uk/m.j.bell1/sentence_analysis/. PMID:24143170

KID Project: an internet-based digital video atlas of capsule endoscopy for research purposes.

PubMed

Koulaouzidis, Anastasios; Iakovidis, Dimitris K; Yung, Diana E; Rondonotti, Emanuele; Kopylov, Uri; Plevris, John N; Toth, Ervin; Eliakim, Abraham; Wurm Johansson, Gabrielle; Marlicz, Wojciech; Mavrogenis, Georgios; Nemeth, Artur; Thorlacius, Henrik; Tontini, Gian Eugenio

2017-06-01

 Capsule endoscopy (CE) has revolutionized small-bowel (SB) investigation. Computational methods can enhance diagnostic yield (DY); however, incorporating machine learning algorithms (MLAs) into CE reading is difficult as large amounts of image annotations are required for training. Current databases lack graphic annotations of pathologies and cannot be used. A novel database, KID, aims to provide a reference for research and development of medical decision support systems (MDSS) for CE.  Open-source software was used for the KID database. Clinicians contribute anonymized, annotated CE images and videos. Graphic annotations are supported by an open-access annotation tool (Ratsnake). We detail an experiment based on the KID database, examining differences in SB lesion measurement between human readers and a MLA. The Jaccard Index (JI) was used to evaluate similarity between annotations by the MLA and human readers.  The MLA performed best in measuring lymphangiectasias with a JI of 81 ± 6 %. The other lesion types were: angioectasias (JI 64 ± 11 %), aphthae (JI 64 ± 8 %), chylous cysts (JI 70 ± 14 %), polypoid lesions (JI 75 ± 21 %), and ulcers (JI 56 ± 9 %).  MLA can perform as well as human readers in the measurement of SB angioectasias in white light (WL). Automated lesion measurement is therefore feasible. KID is currently the only open-source CE database developed specifically to aid development of MDSS. Our experiment demonstrates this potential.

A metabolomic study on high-risk stroke patients determines low levels of serum lysine metabolites: a retrospective cohort study.

PubMed

Lee, Yeseung; Khan, Adnan; Hong, Seri; Jee, Sun Ha; Park, Youngja H

2017-05-30

Identifying changes in serum metabolites during cerebral ischemia is an important approach for early diagnosis of thrombotic stroke. Herein, we highlight novel biomarkers for early diagnosis of patients at high risk of thrombotic stroke using high resolution metabolomics (HRM). In this retrospective cohort study, serum samples obtained from patients at risk of thrombotic stroke (n  =  62) and non-risk individuals (n  =  348) were tested using HRM, coupled with LC-MS/MS, to discriminate between metabolic profiles of control and stroke risk patients. Multivariate analysis and orthogonal partial least square-discriminant analysis (OPLS-DA) were performed to determine the top 5% metabolites within 95% group identities, followed by filtering with p-value <0.05 and annotating significant metabolites using a Metlin database. Mapping identified features from Kyoto Encyclopedia of Genes and Genomes (KEGG) and Mummichog resulted in 341 significant features based on OPLS-DA with p-value <0.05. Among these 341 features, nine discriminated the thrombotic stroke risk group from the control group: low levels of N 6 -acetyl-l-lysine, 5-aminopentanoate, cadaverine, 2-oxoglutarate, nicotinamide, l-valine, S-(2-methylpropionyl)-dihydrolipoamide-E and ubiquinone, and elevated levels of homocysteine sulfinic acid. Further analysis showed that these metabolite biomarkers are specifically related to stroke occurrence, and unrelated to other factors such as diabetes or smoking. Lower levels of lysine catabolites in thrombotic stroke risk patients, as compared to the control, supports targeting these compounds as novel biomarkers for early and non-invasive detection of a thrombotic stroke.

Transcriptomic and metabolomic analyses of cucumber fruit peels reveal a developmental increase in terpenoid glycosides associated with age-related resistance to Phytophthora capsici

PubMed Central

Mansfeld, Ben N; Colle, Marivi; Kang, Yunyan; Jones, A Daniel; Grumet, Rebecca

2017-01-01

The oomycete, Phytophthora capsici, infects cucumber (Cucumis sativus L.) fruit. An age-related resistance (ARR) to this pathogen was previously observed in fruit of cultivar ‘Vlaspik’ and shown to be associated with the peel. Young fruits are highly susceptible, but develop resistance at ~10–12 days post pollination (dpp). Peels from resistant (16 dpp) versus susceptible (8 dpp) age fruit are enriched with genes associated with defense, and methanolic extracts from resistant age peels inhibit pathogen growth. Here we compared developing fruits from ‘Vlaspik’ with those of ‘Gy14’, a line that does not exhibit ARR. Transcriptomic analysis of peels of the two lines at 8 and 16 dpp identified 80 genes that were developmentally upregulated in resistant ‘Vlaspik’ 16 dpp versus 8 dpp, but not in susceptible ‘Gy14’ at 16 dpp. A large number of these genes are annotated to be associated with defense and/or specialized metabolism, including four putative resistance (R) genes, and numerous genes involved in flavonoid and terpenoid synthesis and decoration. Untargeted metabolomic analysis was performed on extracts from 8 and 16 dpp ‘Vlaspik’ and ‘Gy14’ fruit peels using Ultra-Performance Liquid Chromatography and Quadrupole Time-of-Flight Mass Spectrometry. Multivariate analysis of the metabolomes identified 113 ions uniquely abundant in resistant ‘Vlaspik’ 16 dpp peel extracts. The most abundant compounds in this group had relative mass defects consistent with terpenoid glycosides. Two of the three most abundant ions were annotated as glycosylated nor-terpenoid esters. Together, these analyses reveal potential mechanisms by which ARR to P. capsici may be conferred. PMID:28580151

GANESH: software for customized annotation of genome regions.

PubMed

Huntley, Derek; Hummerich, Holger; Smedley, Damian; Kittivoravitkul, Sasivimol; McCarthy, Mark; Little, Peter; Sergot, Marek

2003-09-01

GANESH is a software package designed to support the genetic analysis of regions of human and other genomes. It provides a set of components that may be assembled to construct a self-updating database of DNA sequence, mapping data, and annotations of possible genome features. Once one or more remote sources of data for the target region have been identified, all sequences for that region are downloaded, assimilated, and subjected to a (configurable) set of standard database-searching and genome-analysis packages. The results are stored in compressed form in a relational database, and are updated automatically on a regular schedule so that they are always immediately available in their most up-to-date versions. A Java front-end, executed as a stand alone application or web applet, provides a graphical interface for navigating the database and for viewing the annotations. There are facilities for importing and exporting data in the format of the Distributed Annotation System (DAS), enabling a GANESH database to be used as a component of a DAS configuration. The system has been used to construct databases for about a dozen regions of human chromosomes and for three regions of mouse chromosomes.

MaizeGDB, the maize model organism database

USDA-ARS?s Scientific Manuscript database

MaizeGDB is the maize research community's database for maize genetic and genomic information. In this seminar I will outline our current endeavors including a full website redesign, the status of maize genome assembly and annotation projects, and work toward genome functional annotation. Mechanis...

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.

DynGO: a tool for visualizing and mining of Gene Ontology and its associations

PubMed Central

Liu, Hongfang; Hu, Zhang-Zhi; Wu, Cathy H

2005-01-01

Background A large volume of data and information about genes and gene products has been stored in various molecular biology databases. A major challenge for knowledge discovery using these databases is to identify related genes and gene products in disparate databases. The development of Gene Ontology (GO) as a common vocabulary for annotation allows integrated queries across multiple databases and identification of semantically related genes and gene products (i.e., genes and gene products that have similar GO annotations). Meanwhile, dozens of tools have been developed for browsing, mining or editing GO terms, their hierarchical relationships, or their "associated" genes and gene products (i.e., genes and gene products annotated with GO terms). Tools that allow users to directly search and inspect relations among all GO terms and their associated genes and gene products from multiple databases are needed. Results We present a standalone package called DynGO, which provides several advanced functionalities in addition to the standard browsing capability of the official GO browsing tool (AmiGO). DynGO allows users to conduct batch retrieval of GO annotations for a list of genes and gene products, and semantic retrieval of genes and gene products sharing similar GO annotations. The result are shown in an association tree organized according to GO hierarchies and supported with many dynamic display options such as sorting tree nodes or changing orientation of the tree. For GO curators and frequent GO users, DynGO provides fast and convenient access to GO annotation data. DynGO is generally applicable to any data set where the records are annotated with GO terms, as illustrated by two examples. Conclusion We have presented a standalone package DynGO that provides functionalities to search and browse GO and its association databases as well as several additional functions such as batch retrieval and semantic retrieval. The complete documentation and software are freely available for download from the website . PMID:16091147

RICD: a rice indica cDNA database resource for rice functional genomics.

PubMed

Lu, Tingting; Huang, Xuehui; Zhu, Chuanrang; Huang, Tao; Zhao, Qiang; Xie, Kabing; Xiong, Lizhong; Zhang, Qifa; Han, Bin

2008-11-26

The Oryza sativa L. indica subspecies is the most widely cultivated rice. During the last few years, we have collected over 20,000 putative full-length cDNAs and over 40,000 ESTs isolated from various cDNA libraries of two indica varieties Guangluai 4 and Minghui 63. A database of the rice indica cDNAs was therefore built to provide a comprehensive web data source for searching and retrieving the indica cDNA clones. Rice Indica cDNA Database (RICD) is an online MySQL-PHP driven database with a user-friendly web interface. It allows investigators to query the cDNA clones by keyword, genome position, nucleotide or protein sequence, and putative function. It also provides a series of information, including sequences, protein domain annotations, similarity search results, SNPs and InDels information, and hyperlinks to gene annotation in both The Rice Annotation Project Database (RAP-DB) and The TIGR Rice Genome Annotation Resource, expression atlas in RiceGE and variation report in Gramene of each cDNA. The online rice indica cDNA database provides cDNA resource with comprehensive information to researchers for functional analysis of indica subspecies and for comparative genomics. The RICD database is available through our website http://www.ncgr.ac.cn/ricd.

Systematization of the protein sequence diversity in enzymes related to secondary metabolic pathways in plants, in the context of big data biology inspired by the KNApSAcK motorcycle database.

PubMed

Ikeda, Shun; Abe, Takashi; Nakamura, Yukiko; Kibinge, Nelson; Hirai Morita, Aki; Nakatani, Atsushi; Ono, Naoaki; Ikemura, Toshimichi; Nakamura, Kensuke; Altaf-Ul-Amin, Md; Kanaya, Shigehiko

2013-05-01

Biology is increasingly becoming a data-intensive science with the recent progress of the omics fields, e.g. genomics, transcriptomics, proteomics and metabolomics. The species-metabolite relationship database, KNApSAcK Core, has been widely utilized and cited in metabolomics research, and chronological analysis of that research work has helped to reveal recent trends in metabolomics research. To meet the needs of these trends, the KNApSAcK database has been extended by incorporating a secondary metabolic pathway database called Motorcycle DB. We examined the enzyme sequence diversity related to secondary metabolism by means of batch-learning self-organizing maps (BL-SOMs). Initially, we constructed a map by using a big data matrix consisting of the frequencies of all possible dipeptides in the protein sequence segments of plants and bacteria. The enzyme sequence diversity of the secondary metabolic pathways was examined by identifying clusters of segments associated with certain enzyme groups in the resulting map. The extent of diversity of 15 secondary metabolic enzyme groups is discussed. Data-intensive approaches such as BL-SOM applied to big data matrices are needed for systematizing protein sequences. Handling big data has become an inevitable part of biology.

YMDB 2.0: a significantly expanded version of the yeast metabolome database.

PubMed

Ramirez-Gaona, Miguel; Marcu, Ana; Pon, Allison; Guo, An Chi; Sajed, Tanvir; Wishart, Noah A; Karu, Naama; Djoumbou Feunang, Yannick; Arndt, David; Wishart, David S

2017-01-04

YMDB or the Yeast Metabolome Database (http://www.ymdb.ca/) is a comprehensive database containing extensive information on the genome and metabolome of Saccharomyces cerevisiae Initially released in 2012, the YMDB has gone through a significant expansion and a number of improvements over the past 4 years. This manuscript describes the most recent version of YMDB (YMDB 2.0). More specifically, it provides an updated description of the database that was previously described in the 2012 NAR Database Issue and it details many of the additions and improvements made to the YMDB over that time. Some of the most important changes include a 7-fold increase in the number of compounds in the database (from 2007 to 16 042), a 430-fold increase in the number of metabolic and signaling pathway diagrams (from 66 to 28 734), a 16-fold increase in the number of compounds linked to pathways (from 742 to 12 733), a 17-fold increase in the numbers of compounds with nuclear magnetic resonance or MS spectra (from 783 to 13 173) and an increase in both the number of data fields and the number of links to external databases. In addition to these database expansions, a number of improvements to YMDB's web interface and its data visualization tools have been made. These additions and improvements should greatly improve the ease, the speed and the quantity of data that can be extracted, searched or viewed within YMDB. Overall, we believe these improvements should not only improve the understanding of the metabolism of S. cerevisiae, but also allow more in-depth exploration of its extensive metabolic networks, signaling pathways and biochemistry. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Hymenoptera Genome Database: integrating genome annotations in HymenopteraMine.

PubMed

Elsik, Christine G; Tayal, Aditi; Diesh, Colin M; Unni, Deepak R; Emery, Marianne L; Nguyen, Hung N; Hagen, Darren E

2016-01-04

We report an update of the Hymenoptera Genome Database (HGD) (http://HymenopteraGenome.org), a model organism database for insect species of the order Hymenoptera (ants, bees and wasps). HGD maintains genomic data for 9 bee species, 10 ant species and 1 wasp, including the versions of genome and annotation data sets published by the genome sequencing consortiums and those provided by NCBI. A new data-mining warehouse, HymenopteraMine, based on the InterMine data warehousing system, integrates the genome data with data from external sources and facilitates cross-species analyses based on orthology. New genome browsers and annotation tools based on JBrowse/WebApollo provide easy genome navigation, and viewing of high throughput sequence data sets and can be used for collaborative genome annotation. All of the genomes and annotation data sets are combined into a single BLAST server that allows users to select and combine sequence data sets to search. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Targeted metabolomics and medication classification data from participants in the ADNI1 cohort.

PubMed

St John-Williams, Lisa; Blach, Colette; Toledo, Jon B; Rotroff, Daniel M; Kim, Sungeun; Klavins, Kristaps; Baillie, Rebecca; Han, Xianlin; Mahmoudiandehkordi, Siamak; Jack, John; Massaro, Tyler J; Lucas, Joseph E; Louie, Gregory; Motsinger-Reif, Alison A; Risacher, Shannon L; Saykin, Andrew J; Kastenmüller, Gabi; Arnold, Matthias; Koal, Therese; Moseley, M Arthur; Mangravite, Lara M; Peters, Mette A; Tenenbaum, Jessica D; Thompson, J Will; Kaddurah-Daouk, Rima

2017-10-17

Alzheimer's disease (AD) is the most common neurodegenerative disease presenting major health and economic challenges that continue to grow. Mechanisms of disease are poorly understood but significant data point to metabolic defects that might contribute to disease pathogenesis. The Alzheimer Disease Metabolomics Consortium (ADMC) in partnership with Alzheimer Disease Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for AD. Using targeted and non- targeted metabolomics and lipidomics platforms we are mapping metabolic pathway and network failures across the trajectory of disease. In this report we present quantitative metabolomics data generated on serum from 199 control, 356 mild cognitive impairment and 175 AD subjects enrolled in ADNI1 using AbsoluteIDQ-p180 platform, along with the pipeline for data preprocessing and medication classification for confound correction. The dataset presented here is the first of eight metabolomics datasets being generated for broad biochemical investigation of the AD metabolome. We expect that these collective metabolomics datasets will provide valuable resources for researchers to identify novel molecular mechanisms contributing to AD pathogenesis and disease phenotypes.

Targeted metabolomics and medication classification data from participants in the ADNI1 cohort

PubMed Central

St John-Williams, Lisa; Blach, Colette; Toledo, Jon B.; Rotroff, Daniel M.; Kim, Sungeun; Klavins, Kristaps; Baillie, Rebecca; Han, Xianlin; Mahmoudiandehkordi, Siamak; Jack, John; Massaro, Tyler J.; Lucas, Joseph E.; Louie, Gregory; Motsinger-Reif, Alison A.; Risacher, Shannon L.; Saykin, Andrew J.; Kastenmüller, Gabi; Arnold, Matthias; Koal, Therese; Moseley, M. Arthur; Mangravite, Lara M.; Peters, Mette A.; Tenenbaum, Jessica D.; Thompson, J. Will; Kaddurah-Daouk, Rima

2017-01-01

Alzheimer’s disease (AD) is the most common neurodegenerative disease presenting major health and economic challenges that continue to grow. Mechanisms of disease are poorly understood but significant data point to metabolic defects that might contribute to disease pathogenesis. The Alzheimer Disease Metabolomics Consortium (ADMC) in partnership with Alzheimer Disease Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for AD. Using targeted and non- targeted metabolomics and lipidomics platforms we are mapping metabolic pathway and network failures across the trajectory of disease. In this report we present quantitative metabolomics data generated on serum from 199 control, 356 mild cognitive impairment and 175 AD subjects enrolled in ADNI1 using AbsoluteIDQ-p180 platform, along with the pipeline for data preprocessing and medication classification for confound correction. The dataset presented here is the first of eight metabolomics datasets being generated for broad biochemical investigation of the AD metabolome. We expect that these collective metabolomics datasets will provide valuable resources for researchers to identify novel molecular mechanisms contributing to AD pathogenesis and disease phenotypes. PMID:29039849

From data repositories to submission portals: rethinking the role of domain-specific databases in CollecTF.

PubMed

Kılıç, Sefa; Sagitova, Dinara M; Wolfish, Shoshannah; Bely, Benoit; Courtot, Mélanie; Ciufo, Stacy; Tatusova, Tatiana; O'Donovan, Claire; Chibucos, Marcus C; Martin, Maria J; Erill, Ivan

2016-01-01

Domain-specific databases are essential resources for the biomedical community, leveraging expert knowledge to curate published literature and provide access to referenced data and knowledge. The limited scope of these databases, however, poses important challenges on their infrastructure, visibility, funding and usefulness to the broader scientific community. CollecTF is a community-oriented database documenting experimentally validated transcription factor (TF)-binding sites in the Bacteria domain. In its quest to become a community resource for the annotation of transcriptional regulatory elements in bacterial genomes, CollecTF aims to move away from the conventional data-repository paradigm of domain-specific databases. Through the adoption of well-established ontologies, identifiers and collaborations, CollecTF has progressively become also a portal for the annotation and submission of information on transcriptional regulatory elements to major biological sequence resources (RefSeq, UniProtKB and the Gene Ontology Consortium). This fundamental change in database conception capitalizes on the domain-specific knowledge of contributing communities to provide high-quality annotations, while leveraging the availability of stable information hubs to promote long-term access and provide high-visibility to the data. As a submission portal, CollecTF generates TF-binding site information through direct annotation of RefSeq genome records, definition of TF-based regulatory networks in UniProtKB entries and submission of functional annotations to the Gene Ontology. As a database, CollecTF provides enhanced search and browsing, targeted data exports, binding motif analysis tools and integration with motif discovery and search platforms. This innovative approach will allow CollecTF to focus its limited resources on the generation of high-quality information and the provision of specialized access to the data.Database URL: http://www.collectf.org/. © The Author(s) 2016. Published by Oxford University Press.

Internet Databases of the Properties, Enzymatic Reactions, and Metabolism of Small Molecules—Search Options and Applications in Food Science

PubMed Central

Minkiewicz, Piotr; Darewicz, Małgorzata; Iwaniak, Anna; Bucholska, Justyna; Starowicz, Piotr; Czyrko, Emilia

2016-01-01

Internet databases of small molecules, their enzymatic reactions, and metabolism have emerged as useful tools in food science. Database searching is also introduced as part of chemistry or enzymology courses for food technology students. Such resources support the search for information about single compounds and facilitate the introduction of secondary analyses of large datasets. Information can be retrieved from databases by searching for the compound name or structure, annotating with the help of chemical codes or drawn using molecule editing software. Data mining options may be enhanced by navigating through a network of links and cross-links between databases. Exemplary databases reviewed in this article belong to two classes: tools concerning small molecules (including general and specialized databases annotating food components) and tools annotating enzymes and metabolism. Some problems associated with database application are also discussed. Data summarized in computer databases may be used for calculation of daily intake of bioactive compounds, prediction of metabolism of food components, and their biological activity as well as for prediction of interactions between food component and drugs. PMID:27929431

Internet Databases of the Properties, Enzymatic Reactions, and Metabolism of Small Molecules-Search Options and Applications in Food Science.

PubMed

Minkiewicz, Piotr; Darewicz, Małgorzata; Iwaniak, Anna; Bucholska, Justyna; Starowicz, Piotr; Czyrko, Emilia

2016-12-06

Internet databases of small molecules, their enzymatic reactions, and metabolism have emerged as useful tools in food science. Database searching is also introduced as part of chemistry or enzymology courses for food technology students. Such resources support the search for information about single compounds and facilitate the introduction of secondary analyses of large datasets. Information can be retrieved from databases by searching for the compound name or structure, annotating with the help of chemical codes or drawn using molecule editing software. Data mining options may be enhanced by navigating through a network of links and cross-links between databases. Exemplary databases reviewed in this article belong to two classes: tools concerning small molecules (including general and specialized databases annotating food components) and tools annotating enzymes and metabolism. Some problems associated with database application are also discussed. Data summarized in computer databases may be used for calculation of daily intake of bioactive compounds, prediction of metabolism of food components, and their biological activity as well as for prediction of interactions between food component and drugs.

EST-PAC a web package for EST annotation and protein sequence prediction

PubMed Central

Strahm, Yvan; Powell, David; Lefèvre, Christophe

2006-01-01

With the decreasing cost of DNA sequencing technology and the vast diversity of biological resources, researchers increasingly face the basic challenge of annotating a larger number of expressed sequences tags (EST) from a variety of species. This typically consists of a series of repetitive tasks, which should be automated and easy to use. The results of these annotation tasks need to be stored and organized in a consistent way. All these operations should be self-installing, platform independent, easy to customize and amenable to using distributed bioinformatics resources available on the Internet. In order to address these issues, we present EST-PAC a web oriented multi-platform software package for expressed sequences tag (EST) annotation. EST-PAC provides a solution for the administration of EST and protein sequence annotations accessible through a web interface. Three aspects of EST annotation are automated: 1) searching local or remote biological databases for sequence similarities using Blast services, 2) predicting protein coding sequence from EST data and, 3) annotating predicted protein sequences with functional domain predictions. In practice, EST-PAC integrates the BLASTALL suite, EST-Scan2 and HMMER in a relational database system accessible through a simple web interface. EST-PAC also takes advantage of the relational database to allow consistent storage, powerful queries of results and, management of the annotation process. The system allows users to customize annotation strategies and provides an open-source data-management environment for research and education in bioinformatics. PMID:17147782

High-throughput comparison, functional annotation, and metabolic modeling of plant genomes using the PlantSEED resource

PubMed Central

Seaver, Samuel M. D.; Gerdes, Svetlana; Frelin, Océane; Lerma-Ortiz, Claudia; Bradbury, Louis M. T.; Zallot, Rémi; Hasnain, Ghulam; Niehaus, Thomas D.; El Yacoubi, Basma; Pasternak, Shiran; Olson, Robert; Pusch, Gordon; Overbeek, Ross; Stevens, Rick; de Crécy-Lagard, Valérie; Ware, Doreen; Hanson, Andrew D.; Henry, Christopher S.

2014-01-01

The increasing number of sequenced plant genomes is placing new demands on the methods applied to analyze, annotate, and model these genomes. Today’s annotation pipelines result in inconsistent gene assignments that complicate comparative analyses and prevent efficient construction of metabolic models. To overcome these problems, we have developed the PlantSEED, an integrated, metabolism-centric database to support subsystems-based annotation and metabolic model reconstruction for plant genomes. PlantSEED combines SEED subsystems technology, first developed for microbial genomes, with refined protein families and biochemical data to assign fully consistent functional annotations to orthologous genes, particularly those encoding primary metabolic pathways. Seamless integration with its parent, the prokaryotic SEED database, makes PlantSEED a unique environment for cross-kingdom comparative analysis of plant and bacterial genomes. The consistent annotations imposed by PlantSEED permit rapid reconstruction and modeling of primary metabolism for all plant genomes in the database. This feature opens the unique possibility of model-based assessment of the completeness and accuracy of gene annotation and thus allows computational identification of genes and pathways that are restricted to certain genomes or need better curation. We demonstrate the PlantSEED system by producing consistent annotations for 10 reference genomes. We also produce a functioning metabolic model for each genome, gapfilling to identify missing annotations and proposing gene candidates for missing annotations. Models are built around an extended biomass composition representing the most comprehensive published to date. To our knowledge, our models are the first to be published for seven of the genomes analyzed. PMID:24927599

High-throughput comparison, functional annotation, and metabolic modeling of plant genomes using the PlantSEED resource.

PubMed

Seaver, Samuel M D; Gerdes, Svetlana; Frelin, Océane; Lerma-Ortiz, Claudia; Bradbury, Louis M T; Zallot, Rémi; Hasnain, Ghulam; Niehaus, Thomas D; El Yacoubi, Basma; Pasternak, Shiran; Olson, Robert; Pusch, Gordon; Overbeek, Ross; Stevens, Rick; de Crécy-Lagard, Valérie; Ware, Doreen; Hanson, Andrew D; Henry, Christopher S

2014-07-01

The increasing number of sequenced plant genomes is placing new demands on the methods applied to analyze, annotate, and model these genomes. Today's annotation pipelines result in inconsistent gene assignments that complicate comparative analyses and prevent efficient construction of metabolic models. To overcome these problems, we have developed the PlantSEED, an integrated, metabolism-centric database to support subsystems-based annotation and metabolic model reconstruction for plant genomes. PlantSEED combines SEED subsystems technology, first developed for microbial genomes, with refined protein families and biochemical data to assign fully consistent functional annotations to orthologous genes, particularly those encoding primary metabolic pathways. Seamless integration with its parent, the prokaryotic SEED database, makes PlantSEED a unique environment for cross-kingdom comparative analysis of plant and bacterial genomes. The consistent annotations imposed by PlantSEED permit rapid reconstruction and modeling of primary metabolism for all plant genomes in the database. This feature opens the unique possibility of model-based assessment of the completeness and accuracy of gene annotation and thus allows computational identification of genes and pathways that are restricted to certain genomes or need better curation. We demonstrate the PlantSEED system by producing consistent annotations for 10 reference genomes. We also produce a functioning metabolic model for each genome, gapfilling to identify missing annotations and proposing gene candidates for missing annotations. Models are built around an extended biomass composition representing the most comprehensive published to date. To our knowledge, our models are the first to be published for seven of the genomes analyzed.

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.

KID Project: an internet-based digital video atlas of capsule endoscopy for research purposes

PubMed Central

Koulaouzidis, Anastasios; Iakovidis, Dimitris K.; Yung, Diana E.; Rondonotti, Emanuele; Kopylov, Uri; Plevris, John N.; Toth, Ervin; Eliakim, Abraham; Wurm Johansson, Gabrielle; Marlicz, Wojciech; Mavrogenis, Georgios; Nemeth, Artur; Thorlacius, Henrik; Tontini, Gian Eugenio

2017-01-01

Background and aims  Capsule endoscopy (CE) has revolutionized small-bowel (SB) investigation. Computational methods can enhance diagnostic yield (DY); however, incorporating machine learning algorithms (MLAs) into CE reading is difficult as large amounts of image annotations are required for training. Current databases lack graphic annotations of pathologies and cannot be used. A novel database, KID, aims to provide a reference for research and development of medical decision support systems (MDSS) for CE. Methods  Open-source software was used for the KID database. Clinicians contribute anonymized, annotated CE images and videos. Graphic annotations are supported by an open-access annotation tool (Ratsnake). We detail an experiment based on the KID database, examining differences in SB lesion measurement between human readers and a MLA. The Jaccard Index (JI) was used to evaluate similarity between annotations by the MLA and human readers. Results  The MLA performed best in measuring lymphangiectasias with a JI of 81 ± 6 %. The other lesion types were: angioectasias (JI 64 ± 11 %), aphthae (JI 64 ± 8 %), chylous cysts (JI 70 ± 14 %), polypoid lesions (JI 75 ± 21 %), and ulcers (JI 56 ± 9 %). Conclusion  MLA can perform as well as human readers in the measurement of SB angioectasias in white light (WL). Automated lesion measurement is therefore feasible. KID is currently the only open-source CE database developed specifically to aid development of MDSS. Our experiment demonstrates this potential. PMID:28580415

GeneSCF: a real-time based functional enrichment tool with support for multiple organisms.

PubMed

Subhash, Santhilal; Kanduri, Chandrasekhar

2016-09-13

High-throughput technologies such as ChIP-sequencing, RNA-sequencing, DNA sequencing and quantitative metabolomics generate a huge volume of data. Researchers often rely on functional enrichment tools to interpret the biological significance of the affected genes from these high-throughput studies. However, currently available functional enrichment tools need to be updated frequently to adapt to new entries from the functional database repositories. Hence there is a need for a simplified tool that can perform functional enrichment analysis by using updated information directly from the source databases such as KEGG, Reactome or Gene Ontology etc. In this study, we focused on designing a command-line tool called GeneSCF (Gene Set Clustering based on Functional annotations), that can predict the functionally relevant biological information for a set of genes in a real-time updated manner. It is designed to handle information from more than 4000 organisms from freely available prominent functional databases like KEGG, Reactome and Gene Ontology. We successfully employed our tool on two of published datasets to predict the biologically relevant functional information. The core features of this tool were tested on Linux machines without the need for installation of more dependencies. GeneSCF is more reliable compared to other enrichment tools because of its ability to use reference functional databases in real-time to perform enrichment analysis. It is an easy-to-integrate tool with other pipelines available for downstream analysis of high-throughput data. More importantly, GeneSCF can run multiple gene lists simultaneously on different organisms thereby saving time for the users. Since the tool is designed to be ready-to-use, there is no need for any complex compilation and installation procedures.

Annotations of Mexican bullfighting videos for semantic index

NASA Astrophysics Data System (ADS)

Montoya Obeso, Abraham; Oropesa Morales, Lester Arturo; Fernando Vázquez, Luis; Cocolán Almeda, Sara Ivonne; Stoian, Andrei; García Vázquez, Mireya Saraí; Zamudio Fuentes, Luis Miguel; Montiel Perez, Jesús Yalja; de la O Torres, Saul; Ramírez Acosta, Alejandro Alvaro

2015-09-01

The video annotation is important for web indexing and browsing systems. Indeed, in order to evaluate the performance of video query and mining techniques, databases with concept annotations are required. Therefore, it is necessary generate a database with a semantic indexing that represents the digital content of the Mexican bullfighting atmosphere. This paper proposes a scheme to make complex annotations in a video in the frame of multimedia search engine project. Each video is partitioned using our segmentation algorithm that creates shots of different length and different number of frames. In order to make complex annotations about the video, we use ELAN software. The annotations are done in two steps: First, we take note about the whole content in each shot. Second, we describe the actions as parameters of the camera like direction, position and deepness. As a consequence, we obtain a more complete descriptor of every action. In both cases we use the concepts of the TRECVid 2014 dataset. We also propose new concepts. This methodology allows to generate a database with the necessary information to create descriptors and algorithms capable to detect actions to automatically index and classify new bullfighting multimedia content.

Work and Family Functioning: An Annotated Bibliography Selected from Family Database.

ERIC Educational Resources Information Center

Davis, Mari, Comp.

This annotated bibliography lists works published in Australia on issues regarding work obligations and family responsibilities. All works cited are included in Australia's FAMILY database. The following topics are covered: (1) adolescents and attitudes to employment (14 citations); (2) the aged and employment (20 citations); (3) career…

MIPS: analysis and annotation of proteins from whole genomes

PubMed Central

Mewes, H. W.; Amid, C.; Arnold, R.; Frishman, D.; Güldener, U.; Mannhaupt, G.; Münsterkötter, M.; Pagel, P.; Strack, N.; Stümpflen, V.; Warfsmann, J.; Ruepp, A.

2004-01-01

The Munich Information Center for Protein Sequences (MIPS-GSF), Neuherberg, Germany, provides protein sequence-related information based on whole-genome analysis. The main focus of the work is directed toward the systematic organization of sequence-related attributes as gathered by a variety of algorithms, primary information from experimental data together with information compiled from the scientific literature. MIPS maintains automatically generated and manually annotated genome-specific databases, develops systematic classification schemes for the functional annotation of protein sequences and provides tools for the comprehensive analysis of protein sequences. This report updates the information on the yeast genome (CYGD), the Neurospora crassa genome (MNCDB), the database of complete cDNAs (German Human Genome Project, NGFN), the database of mammalian protein–protein interactions (MPPI), the database of FASTA homologies (SIMAP), and the interface for the fast retrieval of protein-associated information (QUIPOS). The Arabidopsis thaliana database, the rice database, the plant EST databases (MATDB, MOsDB, SPUTNIK), as well as the databases for the comprehensive set of genomes (PEDANT genomes) are described elsewhere in the 2003 and 2004 NAR database issues, respectively. All databases described, and the detailed descriptions of our projects can be accessed through the MIPS web server (http://mips.gsf.de). PMID:14681354

MIPS: analysis and annotation of proteins from whole genomes.

PubMed

Mewes, H W; Amid, C; Arnold, R; Frishman, D; Güldener, U; Mannhaupt, G; Münsterkötter, M; Pagel, P; Strack, N; Stümpflen, V; Warfsmann, J; Ruepp, A

2004-01-01

The Munich Information Center for Protein Sequences (MIPS-GSF), Neuherberg, Germany, provides protein sequence-related information based on whole-genome analysis. The main focus of the work is directed toward the systematic organization of sequence-related attributes as gathered by a variety of algorithms, primary information from experimental data together with information compiled from the scientific literature. MIPS maintains automatically generated and manually annotated genome-specific databases, develops systematic classification schemes for the functional annotation of protein sequences and provides tools for the comprehensive analysis of protein sequences. This report updates the information on the yeast genome (CYGD), the Neurospora crassa genome (MNCDB), the database of complete cDNAs (German Human Genome Project, NGFN), the database of mammalian protein-protein interactions (MPPI), the database of FASTA homologies (SIMAP), and the interface for the fast retrieval of protein-associated information (QUIPOS). The Arabidopsis thaliana database, the rice database, the plant EST databases (MATDB, MOsDB, SPUTNIK), as well as the databases for the comprehensive set of genomes (PEDANT genomes) are described elsewhere in the 2003 and 2004 NAR database issues, respectively. All databases described, and the detailed descriptions of our projects can be accessed through the MIPS web server (http://mips.gsf.de).

Gene annotation from scientific literature using mappings between keyword systems.

PubMed

Pérez, Antonio J; Perez-Iratxeta, Carolina; Bork, Peer; Thode, Guillermo; Andrade, Miguel A

2004-09-01

The description of genes in databases by keywords helps the non-specialist to quickly grasp the properties of a gene and increases the efficiency of computational tools that are applied to gene data (e.g. searching a gene database for sequences related to a particular biological process). However, the association of keywords to genes or protein sequences is a difficult process that ultimately implies examination of the literature related to a gene. To support this task, we present a procedure to derive keywords from the set of scientific abstracts related to a gene. Our system is based on the automated extraction of mappings between related terms from different databases using a model of fuzzy associations that can be applied with all generality to any pair of linked databases. We tested the system by annotating genes of the SWISS-PROT database with keywords derived from the abstracts linked to their entries (stored in the MEDLINE database of scientific references). The performance of the annotation procedure was much better for SWISS-PROT keywords (recall of 47%, precision of 68%) than for Gene Ontology terms (recall of 8%, precision of 67%). The algorithm can be publicly accessed and used for the annotation of sequences through a web server at http://www.bork.embl.de/kat

Development and in silico evaluation of large-scale metabolite identification methods using functional group detection for metabolomics

PubMed Central

Mitchell, Joshua M.; Fan, Teresa W.-M.; Lane, Andrew N.; Moseley, Hunter N. B.

2014-01-01

Large-scale identification of metabolites is key to elucidating and modeling metabolism at the systems level. Advances in metabolomics technologies, particularly ultra-high resolution mass spectrometry (MS) enable comprehensive and rapid analysis of metabolites. However, a significant barrier to meaningful data interpretation is the identification of a wide range of metabolites including unknowns and the determination of their role(s) in various metabolic networks. Chemoselective (CS) probes to tag metabolite functional groups combined with high mass accuracy provide additional structural constraints for metabolite identification and quantification. We have developed a novel algorithm, Chemically Aware Substructure Search (CASS) that efficiently detects functional groups within existing metabolite databases, allowing for combined molecular formula and functional group (from CS tagging) queries to aid in metabolite identification without a priori knowledge. Analysis of the isomeric compounds in both Human Metabolome Database (HMDB) and KEGG Ligand demonstrated a high percentage of isomeric molecular formulae (43 and 28%, respectively), indicating the necessity for techniques such as CS-tagging. Furthermore, these two databases have only moderate overlap in molecular formulae. Thus, it is prudent to use multiple databases in metabolite assignment, since each major metabolite database represents different portions of metabolism within the biosphere. In silico analysis of various CS-tagging strategies under different conditions for adduct formation demonstrate that combined FT-MS derived molecular formulae and CS-tagging can uniquely identify up to 71% of KEGG and 37% of the combined KEGG/HMDB database vs. 41 and 17%, respectively without adduct formation. This difference between database isomer disambiguation highlights the strength of CS-tagging for non-lipid metabolite identification. However, unique identification of complex lipids still needs additional information. PMID:25120557

Phage phenomics: Physiological approaches to characterize novel viral proteins

ScienceCinema

Sanchez, Savannah E. [San Diego State Univ., San Diego, CA (United States); Cuevas, Daniel A. [San Diego State Univ., San Diego, CA (United States); Rostron, Jason E. [San Diego State Univ., San Diego, CA (United States); Liang, Tiffany Y. [San Diego State Univ., San Diego, CA (United States); Pivaroff, Cullen G. [San Diego State Univ., San Diego, CA (United States); Haynes, Matthew R. [San Diego State Univ., San Diego, CA (United States); Nulton, Jim [San Diego State Univ., San Diego, CA (United States); Felts, Ben [San Diego State Univ., San Diego, CA (United States); Bailey, Barbara A. [San Diego State Univ., San Diego, CA (United States); Salamon, Peter [San Diego State Univ., San Diego, CA (United States); Edwards, Robert A. [San Diego State Univ., San Diego, CA (United States); Argonne National Lab. (ANL), Argonne, IL (United States); Burgin, Alex B. [Broad Institute, Cambridge, MA (United States); Segall, Anca M. [San Diego State Univ., San Diego, CA (United States); Rohwer, Forest [San Diego State Univ., San Diego, CA (United States)

2018-06-21

Current investigations into phage-host interactions are dependent on extrapolating knowledge from (meta)genomes. Interestingly, 60 - 95% of all phage sequences share no homology to current annotated proteins. As a result, a large proportion of phage genes are annotated as hypothetical. This reality heavily affects the annotation of both structural and auxiliary metabolic genes. Here we present phenomic methods designed to capture the physiological response(s) of a selected host during expression of one of these unknown phage genes. Multi-phenotype Assay Plates (MAPs) are used to monitor the diversity of host substrate utilization and subsequent biomass formation, while metabolomics provides bi-product analysis by monitoring metabolite abundance and diversity. Both tools are used simultaneously to provide a phenotypic profile associated with expression of a single putative phage open reading frame (ORF). Thus, representative results for both methods are compared, highlighting the phenotypic profile differences of a host carrying either putative structural or metabolic phage genes. In addition, the visualization techniques and high throughput computational pipelines that facilitated experimental analysis are presented.

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

Sanchez, Savannah E.; Cuevas, Daniel A.; Rostron, Jason E.

Current investigations into phage-host interactions are dependent on extrapolating knowledge from (meta)genomes. Interestingly, 60 - 95% of all phage sequences share no homology to current annotated proteins. As a result, a large proportion of phage genes are annotated as hypothetical. This reality heavily affects the annotation of both structural and auxiliary metabolic genes. Here we present phenomic methods designed to capture the physiological response(s) of a selected host during expression of one of these unknown phage genes. Multi-phenotype Assay Plates (MAPs) are used to monitor the diversity of host substrate utilization and subsequent biomass formation, while metabolomics provides bi-product analysismore » by monitoring metabolite abundance and diversity. Both tools are used simultaneously to provide a phenotypic profile associated with expression of a single putative phage open reading frame (ORF). Thus, representative results for both methods are compared, highlighting the phenotypic profile differences of a host carrying either putative structural or metabolic phage genes. In addition, the visualization techniques and high throughput computational pipelines that facilitated experimental analysis are presented.« less

MassTRIX: mass translator into pathways.

PubMed

Suhre, Karsten; Schmitt-Kopplin, Philippe

2008-07-01

Recent technical advances in mass spectrometry (MS) have brought the field of metabolomics to a point where large numbers of metabolites from numerous prokaryotic and eukaryotic organisms can now be easily and precisely detected. The challenge today lies in the correct annotation of these metabolites on the basis of their accurate measured masses. Assignment of bulk chemical formula is generally possible, but without consideration of the biological and genomic context, concrete metabolite annotations remain difficult and uncertain. MassTRIX responds to this challenge by providing a hypothesis-driven approach to high precision MS data annotation. It presents the identified chemical compounds in their genomic context as differentially colored objects on KEGG pathway maps. Information on gene transcription or differences in the gene complement (e.g. samples from different bacterial strains) can be easily added. The user can thus interpret the metabolic state of the organism in the context of its potential and, in the case of submitted transcriptomics data, real enzymatic capacities. The MassTRIX web server is freely accessible at http://masstrix.org.

Columba: an integrated database of proteins, structures, and annotations.

PubMed

Trissl, Silke; Rother, Kristian; Müller, Heiko; Steinke, Thomas; Koch, Ina; Preissner, Robert; Frömmel, Cornelius; Leser, Ulf

2005-03-31

Structural and functional research often requires the computation of sets of protein structures based on certain properties of the proteins, such as sequence features, fold classification, or functional annotation. Compiling such sets using current web resources is tedious because the necessary data are spread over many different databases. To facilitate this task, we have created COLUMBA, an integrated database of annotations of protein structures. COLUMBA currently integrates twelve different databases, including PDB, KEGG, Swiss-Prot, CATH, SCOP, the Gene Ontology, and ENZYME. The database can be searched using either keyword search or data source-specific web forms. Users can thus quickly select and download PDB entries that, for instance, participate in a particular pathway, are classified as containing a certain CATH architecture, are annotated as having a certain molecular function in the Gene Ontology, and whose structures have a resolution under a defined threshold. The results of queries are provided in both machine-readable extensible markup language and human-readable format. The structures themselves can be viewed interactively on the web. The COLUMBA database facilitates the creation of protein structure data sets for many structure-based studies. It allows to combine queries on a number of structure-related databases not covered by other projects at present. Thus, information on both many and few protein structures can be used efficiently. The web interface for COLUMBA is available at http://www.columba-db.de.

ALLocator: an interactive web platform for the analysis of metabolomic LC-ESI-MS datasets, enabling semi-automated, user-revised compound annotation and mass isotopomer ratio analysis.

PubMed

Kessler, Nikolas; Walter, Frederik; Persicke, Marcus; Albaum, Stefan P; Kalinowski, Jörn; Goesmann, Alexander; Niehaus, Karsten; Nattkemper, Tim W

2014-01-01

Adduct formation, fragmentation events and matrix effects impose special challenges to the identification and quantitation of metabolites in LC-ESI-MS datasets. An important step in compound identification is the deconvolution of mass signals. During this processing step, peaks representing adducts, fragments, and isotopologues of the same analyte are allocated to a distinct group, in order to separate peaks from coeluting compounds. From these peak groups, neutral masses and pseudo spectra are derived and used for metabolite identification via mass decomposition and database matching. Quantitation of metabolites is hampered by matrix effects and nonlinear responses in LC-ESI-MS measurements. A common approach to correct for these effects is the addition of a U-13C-labeled internal standard and the calculation of mass isotopomer ratios for each metabolite. Here we present a new web-platform for the analysis of LC-ESI-MS experiments. ALLocator covers the workflow from raw data processing to metabolite identification and mass isotopomer ratio analysis. The integrated processing pipeline for spectra deconvolution "ALLocatorSD" generates pseudo spectra and automatically identifies peaks emerging from the U-13C-labeled internal standard. Information from the latter improves mass decomposition and annotation of neutral losses. ALLocator provides an interactive and dynamic interface to explore and enhance the results in depth. Pseudo spectra of identified metabolites can be stored in user- and method-specific reference lists that can be applied on succeeding datasets. The potential of the software is exemplified in an experiment, in which abundance fold-changes of metabolites of the l-arginine biosynthesis in C. glutamicum type strain ATCC 13032 and l-arginine producing strain ATCC 21831 are compared. Furthermore, the capability for detection and annotation of uncommon large neutral losses is shown by the identification of (γ-)glutamyl dipeptides in the same strains. ALLocator is available online at: https://allocator.cebitec.uni-bielefeld.de. A login is required, but freely available.

A prototypic small molecule database for bronchoalveolar lavage-based metabolomics

NASA Astrophysics Data System (ADS)

Walmsley, Scott; Cruickshank-Quinn, Charmion; Quinn, Kevin; Zhang, Xing; Petrache, Irina; Bowler, Russell P.; Reisdorph, Richard; Reisdorph, Nichole

2018-04-01

The analysis of bronchoalveolar lavage fluid (BALF) using mass spectrometry-based metabolomics can provide insight into lung diseases, such as asthma. However, the important step of compound identification is hindered by the lack of a small molecule database that is specific for BALF. Here we describe prototypic, small molecule databases derived from human BALF samples (n=117). Human BALF was extracted into lipid and aqueous fractions and analyzed using liquid chromatography mass spectrometry. Following filtering to reduce contaminants and artifacts, the resulting BALF databases (BALF-DBs) contain 11,736 lipid and 658 aqueous compounds. Over 10% of these were found in 100% of samples. Testing the BALF-DBs using nested test sets produced a 99% match rate for lipids and 47% match rate for aqueous molecules. Searching an independent dataset resulted in 45% matching to the lipid BALF-DB compared to<25% when general databases are searched. The BALF-DBs are available for download from MetaboLights. Overall, the BALF-DBs can reduce false positives and improve confidence in compound identification compared to when general databases are used.

Artemis and ACT: viewing, annotating and comparing sequences stored in a relational database.

PubMed

Carver, Tim; Berriman, Matthew; Tivey, Adrian; Patel, Chinmay; Böhme, Ulrike; Barrell, Barclay G; Parkhill, Julian; Rajandream, Marie-Adèle

2008-12-01

Artemis and Artemis Comparison Tool (ACT) have become mainstream tools for viewing and annotating sequence data, particularly for microbial genomes. Since its first release, Artemis has been continuously developed and supported with additional functionality for editing and analysing sequences based on feedback from an active user community of laboratory biologists and professional annotators. Nevertheless, its utility has been somewhat restricted by its limitation to reading and writing from flat files. Therefore, a new version of Artemis has been developed, which reads from and writes to a relational database schema, and allows users to annotate more complex, often large and fragmented, genome sequences. Artemis and ACT have now been extended to read and write directly to the Generic Model Organism Database (GMOD, http://www.gmod.org) Chado relational database schema. In addition, a Gene Builder tool has been developed to provide structured forms and tables to edit coordinates of gene models and edit functional annotation, based on standard ontologies, controlled vocabularies and free text. Artemis and ACT are freely available (under a GPL licence) for download (for MacOSX, UNIX and Windows) at the Wellcome Trust Sanger Institute web sites: http://www.sanger.ac.uk/Software/Artemis/ http://www.sanger.ac.uk/Software/ACT/

Next Generation Models for Storage and Representation of Microbial Biological Annotation

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

Quest, Daniel J; Land, Miriam L; Brettin, Thomas S

2010-01-01

Background Traditional genome annotation systems were developed in a very different computing era, one where the World Wide Web was just emerging. Consequently, these systems are built as centralized black boxes focused on generating high quality annotation submissions to GenBank/EMBL supported by expert manual curation. The exponential growth of sequence data drives a growing need for increasingly higher quality and automatically generated annotation. Typical annotation pipelines utilize traditional database technologies, clustered computing resources, Perl, C, and UNIX file systems to process raw sequence data, identify genes, and predict and categorize gene function. These technologies tightly couple the annotation software systemmore » to hardware and third party software (e.g. relational database systems and schemas). This makes annotation systems hard to reproduce, inflexible to modification over time, difficult to assess, difficult to partition across multiple geographic sites, and difficult to understand for those who are not domain experts. These systems are not readily open to scrutiny and therefore not scientifically tractable. The advent of Semantic Web standards such as Resource Description Framework (RDF) and OWL Web Ontology Language (OWL) enables us to construct systems that address these challenges in a new comprehensive way. Results Here, we develop a framework for linking traditional data to OWL-based ontologies in genome annotation. We show how data standards can decouple hardware and third party software tools from annotation pipelines, thereby making annotation pipelines easier to reproduce and assess. An illustrative example shows how TURTLE (Terse RDF Triple Language) can be used as a human readable, but also semantically-aware, equivalent to GenBank/EMBL files. Conclusions The power of this approach lies in its ability to assemble annotation data from multiple databases across multiple locations into a representation that is understandable to researchers. In this way, all researchers, experimental and computational, will more easily understand the informatics processes constructing genome annotation and ultimately be able to help improve the systems that produce them.« less

Metabolomics Analysis of Metabolic Effects of Nicotinamide Phosphoribosyltransferase (NAMPT) Inhibition on Human Cancer Cells

PubMed Central

Tolstikov, Vladimir; Nikolayev, Alexander; Dong, Sucai; Zhao, Genshi; Kuo, Ming-Shang

2014-01-01

Nicotinamide phosphoribosyltransferase (NAMPT) plays an important role in cellular bioenergetics. It is responsible for converting nicotinamide to nicotinamide adenine dinucleotide, an essential molecule in cellular metabolism. NAMPT has been extensively studied over the past decade due to its role as a key regulator of nicotinamide adenine dinucleotide–consuming enzymes. NAMPT is also known as a potential target for therapeutic intervention due to its involvement in disease. In the current study, we used a global mass spectrometry–based metabolomic approach to investigate the effects of FK866, a small molecule inhibitor of NAMPT currently in clinical trials, on metabolic perturbations in human cancer cells. We treated A2780 (ovarian cancer) and HCT-116 (colorectal cancer) cell lines with FK866 in the presence and absence of nicotinic acid. Significant changes were observed in the amino acids metabolism and the purine and pyrimidine metabolism. We also observed metabolic alterations in glycolysis, the citric acid cycle (TCA), and the pentose phosphate pathway. To expand the range of the detected polar metabolites and improve data confidence, we applied a global metabolomics profiling platform by using both non-targeted and targeted hydrophilic (HILIC)-LC-MS and GC-MS analysis. We used Ingenuity Knowledge Base to facilitate the projection of metabolomics data onto metabolic pathways. Several metabolic pathways showed differential responses to FK866 based on several matches to the list of annotated metabolites. This study suggests that global metabolomics can be a useful tool in pharmacological studies of the mechanism of action of drugs at a cellular level. PMID:25486521

Environmental metabolomics: a SWOT analysis (strengths, weaknesses, opportunities, and threats).

PubMed

Miller, Marion G

2007-02-01

Metabolomic approaches have the potential to make an exceptional contribution to understanding how chemicals and other environmental stressors can affect both human and environmental health. However, the application of metabolomics to environmental exposures, although getting underway, has not yet been extensively explored. This review will use a SWOT analysis model to discuss some of the strengths, weaknesses, opportunities, and threats that are apparent to an investigator venturing into this relatively new field. SWOT has been used extensively in business settings to uncover new outlooks and identify problems that would impede progress. The field of environmental metabolomics provides great opportunities for discovery, and this is recognized by a high level of interest in potential applications. However, understanding the biological consequence of environmental exposures can be confounded by inter- and intra-individual differences. Metabolomic profiles can yield a plethora of data, the interpretation of which is complex and still being evaluated and researched. The development of the field will depend on the availability of technologies for data handling and that permit ready access metabolomic databases. Understanding the relevance of metabolomic endpoints to organism health vs adaptation vs variation is an important step in understanding what constitutes a substantive environmental threat. Metabolomic applications in reproductive research are discussed. Overall, the development of a comprehensive mechanistic-based interpretation of metabolomic changes offers the possibility of providing information that will significantly contribute to the protection of human health and the environment.

STANDARDIZATION AND STRUCTURAL ANNOTATION OF PUBLIC TOXICITY DATABASES: IMPROVING SAR CAPABILITIES AND LINKAGE TO 'OMICS DATA

EPA Science Inventory

Standardization and structural annotation of public toxicity databases: Improving SAR capabilities and linkage to 'omics data
Ann M. Richard', ClarLynda Williams', Jamie Burch2
'Nat Health & Environ Res Lab, US EPA, RTP, NC 27711; 2EPA/NC Central Univ Student COOP Trainee<...

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

Metabolomic spectral libraries for data-independent SWATH liquid chromatography mass spectrometry acquisition.

PubMed

Bruderer, Tobias; Varesio, Emmanuel; Hidasi, Anita O; Duchoslav, Eva; Burton, Lyle; Bonner, Ron; Hopfgartner, Gérard

2018-03-01

High-quality mass spectral libraries have become crucial in mass spectrometry-based metabolomics. Here, we investigate a workflow to generate accurate mass discrete and composite spectral libraries for metabolite identification and for SWATH mass spectrometry data processing. Discrete collision energy (5-100 eV) accurate mass spectra were collected for 532 metabolites from the human metabolome database (HMDB) by flow injection analysis and compiled into composite spectra over a large collision energy range (e.g., 10-70 eV). Full scan response factors were also calculated. Software tools based on accurate mass and predictive fragmentation were specially developed and found to be essential for construction and quality control of the spectral library. First, elemental compositions constrained by the elemental composition of the precursor ion were calculated for all fragments. Secondly, all possible fragments were generated from the compound structure and were filtered based on their elemental compositions. From the discrete spectra, it was possible to analyze the specific fragment form at each collision energy and it was found that a relatively large collision energy range (10-70 eV) gives informative MS/MS spectra for library searches. From the composite spectra, it was possible to characterize specific neutral losses as radical losses using in silico fragmentation. Radical losses (generating radical cations) were found to be more prominent than expected. From 532 metabolites, 489 provided a signal in positive mode [M+H] + and 483 in negative mode [M-H] - . MS/MS spectra were obtained for 399 compounds in positive mode and for 462 in negative mode; 329 metabolites generated suitable spectra in both modes. Using the spectral library, LC retention time, response factors to analyze data-independent LC-SWATH-MS data allowed the identification of 39 (positive mode) and 72 (negative mode) metabolites in a plasma pool sample (total 92 metabolites) where 81 previously were reported in HMDB to be found in plasma. Graphical abstract Library generation workflow for LC-SWATH MS, using collision energy spread, accurate mass, and fragment annotation.

De novo assembly and functional annotation of Myrciaria dubia fruit transcriptome reveals multiple metabolic pathways for L-ascorbic acid biosynthesis.

PubMed

Castro, Juan C; Maddox, J Dylan; Cobos, Marianela; Requena, David; Zimic, Mirko; Bombarely, Aureliano; Imán, Sixto A; Cerdeira, Luis A; Medina, Andersson E

2015-11-24

Myrciaria dubia is an Amazonian fruit shrub that produces numerous bioactive phytochemicals, but is best known by its high L-ascorbic acid (AsA) content in fruits. Pronounced variation in AsA content has been observed both within and among individuals, but the genetic factors responsible for this variation are largely unknown. The goals of this research, therefore, were to assemble, characterize, and annotate the fruit transcriptome of M. dubia in order to reconstruct metabolic pathways and determine if multiple pathways contribute to AsA biosynthesis. In total 24,551,882 high-quality sequence reads were de novo assembled into 70,048 unigenes (mean length = 1150 bp, N50 = 1775 bp). Assembled sequences were annotated using BLASTX against public databases such as TAIR, GR-protein, FB, MGI, RGD, ZFIN, SGN, WB, TIGR_CMR, and JCVI-CMR with 75.2 % of unigenes having annotations. Of the three core GO annotation categories, biological processes comprised 53.6 % of the total assigned annotations, whereas cellular components and molecular functions comprised 23.3 and 23.1 %, respectively. Based on the KEGG pathway assignment of the functionally annotated transcripts, five metabolic pathways for AsA biosynthesis were identified: animal-like pathway, myo-inositol pathway, L-gulose pathway, D-mannose/L-galactose pathway, and uronic acid pathway. All transcripts coding enzymes involved in the ascorbate-glutathione cycle were also identified. Finally, we used the assembly to identified 6314 genic microsatellites and 23,481 high quality SNPs. This study describes the first next-generation sequencing effort and transcriptome annotation of a non-model Amazonian plant that is relevant for AsA production and other bioactive phytochemicals. Genes encoding key enzymes were successfully identified and metabolic pathways involved in biosynthesis of AsA, anthocyanins, and other metabolic pathways have been reconstructed. The identification of these genes and pathways is in agreement with the empirically observed capability of M. dubia to synthesize and accumulate AsA and other important molecules, and adds to our current knowledge of the molecular biology and biochemistry of their production in plants. By providing insights into the mechanisms underpinning these metabolic processes, these results can be used to direct efforts to genetically manipulate this organism in order to enhance the production of these bioactive phytochemicals. The accumulation of AsA precursor and discovery of genes associated with their biosynthesis and metabolism in M. dubia is intriguing and worthy of further investigation. The sequences and pathways produced here present the genetic framework required for further studies. Quantitative transcriptomics in concert with studies of the genome, proteome, and metabolome under conditions that stimulate production and accumulation of AsA and their precursors are needed to provide a more comprehensive view of how these pathways for AsA metabolism are regulated and linked in this species.

Livestock metabolomics and the livestock metabolome: A systematic review

PubMed Central

Guo, An Chi; Sajed, Tanvir; Steele, Michael A.; Plastow, Graham S.; Wishart, David S.

2017-01-01

Metabolomics uses advanced analytical chemistry techniques to comprehensively measure large numbers of small molecule metabolites in cells, tissues and biofluids. The ability to rapidly detect and quantify hundreds or even thousands of metabolites within a single sample is helping scientists paint a far more complete picture of system-wide metabolism and biology. Metabolomics is also allowing researchers to focus on measuring the end-products of complex, hard-to-decipher genetic, epigenetic and environmental interactions. As a result, metabolomics has become an increasingly popular “omics” approach to assist with the robust phenotypic characterization of humans, crop plants and model organisms. Indeed, metabolomics is now routinely used in biomedical, nutritional and crop research. It is also being increasingly used in livestock research and livestock monitoring. The purpose of this systematic review is to quantitatively and objectively summarize the current status of livestock metabolomics and to identify emerging trends, preferred technologies and important gaps in the field. In conducting this review we also critically assessed the applications of livestock metabolomics in key areas such as animal health assessment, disease diagnosis, bioproduct characterization and biomarker discovery for highly desirable economic traits (i.e., feed efficiency, growth potential and milk production). A secondary goal of this critical review was to compile data on the known composition of the livestock metabolome (for 5 of the most common livestock species namely cattle, sheep, goats, horses and pigs). These data have been made available through an open access, comprehensive livestock metabolome database (LMDB, available at http://www.lmdb.ca). The LMDB should enable livestock researchers and producers to conduct more targeted metabolomic studies and to identify where further metabolome coverage is needed. PMID:28531195

Livestock metabolomics and the livestock metabolome: A systematic review.

PubMed

Goldansaz, Seyed Ali; Guo, An Chi; Sajed, Tanvir; Steele, Michael A; Plastow, Graham S; Wishart, David S

2017-01-01

Metabolomics uses advanced analytical chemistry techniques to comprehensively measure large numbers of small molecule metabolites in cells, tissues and biofluids. The ability to rapidly detect and quantify hundreds or even thousands of metabolites within a single sample is helping scientists paint a far more complete picture of system-wide metabolism and biology. Metabolomics is also allowing researchers to focus on measuring the end-products of complex, hard-to-decipher genetic, epigenetic and environmental interactions. As a result, metabolomics has become an increasingly popular "omics" approach to assist with the robust phenotypic characterization of humans, crop plants and model organisms. Indeed, metabolomics is now routinely used in biomedical, nutritional and crop research. It is also being increasingly used in livestock research and livestock monitoring. The purpose of this systematic review is to quantitatively and objectively summarize the current status of livestock metabolomics and to identify emerging trends, preferred technologies and important gaps in the field. In conducting this review we also critically assessed the applications of livestock metabolomics in key areas such as animal health assessment, disease diagnosis, bioproduct characterization and biomarker discovery for highly desirable economic traits (i.e., feed efficiency, growth potential and milk production). A secondary goal of this critical review was to compile data on the known composition of the livestock metabolome (for 5 of the most common livestock species namely cattle, sheep, goats, horses and pigs). These data have been made available through an open access, comprehensive livestock metabolome database (LMDB, available at http://www.lmdb.ca). The LMDB should enable livestock researchers and producers to conduct more targeted metabolomic studies and to identify where further metabolome coverage is needed.

Proposed Confidence Scale and ID Score in the Identification of Known-Unknown Compounds Using High Resolution MS Data

NASA Astrophysics Data System (ADS)

Rochat, Bertrand

2017-04-01

High-resolution (HR) MS instruments recording HR-full scan allow analysts to go further beyond pre-acquisition choices. Untargeted acquisition can reveal unexpected compounds or concentrations and can be performed for preliminary diagnosis attempt. Then, revealed compounds will have to be identified for interpretations. Whereas the need of reference standards is mandatory to confirm identification, the diverse information collected from HRMS allows identifying unknown compounds with relatively high degree of confidence without reference standards injected in the same analytical sequence. However, there is a necessity to evaluate the degree of confidence in putative identifications, possibly before further targeted analyses. This is why a confidence scale and a score in the identification of (non-peptidic) known-unknown, defined as compounds with entries in database, is proposed for (LC-) HRMS data. The scale is based on two representative documents edited by the European Commission (2007/657/EC) and the Metabolomics Standard Initiative (MSI), in an attempt to build a bridge between the communities of metabolomics and screening labs. With this confidence scale, an identification (ID) score is determined as [a number, a letter, and a number] (e.g., 2D3), from the following three criteria: I, a General Identification Category (1, confirmed, 2, putatively identified, 3, annotated compounds/classes, and 4, unknown); II, a Chromatography Class based on the relative retention time (from the narrowest tolerance, A, to no chromatographic references, D); and III, an Identification Point Level (1, very high, 2, high, and 3, normal level) based on the number of identification points collected. Three putative identification examples of known-unknown will be presented.

PIERO ontology for analysis of biochemical transformations: effective implementation of reaction information in the IUBMB enzyme list.

PubMed

Kotera, Masaaki; Nishimura, Yosuke; Nakagawa, Zen-ichi; Muto, Ai; Moriya, Yuki; Okamoto, Shinobu; Kawashima, Shuichi; Katayama, Toshiaki; Tokimatsu, Toshiaki; Kanehisa, Minoru; Goto, Susumu

2014-12-01

Genomics is faced with the issue of many partially annotated putative enzyme-encoding genes for which activities have not yet been verified, while metabolomics is faced with the issue of many putative enzyme reactions for which full equations have not been verified. Knowledge of enzymes has been collected by IUBMB, and has been made public as the Enzyme List. To date, however, the terminology of the Enzyme List has not been assessed comprehensively by bioinformatics studies. Instead, most of the bioinformatics studies simply use the identifiers of the enzymes, i.e. the Enzyme Commission (EC) numbers. We investigated the actual usage of terminology throughout the Enzyme List, and demonstrated that the partial characteristics of reactions cannot be retrieved by simply using EC numbers. Thus, we developed a novel ontology, named PIERO, for annotating biochemical transformations as follows. First, the terminology describing enzymatic reactions was retrieved from the Enzyme List, and was grouped into those related to overall reactions and biochemical transformations. Consequently, these terms were mapped onto the actual transformations taken from enzymatic reaction equations. This ontology was linked to Gene Ontology (GO) and EC numbers, allowing the extraction of common partial reaction characteristics from given sets of orthologous genes and the elucidation of possible enzymes from the given transformations. Further future development of the PIERO ontology should enhance the Enzyme List to promote the integration of genomics and metabolomics.

MIPS: a database for genomes and protein sequences

PubMed Central

Mewes, H. W.; Frishman, D.; Güldener, U.; Mannhaupt, G.; Mayer, K.; Mokrejs, M.; Morgenstern, B.; Münsterkötter, M.; Rudd, S.; Weil, B.

2002-01-01

The Munich Information Center for Protein Sequences (MIPS-GSF, Neuherberg, Germany) continues to provide genome-related information in a systematic way. MIPS supports both national and European sequencing and functional analysis projects, develops and maintains automatically generated and manually annotated genome-specific databases, develops systematic classification schemes for the functional annotation of protein sequences, and provides tools for the comprehensive analysis of protein sequences. This report updates the information on the yeast genome (CYGD), the Neurospora crassa genome (MNCDB), the databases for the comprehensive set of genomes (PEDANT genomes), the database of annotated human EST clusters (HIB), the database of complete cDNAs from the DHGP (German Human Genome Project), as well as the project specific databases for the GABI (Genome Analysis in Plants) and HNB (Helmholtz–Netzwerk Bioinformatik) networks. The Arabidospsis thaliana database (MATDB), the database of mitochondrial proteins (MITOP) and our contribution to the PIR International Protein Sequence Database have been described elsewhere [Schoof et al. (2002) Nucleic Acids Res., 30, 91–93; Scharfe et al. (2000) Nucleic Acids Res., 28, 155–158; Barker et al. (2001) Nucleic Acids Res., 29, 29–32]. All databases described, the protein analysis tools provided and the detailed descriptions of our projects can be accessed through the MIPS World Wide Web server (http://mips.gsf.de). PMID:11752246

MIPS: a database for genomes and protein sequences.

PubMed

Mewes, H W; Frishman, D; Güldener, U; Mannhaupt, G; Mayer, K; Mokrejs, M; Morgenstern, B; Münsterkötter, M; Rudd, S; Weil, B

2002-01-01

The Munich Information Center for Protein Sequences (MIPS-GSF, Neuherberg, Germany) continues to provide genome-related information in a systematic way. MIPS supports both national and European sequencing and functional analysis projects, develops and maintains automatically generated and manually annotated genome-specific databases, develops systematic classification schemes for the functional annotation of protein sequences, and provides tools for the comprehensive analysis of protein sequences. This report updates the information on the yeast genome (CYGD), the Neurospora crassa genome (MNCDB), the databases for the comprehensive set of genomes (PEDANT genomes), the database of annotated human EST clusters (HIB), the database of complete cDNAs from the DHGP (German Human Genome Project), as well as the project specific databases for the GABI (Genome Analysis in Plants) and HNB (Helmholtz-Netzwerk Bioinformatik) networks. The Arabidospsis thaliana database (MATDB), the database of mitochondrial proteins (MITOP) and our contribution to the PIR International Protein Sequence Database have been described elsewhere [Schoof et al. (2002) Nucleic Acids Res., 30, 91-93; Scharfe et al. (2000) Nucleic Acids Res., 28, 155-158; Barker et al. (2001) Nucleic Acids Res., 29, 29-32]. All databases described, the protein analysis tools provided and the detailed descriptions of our projects can be accessed through the MIPS World Wide Web server (http://mips.gsf.de).

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.

A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations.

PubMed

Spanier, A B; Caplan, N; Sosna, J; Acar, B; Joskowicz, L

2018-01-01

The goal of medical content-based image retrieval (M-CBIR) is to assist radiologists in the decision-making process by retrieving medical cases similar to a given image. One of the key interests of radiologists is lesions and their annotations, since the patient treatment depends on the lesion diagnosis. Therefore, a key feature of M-CBIR systems is the retrieval of scans with the most similar lesion annotations. To be of value, M-CBIR systems should be fully automatic to handle large case databases. We present a fully automatic end-to-end method for the retrieval of CT scans with similar liver lesion annotations. The input is a database of abdominal CT scans labeled with liver lesions, a query CT scan, and optionally one radiologist-specified lesion annotation of interest. The output is an ordered list of the database CT scans with the most similar liver lesion annotations. The method starts by automatically segmenting the liver in the scan. It then extracts a histogram-based features vector from the segmented region, learns the features' relative importance, and ranks the database scans according to the relative importance measure. The main advantages of our method are that it fully automates the end-to-end querying process, that it uses simple and efficient techniques that are scalable to large datasets, and that it produces quality retrieval results using an unannotated CT scan. Our experimental results on 9 CT queries on a dataset of 41 volumetric CT scans from the 2014 Image CLEF Liver Annotation Task yield an average retrieval accuracy (Normalized Discounted Cumulative Gain index) of 0.77 and 0.84 without/with annotation, respectively. Fully automatic end-to-end retrieval of similar cases based on image information alone, rather that on disease diagnosis, may help radiologists to better diagnose liver lesions.

GarlicESTdb: an online database and mining tool for garlic EST sequences.

PubMed

Kim, Dae-Won; Jung, Tae-Sung; Nam, Seong-Hyeuk; Kwon, Hyuk-Ryul; Kim, Aeri; Chae, Sung-Hwa; Choi, Sang-Haeng; Kim, Dong-Wook; Kim, Ryong Nam; Park, Hong-Seog

2009-05-18

Allium sativum., commonly known as garlic, is a species in the onion genus (Allium), which is a large and diverse one containing over 1,250 species. Its close relatives include chives, onion, leek and shallot. Garlic has been used throughout recorded history for culinary, medicinal use and health benefits. Currently, the interest in garlic is highly increasing due to nutritional and pharmaceutical value including high blood pressure and cholesterol, atherosclerosis and cancer. For all that, there are no comprehensive databases available for Expressed Sequence Tags(EST) of garlic for gene discovery and future efforts of genome annotation. That is why we developed a new garlic database and applications to enable comprehensive analysis of garlic gene expression. GarlicESTdb is an integrated database and mining tool for large-scale garlic (Allium sativum) EST sequencing. A total of 21,595 ESTs collected from an in-house cDNA library were used to construct the database. The analysis pipeline is an automated system written in JAVA and consists of the following components: automatic preprocessing of EST reads, assembly of raw sequences, annotation of the assembled sequences, storage of the analyzed information into MySQL databases, and graphic display of all processed data. A web application was implemented with the latest J2EE (Java 2 Platform Enterprise Edition) software technology (JSP/EJB/JavaServlet) for browsing and querying the database, for creation of dynamic web pages on the client side, and for mapping annotated enzymes to KEGG pathways, the AJAX framework was also used partially. The online resources, such as putative annotation, single nucleotide polymorphisms (SNP) and tandem repeat data sets, can be searched by text, explored on the website, searched using BLAST, and downloaded. To archive more significant BLAST results, a curation system was introduced with which biologists can easily edit best-hit annotation information for others to view. The GarlicESTdb web application is freely available at http://garlicdb.kribb.re.kr. GarlicESTdb is the first incorporated online information database of EST sequences isolated from garlic that can be freely accessed and downloaded. It has many useful features for interactive mining of EST contigs and datasets from each library, including curation of annotated information, expression profiling, information retrieval, and summary of statistics of functional annotation. Consequently, the development of GarlicESTdb will provide a crucial contribution to biologists for data-mining and more efficient experimental studies.

Non-redundant patent sequence databases with value-added annotations at two levels

PubMed Central

Li, Weizhong; McWilliam, Hamish; de la Torre, Ana Richart; Grodowski, Adam; Benediktovich, Irina; Goujon, Mickael; Nauche, Stephane; Lopez, Rodrigo

2010-01-01

The European Bioinformatics Institute (EMBL-EBI) provides public access to patent data, including abstracts, chemical compounds and sequences. Sequences can appear multiple times due to the filing of the same invention with multiple patent offices, or the use of the same sequence by different inventors in different contexts. Information relating to the source invention may be incomplete, and biological information available in patent documents elsewhere may not be reflected in the annotation of the sequence. Search and analysis of these data have become increasingly challenging for both the scientific and intellectual-property communities. Here, we report a collection of non-redundant patent sequence databases, which cover the EMBL-Bank nucleotides patent class and the patent protein databases and contain value-added annotations from patent documents. The databases were created at two levels by the use of sequence MD5 checksums. Sequences within a level-1 cluster are 100% identical over their whole length. Level-2 clusters were defined by sub-grouping level-1 clusters based on patent family information. Value-added annotations, such as publication number corrections, earliest publication dates and feature collations, significantly enhance the quality of the data, allowing for better tracking and cross-referencing. The databases are available format: http://www.ebi.ac.uk/patentdata/nr/. PMID:19884134

Non-redundant patent sequence databases with value-added annotations at two levels.

PubMed

Li, Weizhong; McWilliam, Hamish; de la Torre, Ana Richart; Grodowski, Adam; Benediktovich, Irina; Goujon, Mickael; Nauche, Stephane; Lopez, Rodrigo

2010-01-01

The European Bioinformatics Institute (EMBL-EBI) provides public access to patent data, including abstracts, chemical compounds and sequences. Sequences can appear multiple times due to the filing of the same invention with multiple patent offices, or the use of the same sequence by different inventors in different contexts. Information relating to the source invention may be incomplete, and biological information available in patent documents elsewhere may not be reflected in the annotation of the sequence. Search and analysis of these data have become increasingly challenging for both the scientific and intellectual-property communities. Here, we report a collection of non-redundant patent sequence databases, which cover the EMBL-Bank nucleotides patent class and the patent protein databases and contain value-added annotations from patent documents. The databases were created at two levels by the use of sequence MD5 checksums. Sequences within a level-1 cluster are 100% identical over their whole length. Level-2 clusters were defined by sub-grouping level-1 clusters based on patent family information. Value-added annotations, such as publication number corrections, earliest publication dates and feature collations, significantly enhance the quality of the data, allowing for better tracking and cross-referencing. The databases are available format: http://www.ebi.ac.uk/patentdata/nr/.

Pseudomonas Genome Database: facilitating user-friendly, comprehensive comparisons of microbial genomes.

PubMed

Winsor, Geoffrey L; Van Rossum, Thea; Lo, Raymond; Khaira, Bhavjinder; Whiteside, Matthew D; Hancock, Robert E W; Brinkman, Fiona S L

2009-01-01

Pseudomonas aeruginosa is a well-studied opportunistic pathogen that is particularly known for its intrinsic antimicrobial resistance, diverse metabolic capacity, and its ability to cause life threatening infections in cystic fibrosis patients. The Pseudomonas Genome Database (http://www.pseudomonas.com) was originally developed as a resource for peer-reviewed, continually updated annotation for the Pseudomonas aeruginosa PAO1 reference strain genome. In order to facilitate cross-strain and cross-species genome comparisons with other Pseudomonas species of importance, we have now expanded the database capabilities to include all Pseudomonas species, and have developed or incorporated methods to facilitate high quality comparative genomics. The database contains robust assessment of orthologs, a novel ortholog clustering method, and incorporates five views of the data at the sequence and annotation levels (Gbrowse, Mauve and custom views) to facilitate genome comparisons. A choice of simple and more flexible user-friendly Boolean search features allows researchers to search and compare annotations or sequences within or between genomes. Other features include more accurate protein subcellular localization predictions and a user-friendly, Boolean searchable log file of updates for the reference strain PAO1. This database aims to continue to provide a high quality, annotated genome resource for the research community and is available under an open source license.

Fire-induced water-repellent soils, an annotated bibliography

USGS Publications Warehouse

Kalendovsky, M.A.; Cannon, S.H.

1997-01-01

The development and nature of water-repellent, or hydrophobic, soils are important issues in evaluating hillslope response to fire. The following annotated bibliography was compiled to consolidate existing published research on the topic. Emphasis was placed on the types, causes, effects and measurement techniques of water repellency, particularly with respect to wildfires and prescribed burns. Each annotation includes a general summary of the respective publication, as well as highlights of interest to this focus. Although some references on the development of water repellency without fires, the chemistry of hydrophobic substances, and remediation of water-repellent conditions are included, coverage of these topics is not intended to be comprehensive. To develop this database, the GeoRef, Agricola, and Water Resources Abstracts databases were searched for appropriate references, and the bibliographies of each reference were then reviewed for additional entries. Additional references will be added to this bibliography as they become available. The annotated bibliography can be accessed on the Web at http://geohazards.cr.usgs.gov/html_files/landslides/ofr97-720/biblio.html. A database consisting of the references and keywords is available through a link at the above address. This database was compiled using EndNote2 plus software by Niles and Associates, and is necessary to search the database.

Orienteering: An Annotated Bibliography = Orientierungslauf: Eine kommentierte Bibliographie.

ERIC Educational Resources Information Center

Seiler, Roland, Ed.; Hartmann, Wolfgang, Ed.

1994-01-01

Annotated bibliography of 220 books, monographs, and journal articles on orienteering published 1984-94, from SPOLIT database of the Federal Institute of Sport Science (Cologne, Germany). Annotations in English or German. Ten sections including psychological, physiological, health, sociological, and environmental aspects; training and coaching;…

Evaluating Functional Annotations of Enzymes Using the Gene Ontology.

PubMed

Holliday, Gemma L; Davidson, Rebecca; Akiva, Eyal; Babbitt, Patricia C

2017-01-01

The Gene Ontology (GO) (Ashburner et al., Nat Genet 25(1):25-29, 2000) is a powerful tool in the informatics arsenal of methods for evaluating annotations in a protein dataset. From identifying the nearest well annotated homologue of a protein of interest to predicting where misannotation has occurred to knowing how confident you can be in the annotations assigned to those proteins is critical. In this chapter we explore what makes an enzyme unique and how we can use GO to infer aspects of protein function based on sequence similarity. These can range from identification of misannotation or other errors in a predicted function to accurate function prediction for an enzyme of entirely unknown function. Although GO annotation applies to any gene products, we focus here a describing our approach for hierarchical classification of enzymes in the Structure-Function Linkage Database (SFLD) (Akiva et al., Nucleic Acids Res 42(Database issue):D521-530, 2014) as a guide for informed utilisation of annotation transfer based on GO terms.

Considerations to improve functional annotations in biological databases.

PubMed

Benítez-Páez, Alfonso

2009-12-01

Despite the great effort to design efficient systems allowing the electronic indexation of information concerning genes, proteins, structures, and interactions published daily in scientific journals, some problems are still observed in specific tasks such as functional annotation. The annotation of function is a critical issue for bioinformatic routines, such as for instance, in functional genomics and the further prediction of unknown protein function, which are highly dependent of the quality of existing annotations. Some information management systems evolve to efficiently incorporate information from large-scale projects, but often, annotation of single records from the literature is difficult and slow. In this short report, functional characterizations of a representative sample of the entire set of uncharacterized proteins from Escherichia coli K12 was compiled from Swiss-Prot, PubMed, and EcoCyc and demonstrate a functional annotation deficit in biological databases. Some issues are postulated as causes of the lack of annotation, and different solutions are evaluated and proposed to avoid them. The hope is that as a consequence of these observations, there will be new impetus to improve the speed and quality of functional annotation and ultimately provide updated, reliable information to the scientific community.

VitisExpDB: a database resource for grape functional genomics.

PubMed

Doddapaneni, Harshavardhan; Lin, Hong; Walker, M Andrew; Yao, Jiqiang; Civerolo, Edwin L

2008-02-28

The family Vitaceae consists of many different grape species that grow in a range of climatic conditions. In the past few years, several studies have generated functional genomic information on different Vitis species and cultivars, including the European grape vine, Vitis vinifera. Our goal is to develop a comprehensive web data source for Vitaceae. VitisExpDB is an online MySQL-PHP driven relational database that houses annotated EST and gene expression data for V. vinifera and non-vinifera grape species and varieties. Currently, the database stores approximately 320,000 EST sequences derived from 8 species/hybrids, their annotation (BLAST top match) details and Gene Ontology based structured vocabulary. Putative homologs for each EST in other species and varieties along with information on their percent nucleotide identities, phylogenetic relationship and common primers can be retrieved. The database also includes information on probe sequence and annotation features of the high density 60-mer gene expression chip consisting of approximately 20,000 non-redundant set of ESTs. Finally, the database includes 14 processed global microarray expression profile sets. Data from 12 of these expression profile sets have been mapped onto metabolic pathways. A user-friendly web interface with multiple search indices and extensively hyperlinked result features that permit efficient data retrieval has been developed. Several online bioinformatics tools that interact with the database along with other sequence analysis tools have been added. In addition, users can submit their ESTs to the database. The developed database provides genomic resource to grape community for functional analysis of genes in the collection and for the grape genome annotation and gene function identification. The VitisExpDB database is available through our website http://cropdisease.ars.usda.gov/vitis_at/main-page.htm.

VitisExpDB: A database resource for grape functional genomics

PubMed Central

Doddapaneni, Harshavardhan; Lin, Hong; Walker, M Andrew; Yao, Jiqiang; Civerolo, Edwin L

2008-01-01

Background The family Vitaceae consists of many different grape species that grow in a range of climatic conditions. In the past few years, several studies have generated functional genomic information on different Vitis species and cultivars, including the European grape vine, Vitis vinifera. Our goal is to develop a comprehensive web data source for Vitaceae. Description VitisExpDB is an online MySQL-PHP driven relational database that houses annotated EST and gene expression data for V. vinifera and non-vinifera grape species and varieties. Currently, the database stores ~320,000 EST sequences derived from 8 species/hybrids, their annotation (BLAST top match) details and Gene Ontology based structured vocabulary. Putative homologs for each EST in other species and varieties along with information on their percent nucleotide identities, phylogenetic relationship and common primers can be retrieved. The database also includes information on probe sequence and annotation features of the high density 60-mer gene expression chip consisting of ~20,000 non-redundant set of ESTs. Finally, the database includes 14 processed global microarray expression profile sets. Data from 12 of these expression profile sets have been mapped onto metabolic pathways. A user-friendly web interface with multiple search indices and extensively hyperlinked result features that permit efficient data retrieval has been developed. Several online bioinformatics tools that interact with the database along with other sequence analysis tools have been added. In addition, users can submit their ESTs to the database. Conclusion The developed database provides genomic resource to grape community for functional analysis of genes in the collection and for the grape genome annotation and gene function identification. The VitisExpDB database is available through our website . PMID:18307813

DBATE: database of alternative transcripts expression.

PubMed

Bianchi, Valerio; Colantoni, Alessio; Calderone, Alberto; Ausiello, Gabriele; Ferrè, Fabrizio; Helmer-Citterich, Manuela

2013-01-01

The use of high-throughput RNA sequencing technology (RNA-seq) allows whole transcriptome analysis, providing an unbiased and unabridged view of alternative transcript expression. Coupling splicing variant-specific expression with its functional inference is still an open and difficult issue for which we created the DataBase of Alternative Transcripts Expression (DBATE), a web-based repository storing expression values and functional annotation of alternative splicing variants. We processed 13 large RNA-seq panels from human healthy tissues and in disease conditions, reporting expression levels and functional annotations gathered and integrated from different sources for each splicing variant, using a variant-specific annotation transfer pipeline. The possibility to perform complex queries by cross-referencing different functional annotations permits the retrieval of desired subsets of splicing variant expression values that can be visualized in several ways, from simple to more informative. DBATE is intended as a novel tool to help appreciate how, and possibly why, the transcriptome expression is shaped. DATABASE URL: http://bioinformatica.uniroma2.it/DBATE/.

ERAIZDA: a model for holistic annotation of animal infectious and zoonotic diseases

PubMed Central

Buza, Teresia M.; Jack, Sherman W.; Kirunda, Halid; Khaitsa, Margaret L.; Lawrence, Mark L.; Pruett, Stephen; Peterson, Daniel G.

2015-01-01

There is an urgent need for a unified resource that integrates trans-disciplinary annotations of emerging and reemerging animal infectious and zoonotic diseases. Such data integration will provide wonderful opportunity for epidemiologists, researchers and health policy makers to make data-driven decisions designed to improve animal health. Integrating emerging and reemerging animal infectious and zoonotic disease data from a large variety of sources into a unified open-access resource provides more plausible arguments to achieve better understanding of infectious and zoonotic diseases. We have developed a model for interlinking annotations of these diseases. These diseases are of particular interest because of the threats they pose to animal health, human health and global health security. We demonstrated the application of this model using brucellosis, an infectious and zoonotic disease. Preliminary annotations were deposited into VetBioBase database (http://vetbiobase.igbb.msstate.edu). This database is associated with user-friendly tools to facilitate searching, retrieving and downloading of disease-related information. Database URL: http://vetbiobase.igbb.msstate.edu PMID:26581408

The MetabolomeExpress Project: enabling web-based processing, analysis and transparent dissemination of GC/MS metabolomics datasets.

PubMed

Carroll, Adam J; Badger, Murray R; Harvey Millar, A

2010-07-14

Standardization of analytical approaches and reporting methods via community-wide collaboration can work synergistically with web-tool development to result in rapid community-driven expansion of online data repositories suitable for data mining and meta-analysis. In metabolomics, the inter-laboratory reproducibility of gas-chromatography/mass-spectrometry (GC/MS) makes it an obvious target for such development. While a number of web-tools offer access to datasets and/or tools for raw data processing and statistical analysis, none of these systems are currently set up to act as a public repository by easily accepting, processing and presenting publicly submitted GC/MS metabolomics datasets for public re-analysis. Here, we present MetabolomeExpress, a new File Transfer Protocol (FTP) server and web-tool for the online storage, processing, visualisation and statistical re-analysis of publicly submitted GC/MS metabolomics datasets. Users may search a quality-controlled database of metabolite response statistics from publicly submitted datasets by a number of parameters (eg. metabolite, species, organ/biofluid etc.). Users may also perform meta-analysis comparisons of multiple independent experiments or re-analyse public primary datasets via user-friendly tools for t-test, principal components analysis, hierarchical cluster analysis and correlation analysis. They may interact with chromatograms, mass spectra and peak detection results via an integrated raw data viewer. Researchers who register for a free account may upload (via FTP) their own data to the server for online processing via a novel raw data processing pipeline. MetabolomeExpress https://www.metabolome-express.org provides a new opportunity for the general metabolomics community to transparently present online the raw and processed GC/MS data underlying their metabolomics publications. Transparent sharing of these data will allow researchers to assess data quality and draw their own insights from published metabolomics datasets.

The Co-regulation Data Harvester: Automating gene annotation starting from a transcriptome database

NASA Astrophysics Data System (ADS)

Tsypin, Lev M.; Turkewitz, Aaron P.

Identifying co-regulated genes provides a useful approach for defining pathway-specific machinery in an organism. To be efficient, this approach relies on thorough genome annotation, a process much slower than genome sequencing per se. Tetrahymena thermophila, a unicellular eukaryote, has been a useful model organism and has a fully sequenced but sparsely annotated genome. One important resource for studying this organism has been an online transcriptomic database. We have developed an automated approach to gene annotation in the context of transcriptome data in T. thermophila, called the Co-regulation Data Harvester (CDH). Beginning with a gene of interest, the CDH identifies co-regulated genes by accessing the Tetrahymena transcriptome database. It then identifies their closely related genes (orthologs) in other organisms by using reciprocal BLAST searches. Finally, it collates the annotations of those orthologs' functions, which provides the user with information to help predict the cellular role of the initial query. The CDH, which is freely available, represents a powerful new tool for analyzing cell biological pathways in Tetrahymena. Moreover, to the extent that genes and pathways are conserved between organisms, the inferences obtained via the CDH should be relevant, and can be explored, in many other systems.

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.

GeneFarm, structural and functional annotation of Arabidopsis gene and protein families by a network of experts

PubMed Central

Aubourg, Sébastien; Brunaud, Véronique; Bruyère, Clémence; Cock, Mark; Cooke, Richard; Cottet, Annick; Couloux, Arnaud; Déhais, Patrice; Deléage, Gilbert; Duclert, Aymeric; Echeverria, Manuel; Eschbach, Aimée; Falconet, Denis; Filippi, Ghislain; Gaspin, Christine; Geourjon, Christophe; Grienenberger, Jean-Michel; Houlné, Guy; Jamet, Elisabeth; Lechauve, Frédéric; Leleu, Olivier; Leroy, Philippe; Mache, Régis; Meyer, Christian; Nedjari, Hafed; Negrutiu, Ioan; Orsini, Valérie; Peyretaillade, Eric; Pommier, Cyril; Raes, Jeroen; Risler, Jean-Loup; Rivière, Stéphane; Rombauts, Stéphane; Rouzé, Pierre; Schneider, Michel; Schwob, Philippe; Small, Ian; Soumayet-Kampetenga, Ghislain; Stankovski, Darko; Toffano, Claire; Tognolli, Michael; Caboche, Michel; Lecharny, Alain

2005-01-01

Genomic projects heavily depend on genome annotations and are limited by the current deficiencies in the published predictions of gene structure and function. It follows that, improved annotation will allow better data mining of genomes, and more secure planning and design of experiments. The purpose of the GeneFarm project is to obtain homogeneous, reliable, documented and traceable annotations for Arabidopsis nuclear genes and gene products, and to enter them into an added-value database. This re-annotation project is being performed exhaustively on every member of each gene family. Performing a family-wide annotation makes the task easier and more efficient than a gene-by-gene approach since many features obtained for one gene can be extrapolated to some or all the other genes of a family. A complete annotation procedure based on the most efficient prediction tools available is being used by 16 partner laboratories, each contributing annotated families from its field of expertise. A database, named GeneFarm, and an associated user-friendly interface to query the annotations have been developed. More than 3000 genes distributed over 300 families have been annotated and are available at http://genoplante-info.infobiogen.fr/Genefarm/. Furthermore, collaboration with the Swiss Institute of Bioinformatics is underway to integrate the GeneFarm data into the protein knowledgebase Swiss-Prot. PMID:15608279

1H NMR-metabolomics: can they be a useful tool in our understanding of cardiac arrest?

PubMed

Chalkias, Athanasios; Fanos, Vassilios; Noto, Antonio; Castrén, Maaret; Gulati, Anil; Svavarsdóttir, Hildigunnur; Iacovidou, Nicoletta; Xanthos, Theodoros

2014-05-01

This review focuses on the presentation of the emerging technology of metabolomics, a promising tool for the detection of identifying the unrevealed biological pathways that lead to cardiac arrest. The electronic bases of PubMed, Scopus, and EMBASE were searched. Research terms were identified using the MESH database and were combined thereafter. Initial search terms were "cardiac arrest", "cardiopulmonary resuscitation", "post-cardiac arrest syndrome" combined with "metabolomics". Metabolomics allow the monitoring of hundreds of metabolites from tissues or body fluids and already influence research in the field of cardiac metabolism. This approach has elucidated several pathophysiological mechanisms and identified profiles of metabolic changes that can be used to follow the disease processes occurring in the peri-arrest period. This can be achieved through leveraging the strengths of unbiased metabolome-wide scans, which include thousands of final downstream products of gene transcription, enzyme activity and metabolic products of extraneously administered substances, in order to identify a metabolomic fingerprint associated with an increased risk of cardiac arrest. Although this technology is still under development, metabolomics is a promising tool for elucidating biological pathways and discovering clinical biomarkers, strengthening the efforts for optimizing both the prevention and treatment of cardiac arrest. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Evaluating Computational Gene Ontology Annotations.

PubMed

Škunca, Nives; Roberts, Richard J; Steffen, Martin

2017-01-01

Two avenues to understanding gene function are complementary and often overlapping: experimental work and computational prediction. While experimental annotation generally produces high-quality annotations, it is low throughput. Conversely, computational annotations have broad coverage, but the quality of annotations may be variable, and therefore evaluating the quality of computational annotations is a critical concern.In this chapter, we provide an overview of strategies to evaluate the quality of computational annotations. First, we discuss why evaluating quality in this setting is not trivial. We highlight the various issues that threaten to bias the evaluation of computational annotations, most of which stem from the incompleteness of biological databases. Second, we discuss solutions that address these issues, for example, targeted selection of new experimental annotations and leveraging the existing experimental annotations.

Tidying Up International Nucleotide Sequence Databases: Ecological, Geographical and Sequence Quality Annotation of ITS Sequences of Mycorrhizal Fungi

PubMed Central

Tedersoo, Leho; Abarenkov, Kessy; Nilsson, R. Henrik; Schüssler, Arthur; Grelet, Gwen-Aëlle; Kohout, Petr; Oja, Jane; Bonito, Gregory M.; Veldre, Vilmar; Jairus, Teele; Ryberg, Martin; Larsson, Karl-Henrik; Kõljalg, Urmas

2011-01-01

Sequence analysis of the ribosomal RNA operon, particularly the internal transcribed spacer (ITS) region, provides a powerful tool for identification of mycorrhizal fungi. The sequence data deposited in the International Nucleotide Sequence Databases (INSD) are, however, unfiltered for quality and are often poorly annotated with metadata. To detect chimeric and low-quality sequences and assign the ectomycorrhizal fungi to phylogenetic lineages, fungal ITS sequences were downloaded from INSD, aligned within family-level groups, and examined through phylogenetic analyses and BLAST searches. By combining the fungal sequence database UNITE and the annotation and search tool PlutoF, we also added metadata from the literature to these accessions. Altogether 35,632 sequences belonged to mycorrhizal fungi or originated from ericoid and orchid mycorrhizal roots. Of these sequences, 677 were considered chimeric and 2,174 of low read quality. Information detailing country of collection, geographical coordinates, interacting taxon and isolation source were supplemented to cover 78.0%, 33.0%, 41.7% and 96.4% of the sequences, respectively. These annotated sequences are publicly available via UNITE (http://unite.ut.ee/) for downstream biogeographic, ecological and taxonomic analyses. In European Nucleotide Archive (ENA; http://www.ebi.ac.uk/ena/), the annotated sequences have a special link-out to UNITE. We intend to expand the data annotation to additional genes and all taxonomic groups and functional guilds of fungi. PMID:21949797

Proteogenomic Analysis of Polymorphisms and Gene Annotation Divergences in Prokaryotes using a Clustered Mass Spectrometry-Friendly Database*

PubMed Central

de Souza, Gustavo A.; Arntzen, Magnus Ø.; Fortuin, Suereta; Schürch, Anita C.; Målen, Hiwa; McEvoy, Christopher R. E.; van Soolingen, Dick; Thiede, Bernd; Warren, Robin M.; Wiker, Harald G.

2011-01-01

Precise annotation of genes or open reading frames is still a difficult task that results in divergence even for data generated from the same genomic sequence. This has an impact in further proteomic studies, and also compromises the characterization of clinical isolates with many specific genetic variations that may not be represented in the selected database. We recently developed software called multistrain mass spectrometry prokaryotic database builder (MSMSpdbb) that can merge protein databases from several sources and be applied on any prokaryotic organism, in a proteomic-friendly approach. We generated a database for the Mycobacterium tuberculosis complex (using three strains of Mycobacterium bovis and five of M. tuberculosis), and analyzed data collected from two laboratory strains and two clinical isolates of M. tuberculosis. We identified 2561 proteins, of which 24 were present in M. tuberculosis H37Rv samples, but not annotated in the M. tuberculosis H37Rv genome. We were also able to identify 280 nonsynonymous single amino acid polymorphisms and confirm 367 translational start sites. As a proof of concept we applied the database to whole-genome DNA sequencing data of one of the clinical isolates, which allowed the validation of 116 predicted single amino acid polymorphisms and the annotation of 131 N-terminal start sites. Moreover we identified regions not present in the original M. tuberculosis H37Rv sequence, indicating strain divergence or errors in the reference sequence. In conclusion, we demonstrated the potential of using a merged database to better characterize laboratory or clinical bacterial strains. PMID:21030493

Weighted score-level feature fusion based on Dempster-Shafer evidence theory for action recognition

NASA Astrophysics Data System (ADS)

Zhang, Guoliang; Jia, Songmin; Li, Xiuzhi; Zhang, Xiangyin

2018-01-01

The majority of human action recognition methods use multifeature fusion strategy to improve the classification performance, where the contribution of different features for specific action has not been paid enough attention. We present an extendible and universal weighted score-level feature fusion method using the Dempster-Shafer (DS) evidence theory based on the pipeline of bag-of-visual-words. First, the partially distinctive samples in the training set are selected to construct the validation set. Then, local spatiotemporal features and pose features are extracted from these samples to obtain evidence information. The DS evidence theory and the proposed rule of survival of the fittest are employed to achieve evidence combination and calculate optimal weight vectors of every feature type belonging to each action class. Finally, the recognition results are deduced via the weighted summation strategy. The performance of the established recognition framework is evaluated on Penn Action dataset and a subset of the joint-annotated human metabolome database (sub-JHMDB). The experiment results demonstrate that the proposed feature fusion method can adequately exploit the complementarity among multiple features and improve upon most of the state-of-the-art algorithms on Penn Action and sub-JHMDB datasets.

Review of mass spectrometry-based metabolomics in cancer research

PubMed Central

Liesenfeld, David B.; Habermann, Nina; Owen, Robert W.; Scalbert, Augustin; Ulrich, Cornelia M.

2014-01-01

Metabolomics, the systematic investigation of all metabolites present within a biological system, is used in biomarker development for many human diseases, including cancer. In this review we investigate the current role of mass spectrometry-based metabolomics in cancer research. A literature review was carried out within the databases PubMed, Embase and Web of Knowledge. We included 106 studies reporting on 21 different types of cancer in 7 different sample types. Metabolomics in cancer research is most often used for case-control comparisons. Secondary applications include translational areas, such as patient prognosis, therapy control and tumor classification or grading. Metabolomics is at a developmental stage with respect to epidemiology, with the majority of studies including <100 patients. Standardization is required especially concerning sample preparation and data analysis. In a second part of this review, we reconstructed a metabolic network of cancer patients by quantitatively extracting all reports of altered metabolites: Alterations in energy metabolism, membrane and fatty acid synthesis emerged, with tryptophan levels changed most frequently in various cancers. Metabolomics has the potential to evolve into a standard tool for future applications in epidemiology and translational cancer research, but further, large-scale studies including prospective validation are needed. PMID:24096148

Applied metabolomics in drug discovery.

PubMed

Cuperlovic-Culf, M; Culf, A S

2016-08-01

The metabolic profile is a direct signature of phenotype and biochemical activity following any perturbation. Metabolites are small molecules present in a biological system including natural products as well as drugs and their metabolism by-products depending on the biological system studied. Metabolomics can provide activity information about possible novel drugs and drug scaffolds, indicate interesting targets for drug development and suggest binding partners of compounds. Furthermore, metabolomics can be used for the discovery of novel natural products and in drug development. Metabolomics can enhance the discovery and testing of new drugs and provide insight into the on- and off-target effects of drugs. This review focuses primarily on the application of metabolomics in the discovery of active drugs from natural products and the analysis of chemical libraries and the computational analysis of metabolic networks. Metabolomics methodology, both experimental and analytical is fast developing. At the same time, databases of compounds are ever growing with the inclusion of more molecular and spectral information. An increasing number of systems are being represented by very detailed metabolic network models. Combining these experimental and computational tools with high throughput drug testing and drug discovery techniques can provide new promising compounds and leads.

Clever generation of rich SPARQL queries from annotated relational schema: application to Semantic Web Service creation for biological databases.

PubMed

Wollbrett, Julien; Larmande, Pierre; de Lamotte, Frédéric; Ruiz, Manuel

2013-04-15

In recent years, a large amount of "-omics" data have been produced. However, these data are stored in many different species-specific databases that are managed by different institutes and laboratories. Biologists often need to find and assemble data from disparate sources to perform certain analyses. Searching for these data and assembling them is a time-consuming task. The Semantic Web helps to facilitate interoperability across databases. A common approach involves the development of wrapper systems that map a relational database schema onto existing domain ontologies. However, few attempts have been made to automate the creation of such wrappers. We developed a framework, named BioSemantic, for the creation of Semantic Web Services that are applicable to relational biological databases. This framework makes use of both Semantic Web and Web Services technologies and can be divided into two main parts: (i) the generation and semi-automatic annotation of an RDF view; and (ii) the automatic generation of SPARQL queries and their integration into Semantic Web Services backbones. We have used our framework to integrate genomic data from different plant databases. BioSemantic is a framework that was designed to speed integration of relational databases. We present how it can be used to speed the development of Semantic Web Services for existing relational biological databases. Currently, it creates and annotates RDF views that enable the automatic generation of SPARQL queries. Web Services are also created and deployed automatically, and the semantic annotations of our Web Services are added automatically using SAWSDL attributes. BioSemantic is downloadable at http://southgreen.cirad.fr/?q=content/Biosemantic.

Clever generation of rich SPARQL queries from annotated relational schema: application to Semantic Web Service creation for biological databases

PubMed Central

2013-01-01

Background In recent years, a large amount of “-omics” data have been produced. However, these data are stored in many different species-specific databases that are managed by different institutes and laboratories. Biologists often need to find and assemble data from disparate sources to perform certain analyses. Searching for these data and assembling them is a time-consuming task. The Semantic Web helps to facilitate interoperability across databases. A common approach involves the development of wrapper systems that map a relational database schema onto existing domain ontologies. However, few attempts have been made to automate the creation of such wrappers. Results We developed a framework, named BioSemantic, for the creation of Semantic Web Services that are applicable to relational biological databases. This framework makes use of both Semantic Web and Web Services technologies and can be divided into two main parts: (i) the generation and semi-automatic annotation of an RDF view; and (ii) the automatic generation of SPARQL queries and their integration into Semantic Web Services backbones. We have used our framework to integrate genomic data from different plant databases. Conclusions BioSemantic is a framework that was designed to speed integration of relational databases. We present how it can be used to speed the development of Semantic Web Services for existing relational biological databases. Currently, it creates and annotates RDF views that enable the automatic generation of SPARQL queries. Web Services are also created and deployed automatically, and the semantic annotations of our Web Services are added automatically using SAWSDL attributes. BioSemantic is downloadable at http://southgreen.cirad.fr/?q=content/Biosemantic. PMID:23586394

Phage phenomics: Physiological approaches to characterize novel viral proteins

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

Sanchez, Savannah E.; Cuevas, Daniel A.; Rostron, Jason E.

Current investigations into phage-host interactions are dependent on extrapolating knowledge from (meta)genomes. Interestingly, 60 - 95% of all phage sequences share no homology to current annotated proteins. As a result, a large proportion of phage genes are annotated as hypothetical. This reality heavily affects the annotation of both structural and auxiliary metabolic genes. Here we present phenomic methods designed to capture the physiological response(s) of a selected host during expression of one of these unknown phage genes. Multi-phenotype Assay Plates (MAPs) are used to monitor the diversity of host substrate utilization and subsequent biomass formation, while metabolomics provides bi-product analysismore » by monitoring metabolite abundance and diversity. Both tools are used simultaneously to provide a phenotypic profile associated with expression of a single putative phage open reading frame (ORF). Thus, representative results for both methods are compared, highlighting the phenotypic profile differences of a host carrying either putative structural or metabolic phage genes. In addition, the visualization techniques and high throughput computational pipelines that facilitated experimental analysis are presented.« less

Phage phenomics: Physiological approaches to characterize novel viral proteins

DOE PAGES

Sanchez, Savannah E.; Cuevas, Daniel A.; Rostron, Jason E.; ...

2015-06-11

Current investigations into phage-host interactions are dependent on extrapolating knowledge from (meta)genomes. Interestingly, 60 - 95% of all phage sequences share no homology to current annotated proteins. As a result, a large proportion of phage genes are annotated as hypothetical. This reality heavily affects the annotation of both structural and auxiliary metabolic genes. Here we present phenomic methods designed to capture the physiological response(s) of a selected host during expression of one of these unknown phage genes. Multi-phenotype Assay Plates (MAPs) are used to monitor the diversity of host substrate utilization and subsequent biomass formation, while metabolomics provides bi-product analysismore » by monitoring metabolite abundance and diversity. Both tools are used simultaneously to provide a phenotypic profile associated with expression of a single putative phage open reading frame (ORF). Thus, representative results for both methods are compared, highlighting the phenotypic profile differences of a host carrying either putative structural or metabolic phage genes. In addition, the visualization techniques and high throughput computational pipelines that facilitated experimental analysis are presented.« less

EST databases and web tools for EST projects.

PubMed

Shen, Yao-Qing; O'Brien, Emmet; Koski, Liisa; Lang, B Franz; Burger, Gertraud

2009-01-01

This chapter outlines key considerations for constructing and implementing an EST database. Instead of showing the technological details step by step, emphasis is put on the design of an EST database suited to the specific needs of EST projects and how to choose the most suitable tools. Using TBestDB as an example, we illustrate the essential factors to be considered for database construction and the steps for data population and annotation. This process employs technologies such as PostgreSQL, Perl, and PHP to build the database and interface, and tools such as AutoFACT for data processing and annotation. We discuss these in comparison to other available technologies and tools, and explain the reasons for our choices.

Nutritional metabolomics: Progress in addressing complexity in diet and health

PubMed Central

Jones, Dean P.; Park, Youngja; Ziegler, Thomas R.

2013-01-01

Nutritional metabolomics is rapidly maturing to use small molecule chemical profiling to support integration of diet and nutrition in complex biosystems research. These developments are critical to facilitate transition of nutritional sciences from population-based to individual-based criteria for nutritional research, assessment and management. This review addresses progress in making these approaches manageable for nutrition research. Important concept developments concerning the exposome, predictive health and complex pathobiology, serve to emphasize the central role of diet and nutrition in integrated biosystems models of health and disease. Improved analytic tools and databases for targeted and non-targeted metabolic profiling, along with bioinformatics, pathway mapping and computational modeling, are now used for nutrition research on diet, metabolism, microbiome and health associations. These new developments enable metabolome-wide association studies (MWAS) and provide a foundation for nutritional metabolomics, along with genomics, epigenomics and health phenotyping, to support integrated models required for personalized diet and nutrition forecasting. PMID:22540256

AutoFACT: An Automatic Functional Annotation and Classification Tool

PubMed Central

Koski, Liisa B; Gray, Michael W; Lang, B Franz; Burger, Gertraud

2005-01-01

Background Assignment of function to new molecular sequence data is an essential step in genomics projects. The usual process involves similarity searches of a given sequence against one or more databases, an arduous process for large datasets. Results We present AutoFACT, a fully automated and customizable annotation tool that assigns biologically informative functions to a sequence. Key features of this tool are that it (1) analyzes nucleotide and protein sequence data; (2) determines the most informative functional description by combining multiple BLAST reports from several user-selected databases; (3) assigns putative metabolic pathways, functional classes, enzyme classes, GeneOntology terms and locus names; and (4) generates output in HTML, text and GFF formats for the user's convenience. We have compared AutoFACT to four well-established annotation pipelines. The error rate of functional annotation is estimated to be only between 1–2%. Comparison of AutoFACT to the traditional top-BLAST-hit annotation method shows that our procedure increases the number of functionally informative annotations by approximately 50%. Conclusion AutoFACT will serve as a useful annotation tool for smaller sequencing groups lacking dedicated bioinformatics staff. It is implemented in PERL and runs on LINUX/UNIX platforms. AutoFACT is available at . PMID:15960857

Dictionary-driven protein annotation.

PubMed

Rigoutsos, Isidore; Huynh, Tien; Floratos, Aris; Parida, Laxmi; Platt, Daniel

2002-09-01

Computational methods seeking to automatically determine the properties (functional, structural, physicochemical, etc.) of a protein directly from the sequence have long been the focus of numerous research groups. With the advent of advanced sequencing methods and systems, the number of amino acid sequences that are being deposited in the public databases has been increasing steadily. This has in turn generated a renewed demand for automated approaches that can annotate individual sequences and complete genomes quickly, exhaustively and objectively. In this paper, we present one such approach that is centered around and exploits the Bio-Dictionary, a collection of amino acid patterns that completely covers the natural sequence space and can capture functional and structural signals that have been reused during evolution, within and across protein families. Our annotation approach also makes use of a weighted, position-specific scoring scheme that is unaffected by the over-representation of well-conserved proteins and protein fragments in the databases used. For a given query sequence, the method permits one to determine, in a single pass, the following: local and global similarities between the query and any protein already present in a public database; the likeness of the query to all available archaeal/ bacterial/eukaryotic/viral sequences in the database as a function of amino acid position within the query; the character of secondary structure of the query as a function of amino acid position within the query; the cytoplasmic, transmembrane or extracellular behavior of the query; the nature and position of binding domains, active sites, post-translationally modified sites, signal peptides, etc. In terms of performance, the proposed method is exhaustive, objective and allows for the rapid annotation of individual sequences and full genomes. Annotation examples are presented and discussed in Results, including individual queries and complete genomes that were released publicly after we built the Bio-Dictionary that is used in our experiments. Finally, we have computed the annotations of more than 70 complete genomes and made them available on the World Wide Web at http://cbcsrv.watson.ibm.com/Annotations/.

Homology to peptide pattern for annotation of carbohydrate-active enzymes and prediction of function.

PubMed

Busk, P K; Pilgaard, B; Lezyk, M J; Meyer, A S; Lange, L

2017-04-12

Carbohydrate-active enzymes are found in all organisms and participate in key biological processes. These enzymes are classified in 274 families in the CAZy database but the sequence diversity within each family makes it a major task to identify new family members and to provide basis for prediction of enzyme function. A fast and reliable method for de novo annotation of genes encoding carbohydrate-active enzymes is to identify conserved peptides in the curated enzyme families followed by matching of the conserved peptides to the sequence of interest as demonstrated for the glycosyl hydrolase and the lytic polysaccharide monooxygenase families. This approach not only assigns the enzymes to families but also provides functional prediction of the enzymes with high accuracy. We identified conserved peptides for all enzyme families in the CAZy database with Peptide Pattern Recognition. The conserved peptides were matched to protein sequence for de novo annotation and functional prediction of carbohydrate-active enzymes with the Hotpep method. Annotation of protein sequences from 12 bacterial and 16 fungal genomes to families with Hotpep had an accuracy of 0.84 (measured as F1-score) compared to semiautomatic annotation by the CAZy database whereas the dbCAN HMM-based method had an accuracy of 0.77 with optimized parameters. Furthermore, Hotpep provided a functional prediction with 86% accuracy for the annotated genes. Hotpep is available as a stand-alone application for MS Windows. Hotpep is a state-of-the-art method for automatic annotation and functional prediction of carbohydrate-active enzymes.

The GermOnline cross-species systems browser provides comprehensive information on genes and gene products relevant for sexual reproduction.

PubMed

Gattiker, Alexandre; Niederhauser-Wiederkehr, Christa; Moore, James; Hermida, Leandro; Primig, Michael

2007-01-01

We report a novel release of the GermOnline knowledgebase covering genes relevant for the cell cycle, gametogenesis and fertility. GermOnline was extended into a cross-species systems browser including information on DNA sequence annotation, gene expression and the function of gene products. The database covers eight model organisms and Homo sapiens, for which complete genome annotation data are available. The database is now built around a sophisticated genome browser (Ensembl), our own microarray information management and annotation system (MIMAS) used to extensively describe experimental data obtained with high-density oligonucleotide microarrays (GeneChips) and a comprehensive system for online editing of database entries (MediaWiki). The RNA data include results from classical microarrays as well as tiling arrays that yield information on RNA expression levels, transcript start sites and lengths as well as exon composition. Members of the research community are solicited to help GermOnline curators keep database entries on genes and gene products complete and accurate. The database is accessible at http://www.germonline.org/.

SNPdbe: constructing an nsSNP functional impacts database.

PubMed

Schaefer, Christian; Meier, Alice; Rost, Burkhard; Bromberg, Yana

2012-02-15

Many existing databases annotate experimentally characterized single nucleotide polymorphisms (SNPs). Each non-synonymous SNP (nsSNP) changes one amino acid in the gene product (single amino acid substitution;SAAS). This change can either affect protein function or be neutral in that respect. Most polymorphisms lack experimental annotation of their functional impact. Here, we introduce SNPdbe-SNP database of effects, with predictions of computationally annotated functional impacts of SNPs. Database entries represent nsSNPs in dbSNP and 1000 Genomes collection, as well as variants from UniProt and PMD. SAASs come from >2600 organisms; 'human' being the most prevalent. The impact of each SAAS on protein function is predicted using the SNAP and SIFT algorithms and augmented with experimentally derived function/structure information and disease associations from PMD, OMIM and UniProt. SNPdbe is consistently updated and easily augmented with new sources of information. The database is available as an MySQL dump and via a web front end that allows searches with any combination of organism names, sequences and mutation IDs. http://www.rostlab.org/services/snpdbe.

Combining computational models, semantic annotations and simulation experiments in a graph database

PubMed Central

Henkel, Ron; Wolkenhauer, Olaf; Waltemath, Dagmar

2015-01-01

Model repositories such as the BioModels Database, the CellML Model Repository or JWS Online are frequently accessed to retrieve computational models of biological systems. However, their storage concepts support only restricted types of queries and not all data inside the repositories can be retrieved. In this article we present a storage concept that meets this challenge. It grounds on a graph database, reflects the models’ structure, incorporates semantic annotations and simulation descriptions and ultimately connects different types of model-related data. The connections between heterogeneous model-related data and bio-ontologies enable efficient search via biological facts and grant access to new model features. The introduced concept notably improves the access of computational models and associated simulations in a model repository. This has positive effects on tasks such as model search, retrieval, ranking, matching and filtering. Furthermore, our work for the first time enables CellML- and Systems Biology Markup Language-encoded models to be effectively maintained in one database. We show how these models can be linked via annotations and queried. Database URL: https://sems.uni-rostock.de/projects/masymos/ PMID:25754863

SATPdb: a database of structurally annotated therapeutic peptides

PubMed Central

Singh, Sandeep; Chaudhary, Kumardeep; Dhanda, Sandeep Kumar; Bhalla, Sherry; Usmani, Salman Sadullah; Gautam, Ankur; Tuknait, Abhishek; Agrawal, Piyush; Mathur, Deepika; Raghava, Gajendra P.S.

2016-01-01

SATPdb (http://crdd.osdd.net/raghava/satpdb/) is a database of structurally annotated therapeutic peptides, curated from 22 public domain peptide databases/datasets including 9 of our own. The current version holds 19192 unique experimentally validated therapeutic peptide sequences having length between 2 and 50 amino acids. It covers peptides having natural, non-natural and modified residues. These peptides were systematically grouped into 10 categories based on their major function or therapeutic property like 1099 anticancer, 10585 antimicrobial, 1642 drug delivery and 1698 antihypertensive peptides. We assigned or annotated structure of these therapeutic peptides using structural databases (Protein Data Bank) and state-of-the-art structure prediction methods like I-TASSER, HHsearch and PEPstrMOD. In addition, SATPdb facilitates users in performing various tasks that include: (i) structure and sequence similarity search, (ii) peptide browsing based on their function and properties, (iii) identification of moonlighting peptides and (iv) searching of peptides having desired structure and therapeutic activities. We hope this database will be useful for researchers working in the field of peptide-based therapeutics. PMID:26527728

BioCreative V CDR task corpus: a resource for chemical disease relation extraction.

PubMed

Li, Jiao; Sun, Yueping; Johnson, Robin J; Sciaky, Daniela; Wei, Chih-Hsuan; Leaman, Robert; Davis, Allan Peter; Mattingly, Carolyn J; Wiegers, Thomas C; Lu, Zhiyong

2016-01-01

Community-run, formal evaluations and manually annotated text corpora are critically important for advancing biomedical text-mining research. Recently in BioCreative V, a new challenge was organized for the tasks of disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction. Given the nature of both tasks, a test collection is required to contain both disease/chemical annotations and relation annotations in the same set of articles. Despite previous efforts in biomedical corpus construction, none was found to be sufficient for the task. Thus, we developed our own corpus called BC5CDR during the challenge by inviting a team of Medical Subject Headings (MeSH) indexers for disease/chemical entity annotation and Comparative Toxicogenomics Database (CTD) curators for CID relation annotation. To ensure high annotation quality and productivity, detailed annotation guidelines and automatic annotation tools were provided. The resulting BC5CDR corpus consists of 1500 PubMed articles with 4409 annotated chemicals, 5818 diseases and 3116 chemical-disease interactions. Each entity annotation includes both the mention text spans and normalized concept identifiers, using MeSH as the controlled vocabulary. To ensure accuracy, the entities were first captured independently by two annotators followed by a consensus annotation: The average inter-annotator agreement (IAA) scores were 87.49% and 96.05% for the disease and chemicals, respectively, in the test set according to the Jaccard similarity coefficient. Our corpus was successfully used for the BioCreative V challenge tasks and should serve as a valuable resource for the text-mining research community.Database URL: http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the United States.

Emerging new strategies for successful metabolite identification in metabolomics

PubMed Central

Bingol, Kerem; Bruschweiler-Li, Lei; Li, Dawei; Zhang, Bo; Xie, Mouzhe; Brüschweiler, Rafael

2016-01-01

This review discusses strategies for the identification of metabolites in complex biological mixtures, as encountered in metabolomics, which have emerged in the recent past. These include NMR database-assisted approaches for the identification of commonly known metabolites as well as novel combinations of NMR and MS analysis methods for the identification of unknown metabolites. The use of certain chemical additives to the NMR tube can permit identification of metabolites with specific physical chemical properties. PMID:26915807

GeneTools--application for functional annotation and statistical hypothesis testing.

PubMed

Beisvag, Vidar; Jünge, Frode K R; Bergum, Hallgeir; Jølsum, Lars; Lydersen, Stian; Günther, Clara-Cecilie; Ramampiaro, Heri; Langaas, Mette; Sandvik, Arne K; Laegreid, Astrid

2006-10-24

Modern biology has shifted from "one gene" approaches to methods for genomic-scale analysis like microarray technology, which allow simultaneous measurement of thousands of genes. This has created a need for tools facilitating interpretation of biological data in "batch" mode. However, such tools often leave the investigator with large volumes of apparently unorganized information. To meet this interpretation challenge, gene-set, or cluster testing has become a popular analytical tool. Many gene-set testing methods and software packages are now available, most of which use a variety of statistical tests to assess the genes in a set for biological information. However, the field is still evolving, and there is a great need for "integrated" solutions. GeneTools is a web-service providing access to a database that brings together information from a broad range of resources. The annotation data are updated weekly, guaranteeing that users get data most recently available. Data submitted by the user are stored in the database, where it can easily be updated, shared between users and exported in various formats. GeneTools provides three different tools: i) NMC Annotation Tool, which offers annotations from several databases like UniGene, Entrez Gene, SwissProt and GeneOntology, in both single- and batch search mode. ii) GO Annotator Tool, where users can add new gene ontology (GO) annotations to genes of interest. These user defined GO annotations can be used in further analysis or exported for public distribution. iii) eGOn, a tool for visualization and statistical hypothesis testing of GO category representation. As the first GO tool, eGOn supports hypothesis testing for three different situations (master-target situation, mutually exclusive target-target situation and intersecting target-target situation). An important additional function is an evidence-code filter that allows users, to select the GO annotations for the analysis. GeneTools is the first "all in one" annotation tool, providing users with a rapid extraction of highly relevant gene annotation data for e.g. thousands of genes or clones at once. It allows a user to define and archive new GO annotations and it supports hypothesis testing related to GO category representations. GeneTools is freely available through www.genetools.no

SAbDab: the structural antibody database

PubMed Central

Dunbar, James; Krawczyk, Konrad; Leem, Jinwoo; Baker, Terry; Fuchs, Angelika; Georges, Guy; Shi, Jiye; Deane, Charlotte M.

2014-01-01

Structural antibody database (SAbDab; http://opig.stats.ox.ac.uk/webapps/sabdab) is an online resource containing all the publicly available antibody structures annotated and presented in a consistent fashion. The data are annotated with several properties including experimental information, gene details, correct heavy and light chain pairings, antigen details and, where available, antibody–antigen binding affinity. The user can select structures, according to these attributes as well as structural properties such as complementarity determining region loop conformation and variable domain orientation. Individual structures, datasets and the complete database can be downloaded. PMID:24214988

Metabolomics - the complementary field in systems biology: a review on obesity and type 2 diabetes.

PubMed

Abu Bakar, Mohamad Hafizi; Sarmidi, Mohamad Roji; Cheng, Kian-Kai; Ali Khan, Abid; Suan, Chua Lee; Zaman Huri, Hasniza; Yaakob, Harisun

2015-07-01

Metabolomic studies on obesity and type 2 diabetes mellitus have led to a number of mechanistic insights into biomarker discovery and comprehension of disease progression at metabolic levels. This article reviews a series of metabolomic studies carried out in previous and recent years on obesity and type 2 diabetes, which have shown potential metabolic biomarkers for further evaluation of the diseases. Literature including journals and books from Web of Science, Pubmed and related databases reporting on the metabolomics in these particular disorders are reviewed. We herein discuss the potential of reported metabolic biomarkers for a novel understanding of disease processes. These biomarkers include fatty acids, TCA cycle intermediates, carbohydrates, amino acids, choline and bile acids. The biological activities and aetiological pathways of metabolites of interest in driving these intricate processes are explained. The data from various publications supported metabolomics as an effective strategy in the identification of novel biomarkers for obesity and type 2 diabetes. Accelerating interest in the perspective of metabolomics to complement other fields in systems biology towards the in-depth understanding of the molecular mechanisms underlying the diseases is also well appreciated. In conclusion, metabolomics can be used as one of the alternative approaches in biomarker discovery and the novel understanding of pathophysiological mechanisms in obesity and type 2 diabetes. It can be foreseen that there will be an increasing research interest to combine metabolomics with other omics platforms towards the establishment of detailed mechanistic evidence associated with the disease processes.

AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images.

PubMed

Albarqouni, Shadi; Baur, Christoph; Achilles, Felix; Belagiannis, Vasileios; Demirci, Stefanie; Navab, Nassir

2016-05-01

The lack of publicly available ground-truth data has been identified as the major challenge for transferring recent developments in deep learning to the biomedical imaging domain. Though crowdsourcing has enabled annotation of large scale databases for real world images, its application for biomedical purposes requires a deeper understanding and hence, more precise definition of the actual annotation task. The fact that expert tasks are being outsourced to non-expert users may lead to noisy annotations introducing disagreement between users. Despite being a valuable resource for learning annotation models from crowdsourcing, conventional machine-learning methods may have difficulties dealing with noisy annotations during training. In this manuscript, we present a new concept for learning from crowds that handle data aggregation directly as part of the learning process of the convolutional neural network (CNN) via additional crowdsourcing layer (AggNet). Besides, we present an experimental study on learning from crowds designed to answer the following questions. 1) Can deep CNN be trained with data collected from crowdsourcing? 2) How to adapt the CNN to train on multiple types of annotation datasets (ground truth and crowd-based)? 3) How does the choice of annotation and aggregation affect the accuracy? Our experimental setup involved Annot8, a self-implemented web-platform based on Crowdflower API realizing image annotation tasks for a publicly available biomedical image database. Our results give valuable insights into the functionality of deep CNN learning from crowd annotations and prove the necessity of data aggregation integration.

Patome: a database server for biological sequence annotation and analysis in issued patents and published patent applications.

PubMed

Lee, Byungwook; Kim, Taehyung; Kim, Seon-Kyu; Lee, Kwang H; Lee, Doheon

2007-01-01

With the advent of automated and high-throughput techniques, the number of patent applications containing biological sequences has been increasing rapidly. However, they have attracted relatively little attention compared to other sequence resources. We have built a database server called Patome, which contains biological sequence data disclosed in patents and published applications, as well as their analysis information. The analysis is divided into two steps. The first is an annotation step in which the disclosed sequences were annotated with RefSeq database. The second is an association step where the sequences were linked to Entrez Gene, OMIM and GO databases, and their results were saved as a gene-patent table. From the analysis, we found that 55% of human genes were associated with patenting. The gene-patent table can be used to identify whether a particular gene or disease is related to patenting. Patome is available at http://www.patome.org/; the information is updated bimonthly.

Patome: a database server for biological sequence annotation and analysis in issued patents and published patent applications

PubMed Central

Lee, Byungwook; Kim, Taehyung; Kim, Seon-Kyu; Lee, Kwang H.; Lee, Doheon

2007-01-01

With the advent of automated and high-throughput techniques, the number of patent applications containing biological sequences has been increasing rapidly. However, they have attracted relatively little attention compared to other sequence resources. We have built a database server called Patome, which contains biological sequence data disclosed in patents and published applications, as well as their analysis information. The analysis is divided into two steps. The first is an annotation step in which the disclosed sequences were annotated with RefSeq database. The second is an association step where the sequences were linked to Entrez Gene, OMIM and GO databases, and their results were saved as a gene–patent table. From the analysis, we found that 55% of human genes were associated with patenting. The gene–patent table can be used to identify whether a particular gene or disease is related to patenting. Patome is available at ; the information is updated bimonthly. PMID:17085479

Analysis of disease-associated objects at the Rat Genome Database

PubMed Central

Wang, Shur-Jen; Laulederkind, Stanley J. F.; Hayman, G. T.; Smith, Jennifer R.; Petri, Victoria; Lowry, Timothy F.; Nigam, Rajni; Dwinell, Melinda R.; Worthey, Elizabeth A.; Munzenmaier, Diane H.; Shimoyama, Mary; Jacob, Howard J.

2013-01-01

The Rat Genome Database (RGD) is the premier resource for genetic, genomic and phenotype data for the laboratory rat, Rattus norvegicus. In addition to organizing biological data from rats, the RGD team focuses on manual curation of gene–disease associations for rat, human and mouse. In this work, we have analyzed disease-associated strains, quantitative trait loci (QTL) and genes from rats. These disease objects form the basis for seven disease portals. Among disease portals, the cardiovascular disease and obesity/metabolic syndrome portals have the highest number of rat strains and QTL. These two portals share 398 rat QTL, and these shared QTL are highly concentrated on rat chromosomes 1 and 2. For disease-associated genes, we performed gene ontology (GO) enrichment analysis across portals using RatMine enrichment widgets. Fifteen GO terms, five from each GO aspect, were selected to profile enrichment patterns of each portal. Of the selected biological process (BP) terms, ‘regulation of programmed cell death’ was the top enriched term across all disease portals except in the obesity/metabolic syndrome portal where ‘lipid metabolic process’ was the most enriched term. ‘Cytosol’ and ‘nucleus’ were common cellular component (CC) annotations for disease genes, but only the cancer portal genes were highly enriched with ‘nucleus’ annotations. Similar enrichment patterns were observed in a parallel analysis using the DAVID functional annotation tool. The relationship between the preselected 15 GO terms and disease terms was examined reciprocally by retrieving rat genes annotated with these preselected terms. The individual GO term–annotated gene list showed enrichment in physiologically related diseases. For example, the ‘regulation of blood pressure’ genes were enriched with cardiovascular disease annotations, and the ‘lipid metabolic process’ genes with obesity annotations. Furthermore, we were able to enhance enrichment of neurological diseases by combining ‘G-protein coupled receptor binding’ annotated genes with ‘protein kinase binding’ annotated genes. Database URL: http://rgd.mcw.edu PMID:23794737

TOPSAN: a dynamic web database for structural genomics.

PubMed

Ellrott, Kyle; Zmasek, Christian M; Weekes, Dana; Sri Krishna, S; Bakolitsa, Constantina; Godzik, Adam; Wooley, John

2011-01-01

The Open Protein Structure Annotation Network (TOPSAN) is a web-based collaboration platform for exploring and annotating structures determined by structural genomics efforts. Characterization of those structures presents a challenge since the majority of the proteins themselves have not yet been characterized. Responding to this challenge, the TOPSAN platform facilitates collaborative annotation and investigation via a user-friendly web-based interface pre-populated with automatically generated information. Semantic web technologies expand and enrich TOPSAN's content through links to larger sets of related databases, and thus, enable data integration from disparate sources and data mining via conventional query languages. TOPSAN can be found at http://www.topsan.org.

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.

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).

ERAIZDA: a model for holistic annotation of animal infectious and zoonotic diseases.

PubMed

Buza, Teresia M; Jack, Sherman W; Kirunda, Halid; Khaitsa, Margaret L; Lawrence, Mark L; Pruett, Stephen; Peterson, Daniel G

2015-01-01

There is an urgent need for a unified resource that integrates trans-disciplinary annotations of emerging and reemerging animal infectious and zoonotic diseases. Such data integration will provide wonderful opportunity for epidemiologists, researchers and health policy makers to make data-driven decisions designed to improve animal health. Integrating emerging and reemerging animal infectious and zoonotic disease data from a large variety of sources into a unified open-access resource provides more plausible arguments to achieve better understanding of infectious and zoonotic diseases. We have developed a model for interlinking annotations of these diseases. These diseases are of particular interest because of the threats they pose to animal health, human health and global health security. We demonstrated the application of this model using brucellosis, an infectious and zoonotic disease. Preliminary annotations were deposited into VetBioBase database (http://vetbiobase.igbb.msstate.edu). This database is associated with user-friendly tools to facilitate searching, retrieving and downloading of disease-related information. Database URL: http://vetbiobase.igbb.msstate.edu. © The Author(s) 2015. Published by Oxford University Press.

The Biofuel Feedstock Genomics Resource: a web-based portal and database to enable functional genomics of plant biofuel feedstock species.

PubMed

Childs, Kevin L; Konganti, Kranti; Buell, C Robin

2012-01-01

Major feedstock sources for future biofuel production are likely to be high biomass producing plant species such as poplar, pine, switchgrass, sorghum and maize. One active area of research in these species is genome-enabled improvement of lignocellulosic biofuel feedstock quality and yield. To facilitate genomic-based investigations in these species, we developed the Biofuel Feedstock Genomic Resource (BFGR), a database and web-portal that provides high-quality, uniform and integrated functional annotation of gene and transcript assembly sequences from species of interest to lignocellulosic biofuel feedstock researchers. The BFGR includes sequence data from 54 species and permits researchers to view, analyze and obtain annotation at the gene, transcript, protein and genome level. Annotation of biochemical pathways permits the identification of key genes and transcripts central to the improvement of lignocellulosic properties in these species. The integrated nature of the BFGR in terms of annotation methods, orthologous/paralogous relationships and linkage to seven species with complete genome sequences allows comparative analyses for biofuel feedstock species with limited sequence resources. Database URL: http://bfgr.plantbiology.msu.edu.

bpRNA: large-scale automated annotation and analysis of RNA secondary structure.

PubMed

Danaee, Padideh; Rouches, Mason; Wiley, Michelle; Deng, Dezhong; Huang, Liang; Hendrix, David

2018-05-09

While RNA secondary structure prediction from sequence data has made remarkable progress, there is a need for improved strategies for annotating the features of RNA secondary structures. Here, we present bpRNA, a novel annotation tool capable of parsing RNA structures, including complex pseudoknot-containing RNAs, to yield an objective, precise, compact, unambiguous, easily-interpretable description of all loops, stems, and pseudoknots, along with the positions, sequence, and flanking base pairs of each such structural feature. We also introduce several new informative representations of RNA structure types to improve structure visualization and interpretation. We have further used bpRNA to generate a web-accessible meta-database, 'bpRNA-1m', of over 100 000 single-molecule, known secondary structures; this is both more fully and accurately annotated and over 20-times larger than existing databases. We use a subset of the database with highly similar (≥90% identical) sequences filtered out to report on statistical trends in sequence, flanking base pairs, and length. Both the bpRNA method and the bpRNA-1m database will be valuable resources both for specific analysis of individual RNA molecules and large-scale analyses such as are useful for updating RNA energy parameters for computational thermodynamic predictions, improving machine learning models for structure prediction, and for benchmarking structure-prediction algorithms.

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.

Genic insights from integrated human proteomics in GeneCards.

PubMed

Fishilevich, Simon; Zimmerman, Shahar; Kohn, Asher; Iny Stein, Tsippi; Olender, Tsviya; Kolker, Eugene; Safran, Marilyn; Lancet, Doron

2016-01-01

GeneCards is a one-stop shop for searchable human gene annotations (http://www.genecards.org/). Data are automatically mined from ∼120 sources and presented in an integrated web card for every human gene. We report the application of recent advances in proteomics to enhance gene annotation and classification in GeneCards. First, we constructed the Human Integrated Protein Expression Database (HIPED), a unified database of protein abundance in human tissues, based on the publically available mass spectrometry (MS)-based proteomics sources ProteomicsDB, Multi-Omics Profiling Expression Database, Protein Abundance Across Organisms and The MaxQuant DataBase. The integrated database, residing within GeneCards, compares favourably with its individual sources, covering nearly 90% of human protein-coding genes. For gene annotation and comparisons, we first defined a protein expression vector for each gene, based on normalized abundances in 69 normal human tissues. This vector is portrayed in the GeneCards expression section as a bar graph, allowing visual inspection and comparison. These data are juxtaposed with transcriptome bar graphs. Using the protein expression vectors, we further defined a pairwise metric that helps assess expression-based pairwise proximity. This new metric for finding functional partners complements eight others, including sharing of pathways, gene ontology (GO) terms and domains, implemented in the GeneCards Suite. In parallel, we calculated proteome-based differential expression, highlighting a subset of tissues that overexpress a gene and subserving gene classification. This textual annotation allows users of VarElect, the suite's next-generation phenotyper, to more effectively discover causative disease variants. Finally, we define the protein-RNA expression ratio and correlation as yet another attribute of every gene in each tissue, adding further annotative information. The results constitute a significant enhancement of several GeneCards sections and help promote and organize the genome-wide structural and functional knowledge of the human proteome. Database URL:http://www.genecards.org/. © The Author(s) 2016. Published by Oxford University Press.

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.

microRNAs Databases: Developmental Methodologies, Structural and Functional Annotations.

PubMed

Singh, Nagendra Kumar

2017-09-01

microRNA (miRNA) is an endogenous and evolutionary conserved non-coding RNA, involved in post-transcriptional process as gene repressor and mRNA cleavage through RNA-induced silencing complex (RISC) formation. In RISC, miRNA binds in complementary base pair with targeted mRNA along with Argonaut proteins complex, causes gene repression or endonucleolytic cleavage of mRNAs and results in many diseases and syndromes. After the discovery of miRNA lin-4 and let-7, subsequently large numbers of miRNAs were discovered by low-throughput and high-throughput experimental techniques along with computational process in various biological and metabolic processes. The miRNAs are important non-coding RNA for understanding the complex biological phenomena of organism because it controls the gene regulation. This paper reviews miRNA databases with structural and functional annotations developed by various researchers. These databases contain structural and functional information of animal, plant and virus miRNAs including miRNAs-associated diseases, stress resistance in plant, miRNAs take part in various biological processes, effect of miRNAs interaction on drugs and environment, effect of variance on miRNAs, miRNAs gene expression analysis, sequence of miRNAs, structure of miRNAs. This review focuses on the developmental methodology of miRNA databases such as computational tools and methods used for extraction of miRNAs annotation from different resources or through experiment. This study also discusses the efficiency of user interface design of every database along with current entry and annotations of miRNA (pathways, gene ontology, disease ontology, etc.). Here, an integrated schematic diagram of construction process for databases is also drawn along with tabular and graphical comparison of various types of entries in different databases. Aim of this paper is to present the importance of miRNAs-related resources at a single place.

NABIC marker database: A molecular markers information network of agricultural crops.

PubMed

Kim, Chang-Kug; Seol, Young-Joo; Lee, Dong-Jun; Jeong, In-Seon; Yoon, Ung-Han; Lee, Gang-Seob; Hahn, Jang-Ho; Park, Dong-Suk

2013-01-01

In 2013, National Agricultural Biotechnology Information Center (NABIC) reconstructs a molecular marker database for useful genetic resources. The web-based marker database consists of three major functional categories: map viewer, RSN marker and gene annotation. It provides 7250 marker locations, 3301 RSN marker property, 3280 molecular marker annotation information in agricultural plants. The individual molecular marker provides information such as marker name, expressed sequence tag number, gene definition and general marker information. This updated marker-based database provides useful information through a user-friendly web interface that assisted in tracing any new structures of the chromosomes and gene positional functions using specific molecular markers. The database is available for free at http://nabic.rda.go.kr/gere/rice/molecularMarkers/

Qualitative metabolome analysis of human cerebrospinal fluid by 13C-/12C-isotope dansylation labeling combined with liquid chromatography Fourier transform ion cyclotron resonance mass spectrometry.

PubMed

Guo, Kevin; Bamforth, Fiona; Li, Liang

2011-02-01

Metabolome analysis of human cerebrospinal fluid (CSF) is challenging because of low abundance of metabolites present in a small volume of sample. We describe and apply a sensitive isotope labeling LC-MS technique for qualitative analysis of the CSF metabolome. After a CSF sample is divided into two aliquots, they are labeled by (13)C-dansyl and (12)C-dansyl chloride, respectively. The differentially labeled aliquots are then mixed and subjected to LC-MS using Fourier-transform ion cyclotron resonance mass spectrometry (FTICR MS). Dansylation offers significant improvement in the performance of chromatography separation and detection sensitivity. Moreover, peaks detected in the mass spectra can be readily analyzed for ion pair recognition and database search based on accurate mass and/or retention time information. It is shown that about 14,000 features can be detected in a 25-min LC-FTICR MS run of a dansyl-labeled CSF sample, from which about 500 metabolites can be profiled. Results from four CSF samples are compared to gauge the detectability of metabolites by this method. About 261 metabolites are commonly detected in replicate runs of four samples. In total, 1132 unique metabolite ion pairs are detected and 347 pairs (31%) matched with at least one metabolite in the Human Metabolome Database. We also report a dansylation library of 220 standard compounds and, using this library, about 85 metabolites can be positively identified. Among them, 21 metabolites have never been reported to be associated with CSF. These results illustrate that the dansylation LC-FTICR MS method can be used to analyze the CSF metabolome in a more comprehensive manner. © American Society for Mass Spectrometry, 2011

Qualitative Metabolome Analysis of Human Cerebrospinal Fluid by 13C-/12C-Isotope Dansylation Labeling Combined with Liquid Chromatography Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

NASA Astrophysics Data System (ADS)

Guo, Kevin; Bamforth, Fiona; Li, Liang

2011-02-01

Metabolome analysis of human cerebrospinal fluid (CSF) is challenging because of low abundance of metabolites present in a small volume of sample. We describe and apply a sensitive isotope labeling LC-MS technique for qualitative analysis of the CSF metabolome. After a CSF sample is divided into two aliquots, they are labeled by 13C-dansyl and 12C-dansyl chloride, respectively. The differentially labeled aliquots are then mixed and subjected to LC-MS using Fourier-transform ion cyclotron resonance mass spectrometry (FTICR MS). Dansylation offers significant improvement in the performance of chromatography separation and detection sensitivity. Moreover, peaks detected in the mass spectra can be readily analyzed for ion pair recognition and database search based on accurate mass and/or retention time information. It is shown that about 14,000 features can be detected in a 25-min LC-FTICR MS run of a dansyl-labeled CSF sample, from which about 500 metabolites can be profiled. Results from four CSF samples are compared to gauge the detectability of metabolites by this method. About 261 metabolites are commonly detected in replicate runs of four samples. In total, 1132 unique metabolite ion pairs are detected and 347 pairs (31%) matched with at least one metabolite in the Human Metabolome Database. We also report a dansylation library of 220 standard compounds and, using this library, about 85 metabolites can be positively identified. Among them, 21 metabolites have never been reported to be associated with CSF. These results illustrate that the dansylation LC-FTICR MS method can be used to analyze the CSF metabolome in a more comprehensive manner.

EuCAP, a Eukaryotic Community Annotation Package, and its application to the rice genome

PubMed Central

Thibaud-Nissen, Françoise; Campbell, Matthew; Hamilton, John P; Zhu, Wei; Buell, C Robin

2007-01-01

Background Despite the improvements of tools for automated annotation of genome sequences, manual curation at the structural and functional level can provide an increased level of refinement to genome annotation. The Institute for Genomic Research Rice Genome Annotation (hereafter named the Osa1 Genome Annotation) is the product of an automated pipeline and, for this reason, will benefit from the input of biologists with expertise in rice and/or particular gene families. Leveraging knowledge from a dispersed community of scientists is a demonstrated way of improving a genome annotation. This requires tools that facilitate 1) the submission of gene annotation to an annotation project, 2) the review of the submitted models by project annotators, and 3) the incorporation of the submitted models in the ongoing annotation effort. Results We have developed the Eukaryotic Community Annotation Package (EuCAP), an annotation tool, and have applied it to the rice genome. The primary level of curation by community annotators (CA) has been the annotation of gene families. Annotation can be submitted by email or through the EuCAP Web Tool. The CA models are aligned to the rice pseudomolecules and the coordinates of these alignments, along with functional annotation, are stored in the MySQL EuCAP Gene Model database. Web pages displaying the alignments of the CA models to the Osa1 Genome models are automatically generated from the EuCAP Gene Model database. The alignments are reviewed by the project annotators (PAs) in the context of experimental evidence. Upon approval by the PAs, the CA models, along with the corresponding functional annotations, are integrated into the Osa1 Genome Annotation. The CA annotations, grouped by family, are displayed on the Community Annotation pages of the project website , as well as in the Community Annotation track of the Genome Browser. Conclusion We have applied EuCAP to rice. As of July 2007, the structural and/or functional annotation of 1,094 genes representing 57 families have been deposited and integrated into the current gene set. All of the EuCAP components are open-source, thereby allowing the implementation of EuCAP for the annotation of other genomes. EuCAP is available at . PMID:17961238

A Linked Data-Based Collaborative Annotation System for Increasing Learning Achievements

ERIC Educational Resources Information Center

Zarzour, Hafed; Sellami, Mokhtar

2017-01-01

With the emergence of the Web 2.0, collaborative annotation practices have become more mature in the field of learning. In this context, several recent studies have shown the powerful effects of the integration of annotation mechanism in learning process. However, most of these studies provide poor support for semantically structured resources,…

MetaDB a Data Processing Workflow in Untargeted MS-Based Metabolomics Experiments.

PubMed

Franceschi, Pietro; Mylonas, Roman; Shahaf, Nir; Scholz, Matthias; Arapitsas, Panagiotis; Masuero, Domenico; Weingart, Georg; Carlin, Silvia; Vrhovsek, Urska; Mattivi, Fulvio; Wehrens, Ron

2014-01-01

Due to their sensitivity and speed, mass-spectrometry based analytical technologies are widely used to in metabolomics to characterize biological phenomena. To address issues like metadata organization, quality assessment, data processing, data storage, and, finally, submission to public repositories, bioinformatic pipelines of a non-interactive nature are often employed, complementing the interactive software used for initial inspection and visualization of the data. These pipelines often are created as open-source software allowing the complete and exhaustive documentation of each step, ensuring the reproducibility of the analysis of extensive and often expensive experiments. In this paper, we will review the major steps which constitute such a data processing pipeline, discussing them in the context of an open-source software for untargeted MS-based metabolomics experiments recently developed at our institute. The software has been developed by integrating our metaMS R package with a user-friendly web-based application written in Grails. MetaMS takes care of data pre-processing and annotation, while the interface deals with the creation of the sample lists, the organization of the data storage, and the generation of survey plots for quality assessment. Experimental and biological metadata are stored in the ISA-Tab format making the proposed pipeline fully integrated with the Metabolights framework.

Improving Visualization and Interpretation of Metabolome-Wide Association Studies: An Application in a Population-Based Cohort Using Untargeted 1H NMR Metabolic Profiling.

PubMed

Castagné, Raphaële; Boulangé, Claire Laurence; Karaman, Ibrahim; Campanella, Gianluca; Santos Ferreira, Diana L; Kaluarachchi, Manuja R; Lehne, Benjamin; Moayyeri, Alireza; Lewis, Matthew R; Spagou, Konstantina; Dona, Anthony C; Evangelos, Vangelis; Tracy, Russell; Greenland, Philip; Lindon, John C; Herrington, David; Ebbels, Timothy M D; Elliott, Paul; Tzoulaki, Ioanna; Chadeau-Hyam, Marc

2017-10-06

1 H NMR spectroscopy of biofluids generates reproducible data allowing detection and quantification of small molecules in large population cohorts. Statistical models to analyze such data are now well-established, and the use of univariate metabolome wide association studies (MWAS) investigating the spectral features separately has emerged as a computationally efficient and interpretable alternative to multivariate models. The MWAS rely on the accurate estimation of a metabolome wide significance level (MWSL) to be applied to control the family wise error rate. Subsequent interpretation requires efficient visualization and formal feature annotation, which, in-turn, call for efficient prioritization of spectral variables of interest. Using human serum 1 H NMR spectroscopic profiles from 3948 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we have performed a series of MWAS for serum levels of glucose. We first propose an extension of the conventional MWSL that yields stable estimates of the MWSL across the different model parameterizations and distributional features of the outcome. We propose both efficient visualization methods and a strategy based on subsampling and internal validation to prioritize the associations. Our work proposes and illustrates practical and scalable solutions to facilitate the implementation of the MWAS approach and improve interpretation in large cohort studies.

Improving Visualization and Interpretation of Metabolome-Wide Association Studies: An Application in a Population-Based Cohort Using Untargeted 1H NMR Metabolic Profiling

PubMed Central

2017-01-01

1H NMR spectroscopy of biofluids generates reproducible data allowing detection and quantification of small molecules in large population cohorts. Statistical models to analyze such data are now well-established, and the use of univariate metabolome wide association studies (MWAS) investigating the spectral features separately has emerged as a computationally efficient and interpretable alternative to multivariate models. The MWAS rely on the accurate estimation of a metabolome wide significance level (MWSL) to be applied to control the family wise error rate. Subsequent interpretation requires efficient visualization and formal feature annotation, which, in-turn, call for efficient prioritization of spectral variables of interest. Using human serum 1H NMR spectroscopic profiles from 3948 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we have performed a series of MWAS for serum levels of glucose. We first propose an extension of the conventional MWSL that yields stable estimates of the MWSL across the different model parameterizations and distributional features of the outcome. We propose both efficient visualization methods and a strategy based on subsampling and internal validation to prioritize the associations. Our work proposes and illustrates practical and scalable solutions to facilitate the implementation of the MWAS approach and improve interpretation in large cohort studies. PMID:28823158

The genome and phenome of the green alga Chloroidium sp. UTEX 3007 reveal adaptive traits for desert acclimatization.

PubMed

Nelson, David R; Khraiwesh, Basel; Fu, Weiqi; Alseekh, Saleh; Jaiswal, Ashish; Chaiboonchoe, Amphun; Hazzouri, Khaled M; O'Connor, Matthew J; Butterfoss, Glenn L; Drou, Nizar; Rowe, Jillian D; Harb, Jamil; Fernie, Alisdair R; Gunsalus, Kristin C; Salehi-Ashtiani, Kourosh

2017-06-17

To investigate the phenomic and genomic traits that allow green algae to survive in deserts, we characterized a ubiquitous species, Chloroidium sp. UTEX 3007 , which we isolated from multiple locations in the United Arab Emirates (UAE). Metabolomic analyses of Chloroidium sp. UTEX 3007 indicated that the alga accumulates a broad range of carbon sources, including several desiccation tolerance-promoting sugars and unusually large stores of palmitate. Growth assays revealed capacities to grow in salinities from zero to 60 g/L and to grow heterotrophically on >40 distinct carbon sources. Assembly and annotation of genomic reads yielded a 52.5 Mbp genome with 8153 functionally annotated genes. Comparison with other sequenced green algae revealed unique protein families involved in osmotic stress tolerance and saccharide metabolism that support phenomic studies. Our results reveal the robust and flexible biology utilized by a green alga to successfully inhabit a desert coastline.

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

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.

Exploring the dark foldable proteome by considering hydrophobic amino acids topology

PubMed Central

Bitard-Feildel, Tristan; Callebaut, Isabelle

2017-01-01

The protein universe corresponds to the set of all proteins found in all organisms. A way to explore it is by taking into account the domain content of the proteins. However, some part of sequences and many entire sequences remain un-annotated despite a converging number of domain families. The un-annotated part of the protein universe is referred to as the dark proteome and remains poorly characterized. In this study, we quantify the amount of foldable domains within the dark proteome by using the hydrophobic cluster analysis methodology. These un-annotated foldable domains were grouped using a combination of remote homology searches and domain annotations, leading to define different levels of darkness. The dark foldable domains were analyzed to understand what make them different from domains stored in databases and thus difficult to annotate. The un-annotated domains of the dark proteome universe display specific features relative to database domains: shorter length, non-canonical content and particular topology in hydrophobic residues, higher propensity for disorder, and a higher energy. These features make them hard to relate to known families. Based on these observations, we emphasize that domain annotation methodologies can still be improved to fully apprehend and decipher the molecular evolution of the protein universe. PMID:28134276

Molecular signatures database (MSigDB) 3.0.

PubMed

Liberzon, Arthur; Subramanian, Aravind; Pinchback, Reid; Thorvaldsdóttir, Helga; Tamayo, Pablo; Mesirov, Jill P

2011-06-15

Well-annotated gene sets representing the universe of the biological processes are critical for meaningful and insightful interpretation of large-scale genomic data. The Molecular Signatures Database (MSigDB) is one of the most widely used repositories of such sets. We report the availability of a new version of the database, MSigDB 3.0, with over 6700 gene sets, a complete revision of the collection of canonical pathways and experimental signatures from publications, enhanced annotations and upgrades to the web site. MSigDB is freely available for non-commercial use at http://www.broadinstitute.org/msigdb.

Genome Annotation Generator: a simple tool for generating and correcting WGS annotation tables for NCBI submission.

PubMed

Geib, Scott M; Hall, Brian; Derego, Theodore; Bremer, Forest T; Cannoles, Kyle; Sim, Sheina B

2018-04-01

One of the most overlooked, yet critical, components of a whole genome sequencing (WGS) project is the submission and curation of the data to a genomic repository, most commonly the National Center for Biotechnology Information (NCBI). While large genome centers or genome groups have developed software tools for post-annotation assembly filtering, annotation, and conversion into the NCBI's annotation table format, these tools typically require back-end setup and connection to an Structured Query Language (SQL) database and/or some knowledge of programming (Perl, Python) to implement. With WGS becoming commonplace, genome sequencing projects are moving away from the genome centers and into the ecology or biology lab, where fewer resources are present to support the process of genome assembly curation. To fill this gap, we developed software to assess, filter, and transfer annotation and convert a draft genome assembly and annotation set into the NCBI annotation table (.tbl) format, facilitating submission to the NCBI Genome Assembly database. This software has no dependencies, is compatible across platforms, and utilizes a simple command to perform a variety of simple and complex post-analysis, pre-NCBI submission WGS project tasks. The Genome Annotation Generator is a consistent and user-friendly bioinformatics tool that can be used to generate a .tbl file that is consistent with the NCBI submission pipeline. The Genome Annotation Generator achieves the goal of providing a publicly available tool that will facilitate the submission of annotated genome assemblies to the NCBI. It is useful for any individual researcher or research group that wishes to submit a genome assembly of their study system to the NCBI.

Genome Annotation Generator: a simple tool for generating and correcting WGS annotation tables for NCBI submission

PubMed Central

Hall, Brian; Derego, Theodore; Bremer, Forest T; Cannoles, Kyle

2018-01-01

Abstract Background One of the most overlooked, yet critical, components of a whole genome sequencing (WGS) project is the submission and curation of the data to a genomic repository, most commonly the National Center for Biotechnology Information (NCBI). While large genome centers or genome groups have developed software tools for post-annotation assembly filtering, annotation, and conversion into the NCBI’s annotation table format, these tools typically require back-end setup and connection to an Structured Query Language (SQL) database and/or some knowledge of programming (Perl, Python) to implement. With WGS becoming commonplace, genome sequencing projects are moving away from the genome centers and into the ecology or biology lab, where fewer resources are present to support the process of genome assembly curation. To fill this gap, we developed software to assess, filter, and transfer annotation and convert a draft genome assembly and annotation set into the NCBI annotation table (.tbl) format, facilitating submission to the NCBI Genome Assembly database. This software has no dependencies, is compatible across platforms, and utilizes a simple command to perform a variety of simple and complex post-analysis, pre-NCBI submission WGS project tasks. Findings The Genome Annotation Generator is a consistent and user-friendly bioinformatics tool that can be used to generate a .tbl file that is consistent with the NCBI submission pipeline Conclusions The Genome Annotation Generator achieves the goal of providing a publicly available tool that will facilitate the submission of annotated genome assemblies to the NCBI. It is useful for any individual researcher or research group that wishes to submit a genome assembly of their study system to the NCBI. PMID:29635297

MitoFish and MiFish Pipeline: A Mitochondrial Genome Database of Fish with an Analysis Pipeline for Environmental DNA Metabarcoding.

PubMed

Sato, Yukuto; Miya, Masaki; Fukunaga, Tsukasa; Sado, Tetsuya; Iwasaki, Wataru

2018-06-01

Fish mitochondrial genome (mitogenome) data form a fundamental basis for revealing vertebrate evolution and hydrosphere ecology. Here, we report recent functional updates of MitoFish, which is a database of fish mitogenomes with a precise annotation pipeline MitoAnnotator. Most importantly, we describe implementation of MiFish pipeline for metabarcoding analysis of fish mitochondrial environmental DNA, which is a fast-emerging and powerful technology in fish studies. MitoFish, MitoAnnotator, and MiFish pipeline constitute a key platform for studies of fish evolution, ecology, and conservation, and are freely available at http://mitofish.aori.u-tokyo.ac.jp/ (last accessed April 7th, 2018).

The metabolomic approach identifies a biological signature of low-dose chronic exposure to cesium 137.

PubMed

Grison, Stéphane; Martin, Jean-Charles; Grandcolas, Line; Banzet, Nathalie; Blanchardon, Eric; Tourlonias, Elie; Defoort, Catherine; Favé, Gaëlle; Bott, Romain; Dublineau, Isabelle; Gourmelon, Patrick; Souidi, Maâmar

2012-01-01

Reports have described apparent biological effects of (137)Cs (the most persistent dispersed radionuclide) irradiation in people living in Chernobyl-contaminated territory. The sensitive analytical technology described here should now help assess the relation of this contamination to the observed effects. A rat model chronically exposed to (137)Cs through drinking water was developed to identify biomarkers of radiation-induced metabolic disorders, and the biological impact was evaluated by a metabolomic approach that allowed us to detect several hundred metabolites in biofluids and assess their association with disease states. After collection of plasma and urine from contaminated and non-contaminated rats at the end of the 9-months contamination period, analysis with a LC-MS system detected 742 features in urine and 1309 in plasma. Biostatistical discriminant analysis extracted a subset of 26 metabolite signals (2 urinary, 4 plasma non-polar, and 19 plasma polar metabolites) that in combination were able to predict from 68 up to 94% of the contaminated rats, depending on the prediction method used, with a misclassification rate as low as 5.3%. The difference in this metabolic score between the contaminated and non-contaminated rats was highly significant (P = 0.019 after ANOVA cross-validation). In conclusion, our proof-of-principle study demonstrated for the first time the usefulness of a metabolomic approach for addressing biological effects of chronic low-dose contamination. We can conclude that a metabolomic signature discriminated (137)Cs-contaminated from control animals in our model. Further validation is nevertheless required together with full annotation of the metabolic indicators.

Driven to Abstraction: Abstracts and Annotations as Stiles for the Boundaries of Composition Studies.

ERIC Educational Resources Information Center

Bolin, Bill

This paper describes one academic author's consternation when he came across an annotation of one of his scholarly articles from the "Journal of Basic Writing" in the ERIC database. The paper recounts that the author was disconcerted to find that the annotation was misleading, describing as his main point something that his article warns…

Dizeez: An Online Game for Human Gene-Disease Annotation

PubMed Central

Loguercio, Salvatore; Good, Benjamin M.; Su, Andrew I.

2013-01-01

Structured gene annotations are a foundation upon which many bioinformatics and statistical analyses are built. However the structured annotations available in public databases are a sparse representation of biological knowledge as a whole. The rate of biomedical data generation is such that centralized biocuration efforts struggle to keep up. New models for gene annotation need to be explored that expand the pace at which we are able to structure biomedical knowledge. Recently, online games have emerged as an effective way to recruit, engage and organize large numbers of volunteers to help address difficult biological challenges. For example, games have been successfully developed for protein folding (Foldit), multiple sequence alignment (Phylo) and RNA structure design (EteRNA). Here we present Dizeez, a simple online game built with the purpose of structuring knowledge of gene-disease associations. Preliminary results from game play online and at scientific conferences suggest that Dizeez is producing valid gene-disease annotations not yet present in any public database. These early results provide a basic proof of principle that online games can be successfully applied to the challenge of gene annotation. Dizeez is available at http://genegames.org. PMID:23951102

An annotated corpus with nanomedicine and pharmacokinetic parameters

PubMed Central

Lewinski, Nastassja A; Jimenez, Ivan; McInnes, Bridget T

2017-01-01

A vast amount of data on nanomedicines is being generated and published, and natural language processing (NLP) approaches can automate the extraction of unstructured text-based data. Annotated corpora are a key resource for NLP and information extraction methods which employ machine learning. Although corpora are available for pharmaceuticals, resources for nanomedicines and nanotechnology are still limited. To foster nanotechnology text mining (NanoNLP) efforts, we have constructed a corpus of annotated drug product inserts taken from the US Food and Drug Administration’s Drugs@FDA online database. In this work, we present the development of the Engineered Nanomedicine Database corpus to support the evaluation of nanomedicine entity extraction. The data were manually annotated for 21 entity mentions consisting of nanomedicine physicochemical characterization, exposure, and biologic response information of 41 Food and Drug Administration-approved nanomedicines. We evaluate the reliability of the manual annotations and demonstrate the use of the corpus by evaluating two state-of-the-art named entity extraction systems, OpenNLP and Stanford NER. The annotated corpus is available open source and, based on these results, guidelines and suggestions for future development of additional nanomedicine corpora are provided. PMID:29066897

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

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.

A data model and database for high-resolution pathology analytical image informatics.

PubMed

Wang, Fusheng; Kong, Jun; Cooper, Lee; Pan, Tony; Kurc, Tahsin; Chen, Wenjin; Sharma, Ashish; Niedermayr, Cristobal; Oh, Tae W; Brat, Daniel; Farris, Alton B; Foran, David J; Saltz, Joel

2011-01-01

The systematic analysis of imaged pathology specimens often results in a vast amount of morphological information at both the cellular and sub-cellular scales. While microscopy scanners and computerized analysis are capable of capturing and analyzing data rapidly, microscopy image data remain underutilized in research and clinical settings. One major obstacle which tends to reduce wider adoption of these new technologies throughout the clinical and scientific communities is the challenge of managing, querying, and integrating the vast amounts of data resulting from the analysis of large digital pathology datasets. This paper presents a data model, which addresses these challenges, and demonstrates its implementation in a relational database system. This paper describes a data model, referred to as Pathology Analytic Imaging Standards (PAIS), and a database implementation, which are designed to support the data management and query requirements of detailed characterization of micro-anatomic morphology through many interrelated analysis pipelines on whole-slide images and tissue microarrays (TMAs). (1) Development of a data model capable of efficiently representing and storing virtual slide related image, annotation, markup, and feature information. (2) Development of a database, based on the data model, capable of supporting queries for data retrieval based on analysis and image metadata, queries for comparison of results from different analyses, and spatial queries on segmented regions, features, and classified objects. The work described in this paper is motivated by the challenges associated with characterization of micro-scale features for comparative and correlative analyses involving whole-slides tissue images and TMAs. Technologies for digitizing tissues have advanced significantly in the past decade. Slide scanners are capable of producing high-magnification, high-resolution images from whole slides and TMAs within several minutes. Hence, it is becoming increasingly feasible for basic, clinical, and translational research studies to produce thousands of whole-slide images. Systematic analysis of these large datasets requires efficient data management support for representing and indexing results from hundreds of interrelated analyses generating very large volumes of quantifications such as shape and texture and of classifications of the quantified features. We have designed a data model and a database to address the data management requirements of detailed characterization of micro-anatomic morphology through many interrelated analysis pipelines. The data model represents virtual slide related image, annotation, markup and feature information. The database supports a wide range of metadata and spatial queries on images, annotations, markups, and features. We currently have three databases running on a Dell PowerEdge T410 server with CentOS 5.5 Linux operating system. The database server is IBM DB2 Enterprise Edition 9.7.2. The set of databases consists of 1) a TMA database containing image analysis results from 4740 cases of breast cancer, with 641 MB storage size; 2) an algorithm validation database, which stores markups and annotations from two segmentation algorithms and two parameter sets on 18 selected slides, with 66 GB storage size; and 3) an in silico brain tumor study database comprising results from 307 TCGA slides, with 365 GB storage size. The latter two databases also contain human-generated annotations and markups for regions and nuclei. Modeling and managing pathology image analysis results in a database provide immediate benefits on the value and usability of data in a research study. The database provides powerful query capabilities, which are otherwise difficult or cumbersome to support by other approaches such as programming languages. Standardized, semantic annotated data representation and interfaces also make it possible to more efficiently share image data and analysis results.

Spatio-temporal distribution and natural variation of metabolites in citrus fruits.

PubMed

Wang, Shouchuang; Tu, Hong; Wan, Jian; Chen, Wei; Liu, Xianqing; Luo, Jie; Xu, Juan; Zhang, Hongyan

2016-05-15

To study the natural variation and spatio-temporal accumulation of citrus metabolites, liquid chromatography tandem mass spectrometry (LC-MS) based metabolome analysis was performed on four fruit tissues (flavedo, albedo, segment membrane and juice sacs) and different Citrus species (lemon, pummelo and grapefruit, sweet orange and mandarin). Using a non-targeted metabolomics approach, more than 2000 metabolite signals were detected, from which more than 54 metabolites, including amino acids, flavonoids and limonoids, were identified/annotated. Differential accumulation patterns of both primary metabolites and secondary metabolites in various tissues and species were revealed by our study. Further investigation indicated that flavedo accumulates more flavonoids while juice sacs contain more amino acids. Besides this, cluster analysis based on the levels of metabolites detected in 47 individual Citrus accessions clearly grouped them into four distinct clusters: pummelos and grapefruits, lemons, sweet oranges and mandarins, while the cluster of pummelos and grapefruits lay distinctly apart from the other three species. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Genome-Wide Association Study of the Human Metabolome in a Community-Based Cohort

PubMed Central

Rhee, Eugene P.; Ho, Jennifer E.; Chen, Ming-Huei; Shen, Dongxiao; Cheng, Susan; Larson, Martin G.; Ghorbani, Anahita; Shi, Xu; Helenius, Iiro T.; O’Donnell, Christopher J.; Souza, Amanda L.; Deik, Amy; Pierce, Kerry A.; Bullock, Kevin; Walford, Geoffrey A.; Vasan, Ramachandran S.; Florez, Jose C.; Clish, Clary; Yeh, J.-R. Joanna; Wang, Thomas J.; Gerszten, Robert E.

2014-01-01

SUMMARY Because metabolites are hypothesized to play key roles as markers and effectors of cardio-metabolic diseases, recent studies have sought to annotate the genetic determinants of circulating metabolite levels. We report a genome-wide association study (GWAS) of 217 plasma metabolites, including >100 not measured in prior GWAS, in 2,076 participants of the Framingham Heart Study. For the majority of analytes, we find that estimated heritability explains >20% of inter-individual variation, and that variation attributable to heritable factors is greater than that attributable to clinical factors. Further, we identify 31 genetic loci associated with plasma metabolites, including 23 that have not previously been reported. Importantly, we include GWAS results for all surveyed metabolites, and demonstrate how this information highlights a role for AGXT2 in cholesterol ester and triacylglycerol metabolism. Thus, our study outlines the relative contributions of inherited and clinical factors on the plasma metabolome and provides a resource for metabolism research. PMID:23823483

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

Ensembl core software resources: storage and programmatic access for DNA sequence and genome annotation.

PubMed

Ruffier, Magali; Kähäri, Andreas; Komorowska, Monika; Keenan, Stephen; Laird, Matthew; Longden, Ian; Proctor, Glenn; Searle, Steve; Staines, Daniel; Taylor, Kieron; Vullo, Alessandro; Yates, Andrew; Zerbino, Daniel; Flicek, Paul

2017-01-01

The Ensembl software resources are a stable infrastructure to store, access and manipulate genome assemblies and their functional annotations. The Ensembl 'Core' database and Application Programming Interface (API) was our first major piece of software infrastructure and remains at the centre of all of our genome resources. Since its initial design more than fifteen years ago, the number of publicly available genomic, transcriptomic and proteomic datasets has grown enormously, accelerated by continuous advances in DNA-sequencing technology. Initially intended to provide annotation for the reference human genome, we have extended our framework to support the genomes of all species as well as richer assembly models. Cross-referenced links to other informatics resources facilitate searching our database with a variety of popular identifiers such as UniProt and RefSeq. Our comprehensive and robust framework storing a large diversity of genome annotations in one location serves as a platform for other groups to generate and maintain their own tailored annotation. We welcome reuse and contributions: our databases and APIs are publicly available, all of our source code is released with a permissive Apache v2.0 licence at http://github.com/Ensembl and we have an active developer mailing list ( http://www.ensembl.org/info/about/contact/index.html ). http://www.ensembl.org. © The Author(s) 2017. Published by Oxford University Press.

De novo RNA-seq and functional annotation of Ornithonyssus bacoti.

PubMed

Niu, DongLing; Wang, RuiLing; Zhao, YaE; Yang, Rui; Hu, Li

2018-06-01

Ornithonyssus bacoti (Hirst) (Acari: Macronyssidae) is a vector and reservoir of pathogens causing serious infectious diseases, such as epidemic hemorrhagic fever, endemic typhus, tularemia, and leptospirosis. Its genome and transcriptome data are lacking in public databases. In this study, total RNA was extracted from live O. bacoti to conduct RNA-seq, functional annotation, coding domain sequence (CDS) prediction and simple sequence repeats (SSRs) detection. The results showed that 65.8 million clean reads were generated and assembled into 72,185 unigenes, of which 49.4% were annotated by seven functional databases. 23,121 unigenes were annotated and assigned to 457 species by non-redundant protein sequence database. The BLAST top-two hit species were Metaseiulus occidentalis and Ixodes scapularis. The procedure detected 12,426 SSRs, of which tri- and di-nucleotides were the most abundant types and the representative motifs were AAT/ATT and AC/GT. 26,936 CDS were predicted with a mean length of 711 bp. 87 unigenes of 30 functional genes, which are usually involved in stress responses, drug resistance, movement, metabolism and allergy, were further identified by bioinformatics methods. The unigenes putatively encoding cytochrome P450 proteins were further analyzed phylogenetically. In conclusion, this study completed the RNA-seq and functional annotation of O. bacoti successfully, which provides reliable molecular data for its future studies of gene function and molecular markers.

Introducing meta-services for biomedical information extraction

PubMed Central

Leitner, Florian; Krallinger, Martin; Rodriguez-Penagos, Carlos; Hakenberg, Jörg; Plake, Conrad; Kuo, Cheng-Ju; Hsu, Chun-Nan; Tsai, Richard Tzong-Han; Hung, Hsi-Chuan; Lau, William W; Johnson, Calvin A; Sætre, Rune; Yoshida, Kazuhiro; Chen, Yan Hua; Kim, Sun; Shin, Soo-Yong; Zhang, Byoung-Tak; Baumgartner, William A; Hunter, Lawrence; Haddow, Barry; Matthews, Michael; Wang, Xinglong; Ruch, Patrick; Ehrler, Frédéric; Özgür, Arzucan; Erkan, Güneş; Radev, Dragomir R; Krauthammer, Michael; Luong, ThaiBinh; Hoffmann, Robert; Sander, Chris; Valencia, Alfonso

2008-01-01

We introduce the first meta-service for information extraction in molecular biology, the BioCreative MetaServer (BCMS; ). This prototype platform is a joint effort of 13 research groups and provides automatically generated annotations for PubMed/Medline abstracts. Annotation types cover gene names, gene IDs, species, and protein-protein interactions. The annotations are distributed by the meta-server in both human and machine readable formats (HTML/XML). This service is intended to be used by biomedical researchers and database annotators, and in biomedical language processing. The platform allows direct comparison, unified access, and result aggregation of the annotations. PMID:18834497

Covering Chemical Diversity of Genetically-Modified Tomatoes Using Metabolomics for Objective Substantial Equivalence Assessment

PubMed Central

Hirai, Tadayoshi; Oikawa, Akira; Matsuda, Fumio; Fukushima, Atsushi; Arita, Masanori; Watanabe, Shin; Yano, Megumu; Hiwasa-Tanase, Kyoko; Ezura, Hiroshi; Saito, Kazuki

2011-01-01

As metabolomics can provide a biochemical snapshot of an organism's phenotype it is a promising approach for charting the unintended effects of genetic modification. A critical obstacle for this application is the inherently limited metabolomic coverage of any single analytical platform. We propose using multiple analytical platforms for the direct acquisition of an interpretable data set of estimable chemical diversity. As an example, we report an application of our multi-platform approach that assesses the substantial equivalence of tomatoes over-expressing the taste-modifying protein miraculin. In combination, the chosen platforms detected compounds that represent 86% of the estimated chemical diversity of the metabolites listed in the LycoCyc database. Following a proof-of-safety approach, we show that % had an acceptable range of variation while simultaneously indicating a reproducible transformation-related metabolic signature. We conclude that multi-platform metabolomics is an approach that is both sensitive and robust and that it constitutes a good starting point for characterizing genetically modified organisms. PMID:21359231

Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling

DOE PAGES

Benton, H. Paul; Ivanisevic, Julijana; Mahieu, Nathaniel G.; ...

2014-12-12

An autonomous metabolomic workflow combining mass spectrometry analysis with tandem mass spectrometry data acquisition was designed to allow for simultaneous data processing and metabolite characterization. Although previously tandem mass spectrometry data have been generated on the fly, the experiments described herein combine this technology with the bioinformatic resources of XCMS and METLIN. We can analyze large profiling datasets and simultaneously obtain structural identifications, as a result of this unique integration. Furthermore, validation of the workflow on bacterial samples allowed the profiling on the order of a thousand metabolite features with simultaneous tandem mass spectra data acquisition. The tandem mass spectrometrymore » data acquisition enabled automatic search and matching against the METLIN tandem mass spectrometry database, shortening the current workflow from days to hours. Overall, the autonomous approach to untargeted metabolomics provides an efficient means of metabolomic profiling, and will ultimately allow the more rapid integration of comparative analyses, metabolite identification, and data analysis at a systems biology level.« less

Non-targeted metabolomics combined with genetic analyses identifies bile acid synthesis and phospholipid metabolism as being associated with incident type 2 diabetes

PubMed Central

Salihovic, Samira; Brandmaier, Stefan; Nowak, Christoph; Ganna, Andrea; Gustafsson, Stefan; Broeckling, Corey D.; Prenni, Jessica E.; Kastenmüller, Gabi; Peters, Annette; Magnusson, Patrik K.; Wang-Sattler, Rui; Giedraitis, Vilmantas; Berne, Christian; Gieger, Christian; Pedersen, Nancy L.; Ingelsson, Erik; Lind, Lars

2017-01-01

Aims/hypothesis Identification of novel biomarkers for type 2 diabetes and their genetic determinants could lead to improved understanding of causal pathways and improve risk prediction. Methods In this study, we used data from non-targeted metabolomics performed using liquid chromatography coupled with tandem mass spectrometry in three Swedish cohorts (Uppsala Longitudinal Study of Adult Men [ULSAM], n = 1138; Prospective Investigation of the Vasculature in Uppsala Seniors [PIVUS], n = 970; TwinGene, n = 1630). Metabolites associated with impaired fasting glucose (IFG) and/or prevalent type 2 diabetes were assessed for associations with incident type 2 diabetes in the three cohorts followed by replication attempts in the Cooperative Health Research in the Region of Augsburg (KORA) S4 cohort (n = 855). Assessment of the association of metabolite-regulating genetic variants with type 2 diabetes was done using data from a meta-analysis of genome-wide association studies. Results Out of 5961 investigated metabolic features, 1120 were associated with prevalent type 2 diabetes and IFG and 70 were annotated to metabolites and replicated in the three cohorts. Fifteen metabolites were associated with incident type 2 diabetes in the four cohorts combined (358 events) following adjustment for age, sex, BMI, waist circumference and fasting glucose. Novel findings included associations of higher values of the bile acid deoxycholic acid and monoacylglyceride 18:2 and lower concentrations of cortisol with type 2 diabetes risk. However, adding metabolites to an existing risk score improved model fit only marginally. A genetic variant within the CYP7A1 locus, encoding the rate-limiting enzyme in bile acid synthesis, was found to be associated with lower concentrations of deoxycholic acid, higher concentrations of LDL-cholesterol and lower type 2 diabetes risk. Variants in or near SGPP1, GCKR and FADS1/2 were associated with diabetes-associated phospholipids and type 2 diabetes. Conclusions/interpretation We found evidence that the metabolism of bile acids and phospholipids shares some common genetic origin with type 2 diabetes. Access to research materials Metabolomics data have been deposited in the Metabolights database, with accession numbers MTBLS93 (TwinGene), MTBLS124 (ULSAM) and MTBLS90 (PIVUS). PMID:27406814

SIMAP—the database of all-against-all protein sequence similarities and annotations with new interfaces and increased coverage

PubMed Central

Arnold, Roland; Goldenberg, Florian; Mewes, Hans-Werner; Rattei, Thomas

2014-01-01

The Similarity Matrix of Proteins (SIMAP, http://mips.gsf.de/simap/) database has been designed to massively accelerate computationally expensive protein sequence analysis tasks in bioinformatics. It provides pre-calculated sequence similarities interconnecting the entire known protein sequence universe, complemented by pre-calculated protein features and domains, similarity clusters and functional annotations. SIMAP covers all major public protein databases as well as many consistently re-annotated metagenomes from different repositories. As of September 2013, SIMAP contains >163 million proteins corresponding to ∼70 million non-redundant sequences. SIMAP uses the sensitive FASTA search heuristics, the Smith–Waterman alignment algorithm, the InterPro database of protein domain models and the BLAST2GO functional annotation algorithm. SIMAP assists biologists by facilitating the interactive exploration of the protein sequence universe. Web-Service and DAS interfaces allow connecting SIMAP with any other bioinformatic tool and resource. All-against-all protein sequence similarity matrices of project-specific protein collections are generated on request. Recent improvements allow SIMAP to cover the rapidly growing sequenced protein sequence universe. New Web-Service interfaces enhance the connectivity of SIMAP. Novel tools for interactive extraction of protein similarity networks have been added. Open access to SIMAP is provided through the web portal; the portal also contains instructions and links for software access and flat file downloads. PMID:24165881

Dictionary-driven protein annotation

PubMed Central

Rigoutsos, Isidore; Huynh, Tien; Floratos, Aris; Parida, Laxmi; Platt, Daniel

2002-01-01

Computational methods seeking to automatically determine the properties (functional, structural, physicochemical, etc.) of a protein directly from the sequence have long been the focus of numerous research groups. With the advent of advanced sequencing methods and systems, the number of amino acid sequences that are being deposited in the public databases has been increasing steadily. This has in turn generated a renewed demand for automated approaches that can annotate individual sequences and complete genomes quickly, exhaustively and objectively. In this paper, we present one such approach that is centered around and exploits the Bio-Dictionary, a collection of amino acid patterns that completely covers the natural sequence space and can capture functional and structural signals that have been reused during evolution, within and across protein families. Our annotation approach also makes use of a weighted, position-specific scoring scheme that is unaffected by the over-representation of well-conserved proteins and protein fragments in the databases used. For a given query sequence, the method permits one to determine, in a single pass, the following: local and global similarities between the query and any protein already present in a public database; the likeness of the query to all available archaeal/bacterial/eukaryotic/viral sequences in the database as a function of amino acid position within the query; the character of secondary structure of the query as a function of amino acid position within the query; the cytoplasmic, transmembrane or extracellular behavior of the query; the nature and position of binding domains, active sites, post-translationally modified sites, signal peptides, etc. In terms of performance, the proposed method is exhaustive, objective and allows for the rapid annotation of individual sequences and full genomes. Annotation examples are presented and discussed in Results, including individual queries and complete genomes that were released publicly after we built the Bio-Dictionary that is used in our experiments. Finally, we have computed the annotations of more than 70 complete genomes and made them available on the World Wide Web at http://cbcsrv.watson.ibm.com/Annotations/. PMID:12202776

The new modern era of yeast genomics: community sequencing and the resulting annotation of multiple Saccharomyces cerevisiae strains at the Saccharomyces Genome Database

PubMed Central

Engel, Stacia R.; Cherry, J. Michael

2013-01-01

The first completed eukaryotic genome sequence was that of the yeast Saccharomyces cerevisiae, and the Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) is the original model organism database. SGD remains the authoritative community resource for the S. cerevisiae reference genome sequence and its annotation, and continues to provide comprehensive biological information correlated with S. cerevisiae genes and their products. A diverse set of yeast strains have been sequenced to explore commercial and laboratory applications, and a brief history of those strains is provided. The publication of these new genomes has motivated the creation of new tools, and SGD will annotate and provide comparative analyses of these sequences, correlating changes with variations in strain phenotypes and protein function. We are entering a new era at SGD, as we incorporate these new sequences and make them accessible to the scientific community, all in an effort to continue in our mission of educating researchers and facilitating discovery. Database URL: http://www.yeastgenome.org/ PMID:23487186

BGD: a database of bat genomes.

PubMed

Fang, Jianfei; Wang, Xuan; Mu, Shuo; Zhang, Shuyi; Dong, Dong

2015-01-01

Bats account for ~20% of mammalian species, and are the only mammals with true powered flight. For the sake of their specialized phenotypic traits, many researches have been devoted to examine the evolution of bats. Until now, some whole genome sequences of bats have been assembled and annotated, however, a uniform resource for the annotated bat genomes is still unavailable. To make the extensive data associated with the bat genomes accessible to the general biological communities, we established a Bat Genome Database (BGD). BGD is an open-access, web-available portal that integrates available data of bat genomes and genes. It hosts data from six bat species, including two megabats and four microbats. Users can query the gene annotations using efficient searching engine, and it offers browsable tracks of bat genomes. Furthermore, an easy-to-use phylogenetic analysis tool was also provided to facilitate online phylogeny study of genes. To the best of our knowledge, BGD is the first database of bat genomes. It will extend our understanding of the bat evolution and be advantageous to the bat sequences analysis. BGD is freely available at: http://donglab.ecnu.edu.cn/databases/BatGenome/.

The UCSC Genome Browser database: extensions and updates 2013.

PubMed

Meyer, Laurence R; Zweig, Ann S; Hinrichs, Angie S; Karolchik, Donna; Kuhn, Robert M; Wong, Matthew; Sloan, Cricket A; Rosenbloom, Kate R; Roe, Greg; Rhead, Brooke; Raney, Brian J; Pohl, Andy; Malladi, Venkat S; Li, Chin H; Lee, Brian T; Learned, Katrina; Kirkup, Vanessa; Hsu, Fan; Heitner, Steve; Harte, Rachel A; Haeussler, Maximilian; Guruvadoo, Luvina; Goldman, Mary; Giardine, Belinda M; Fujita, Pauline A; Dreszer, Timothy R; Diekhans, Mark; Cline, Melissa S; Clawson, Hiram; Barber, Galt P; Haussler, David; Kent, W James

2013-01-01

The University of California Santa Cruz (UCSC) Genome Browser (http://genome.ucsc.edu) offers online public access to a growing database of genomic sequence and annotations for a wide variety of organisms. The Browser is an integrated tool set for visualizing, comparing, analysing and sharing both publicly available and user-generated genomic datasets. As of September 2012, genomic sequence and a basic set of annotation 'tracks' are provided for 63 organisms, including 26 mammals, 13 non-mammal vertebrates, 3 invertebrate deuterostomes, 13 insects, 6 worms, yeast and sea hare. In the past year 19 new genome assemblies have been added, and we anticipate releasing another 28 in early 2013. Further, a large number of annotation tracks have been either added, updated by contributors or remapped to the latest human reference genome. Among these are an updated UCSC Genes track for human and mouse assemblies. We have also introduced several features to improve usability, including new navigation menus. This article provides an update to the UCSC Genome Browser database, which has been previously featured in the Database issue of this journal.

EuroPineDB: a high-coverage web database for maritime pine transcriptome

PubMed Central

2011-01-01

Background Pinus pinaster is an economically and ecologically important species that is becoming a woody gymnosperm model. Its enormous genome size makes whole-genome sequencing approaches are hard to apply. Therefore, the expressed portion of the genome has to be characterised and the results and annotations have to be stored in dedicated databases. Description EuroPineDB is the largest sequence collection available for a single pine species, Pinus pinaster (maritime pine), since it comprises 951 641 raw sequence reads obtained from non-normalised cDNA libraries and high-throughput sequencing from adult (xylem, phloem, roots, stem, needles, cones, strobili) and embryonic (germinated embryos, buds, callus) maritime pine tissues. Using open-source tools, sequences were optimally pre-processed, assembled, and extensively annotated (GO, EC and KEGG terms, descriptions, SNPs, SSRs, ORFs and InterPro codes). As a result, a 10.5× P. pinaster genome was covered and assembled in 55 322 UniGenes. A total of 32 919 (59.5%) of P. pinaster UniGenes were annotated with at least one description, revealing at least 18 466 different genes. The complete database, which is designed to be scalable, maintainable, and expandable, is freely available at: http://www.scbi.uma.es/pindb/. It can be retrieved by gene libraries, pine species, annotations, UniGenes and microarrays (i.e., the sequences are distributed in two-colour microarrays; this is the only conifer database that provides this information) and will be periodically updated. Small assemblies can be viewed using a dedicated visualisation tool that connects them with SNPs. Any sequence or annotation set shown on-screen can be downloaded. Retrieval mechanisms for sequences and gene annotations are provided. Conclusions The EuroPineDB with its integrated information can be used to reveal new knowledge, offers an easy-to-use collection of information to directly support experimental work (including microarray hybridisation), and provides deeper knowledge on the maritime pine transcriptome. PMID:21762488

Orchid: a novel management, annotation and machine learning framework for analyzing cancer mutations.

PubMed

Cario, Clinton L; Witte, John S

2018-03-15

As whole-genome tumor sequence and biological annotation datasets grow in size, number and content, there is an increasing basic science and clinical need for efficient and accurate data management and analysis software. With the emergence of increasingly sophisticated data stores, execution environments and machine learning algorithms, there is also a need for the integration of functionality across frameworks. We present orchid, a python based software package for the management, annotation and machine learning of cancer mutations. Building on technologies of parallel workflow execution, in-memory database storage and machine learning analytics, orchid efficiently handles millions of mutations and hundreds of features in an easy-to-use manner. We describe the implementation of orchid and demonstrate its ability to distinguish tissue of origin in 12 tumor types based on 339 features using a random forest classifier. Orchid and our annotated tumor mutation database are freely available at https://github.com/wittelab/orchid. Software is implemented in python 2.7, and makes use of MySQL or MemSQL databases. Groovy 2.4.5 is optionally required for parallel workflow execution. JWitte@ucsf.edu. Supplementary data are available at Bioinformatics online.

GenomeRNAi: a database for cell-based RNAi phenotypes.

PubMed

Horn, Thomas; Arziman, Zeynep; Berger, Juerg; Boutros, Michael

2007-01-01

RNA interference (RNAi) has emerged as a powerful tool to generate loss-of-function phenotypes in a variety of organisms. Combined with the sequence information of almost completely annotated genomes, RNAi technologies have opened new avenues to conduct systematic genetic screens for every annotated gene in the genome. As increasing large datasets of RNAi-induced phenotypes become available, an important challenge remains the systematic integration and annotation of functional information. Genome-wide RNAi screens have been performed both in Caenorhabditis elegans and Drosophila for a variety of phenotypes and several RNAi libraries have become available to assess phenotypes for almost every gene in the genome. These screens were performed using different types of assays from visible phenotypes to focused transcriptional readouts and provide a rich data source for functional annotation across different species. The GenomeRNAi database provides access to published RNAi phenotypes obtained from cell-based screens and maps them to their genomic locus, including possible non-specific regions. The database also gives access to sequence information of RNAi probes used in various screens. It can be searched by phenotype, by gene, by RNAi probe or by sequence and is accessible at http://rnai.dkfz.de.

GenomeRNAi: a database for cell-based RNAi phenotypes

PubMed Central

Horn, Thomas; Arziman, Zeynep; Berger, Juerg; Boutros, Michael

2007-01-01

RNA interference (RNAi) has emerged as a powerful tool to generate loss-of-function phenotypes in a variety of organisms. Combined with the sequence information of almost completely annotated genomes, RNAi technologies have opened new avenues to conduct systematic genetic screens for every annotated gene in the genome. As increasing large datasets of RNAi-induced phenotypes become available, an important challenge remains the systematic integration and annotation of functional information. Genome-wide RNAi screens have been performed both in Caenorhabditis elegans and Drosophila for a variety of phenotypes and several RNAi libraries have become available to assess phenotypes for almost every gene in the genome. These screens were performed using different types of assays from visible phenotypes to focused transcriptional readouts and provide a rich data source for functional annotation across different species. The GenomeRNAi database provides access to published RNAi phenotypes obtained from cell-based screens and maps them to their genomic locus, including possible non-specific regions. The database also gives access to sequence information of RNAi probes used in various screens. It can be searched by phenotype, by gene, by RNAi probe or by sequence and is accessible at PMID:17135194

Modeling loosely annotated images using both given and imagined annotations

NASA Astrophysics Data System (ADS)

Tang, Hong; Boujemaa, Nozha; Chen, Yunhao; Deng, Lei

2011-12-01

In this paper, we present an approach to learn latent semantic analysis models from loosely annotated images for automatic image annotation and indexing. The given annotation in training images is loose due to: 1. ambiguous correspondences between visual features and annotated keywords; 2. incomplete lists of annotated keywords. The second reason motivates us to enrich the incomplete annotation in a simple way before learning a topic model. In particular, some ``imagined'' keywords are poured into the incomplete annotation through measuring similarity between keywords in terms of their co-occurrence. Then, both given and imagined annotations are employed to learn probabilistic topic models for automatically annotating new images. We conduct experiments on two image databases (i.e., Corel and ESP) coupled with their loose annotations, and compare the proposed method with state-of-the-art discrete annotation methods. The proposed method improves word-driven probability latent semantic analysis (PLSA-words) up to a comparable performance with the best discrete annotation method, while a merit of PLSA-words is still kept, i.e., a wider semantic range.

Non-targeted metabolomics and lipidomics LC-MS data from maternal plasma of 180 healthy pregnant women.

PubMed

Luan, Hemi; Meng, Nan; Liu, Ping; Fu, Jin; Chen, Xiaomin; Rao, Weiqiao; Jiang, Hui; Xu, Xun; Cai, Zongwei; Wang, Jun

2015-01-01

Metabolomics has the potential to be a powerful and sensitive approach for investigating the low molecular weight metabolite profiles present in maternal fluids and their role in pregnancy. In this Data Note, LC-MS metabolome, lipidome and carnitine profiling data were collected from 180 healthy pregnant women, representing six time points spanning all three trimesters, and providing sufficient coverage to model the progression of normal pregnancy. As a relatively large scale, real-world dataset with robust numbers of quality control samples, the data are expected to prove useful for algorithm optimization and development, with the potential to augment studies into abnormal pregnancy. All data and ISA-TAB format enriched metadata are available for download in the MetaboLights and GigaScience databases.

A graph-based semantic similarity measure for the gene ontology.

PubMed

Alvarez, Marco A; Yan, Changhui

2011-12-01

Existing methods for calculating semantic similarities between pairs of Gene Ontology (GO) terms and gene products often rely on external databases like Gene Ontology Annotation (GOA) that annotate gene products using the GO terms. This dependency leads to some limitations in real applications. Here, we present a semantic similarity algorithm (SSA), that relies exclusively on the GO. When calculating the semantic similarity between a pair of input GO terms, SSA takes into account the shortest path between them, the depth of their nearest common ancestor, and a novel similarity score calculated between the definitions of the involved GO terms. In our work, we use SSA to calculate semantic similarities between pairs of proteins by combining pairwise semantic similarities between the GO terms that annotate the involved proteins. The reliability of SSA was evaluated by comparing the resulting semantic similarities between proteins with the functional similarities between proteins derived from expert annotations or sequence similarity. Comparisons with existing state-of-the-art methods showed that SSA is highly competitive with the other methods. SSA provides a reliable measure for semantics similarity independent of external databases of functional-annotation observations.

Enabling comparative modeling of closely related genomes: Example genus Brucella

DOE PAGES

Faria, José P.; Edirisinghe, Janaka N.; Davis, James J.; ...

2014-03-08

For many scientific applications, it is highly desirable to be able to compare metabolic models of closely related genomes. In this study, we attempt to raise awareness to the fact that taking annotated genomes from public repositories and using them for metabolic model reconstructions is far from being trivial due to annotation inconsistencies. We are proposing a protocol for comparative analysis of metabolic models on closely related genomes, using fifteen strains of genus Brucella, which contains pathogens of both humans and livestock. This study lead to the identification and subsequent correction of inconsistent annotations in the SEED database, as wellmore » as the identification of 31 biochemical reactions that are common to Brucella, which are not originally identified by automated metabolic reconstructions. We are currently implementing this protocol for improving automated annotations within the SEED database and these improvements have been propagated into PATRIC, Model-SEED, KBase and RAST. This method is an enabling step for the future creation of consistent annotation systems and high-quality model reconstructions that will support in predicting accurate phenotypes such as pathogenicity, media requirements or type of respiration.« less

Enabling comparative modeling of closely related genomes: Example genus Brucella

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

Faria, José P.; Edirisinghe, Janaka N.; Davis, James J.

For many scientific applications, it is highly desirable to be able to compare metabolic models of closely related genomes. In this study, we attempt to raise awareness to the fact that taking annotated genomes from public repositories and using them for metabolic model reconstructions is far from being trivial due to annotation inconsistencies. We are proposing a protocol for comparative analysis of metabolic models on closely related genomes, using fifteen strains of genus Brucella, which contains pathogens of both humans and livestock. This study lead to the identification and subsequent correction of inconsistent annotations in the SEED database, as wellmore » as the identification of 31 biochemical reactions that are common to Brucella, which are not originally identified by automated metabolic reconstructions. We are currently implementing this protocol for improving automated annotations within the SEED database and these improvements have been propagated into PATRIC, Model-SEED, KBase and RAST. This method is an enabling step for the future creation of consistent annotation systems and high-quality model reconstructions that will support in predicting accurate phenotypes such as pathogenicity, media requirements or type of respiration.« less

Automated Gene Ontology annotation for anonymous sequence data.

PubMed

Hennig, Steffen; Groth, Detlef; Lehrach, Hans

2003-07-01

Gene Ontology (GO) is the most widely accepted attempt to construct a unified and structured vocabulary for the description of genes and their products in any organism. Annotation by GO terms is performed in most of the current genome projects, which besides generality has the advantage of being very convenient for computer based classification methods. However, direct use of GO in small sequencing projects is not easy, especially for species not commonly represented in public databases. We present a software package (GOblet), which performs annotation based on GO terms for anonymous cDNA or protein sequences. It uses the species independent GO structure and vocabulary together with a series of protein databases collected from various sites, to perform a detailed GO annotation by sequence similarity searches. The sensitivity and the reference protein sets can be selected by the user. GOblet runs automatically and is available as a public service on our web server. The paper also addresses the reliability of automated GO annotations by using a reference set of more than 6000 human proteins. The GOblet server is accessible at http://goblet.molgen.mpg.de.

MIPS: a database for genomes and protein sequences.

PubMed Central

Mewes, H W; Heumann, K; Kaps, A; Mayer, K; Pfeiffer, F; Stocker, S; Frishman, D

1999-01-01

The Munich Information Center for Protein Sequences (MIPS-GSF), Martinsried near Munich, Germany, develops and maintains genome oriented databases. It is commonplace that the amount of sequence data available increases rapidly, but not the capacity of qualified manual annotation at the sequence databases. Therefore, our strategy aims to cope with the data stream by the comprehensive application of analysis tools to sequences of complete genomes, the systematic classification of protein sequences and the active support of sequence analysis and functional genomics projects. This report describes the systematic and up-to-date analysis of genomes (PEDANT), a comprehensive database of the yeast genome (MYGD), a database reflecting the progress in sequencing the Arabidopsis thaliana genome (MATD), the database of assembled, annotated human EST clusters (MEST), and the collection of protein sequence data within the framework of the PIR-International Protein Sequence Database (described elsewhere in this volume). MIPS provides access through its WWW server (http://www.mips.biochem.mpg.de) to a spectrum of generic databases, including the above mentioned as well as a database of protein families (PROTFAM), the MITOP database, and the all-against-all FASTA database. PMID:9847138

The Nuclear Protein Database (NPD): sub-nuclear localisation and functional annotation of the nuclear proteome

PubMed Central

Dellaire, G.; Farrall, R.; Bickmore, W.A.

2003-01-01

The Nuclear Protein Database (NPD) is a curated database that contains information on more than 1300 vertebrate proteins that are thought, or are known, to localise to the cell nucleus. Each entry is annotated with information on predicted protein size and isoelectric point, as well as any repeats, motifs or domains within the protein sequence. In addition, information on the sub-nuclear localisation of each protein is provided and the biological and molecular functions are described using Gene Ontology (GO) terms. The database is searchable by keyword, protein name, sub-nuclear compartment and protein domain/motif. Links to other databases are provided (e.g. Entrez, SWISS-PROT, OMIM, PubMed, PubMed Central). Thus, NPD provides a gateway through which the nuclear proteome may be explored. The database can be accessed at http://npd.hgu.mrc.ac.uk and is updated monthly. PMID:12520015

Computer systems for annotation of single molecule fragments

DOEpatents

Schwartz, David Charles; Severin, Jessica

2016-07-19

There are provided computer systems for visualizing and annotating single molecule images. Annotation systems in accordance with this disclosure allow a user to mark and annotate single molecules of interest and their restriction enzyme cut sites thereby determining the restriction fragments of single nucleic acid molecules. The markings and annotations may be automatically generated by the system in certain embodiments and they may be overlaid translucently onto the single molecule images. An image caching system may be implemented in the computer annotation systems to reduce image processing time. The annotation systems include one or more connectors connecting to one or more databases capable of storing single molecule data as well as other biomedical data. Such diverse array of data can be retrieved and used to validate the markings and annotations. The annotation systems may be implemented and deployed over a computer network. They may be ergonomically optimized to facilitate user interactions.

Image-based diagnostic aid for interstitial lung disease with secondary data integration

NASA Astrophysics Data System (ADS)

Depeursinge, Adrien; Müller, Henning; Hidki, Asmâa; Poletti, Pierre-Alexandre; Platon, Alexandra; Geissbuhler, Antoine

2007-03-01

Interstitial lung diseases (ILDs) are a relatively heterogeneous group of around 150 illnesses with often very unspecific symptoms. The most complete imaging method for the characterisation of ILDs is the high-resolution computed tomography (HRCT) of the chest but a correct interpretation of these images is difficult even for specialists as many diseases are rare and thus little experience exists. Moreover, interpreting HRCT images requires knowledge of the context defined by clinical data of the studied case. A computerised diagnostic aid tool based on HRCT images with associated medical data to retrieve similar cases of ILDs from a dedicated database can bring quick and precious information for example for emergency radiologists. The experience from a pilot project highlighted the need for detailed database containing high-quality annotations in addition to clinical data. The state of the art is studied to identify requirements for image-based diagnostic aid for interstitial lung disease with secondary data integration. The data acquisition steps are detailed. The selection of the most relevant clinical parameters is done in collaboration with lung specialists from current literature, along with knowledge bases of computer-based diagnostic decision support systems. In order to perform high-quality annotations of the interstitial lung tissue in the HRCT images an annotation software and its own file format is implemented for DICOM images. A multimedia database is implemented to store ILD cases with clinical data and annotated image series. Cases from the University & University Hospitals of Geneva (HUG) are retrospectively and prospectively collected to populate the database. Currently, 59 cases with certified diagnosis and their clinical parameters are stored in the database as well as 254 image series of which 26 have their regions of interest annotated. The available data was used to test primary visual features for the classification of lung tissue patterns. These features show good discriminative properties for the separation of five classes of visual observations.

DNApod: DNA polymorphism annotation database from next-generation sequence read archives.

PubMed

Mochizuki, Takako; Tanizawa, Yasuhiro; Fujisawa, Takatomo; Ohta, Tazro; Nikoh, Naruo; Shimizu, Tokurou; Toyoda, Atsushi; Fujiyama, Asao; Kurata, Nori; Nagasaki, Hideki; Kaminuma, Eli; Nakamura, Yasukazu

2017-01-01

With the rapid advances in next-generation sequencing (NGS), datasets for DNA polymorphisms among various species and strains have been produced, stored, and distributed. However, reliability varies among these datasets because the experimental and analytical conditions used differ among assays. Furthermore, such datasets have been frequently distributed from the websites of individual sequencing projects. It is desirable to integrate DNA polymorphism data into one database featuring uniform quality control that is distributed from a single platform at a single place. DNA polymorphism annotation database (DNApod; http://tga.nig.ac.jp/dnapod/) is an integrated database that stores genome-wide DNA polymorphism datasets acquired under uniform analytical conditions, and this includes uniformity in the quality of the raw data, the reference genome version, and evaluation algorithms. DNApod genotypic data are re-analyzed whole-genome shotgun datasets extracted from sequence read archives, and DNApod distributes genome-wide DNA polymorphism datasets and known-gene annotations for each DNA polymorphism. This new database was developed for storing genome-wide DNA polymorphism datasets of plants, with crops being the first priority. Here, we describe our analyzed data for 679, 404, and 66 strains of rice, maize, and sorghum, respectively. The analytical methods are available as a DNApod workflow in an NGS annotation system of the DNA Data Bank of Japan and a virtual machine image. Furthermore, DNApod provides tables of links of identifiers between DNApod genotypic data and public phenotypic data. To advance the sharing of organism knowledge, DNApod offers basic and ubiquitous functions for multiple alignment and phylogenetic tree construction by using orthologous gene information.

DNApod: DNA polymorphism annotation database from next-generation sequence read archives

PubMed Central

Mochizuki, Takako; Tanizawa, Yasuhiro; Fujisawa, Takatomo; Ohta, Tazro; Nikoh, Naruo; Shimizu, Tokurou; Toyoda, Atsushi; Fujiyama, Asao; Kurata, Nori; Nagasaki, Hideki; Kaminuma, Eli; Nakamura, Yasukazu

2017-01-01

With the rapid advances in next-generation sequencing (NGS), datasets for DNA polymorphisms among various species and strains have been produced, stored, and distributed. However, reliability varies among these datasets because the experimental and analytical conditions used differ among assays. Furthermore, such datasets have been frequently distributed from the websites of individual sequencing projects. It is desirable to integrate DNA polymorphism data into one database featuring uniform quality control that is distributed from a single platform at a single place. DNA polymorphism annotation database (DNApod; http://tga.nig.ac.jp/dnapod/) is an integrated database that stores genome-wide DNA polymorphism datasets acquired under uniform analytical conditions, and this includes uniformity in the quality of the raw data, the reference genome version, and evaluation algorithms. DNApod genotypic data are re-analyzed whole-genome shotgun datasets extracted from sequence read archives, and DNApod distributes genome-wide DNA polymorphism datasets and known-gene annotations for each DNA polymorphism. This new database was developed for storing genome-wide DNA polymorphism datasets of plants, with crops being the first priority. Here, we describe our analyzed data for 679, 404, and 66 strains of rice, maize, and sorghum, respectively. The analytical methods are available as a DNApod workflow in an NGS annotation system of the DNA Data Bank of Japan and a virtual machine image. Furthermore, DNApod provides tables of links of identifiers between DNApod genotypic data and public phenotypic data. To advance the sharing of organism knowledge, DNApod offers basic and ubiquitous functions for multiple alignment and phylogenetic tree construction by using orthologous gene information. PMID:28234924

Negative Example Selection for Protein Function Prediction: The NoGO Database

PubMed Central

Youngs, Noah; Penfold-Brown, Duncan; Bonneau, Richard; Shasha, Dennis

2014-01-01

Negative examples – genes that are known not to carry out a given protein function – are rarely recorded in genome and proteome annotation databases, such as the Gene Ontology database. Negative examples are required, however, for several of the most powerful machine learning methods for integrative protein function prediction. Most protein function prediction efforts have relied on a variety of heuristics for the choice of negative examples. Determining the accuracy of methods for negative example prediction is itself a non-trivial task, given that the Open World Assumption as applied to gene annotations rules out many traditional validation metrics. We present a rigorous comparison of these heuristics, utilizing a temporal holdout, and a novel evaluation strategy for negative examples. We add to this comparison several algorithms adapted from Positive-Unlabeled learning scenarios in text-classification, which are the current state of the art methods for generating negative examples in low-density annotation contexts. Lastly, we present two novel algorithms of our own construction, one based on empirical conditional probability, and the other using topic modeling applied to genes and annotations. We demonstrate that our algorithms achieve significantly fewer incorrect negative example predictions than the current state of the art, using multiple benchmarks covering multiple organisms. Our methods may be applied to generate negative examples for any type of method that deals with protein function, and to this end we provide a database of negative examples in several well-studied organisms, for general use (The NoGO database, available at: bonneaulab.bio.nyu.edu/nogo.html). PMID:24922051

Quality of Computationally Inferred Gene Ontology Annotations

PubMed Central

Škunca, Nives; Altenhoff, Adrian; Dessimoz, Christophe

2012-01-01

Gene Ontology (GO) has established itself as the undisputed standard for protein function annotation. Most annotations are inferred electronically, i.e. without individual curator supervision, but they are widely considered unreliable. At the same time, we crucially depend on those automated annotations, as most newly sequenced genomes are non-model organisms. Here, we introduce a methodology to systematically and quantitatively evaluate electronic annotations. By exploiting changes in successive releases of the UniProt Gene Ontology Annotation database, we assessed the quality of electronic annotations in terms of specificity, reliability, and coverage. Overall, we not only found that electronic annotations have significantly improved in recent years, but also that their reliability now rivals that of annotations inferred by curators when they use evidence other than experiments from primary literature. This work provides the means to identify the subset of electronic annotations that can be relied upon—an important outcome given that >98% of all annotations are inferred without direct curation. PMID:22693439

MalaCards: an integrated compendium for diseases and their annotation

PubMed Central

Rappaport, Noa; Nativ, Noam; Stelzer, Gil; Twik, Michal; Guan-Golan, Yaron; Iny Stein, Tsippi; Bahir, Iris; Belinky, Frida; Morrey, C. Paul; Safran, Marilyn; Lancet, Doron

2013-01-01

Comprehensive disease classification, integration and annotation are crucial for biomedical discovery. At present, disease compilation is incomplete, heterogeneous and often lacking systematic inquiry mechanisms. We introduce MalaCards, an integrated database of human maladies and their annotations, modeled on the architecture and strategy of the GeneCards database of human genes. MalaCards mines and merges 44 data sources to generate a computerized card for each of 16 919 human diseases. Each MalaCard contains disease-specific prioritized annotations, as well as inter-disease connections, empowered by the GeneCards relational database, its searches and GeneDecks set analyses. First, we generate a disease list from 15 ranked sources, using disease-name unification heuristics. Next, we use four schemes to populate MalaCards sections: (i) directly interrogating disease resources, to establish integrated disease names, synonyms, summaries, drugs/therapeutics, clinical features, genetic tests and anatomical context; (ii) searching GeneCards for related publications, and for associated genes with corresponding relevance scores; (iii) analyzing disease-associated gene sets in GeneDecks to yield affiliated pathways, phenotypes, compounds and GO terms, sorted by a composite relevance score and presented with GeneCards links; and (iv) searching within MalaCards itself, e.g. for additional related diseases and anatomical context. The latter forms the basis for the construction of a disease network, based on shared MalaCards annotations, embodying associations based on etiology, clinical features and clinical conditions. This broadly disposed network has a power-law degree distribution, suggesting that this might be an inherent property of such networks. Work in progress includes hierarchical malady classification, ontological mapping and disease set analyses, striving to make MalaCards an even more effective tool for biomedical research. Database URL: http://www.malacards.org/ PMID:23584832

Exploratory Application of Neuropharmacometabolomics in Severe Childhood Traumatic Brain Injury.

PubMed

Hagos, Fanuel T; Empey, Philip E; Wang, Pengcheng; Ma, Xiaochao; Poloyac, Samuel M; Bayır, Hülya; Kochanek, Patrick M; Bell, Michael J; Clark, Robert S B

2018-05-07

To employ metabolomics-based pathway and network analyses to evaluate the cerebrospinal fluid metabolome after severe traumatic brain injury in children and the capacity of combination therapy with probenecid and N-acetylcysteine to impact glutathione-related and other pathways and networks, relative to placebo treatment. Analysis of cerebrospinal fluid obtained from children enrolled in an Institutional Review Board-approved, randomized, placebo-controlled trial of a combination of probenecid and N-acetylcysteine after severe traumatic brain injury (Trial Registration NCT01322009). Thirty-six-bed PICU in a university-affiliated children's hospital. Twelve children 2-18 years old after severe traumatic brain injury and five age-matched control subjects. Probenecid (25 mg/kg) and N-acetylcysteine (140 mg/kg) or placebo administered via naso/orogastric tube. The cerebrospinal fluid metabolome was analyzed in samples from traumatic brain injury patients 24 hours after the first dose of drugs or placebo and control subjects. Feature detection, retention time, alignment, annotation, and principal component analysis and statistical analysis were conducted using XCMS-online. The software "mummichog" was used for pathway and network analyses. A two-component principal component analysis revealed clustering of each of the groups, with distinct metabolomics signatures. Several novel pathways with plausible mechanistic involvement in traumatic brain injury were identified. A combination of metabolomics and pathway/network analyses showed that seven glutathione-centered pathways and two networks were enriched in the cerebrospinal fluid of traumatic brain injury patients treated with probenecid and N-acetylcysteine versus placebo-treated patients. Several additional pathways/networks consisting of components that are known substrates of probenecid-inhibitable transporters were also identified, providing additional mechanistic validation. This proof-of-concept neuropharmacometabolomics assessment reveals alterations in known and previously unidentified metabolic pathways and supports therapeutic target engagement of the combination of probenecid and N-acetylcysteine treatment after severe traumatic brain injury in children.

Metabolomic Profiles of Dinophysis acuminata and Dinophysis acuta Using Non-Targeted High-Resolution Mass Spectrometry: Effect of Nutritional Status and Prey.

PubMed

García-Portela, María; Reguera, Beatriz; Sibat, Manoella; Altenburger, Andreas; Rodríguez, Francisco; Hess, Philipp

2018-04-26

Photosynthetic species of the genus Dinophysis are obligate mixotrophs with temporary plastids (kleptoplastids) that are acquired from the ciliate Mesodinium rubrum , which feeds on cryptophytes of the Teleaulax-Plagioselmis-Geminigera clade. A metabolomic study of the three-species food chain Dinophysis-Mesodinium-Teleaulax was carried out using mass spectrometric analysis of extracts of batch-cultured cells of each level of that food chain. The main goal was to compare the metabolomic expression of Galician strains of Dinophysis acuminata and D. acuta that were subjected to different feeding regimes (well-fed and prey-limited) and feeding on two Mesodinium (Spanish and Danish) strains. Both Dinophysis species were able to grow while feeding on both Mesodinium strains, although differences in growth rates were observed. Toxin and metabolomic profiles of the two Dinophysis species were significantly different, and also varied between different feeding regimes and different prey organisms. Furthermore, significantly different metabolomes were expressed by a strain of D. acuminata that was feeding on different strains of the ciliate Mesodinium rubrum . Both species-specific metabolites and those common to D. acuminata and D. acuta were tentatively identified by screening of METLIN and Marine Natural Products Dictionary databases. This first metabolomic study applied to Dinophysis acuminata and D.acuta in culture establishes a basis for the chemical inventory of these species.

Emerging field of metabolomics: big promise for cancer biomarker identification and drug discovery.

PubMed

Patel, Seema; Ahmed, Shadab

2015-03-25

Most cancers are lethal and metabolic alterations are considered a hallmark of this deadly disease. Genomics and proteomics have contributed vastly to understand cancer biology. Still there are missing links as downstream to them molecular divergence occurs. Metabolomics, the omic science that furnishes a dynamic portrait of metabolic profile is expected to bridge these gaps and boost cancer research. Metabolites being the end products are more stable than mRNAs or proteins. Previous studies have shown the efficacy of metabolomics in identifying biomarkers associated with diagnosis, prognosis and treatment of cancer. Metabolites are highly informative about the functional status of the biological system, owing to their proximity to organismal phenotypes. Scores of publications have reported about high-throughput data generation by cutting-edge analytic platforms (mass spectrometry and nuclear magnetic resonance). Further sophisticated statistical softwares (chemometrics) have enabled meaningful information extraction from the metabolomic data. Metabolomics studies have demonstrated the perturbation in glycolysis, tricarboxylic acid cycle, choline and fatty acid metabolism as traits of cancer cells. This review discusses the latest progress in this field, the future trends and the deficiencies to be surmounted for optimally implementation in oncology. The authors scoured through the most recent, high-impact papers archived in Pubmed, ScienceDirect, Wiley and Springer databases to compile this review to pique the interest of researchers towards cancer metabolomics. Copyright © 2014 Elsevier B.V. All rights reserved.

MicroScope: a platform for microbial genome annotation and comparative genomics

PubMed Central

Vallenet, D.; Engelen, S.; Mornico, D.; Cruveiller, S.; Fleury, L.; Lajus, A.; Rouy, Z.; Roche, D.; Salvignol, G.; Scarpelli, C.; Médigue, C.

2009-01-01

The initial outcome of genome sequencing is the creation of long text strings written in a four letter alphabet. The role of in silico sequence analysis is to assist biologists in the act of associating biological knowledge with these sequences, allowing investigators to make inferences and predictions that can be tested experimentally. A wide variety of software is available to the scientific community, and can be used to identify genomic objects, before predicting their biological functions. However, only a limited number of biologically interesting features can be revealed from an isolated sequence. Comparative genomics tools, on the other hand, by bringing together the information contained in numerous genomes simultaneously, allow annotators to make inferences based on the idea that evolution and natural selection are central to the definition of all biological processes. We have developed the MicroScope platform in order to offer a web-based framework for the systematic and efficient revision of microbial genome annotation and comparative analysis (http://www.genoscope.cns.fr/agc/microscope). Starting with the description of the flow chart of the annotation processes implemented in the MicroScope pipeline, and the development of traditional and novel microbial annotation and comparative analysis tools, this article emphasizes the essential role of expert annotation as a complement of automatic annotation. Several examples illustrate the use of implemented tools for the review and curation of annotations of both new and publicly available microbial genomes within MicroScope’s rich integrated genome framework. The platform is used as a viewer in order to browse updated annotation information of available microbial genomes (more than 440 organisms to date), and in the context of new annotation projects (117 bacterial genomes). The human expertise gathered in the MicroScope database (about 280,000 independent annotations) contributes to improve the quality of microbial genome annotation, especially for genomes initially analyzed by automatic procedures alone. Database URLs: http://www.genoscope.cns.fr/agc/mage and http://www.genoscope.cns.fr/agc/microcyc PMID:20157493

MicroScope: a platform for microbial genome annotation and comparative genomics.

PubMed

Vallenet, D; Engelen, S; Mornico, D; Cruveiller, S; Fleury, L; Lajus, A; Rouy, Z; Roche, D; Salvignol, G; Scarpelli, C; Médigue, C

2009-01-01

The initial outcome of genome sequencing is the creation of long text strings written in a four letter alphabet. The role of in silico sequence analysis is to assist biologists in the act of associating biological knowledge with these sequences, allowing investigators to make inferences and predictions that can be tested experimentally. A wide variety of software is available to the scientific community, and can be used to identify genomic objects, before predicting their biological functions. However, only a limited number of biologically interesting features can be revealed from an isolated sequence. Comparative genomics tools, on the other hand, by bringing together the information contained in numerous genomes simultaneously, allow annotators to make inferences based on the idea that evolution and natural selection are central to the definition of all biological processes. We have developed the MicroScope platform in order to offer a web-based framework for the systematic and efficient revision of microbial genome annotation and comparative analysis (http://www.genoscope.cns.fr/agc/microscope). Starting with the description of the flow chart of the annotation processes implemented in the MicroScope pipeline, and the development of traditional and novel microbial annotation and comparative analysis tools, this article emphasizes the essential role of expert annotation as a complement of automatic annotation. Several examples illustrate the use of implemented tools for the review and curation of annotations of both new and publicly available microbial genomes within MicroScope's rich integrated genome framework. The platform is used as a viewer in order to browse updated annotation information of available microbial genomes (more than 440 organisms to date), and in the context of new annotation projects (117 bacterial genomes). The human expertise gathered in the MicroScope database (about 280,000 independent annotations) contributes to improve the quality of microbial genome annotation, especially for genomes initially analyzed by automatic procedures alone.Database URLs: http://www.genoscope.cns.fr/agc/mage and http://www.genoscope.cns.fr/agc/microcyc.

MetMSLine: an automated and fully integrated pipeline for rapid processing of high-resolution LC-MS metabolomic datasets.

PubMed

Edmands, William M B; Barupal, Dinesh K; Scalbert, Augustin

2015-03-01

MetMSLine represents a complete collection of functions in the R programming language as an accessible GUI for biomarker discovery in large-scale liquid-chromatography high-resolution mass spectral datasets from acquisition through to final metabolite identification forming a backend to output from any peak-picking software such as XCMS. MetMSLine automatically creates subdirectories, data tables and relevant figures at the following steps: (i) signal smoothing, normalization, filtration and noise transformation (PreProc.QC.LSC.R); (ii) PCA and automatic outlier removal (Auto.PCA.R); (iii) automatic regression, biomarker selection, hierarchical clustering and cluster ion/artefact identification (Auto.MV.Regress.R); (iv) Biomarker-MS/MS fragmentation spectra matching and fragment/neutral loss annotation (Auto.MS.MS.match.R) and (v) semi-targeted metabolite identification based on a list of theoretical masses obtained from public databases (DBAnnotate.R). All source code and suggested parameters are available in an un-encapsulated layout on http://wmbedmands.github.io/MetMSLine/. Readme files and a synthetic dataset of both X-variables (simulated LC-MS data), Y-variables (simulated continuous variables) and metabolite theoretical masses are also available on our GitHub repository. © The Author 2014. Published by Oxford University Press.

MetMSLine: an automated and fully integrated pipeline for rapid processing of high-resolution LC–MS metabolomic datasets

PubMed Central

Edmands, William M. B.; Barupal, Dinesh K.; Scalbert, Augustin

2015-01-01

Summary: MetMSLine represents a complete collection of functions in the R programming language as an accessible GUI for biomarker discovery in large-scale liquid-chromatography high-resolution mass spectral datasets from acquisition through to final metabolite identification forming a backend to output from any peak-picking software such as XCMS. MetMSLine automatically creates subdirectories, data tables and relevant figures at the following steps: (i) signal smoothing, normalization, filtration and noise transformation (PreProc.QC.LSC.R); (ii) PCA and automatic outlier removal (Auto.PCA.R); (iii) automatic regression, biomarker selection, hierarchical clustering and cluster ion/artefact identification (Auto.MV.Regress.R); (iv) Biomarker—MS/MS fragmentation spectra matching and fragment/neutral loss annotation (Auto.MS.MS.match.R) and (v) semi-targeted metabolite identification based on a list of theoretical masses obtained from public databases (DBAnnotate.R). Availability and implementation: All source code and suggested parameters are available in an un-encapsulated layout on http://wmbedmands.github.io/MetMSLine/. Readme files and a synthetic dataset of both X-variables (simulated LC–MS data), Y-variables (simulated continuous variables) and metabolite theoretical masses are also available on our GitHub repository. Contact: ScalbertA@iarc.fr PMID:25348215

CSE database: extended annotations and new recommendations for ECG software testing.

PubMed

Smíšek, Radovan; Maršánová, Lucie; Němcová, Andrea; Vítek, Martin; Kozumplík, Jiří; Nováková, Marie

2017-08-01

Nowadays, cardiovascular diseases represent the most common cause of death in western countries. Among various examination techniques, electrocardiography (ECG) is still a highly valuable tool used for the diagnosis of many cardiovascular disorders. In order to diagnose a person based on ECG, cardiologists can use automatic diagnostic algorithms. Research in this area is still necessary. In order to compare various algorithms correctly, it is necessary to test them on standard annotated databases, such as the Common Standards for Quantitative Electrocardiography (CSE) database. According to Scopus, the CSE database is the second most cited standard database. There were two main objectives in this work. First, new diagnoses were added to the CSE database, which extended its original annotations. Second, new recommendations for diagnostic software quality estimation were established. The ECG recordings were diagnosed by five new cardiologists independently, and in total, 59 different diagnoses were found. Such a large number of diagnoses is unique, even in terms of standard databases. Based on the cardiologists' diagnoses, a four-round consensus (4R consensus) was established. Such a 4R consensus means a correct final diagnosis, which should ideally be the output of any tested classification software. The accuracy of the cardiologists' diagnoses compared with the 4R consensus was the basis for the establishment of accuracy recommendations. The accuracy was determined in terms of sensitivity = 79.20-86.81%, positive predictive value = 79.10-87.11%, and the Jaccard coefficient = 72.21-81.14%, respectively. Within these ranges, the accuracy of the software is comparable with the accuracy of cardiologists. The accuracy quantification of the correct classification is unique. Diagnostic software developers can objectively evaluate the success of their algorithm and promote its further development. The annotations and recommendations proposed in this work will allow for faster development and testing of classification software. As a result, this might facilitate cardiologists' work and lead to faster diagnoses and earlier treatment.

PR Bibliography, 1994.

ERIC Educational Resources Information Center

Walker, Albert, Ed.

1994-01-01

Based on searches of databases and over 140 periodicals, this annotated bibliography presents a representative collection of books and journal articles related to the knowledge and practice of public relations published in 1993. The annotated bibliography is subdivided into 35 different categories, including business credibility and ethics;…

Gene Ontology annotations at SGD: new data sources and annotation methods

PubMed Central

Hong, Eurie L.; Balakrishnan, Rama; Dong, Qing; Christie, Karen R.; Park, Julie; Binkley, Gail; Costanzo, Maria C.; Dwight, Selina S.; Engel, Stacia R.; Fisk, Dianna G.; Hirschman, Jodi E.; Hitz, Benjamin C.; Krieger, Cynthia J.; Livstone, Michael S.; Miyasato, Stuart R.; Nash, Robert S.; Oughtred, Rose; Skrzypek, Marek S.; Weng, Shuai; Wong, Edith D.; Zhu, Kathy K.; Dolinski, Kara; Botstein, David; Cherry, J. Michael

2008-01-01

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) collects and organizes biological information about the chromosomal features and gene products of the budding yeast Saccharomyces cerevisiae. Although published data from traditional experimental methods are the primary sources of evidence supporting Gene Ontology (GO) annotations for a gene product, high-throughput experiments and computational predictions can also provide valuable insights in the absence of an extensive body of literature. Therefore, GO annotations available at SGD now include high-throughput data as well as computational predictions provided by the GO Annotation Project (GOA UniProt; http://www.ebi.ac.uk/GOA/). Because the annotation method used to assign GO annotations varies by data source, GO resources at SGD have been modified to distinguish data sources and annotation methods. In addition to providing information for genes that have not been experimentally characterized, GO annotations from independent sources can be compared to those made by SGD to help keep the literature-based GO annotations current. PMID:17982175

ESTuber db: an online database for Tuber borchii EST sequences.

PubMed

Lazzari, Barbara; Caprera, Andrea; Cosentino, Cristian; Stella, Alessandra; Milanesi, Luciano; Viotti, Angelo

2007-03-08

The ESTuber database (http://www.itb.cnr.it/estuber) includes 3,271 Tuber borchii expressed sequence tags (EST). The dataset consists of 2,389 sequences from an in-house prepared cDNA library from truffle vegetative hyphae, and 882 sequences downloaded from GenBank and representing four libraries from white truffle mycelia and ascocarps at different developmental stages. An automated pipeline was prepared to process EST sequences using public software integrated by in-house developed Perl scripts. Data were collected in a MySQL database, which can be queried via a php-based web interface. Sequences included in the ESTuber db were clustered and annotated against three databases: the GenBank nr database, the UniProtKB database and a third in-house prepared database of fungi genomic sequences. An algorithm was implemented to infer statistical classification among Gene Ontology categories from the ontology occurrences deduced from the annotation procedure against the UniProtKB database. Ontologies were also deduced from the annotation of more than 130,000 EST sequences from five filamentous fungi, for intra-species comparison purposes. Further analyses were performed on the ESTuber db dataset, including tandem repeats search and comparison of the putative protein dataset inferred from the EST sequences to the PROSITE database for protein patterns identification. All the analyses were performed both on the complete sequence dataset and on the contig consensus sequences generated by the EST assembly procedure. The resulting web site is a resource of data and links related to truffle expressed genes. The Sequence Report and Contig Report pages are the web interface core structures which, together with the Text search utility and the Blast utility, allow easy access to the data stored in the database.

The Plant Ontology: A Tool for Plant Genomics.

PubMed

Cooper, Laurel; Jaiswal, Pankaj

2016-01-01

The use of controlled, structured vocabularies (ontologies) has become a critical tool for scientists in the post-genomic era of massive datasets. Adoption and integration of common vocabularies and annotation practices enables cross-species comparative analyses and increases data sharing and reusability. The Plant Ontology (PO; http://www.plantontology.org/ ) describes plant anatomy, morphology, and the stages of plant development, and offers a database of plant genomics annotations associated to the PO terms. The scope of the PO has grown from its original design covering only rice, maize, and Arabidopsis, and now includes terms to describe all green plants from angiosperms to green algae.This chapter introduces how the PO and other related ontologies are constructed and organized, including languages and software used for ontology development, and provides an overview of the key features. Detailed instructions illustrate how to search and browse the PO database and access the associated annotation data. Users are encouraged to provide input on the ontology through the online term request form and contribute datasets for integration in the PO database.

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.

CHEMICAL STRUCTURE INDEXING OF TOXICITY DATA ON ...

EPA Pesticide Factsheets

Standardized chemical structure annotation of public toxicity databases and information resources is playing an increasingly important role in the 'flattening' and integration of diverse sets of biological activity data on the Internet. This review discusses public initiatives that are accelerating the pace of this transformation, with particular reference to toxicology-related chemical information. Chemical content annotators, structure locator services, large structure/data aggregator web sites, structure browsers, International Union of Pure and Applied Chemistry (IUPAC) International Chemical Identifier (InChI) codes, toxicity data models and public chemical/biological activity profiling initiatives are all playing a role in overcoming barriers to the integration of toxicity data, and are bringing researchers closer to the reality of a mineable chemical Semantic Web. An example of this integration of data is provided by the collaboration among researchers involved with the Distributed Structure-Searchable Toxicity (DSSTox) project, the Carcinogenic Potency Project, projects at the National Cancer Institute and the PubChem database. Standardizing chemical structure annotation of public toxicity databases

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.

DNAtraffic--a new database for systems biology of DNA dynamics during the cell life.

PubMed

Kuchta, Krzysztof; Barszcz, Daniela; Grzesiuk, Elzbieta; Pomorski, Pawel; Krwawicz, Joanna

2012-01-01

DNAtraffic (http://dnatraffic.ibb.waw.pl/) is dedicated to be a unique comprehensive and richly annotated database of genome dynamics during the cell life. It contains extensive data on the nomenclature, ontology, structure and function of proteins related to the DNA integrity mechanisms such as chromatin remodeling, histone modifications, DNA repair and damage response from eight organisms: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Escherichia coli and Arabidopsis thaliana. DNAtraffic contains comprehensive information on the diseases related to the assembled human proteins. DNAtraffic is richly annotated in the systemic information on the nomenclature, chemistry and structure of DNA damage and their sources, including environmental agents or commonly used drugs targeting nucleic acids and/or proteins involved in the maintenance of genome stability. One of the DNAtraffic database aim is to create the first platform of the combinatorial complexity of DNA network analysis. Database includes illustrations of pathways, damage, proteins and drugs. Since DNAtraffic is designed to cover a broad spectrum of scientific disciplines, it has to be extensively linked to numerous external data sources. Our database represents the result of the manual annotation work aimed at making the DNAtraffic much more useful for a wide range of systems biology applications.

DNAtraffic—a new database for systems biology of DNA dynamics during the cell life

PubMed Central

Kuchta, Krzysztof; Barszcz, Daniela; Grzesiuk, Elzbieta; Pomorski, Pawel; Krwawicz, Joanna

2012-01-01

DNAtraffic (http://dnatraffic.ibb.waw.pl/) is dedicated to be a unique comprehensive and richly annotated database of genome dynamics during the cell life. It contains extensive data on the nomenclature, ontology, structure and function of proteins related to the DNA integrity mechanisms such as chromatin remodeling, histone modifications, DNA repair and damage response from eight organisms: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Escherichia coli and Arabidopsis thaliana. DNAtraffic contains comprehensive information on the diseases related to the assembled human proteins. DNAtraffic is richly annotated in the systemic information on the nomenclature, chemistry and structure of DNA damage and their sources, including environmental agents or commonly used drugs targeting nucleic acids and/or proteins involved in the maintenance of genome stability. One of the DNAtraffic database aim is to create the first platform of the combinatorial complexity of DNA network analysis. Database includes illustrations of pathways, damage, proteins and drugs. Since DNAtraffic is designed to cover a broad spectrum of scientific disciplines, it has to be extensively linked to numerous external data sources. Our database represents the result of the manual annotation work aimed at making the DNAtraffic much more useful for a wide range of systems biology applications. PMID:22110027

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.

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.

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

MODBASE, a database of annotated comparative protein structure models

PubMed Central

Pieper, Ursula; Eswar, Narayanan; Stuart, Ashley C.; Ilyin, Valentin A.; Sali, Andrej

2002-01-01

MODBASE (http://guitar.rockefeller.edu/modbase) is a relational database of annotated comparative protein structure models for all available protein sequences matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on PSI-BLAST, IMPALA and MODELLER. MODBASE uses the MySQL relational database management system for flexible and efficient querying, and the MODVIEW Netscape plugin for viewing and manipulating multiple sequences and structures. It is updated regularly to reflect the growth of the protein sequence and structure databases, as well as improvements in the software for calculating the models. For ease of access, MODBASE is organized into different datasets. The largest dataset contains models for domains in 304 517 out of 539 171 unique protein sequences in the complete TrEMBL database (23 March 2001); only models based on significant alignments (PSI-BLAST E-value < 10–4) and models assessed to have the correct fold are included. Other datasets include models for target selection and structure-based annotation by the New York Structural Genomics Research Consortium, models for prediction of genes in the Drosophila melanogaster genome, models for structure determination of several ribosomal particles and models calculated by the MODWEB comparative modeling web server. PMID:11752309

Current and future trends in marine image annotation software

NASA Astrophysics Data System (ADS)

Gomes-Pereira, Jose Nuno; Auger, Vincent; Beisiegel, Kolja; Benjamin, Robert; Bergmann, Melanie; Bowden, David; Buhl-Mortensen, Pal; De Leo, Fabio C.; Dionísio, Gisela; Durden, Jennifer M.; Edwards, Luke; Friedman, Ariell; Greinert, Jens; Jacobsen-Stout, Nancy; Lerner, Steve; Leslie, Murray; Nattkemper, Tim W.; Sameoto, Jessica A.; Schoening, Timm; Schouten, Ronald; Seager, James; Singh, Hanumant; Soubigou, Olivier; Tojeira, Inês; van den Beld, Inge; Dias, Frederico; Tempera, Fernando; Santos, Ricardo S.

2016-12-01

Given the need to describe, analyze and index large quantities of marine imagery data for exploration and monitoring activities, a range of specialized image annotation tools have been developed worldwide. Image annotation - the process of transposing objects or events represented in a video or still image to the semantic level, may involve human interactions and computer-assisted solutions. Marine image annotation software (MIAS) have enabled over 500 publications to date. We review the functioning, application trends and developments, by comparing general and advanced features of 23 different tools utilized in underwater image analysis. MIAS requiring human input are basically a graphical user interface, with a video player or image browser that recognizes a specific time code or image code, allowing to log events in a time-stamped (and/or geo-referenced) manner. MIAS differ from similar software by the capability of integrating data associated to video collection, the most simple being the position coordinates of the video recording platform. MIAS have three main characteristics: annotating events in real time, posteriorly to annotation and interact with a database. These range from simple annotation interfaces, to full onboard data management systems, with a variety of toolboxes. Advanced packages allow to input and display data from multiple sensors or multiple annotators via intranet or internet. Posterior human-mediated annotation often include tools for data display and image analysis, e.g. length, area, image segmentation, point count; and in a few cases the possibility of browsing and editing previous dive logs or to analyze the annotations. The interaction with a database allows the automatic integration of annotations from different surveys, repeated annotation and collaborative annotation of shared datasets, browsing and querying of data. Progress in the field of automated annotation is mostly in post processing, for stable platforms or still images. Integration into available MIAS is currently limited to semi-automated processes of pixel recognition through computer-vision modules that compile expert-based knowledge. Important topics aiding the choice of a specific software are outlined, the ideal software is discussed and future trends are presented.

Plant genome and transcriptome annotations: from misconceptions to simple solutions

PubMed Central

Bolger, Marie E; Arsova, Borjana; Usadel, Björn

2018-01-01

Abstract Next-generation sequencing has triggered an explosion of available genomic and transcriptomic resources in the plant sciences. Although genome and transcriptome sequencing has become orders of magnitudes cheaper and more efficient, often the functional annotation process is lagging behind. This might be hampered by the lack of a comprehensive enumeration of simple-to-use tools available to the plant researcher. In this comprehensive review, we present (i) typical ontologies to be used in the plant sciences, (ii) useful databases and resources used for functional annotation, (iii) what to expect from an annotated plant genome, (iv) an automated annotation pipeline and (v) a recipe and reference chart outlining typical steps used to annotate plant genomes/transcriptomes using publicly available resources. PMID:28062412

Metabolome analysis of biosynthetic mutants reveals a diversity of metabolic changes and allows identification of a large number of new compounds in Arabidopsis.

PubMed

Böttcher, Christoph; von Roepenack-Lahaye, Edda; Schmidt, Jürgen; Schmotz, Constanze; Neumann, Steffen; Scheel, Dierk; Clemens, Stephan

2008-08-01

Metabolomics is facing a major challenge: the lack of knowledge about metabolites present in a given biological system. Thus, large-scale discovery of metabolites is considered an essential step toward a better understanding of plant metabolism. We show here that the application of a metabolomics approach generating structural information for the analysis of Arabidopsis (Arabidopsis thaliana) mutants allows the efficient cataloging of metabolites. Fifty-six percent of the features that showed significant differences in abundance between seeds of wild-type, transparent testa4, and transparent testa5 plants could be annotated. Seventy-five compounds were structurally characterized, 21 of which could be identified. About 40 compounds had not been known from Arabidopsis before. Also, the high-resolution analysis revealed an unanticipated expansion of metabolic conversions upstream of biosynthetic blocks. Deficiency in chalcone synthase results in the increased seed-specific biosynthesis of a range of phenolic choline esters. Similarly, a lack of chalcone isomerase activity leads to the accumulation of various naringenin chalcone derivatives. Furthermore, our data provide insight into the connection between p-coumaroyl-coenzyme A-dependent pathways. Lack of flavonoid biosynthesis results in elevated synthesis not only of p-coumarate-derived choline esters but also of sinapate-derived metabolites. However, sinapoylcholine is not the only accumulating end product. Instead, we observed specific and sophisticated changes in the complex pattern of sinapate derivatives.

DaMold: A data-mining platform for variant annotation and visualization in molecular diagnostics research.

PubMed

Pandey, Ram Vinay; Pabinger, Stephan; Kriegner, Albert; Weinhäusel, Andreas

2017-07-01

Next-generation sequencing (NGS) has become a powerful and efficient tool for routine mutation screening in clinical research. As each NGS test yields hundreds of variants, the current challenge is to meaningfully interpret the data and select potential candidates. Analyzing each variant while manually investigating several relevant databases to collect specific information is a cumbersome and time-consuming process, and it requires expertise and familiarity with these databases. Thus, a tool that can seamlessly annotate variants with clinically relevant databases under one common interface would be of great help for variant annotation, cross-referencing, and visualization. This tool would allow variants to be processed in an automated and high-throughput manner and facilitate the investigation of variants in several genome browsers. Several analysis tools are available for raw sequencing-read processing and variant identification, but an automated variant filtering, annotation, cross-referencing, and visualization tool is still lacking. To fulfill these requirements, we developed DaMold, a Web-based, user-friendly tool that can filter and annotate variants and can access and compile information from 37 resources. It is easy to use, provides flexible input options, and accepts variants from NGS and Sanger sequencing as well as hotspots in VCF and BED formats. DaMold is available as an online application at http://damold.platomics.com/index.html, and as a Docker container and virtual machine at https://sourceforge.net/projects/damold/. © 2017 Wiley Periodicals, Inc.

Metabolomics of Small Numbers of Cells: Metabolomic Profiling of 100, 1000, and 10000 Human Breast Cancer Cells.

PubMed

Luo, Xian; Li, Liang

2017-11-07

In cellular metabolomics, it is desirable to carry out metabolomic profiling using a small number of cells in order to save time and cost. In some applications (e.g., working with circulating tumor cells in blood), only a limited number of cells are available for analysis. In this report, we describe a method based on high-performance chemical isotope labeling (CIL) nanoflow liquid chromatography mass spectrometry (nanoLC-MS) for high-coverage metabolomic analysis of small numbers of cells (i.e., ≤10000 cells). As an example, 12 C-/ 13 C-dansyl labeling of the metabolites in lysates of 100, 1000, and 10000 MCF-7 breast cancer cells was carried out using a new labeling protocol tailored to handle small amounts of metabolites. Chemical-vapor-assisted ionization in a captivespray interface was optimized for improving metabolite ionization and increasing robustness of nanoLC-MS. Compared to microflow LC-MS, the nanoflow system provided much improved metabolite detectability with a significantly reduced sample amount required for analysis. Experimental duplicate analyses of biological triplicates resulted in the detection of 1620 ± 148, 2091 ± 89 and 2402 ± 80 (n = 6) peak pairs or metabolites in the amine/phenol submetabolome from the 12 C-/ 13 C-dansyl labeled lysates of 100, 1000, and 10000 cells, respectively. About 63-69% of these peak pairs could be either identified using dansyl labeled standard library or mass-matched to chemical structures in human metabolome databases. We envisage the routine applications of this method for high-coverage quantitative cellular metabolomics using a starting material of 10000 cells. Even for analyzing 100 or 1000 cells, although the metabolomic coverage is reduced from the maximal coverage, this method can still detect thousands of metabolites, allowing the analysis of a large fraction of the metabolome and focused analysis of the detectable metabolites.

Determining conserved metabolic biomarkers from a million database queries.

PubMed

Kurczy, Michael E; Ivanisevic, Julijana; Johnson, Caroline H; Uritboonthai, Winnie; Hoang, Linh; Fang, Mingliang; Hicks, Matthew; Aldebot, Anthony; Rinehart, Duane; Mellander, Lisa J; Tautenhahn, Ralf; Patti, Gary J; Spilker, Mary E; Benton, H Paul; Siuzdak, Gary

2015-12-01

Metabolite databases provide a unique window into metabolome research allowing the most commonly searched biomarkers to be catalogued. Omic scale metabolite profiling, or metabolomics, is finding increased utility in biomarker discovery largely driven by improvements in analytical technologies and the concurrent developments in bioinformatics. However, the successful translation of biomarkers into clinical or biologically relevant indicators is limited. With the aim of improving the discovery of translatable metabolite biomarkers, we present search analytics for over one million METLIN metabolite database queries. The most common metabolites found in METLIN were cross-correlated against XCMS Online, the widely used cloud-based data processing and pathway analysis platform. Analysis of the METLIN and XCMS common metabolite data has two primary implications: these metabolites, might indicate a conserved metabolic response to stressors and, this data may be used to gauge the relative uniqueness of potential biomarkers. METLIN can be accessed by logging on to: https://metlin.scripps.edu siuzdak@scripps.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Standardized description of scientific evidence using the Evidence Ontology (ECO)

PubMed Central

Chibucos, Marcus C.; Mungall, Christopher J.; Balakrishnan, Rama; Christie, Karen R.; Huntley, Rachael P.; White, Owen; Blake, Judith A.; Lewis, Suzanna E.; Giglio, Michelle

2014-01-01

The Evidence Ontology (ECO) is a structured, controlled vocabulary for capturing evidence in biological research. ECO includes diverse terms for categorizing evidence that supports annotation assertions including experimental types, computational methods, author statements and curator inferences. Using ECO, annotation assertions can be distinguished according to the evidence they are based on such as those made by curators versus those automatically computed or those made via high-throughput data review versus single test experiments. Originally created for capturing evidence associated with Gene Ontology annotations, ECO is now used in other capacities by many additional annotation resources including UniProt, Mouse Genome Informatics, Saccharomyces Genome Database, PomBase, the Protein Information Resource and others. Information on the development and use of ECO can be found at http://evidenceontology.org. The ontology is freely available under Creative Commons license (CC BY-SA 3.0), and can be downloaded in both Open Biological Ontologies and Web Ontology Language formats at http://code.google.com/p/evidenceontology. Also at this site is a tracker for user submission of term requests and questions. ECO remains under active development in response to user-requested terms and in collaborations with other ontologies and database resources. Database URL: Evidence Ontology Web site: http://evidenceontology.org PMID:25052702

The Aspergillus Genome Database: multispecies curation and incorporation of RNA-Seq data to improve structural gene annotations.

PubMed

Cerqueira, Gustavo C; Arnaud, Martha B; Inglis, Diane O; Skrzypek, Marek S; Binkley, Gail; Simison, Matt; Miyasato, Stuart R; Binkley, Jonathan; Orvis, Joshua; Shah, Prachi; Wymore, Farrell; Sherlock, Gavin; Wortman, Jennifer R

2014-01-01

The Aspergillus Genome Database (AspGD; http://www.aspgd.org) is a freely available web-based resource that was designed for Aspergillus researchers and is also a valuable source of information for the entire fungal research community. In addition to being a repository and central point of access to genome, transcriptome and polymorphism data, AspGD hosts a comprehensive comparative genomics toolbox that facilitates the exploration of precomputed orthologs among the 20 currently available Aspergillus genomes. AspGD curators perform gene product annotation based on review of the literature for four key Aspergillus species: Aspergillus nidulans, Aspergillus oryzae, Aspergillus fumigatus and Aspergillus niger. We have iteratively improved the structural annotation of Aspergillus genomes through the analysis of publicly available transcription data, mostly expressed sequenced tags, as described in a previous NAR Database article (Arnaud et al. 2012). In this update, we report substantive structural annotation improvements for A. nidulans, A. oryzae and A. fumigatus genomes based on recently available RNA-Seq data. Over 26 000 loci were updated across these species; although those primarily comprise the addition and extension of untranslated regions (UTRs), the new analysis also enabled over 1000 modifications affecting the coding sequence of genes in each target genome.

Identification of Functionally Related Enzymes by Learning-to-Rank Methods.

PubMed

Stock, Michiel; Fober, Thomas; Hüllermeier, Eyke; Glinca, Serghei; Klebe, Gerhard; Pahikkala, Tapio; Airola, Antti; De Baets, Bernard; Waegeman, Willem

2014-01-01

Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually departs from some notion of similarity, comparing two enzymes by looking for correspondences in their sequences, structures or surfaces. For a given query, the search operation results in a ranking of the enzymes in the database, from very similar to dissimilar enzymes, while information about the biological function of annotated database enzymes is ignored. In this work, we show that rankings of that kind can be substantially improved by applying kernel-based learning algorithms. This approach enables the detection of statistical dependencies between similarities of the active cleft and the biological function of annotated enzymes. This is in contrast to search-based approaches, which do not take annotated training data into account. Similarity measures based on the active cleft are known to outperform sequence-based or structure-based measures under certain conditions. We consider the Enzyme Commission (EC) classification hierarchy for obtaining annotated enzymes during the training phase. The results of a set of sizeable experiments indicate a consistent and significant improvement for a set of similarity measures that exploit information about small cavities in the surface of enzymes.

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.

Design and implementation of a database for Brucella melitensis genome annotation.

PubMed

De Hertogh, Benoît; Lahlimi, Leïla; Lambert, Christophe; Letesson, Jean-Jacques; Depiereux, Eric

2008-03-18

The genome sequences of three Brucella biovars and of some species close to Brucella sp. have become available, leading to new relationship analysis. Moreover, the automatic genome annotation of the pathogenic bacteria Brucella melitensis has been manually corrected by a consortium of experts, leading to 899 modifications of start sites predictions among the 3198 open reading frames (ORFs) examined. This new annotation, coupled with the results of automatic annotation tools of the complete genome sequences of the B. melitensis genome (including BLASTs to 9 genomes close to Brucella), provides numerous data sets related to predicted functions, biochemical properties and phylogenic comparisons. To made these results available, alphaPAGe, a functional auto-updatable database of the corrected sequence genome of B. melitensis, has been built, using the entity-relationship (ER) approach and a multi-purpose database structure. A friendly graphical user interface has been designed, and users can carry out different kinds of information by three levels of queries: (1) the basic search use the classical keywords or sequence identifiers; (2) the original advanced search engine allows to combine (by using logical operators) numerous criteria: (a) keywords (textual comparison) related to the pCDS's function, family domains and cellular localization; (b) physico-chemical characteristics (numerical comparison) such as isoelectric point or molecular weight and structural criteria such as the nucleic length or the number of transmembrane helix (TMH); (c) similarity scores with Escherichia coli and 10 species phylogenetically close to B. melitensis; (3) complex queries can be performed by using a SQL field, which allows all queries respecting the database's structure. The database is publicly available through a Web server at the following url: http://www.fundp.ac.be/urbm/bioinfo/aPAGe.

De novo transcriptomic analysis and development of EST-SSRs for Sorbus pohuashanensis (Hance) Hedl.

PubMed Central

Guan, Xuelian; Fu, Qiang; Zhang, Ze; Hu, Zenghui; Zheng, Jian; Lu, Yizeng; Li, Wei

2017-01-01

Sorbus pohuashanensis is a native tree species of northern China that is used for a variety of ecological purposes. The species is often grown as an ornamental landscape tree because of its beautiful form, silver flowers in early summer, attractive pinnate leaves in summer, and red leaves and fruits in autumn. However, development and further utilization of the species are hindered by the lack of comprehensive genetic information, which impedes research into its genetics and molecular biology. Recent advances in de novo transcriptome sequencing (RNA-seq) technology have provided an effective means to obtain genomic information from non-model species. Here, we applied RNA-seq for sequencing S. pohuashanensis leaves and obtained a total of 137,506 clean reads. After assembly, 96,213 unigenes with an average length of 770 bp were obtained. We found that 64.5% of the unigenes could be annotated using bioinformatics tools to analyze gene function and alignment with the NCBI database. Overall, 59,089 unigenes were annotated using the Nr database(non-redundant protein database), 35,225 unigenes were annotated using the GO (Gene Ontology categories) database, and 33,168 unigenes were annotated using COG (Cluster of Orthologous Groups). Analysis of the unigenes using the KEGG (Kyoto Encyclopedia of Genes and Genomes) database indicated that 13,953 unigenes were involved in 322 metabolic pathways. Finally, simple sequence repeat (SSR) site detection identified 6,604 unigenes that included EST-SSRs and a total of 7,473 EST-SSRs in the unigene sequences. Fifteen polymorphic SSRs were screened and found to be of use for future genetic research. These unigene sequences will provide important genetic resources for genetic improvement and investigation of biochemical processes in S. pohuashanensis. PMID:28614366

Annotated checklist and database for vascular plants of the Jemez Mountains

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

Foxx, T. S.; Pierce, L.; Tierney, G. D.

Studies done in the last 40 years have provided information to construct a checklist of the Jemez Mountains. The present database and checklist builds on the basic list compiled by Teralene Foxx and Gail Tierney in the early 1980s. The checklist is annotated with taxonomic information, geographic and biological information, economic uses, wildlife cover, revegetation potential, and ethnographic uses. There are nearly 1000 species that have been noted for the Jemez Mountains. This list is cross-referenced with the US Department of Agriculture Natural Resource Conservation Service PLANTS database species names and acronyms. All information will soon be available on amore » Web Page.« less

DeTEXT: A Database for Evaluating Text Extraction from Biomedical Literature Figures

PubMed Central

Yin, Xu-Cheng; Yang, Chun; Pei, Wei-Yi; Man, Haixia; Zhang, Jun; Learned-Miller, Erik; Yu, Hong

2015-01-01

Hundreds of millions of figures are available in biomedical literature, representing important biomedical experimental evidence. Since text is a rich source of information in figures, automatically extracting such text may assist in the task of mining figure information. A high-quality ground truth standard can greatly facilitate the development of an automated system. This article describes DeTEXT: A database for evaluating text extraction from biomedical literature figures. It is the first publicly available, human-annotated, high quality, and large-scale figure-text dataset with 288 full-text articles, 500 biomedical figures, and 9308 text regions. This article describes how figures were selected from open-access full-text biomedical articles and how annotation guidelines and annotation tools were developed. We also discuss the inter-annotator agreement and the reliability of the annotations. We summarize the statistics of the DeTEXT data and make available evaluation protocols for DeTEXT. Finally we lay out challenges we observed in the automated detection and recognition of figure text and discuss research directions in this area. DeTEXT is publicly available for downloading at http://prir.ustb.edu.cn/DeTEXT/. PMID:25951377

ExpTreeDB: web-based query and visualization of manually annotated gene expression profiling experiments of human and mouse from GEO.

PubMed

Ni, Ming; Ye, Fuqiang; Zhu, Juanjuan; Li, Zongwei; Yang, Shuai; Yang, Bite; Han, Lu; Wu, Yongge; Chen, Ying; Li, Fei; Wang, Shengqi; Bo, Xiaochen

2014-12-01

Numerous public microarray datasets are valuable resources for the scientific communities. Several online tools have made great steps to use these data by querying related datasets with users' own gene signatures or expression profiles. However, dataset annotation and result exhibition still need to be improved. ExpTreeDB is a database that allows for queries on human and mouse microarray experiments from Gene Expression Omnibus with gene signatures or profiles. Compared with similar applications, ExpTreeDB pays more attention to dataset annotations and result visualization. We introduced a multiple-level annotation system to depict and organize original experiments. For example, a tamoxifen-treated cell line experiment is hierarchically annotated as 'agent→drug→estrogen receptor antagonist→tamoxifen'. Consequently, retrieved results are exhibited by an interactive tree-structured graphics, which provide an overview for related experiments and might enlighten users on key items of interest. The database is freely available at http://biotech.bmi.ac.cn/ExpTreeDB. Web site is implemented in Perl, PHP, R, MySQL and Apache. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

LS-SNP: large-scale annotation of coding non-synonymous SNPs based on multiple information sources.

PubMed

Karchin, Rachel; Diekhans, Mark; Kelly, Libusha; Thomas, Daryl J; Pieper, Ursula; Eswar, Narayanan; Haussler, David; Sali, Andrej

2005-06-15

The NCBI dbSNP database lists over 9 million single nucleotide polymorphisms (SNPs) in the human genome, but currently contains limited annotation information. SNPs that result in amino acid residue changes (nsSNPs) are of critical importance in variation between individuals, including disease and drug sensitivity. We have developed LS-SNP, a genomic scale software pipeline to annotate nsSNPs. LS-SNP comprehensively maps nsSNPs onto protein sequences, functional pathways and comparative protein structure models, and predicts positions where nsSNPs destabilize proteins, interfere with the formation of domain-domain interfaces, have an effect on protein-ligand binding or severely impact human health. It currently annotates 28,043 validated SNPs that produce amino acid residue substitutions in human proteins from the SwissProt/TrEMBL database. Annotations can be viewed via a web interface either in the context of a genomic region or by selecting sets of SNPs, genes, proteins or pathways. These results are useful for identifying candidate functional SNPs within a gene, haplotype or pathway and in probing molecular mechanisms responsible for functional impacts of nsSNPs. http://www.salilab.org/LS-SNP CONTACT: rachelk@salilab.org http://salilab.org/LS-SNP/supp-info.pdf.

Systematic analysis of the polyphenol metabolome using the Phenol-Explorer database.

PubMed

Rothwell, Joseph A; Urpi-Sarda, Mireia; Boto-Ordoñez, Maria; Llorach, Rafael; Farran-Codina, Andreu; Barupal, Dinesh Kumar; Neveu, Vanessa; Manach, Claudine; Andres-Lacueva, Cristina; Scalbert, Augustin

2016-01-01

The Phenol-Explorer web database details 383 polyphenol metabolites identified in human and animal biofluids from 221 publications. Here, we exploit these data to characterize and visualize the polyphenol metabolome, the set of all metabolites derived from phenolic food components. Qualitative and quantitative data on 383 polyphenol metabolites as described in 424 human and animal intervention studies were systematically analyzed. Of these metabolites, 301 were identified without prior enzymatic hydrolysis of biofluids, and included glucuronide and sulfate esters, glycosides, aglycones, and O-methyl ethers. Around one-third of these compounds are also known as food constituents and corresponded to polyphenols absorbed without further metabolism. Many ring-cleavage metabolites formed by gut microbiota were noted, mostly derived from hydroxycinnamates, flavanols, and flavonols. Median maximum plasma concentrations (C(max)) of all human metabolites were 0.09 and 0.32 μM when consumed from foods or dietary supplements, respectively. Median time to reach maximum plasma concentration in humans (T(max)) was 2.18 h. These data show the complexity of the polyphenol metabolome and the need to take into account biotransformations to understand in vivo bioactivities and the role of dietary polyphenols in health and disease. © 2015 The Authors. Molecular Nutrition & Food Research published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Cerebrospinal fluid metabolomic profiling in tuberculous and viral meningitis: Screening potential markers for differential diagnosis.

PubMed

Li, Zihui; Du, Boping; Li, Jing; Zhang, Jinli; Zheng, Xiaojing; Jia, Hongyan; Xing, Aiying; Sun, Qi; Liu, Fei; Zhang, Zongde

2017-03-01

Tuberculous meningitis (TBM) is the most severe and frequent form of central nervous system tuberculosis. The current lack of efficient diagnostic tests makes it difficult to differentiate TBM from other common types of meningitis, especially viral meningitis (VM). Metabolomics is an important tool to identify disease-specific biomarkers. However, little metabolomic information is available on adult TBM. We used 1 H nuclear magnetic resonance-based metabolomics to investigate the metabolic features of the CSF from 18 TBM and 20 VM patients. Principal component analysis and orthogonal signal correction-partial least squares-discriminant analysis (OSC-PLS-DA) were applied to analyze profiling data. Metabolites were identified using the Human Metabolome Database and pathway analysis was performed with MetaboAnalyst 3.0. The OSC-PLS-DA model could distinguish TBM from VM with high reliability. A total of 25 key metabolites that contributed to their discrimination were identified, including some, such as betaine and cyclohexane, rarely reported before in TBM. Pathway analysis indicated that amino acid and energy metabolism was significantly different in the CSF of TBM compared with VM. Twenty-five key metabolites identified in our study may be potential biomarkers for TBM differential diagnosis and are worthy of further investigation. Copyright © 2017 Elsevier B.V. All rights reserved.

Shotgun metabolomic approach based on mass spectrometry for hepatic mitochondria of mice under arsenic exposure.

PubMed

García-Sevillano, M A; García-Barrera, T; Navarro, F; Montero-Lobato, Z; Gómez-Ariza, J L

2015-04-01

Mass spectrometry (MS)-based toxicometabolomics requires analytical approaches for obtaining unbiased metabolic profiles. The present work explores the general application of direct infusion MS using a high mass resolution analyzer (a hybrid systems triple quadrupole-time-of-flight) and a complementary gas chromatography-MS analysis to mitochondria extracts from mouse hepatic cells, emphasizing on mitochondria isolation from hepatic cells with a commercial kit, sample treatment after cell lysis, comprehensive metabolomic analysis and pattern recognition from metabolic profiles. Finally, the metabolomic platform was successfully checked on a case-study based on the exposure experiment of mice Mus musculus to inorganic arsenic during 12 days. Endogenous metabolites alterations were recognized by partial least squares-discriminant analysis. Subsequently, metabolites were identified by combining MS/MS analysis and metabolomics databases. This work reports for the first time the effects of As-exposure on hepatic mitochondria metabolic pathways based on MS, and reveals disturbances in Krebs cycle, β-oxidation pathway, amino acids degradation and perturbations in creatine levels. This non-target analysis provides extensive metabolic information from mitochondrial organelle, which could be applied to toxicology, pharmacology and clinical studies.

Managing and Querying Image Annotation and Markup in XML.

PubMed

Wang, Fusheng; Pan, Tony; Sharma, Ashish; Saltz, Joel

2010-01-01

Proprietary approaches for representing annotations and image markup are serious barriers for researchers to share image data and knowledge. The Annotation and Image Markup (AIM) project is developing a standard based information model for image annotation and markup in health care and clinical trial environments. The complex hierarchical structures of AIM data model pose new challenges for managing such data in terms of performance and support of complex queries. In this paper, we present our work on managing AIM data through a native XML approach, and supporting complex image and annotation queries through native extension of XQuery language. Through integration with xService, AIM databases can now be conveniently shared through caGrid.

Managing and Querying Image Annotation and Markup in XML

PubMed Central

Wang, Fusheng; Pan, Tony; Sharma, Ashish; Saltz, Joel

2010-01-01

Proprietary approaches for representing annotations and image markup are serious barriers for researchers to share image data and knowledge. The Annotation and Image Markup (AIM) project is developing a standard based information model for image annotation and markup in health care and clinical trial environments. The complex hierarchical structures of AIM data model pose new challenges for managing such data in terms of performance and support of complex queries. In this paper, we present our work on managing AIM data through a native XML approach, and supporting complex image and annotation queries through native extension of XQuery language. Through integration with xService, AIM databases can now be conveniently shared through caGrid. PMID:21218167

The genome and phenome of the green alga Chloroidium sp. UTEX 3007 reveal adaptive traits for desert acclimatization

PubMed Central

Nelson, David R; Khraiwesh, Basel; Fu, Weiqi; Alseekh, Saleh; Jaiswal, Ashish; Chaiboonchoe, Amphun; Hazzouri, Khaled M; O’Connor, Matthew J; Butterfoss, Glenn L; Drou, Nizar; Rowe, Jillian D; Harb, Jamil; Fernie, Alisdair R; Gunsalus, Kristin C; Salehi-Ashtiani, Kourosh

2017-01-01

To investigate the phenomic and genomic traits that allow green algae to survive in deserts, we characterized a ubiquitous species, Chloroidium sp. UTEX 3007, which we isolated from multiple locations in the United Arab Emirates (UAE). Metabolomic analyses of Chloroidium sp. UTEX 3007 indicated that the alga accumulates a broad range of carbon sources, including several desiccation tolerance-promoting sugars and unusually large stores of palmitate. Growth assays revealed capacities to grow in salinities from zero to 60 g/L and to grow heterotrophically on >40 distinct carbon sources. Assembly and annotation of genomic reads yielded a 52.5 Mbp genome with 8153 functionally annotated genes. Comparison with other sequenced green algae revealed unique protein families involved in osmotic stress tolerance and saccharide metabolism that support phenomic studies. Our results reveal the robust and flexible biology utilized by a green alga to successfully inhabit a desert coastline. DOI: http://dx.doi.org/10.7554/eLife.25783.001 PMID:28623667

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

Chado controller: advanced annotation management with a community annotation system.

PubMed

Guignon, Valentin; Droc, Gaëtan; Alaux, Michael; Baurens, Franc-Christophe; Garsmeur, Olivier; Poiron, Claire; Carver, Tim; Rouard, Mathieu; Bocs, Stéphanie

2012-04-01

We developed a controller that is compliant with the Chado database schema, GBrowse and genome annotation-editing tools such as Artemis and Apollo. It enables the management of public and private data, monitors manual annotation (with controlled vocabularies, structural and functional annotation controls) and stores versions of annotation for all modified features. The Chado controller uses PostgreSQL and Perl. The Chado Controller package is available for download at http://www.gnpannot.org/content/chado-controller and runs on any Unix-like operating system, and documentation is available at http://www.gnpannot.org/content/chado-controller-doc The system can be tested using the GNPAnnot Sandbox at http://www.gnpannot.org/content/gnpannot-sandbox-form valentin.guignon@cirad.fr; stephanie.sidibe-bocs@cirad.fr Supplementary data are available at Bioinformatics online.

Genome and proteome annotation: organization, interpretation and integration

PubMed Central

Reeves, Gabrielle A.; Talavera, David; Thornton, Janet M.

2008-01-01

Recent years have seen a huge increase in the generation of genomic and proteomic data. This has been due to improvements in current biological methodologies, the development of new experimental techniques and the use of computers as support tools. All these raw data are useless if they cannot be properly analysed, annotated, stored and displayed. Consequently, a vast number of resources have been created to present the data to the wider community. Annotation tools and databases provide the means to disseminate these data and to comprehend their biological importance. This review examines the various aspects of annotation: type, methodology and availability. Moreover, it puts a special interest on novel annotation fields, such as that of phenotypes, and highlights the recent efforts focused on the integrating annotations. PMID:19019817

GelScape: a web-based server for interactively annotating, manipulating, comparing and archiving 1D and 2D gel images.

PubMed

Young, Nelson; Chang, Zhan; Wishart, David S

2004-04-12

GelScape is a web-based tool that permits facile, interactive annotation, comparison, manipulation and storage of protein gel images. It uses Java applet-servlet technology to allow rapid, remote image handling and image processing in a platform-independent manner. It supports many of the features found in commercial, stand-alone gel analysis software including spot annotation, spot integration, gel warping, image resizing, HTML image mapping, image overlaying as well as the storage of gel image and gel annotation data in compliance with Federated Gel Database requirements.

An annotation system for 3D fluid flow visualization

NASA Technical Reports Server (NTRS)

Loughlin, Maria M.; Hughes, John F.

1995-01-01

Annotation is a key activity of data analysis. However, current systems for data analysis focus almost exclusively on visualization. We propose a system which integrates annotations into a visualization system. Annotations are embedded in 3D data space, using the Post-it metaphor. This embedding allows contextual-based information storage and retrieval, and facilitates information sharing in collaborative environments. We provide a traditional database filter and a Magic Lens filter to create specialized views of the data. The system has been customized for fluid flow applications, with features which allow users to store parameters of visualization tools and sketch 3D volumes.

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

Abstracting/Annotating. ERIC Processing Manual, Section VI.

ERIC Educational Resources Information Center

Brandhorst, Ted, Ed.

Rules and guidelines are provided for the preparation of abstracts and annotations for documents and journal articles entering the ERIC database. Various types of abstracts are defined, including the Informative, Indicative, and mixed Informative-Indicative. Advice is given on how to select the abstract type appropriate for the particular…

Specialized microbial databases for inductive exploration of microbial genome sequences

PubMed Central

Fang, Gang; Ho, Christine; Qiu, Yaowu; Cubas, Virginie; Yu, Zhou; Cabau, Cédric; Cheung, Frankie; Moszer, Ivan; Danchin, Antoine

2005-01-01

Background The enormous amount of genome sequence data asks for user-oriented databases to manage sequences and annotations. Queries must include search tools permitting function identification through exploration of related objects. Methods The GenoList package for collecting and mining microbial genome databases has been rewritten using MySQL as the database management system. Functions that were not available in MySQL, such as nested subquery, have been implemented. Results Inductive reasoning in the study of genomes starts from "islands of knowledge", centered around genes with some known background. With this concept of "neighborhood" in mind, a modified version of the GenoList structure has been used for organizing sequence data from prokaryotic genomes of particular interest in China. GenoChore , a set of 17 specialized end-user-oriented microbial databases (including one instance of Microsporidia, Encephalitozoon cuniculi, a member of Eukarya) has been made publicly available. These databases allow the user to browse genome sequence and annotation data using standard queries. In addition they provide a weekly update of searches against the world-wide protein sequences data libraries, allowing one to monitor annotation updates on genes of interest. Finally, they allow users to search for patterns in DNA or protein sequences, taking into account a clustering of genes into formal operons, as well as providing extra facilities to query sequences using predefined sequence patterns. Conclusion This growing set of specialized microbial databases organize data created by the first Chinese bacterial genome programs (ThermaList, Thermoanaerobacter tencongensis, LeptoList, with two different genomes of Leptospira interrogans and SepiList, Staphylococcus epidermidis) associated to related organisms for comparison. PMID:15698474

MS2Analyzer: A Software for Small Molecule Substructure Annotations from Accurate Tandem Mass Spectra

PubMed Central

2015-01-01

Systematic analysis and interpretation of the large number of tandem mass spectra (MS/MS) obtained in metabolomics experiments is a bottleneck in discovery-driven research. MS/MS mass spectral libraries are small compared to all known small molecule structures and are often not freely available. MS2Analyzer was therefore developed to enable user-defined searches of thousands of spectra for mass spectral features such as neutral losses, m/z differences, and product and precursor ions from MS/MS spectra in MSP/MGF files. The software is freely available at http://fiehnlab.ucdavis.edu/projects/MS2Analyzer/. As the reference query set, 147 literature-reported neutral losses and their corresponding substructures were collected. This set was tested for accuracy of linking neutral loss analysis to substructure annotations using 19 329 accurate mass tandem mass spectra of structurally known compounds from the NIST11 MS/MS library. Validation studies showed that 92.1 ± 6.4% of 13 typical neutral losses such as acetylations, cysteine conjugates, or glycosylations are correct annotating the associated substructures, while the absence of mass spectra features does not necessarily imply the absence of such substructures. Use of this tool has been successfully demonstrated for complex lipids in microalgae. PMID:25263576

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.

Brassica database (BRAD) version 2.0: integrating and mining Brassicaceae species genomic resources.

PubMed

Wang, Xiaobo; Wu, Jian; Liang, Jianli; Cheng, Feng; Wang, Xiaowu

2015-01-01

The Brassica database (BRAD) was built initially to assist users apply Brassica rapa and Arabidopsis thaliana genomic data efficiently to their research. However, many Brassicaceae genomes have been sequenced and released after its construction. These genomes are rich resources for comparative genomics, gene annotation and functional evolutionary studies of Brassica crops. Therefore, we have updated BRAD to version 2.0 (V2.0). In BRAD V2.0, 11 more Brassicaceae genomes have been integrated into the database, namely those of Arabidopsis lyrata, Aethionema arabicum, Brassica oleracea, Brassica napus, Camelina sativa, Capsella rubella, Leavenworthia alabamica, Sisymbrium irio and three extremophiles Schrenkiella parvula, Thellungiella halophila and Thellungiella salsuginea. BRAD V2.0 provides plots of syntenic genomic fragments between pairs of Brassicaceae species, from the level of chromosomes to genomic blocks. The Generic Synteny Browser (GBrowse_syn), a module of the Genome Browser (GBrowse), is used to show syntenic relationships between multiple genomes. Search functions for retrieving syntenic and non-syntenic orthologs, as well as their annotation and sequences are also provided. Furthermore, genome and annotation information have been imported into GBrowse so that all functional elements can be visualized in one frame. We plan to continually update BRAD by integrating more Brassicaceae genomes into the database. Database URL: http://brassicadb.org/brad/. © The Author(s) 2015. Published by Oxford University Press.

Reptilian Transcriptomes v2.0: An Extensive Resource for Sauropsida Genomics and Transcriptomics

PubMed Central

Tzika, Athanasia C.; Ullate-Agote, Asier; Grbic, Djordje; Milinkovitch, Michel C.

2015-01-01

Despite the availability of deep-sequencing techniques, genomic and transcriptomic data remain unevenly distributed across phylogenetic groups. For example, reptiles are poorly represented in sequence databases, hindering functional evolutionary and developmental studies in these lineages substantially more diverse than mammals. In addition, different studies use different assembly and annotation protocols, inhibiting meaningful comparisons. Here, we present the “Reptilian Transcriptomes Database 2.0,” which provides extensive annotation of transcriptomes and genomes from species covering the major reptilian lineages. To this end, we sequenced normalized complementary DNA libraries of multiple adult tissues and various embryonic stages of the leopard gecko and the corn snake and gathered published reptilian sequence data sets from representatives of the four extant orders of reptiles: Squamata (snakes and lizards), the tuatara, crocodiles, and turtles. The LANE runner 2.0 software was implemented to annotate all assemblies within a single integrated pipeline. We show that this approach increases the annotation completeness of the assembled transcriptomes/genomes. We then built large concatenated protein alignments of single-copy genes and inferred phylogenetic trees that support the positions of turtles and the tuatara as sister groups of Archosauria and Squamata, respectively. The Reptilian Transcriptomes Database 2.0 resource will be updated to include selected new data sets as they become available, thus making it a reference for differential expression studies, comparative genomics and transcriptomics, linkage mapping, molecular ecology, and phylogenomic analyses involving reptiles. The database is available at www.reptilian-transcriptomes.org and can be enquired using a wwwblast server installed at the University of Geneva. PMID:26133641

Semantically Interoperable XML Data

PubMed Central

Vergara-Niedermayr, Cristobal; Wang, Fusheng; Pan, Tony; Kurc, Tahsin; Saltz, Joel

2013-01-01

XML is ubiquitously used as an information exchange platform for web-based applications in healthcare, life sciences, and many other domains. Proliferating XML data are now managed through latest native XML database technologies. XML data sources conforming to common XML schemas could be shared and integrated with syntactic interoperability. Semantic interoperability can be achieved through semantic annotations of data models using common data elements linked to concepts from ontologies. In this paper, we present a framework and software system to support the development of semantic interoperable XML based data sources that can be shared through a Grid infrastructure. We also present our work on supporting semantic validated XML data through semantic annotations for XML Schema, semantic validation and semantic authoring of XML data. We demonstrate the use of the system for a biomedical database of medical image annotations and markups. PMID:25298789

RefSeq microbial genomes database: new representation and annotation strategy.

PubMed

Tatusova, Tatiana; Ciufo, Stacy; Fedorov, Boris; O'Neill, Kathleen; Tolstoy, Igor

2014-01-01

The source of the microbial genomic sequences in the RefSeq collection is the set of primary sequence records submitted to the International Nucleotide Sequence Database public archives. These can be accessed through the Entrez search and retrieval system at http://www.ncbi.nlm.nih.gov/genome. Next-generation sequencing has enabled researchers to perform genomic sequencing at rates that were unimaginable in the past. Microbial genomes can now be sequenced in a matter of hours, which has led to a significant increase in the number of assembled genomes deposited in the public archives. This huge increase in DNA sequence data presents new challenges for the annotation, analysis and visualization bioinformatics tools. New strategies have been developed for the annotation and representation of reference genomes and sequence variations derived from population studies and clinical outbreaks.

Database constraints applied to metabolic pathway reconstruction tools.

PubMed

Vilaplana, Jordi; Solsona, Francesc; Teixido, Ivan; Usié, Anabel; Karathia, Hiren; Alves, Rui; Mateo, Jordi

2014-01-01

Our group developed two biological applications, Biblio-MetReS and Homol-MetReS, accessing the same database of organisms with annotated genes. Biblio-MetReS is a data-mining application that facilitates the reconstruction of molecular networks based on automated text-mining analysis of published scientific literature. Homol-MetReS allows functional (re)annotation of proteomes, to properly identify both the individual proteins involved in the process(es) of interest and their function. It also enables the sets of proteins involved in the process(es) in different organisms to be compared directly. The efficiency of these biological applications is directly related to the design of the shared database. We classified and analyzed the different kinds of access to the database. Based on this study, we tried to adjust and tune the configurable parameters of the database server to reach the best performance of the communication data link to/from the database system. Different database technologies were analyzed. We started the study with a public relational SQL database, MySQL. Then, the same database was implemented by a MapReduce-based database named HBase. The results indicated that the standard configuration of MySQL gives an acceptable performance for low or medium size databases. Nevertheless, tuning database parameters can greatly improve the performance and lead to very competitive runtimes.

Biases in the Experimental Annotations of Protein Function and Their Effect on Our Understanding of Protein Function Space

PubMed Central

Schnoes, Alexandra M.; Ream, David C.; Thorman, Alexander W.; Babbitt, Patricia C.; Friedberg, Iddo

2013-01-01

The ongoing functional annotation of proteins relies upon the work of curators to capture experimental findings from scientific literature and apply them to protein sequence and structure data. However, with the increasing use of high-throughput experimental assays, a small number of experimental studies dominate the functional protein annotations collected in databases. Here, we investigate just how prevalent is the “few articles - many proteins” phenomenon. We examine the experimentally validated annotation of proteins provided by several groups in the GO Consortium, and show that the distribution of proteins per published study is exponential, with 0.14% of articles providing the source of annotations for 25% of the proteins in the UniProt-GOA compilation. Since each of the dominant articles describes the use of an assay that can find only one function or a small group of functions, this leads to substantial biases in what we know about the function of many proteins. Mass-spectrometry, microscopy and RNAi experiments dominate high throughput experiments. Consequently, the functional information derived from these experiments is mostly of the subcellular location of proteins, and of the participation of proteins in embryonic developmental pathways. For some organisms, the information provided by different studies overlap by a large amount. We also show that the information provided by high throughput experiments is less specific than those provided by low throughput experiments. Given the experimental techniques available, certain biases in protein function annotation due to high-throughput experiments are unavoidable. Knowing that these biases exist and understanding their characteristics and extent is important for database curators, developers of function annotation programs, and anyone who uses protein function annotation data to plan experiments. PMID:23737737

Evaluation of relational and NoSQL database architectures to manage genomic annotations.

PubMed

Schulz, Wade L; Nelson, Brent G; Felker, Donn K; Durant, Thomas J S; Torres, Richard

2016-12-01

While the adoption of next generation sequencing has rapidly expanded, the informatics infrastructure used to manage the data generated by this technology has not kept pace. Historically, relational databases have provided much of the framework for data storage and retrieval. Newer technologies based on NoSQL architectures may provide significant advantages in storage and query efficiency, thereby reducing the cost of data management. But their relative advantage when applied to biomedical data sets, such as genetic data, has not been characterized. To this end, we compared the storage, indexing, and query efficiency of a common relational database (MySQL), a document-oriented NoSQL database (MongoDB), and a relational database with NoSQL support (PostgreSQL). When used to store genomic annotations from the dbSNP database, we found the NoSQL architectures to outperform traditional, relational models for speed of data storage, indexing, and query retrieval in nearly every operation. These findings strongly support the use of novel database technologies to improve the efficiency of data management within the biological sciences. Copyright © 2016 Elsevier Inc. All rights reserved.

'RetinoGenetics': a comprehensive mutation database for genes related to inherited retinal degeneration.

PubMed

Ran, Xia; Cai, Wei-Jun; Huang, Xiu-Feng; Liu, Qi; Lu, Fan; Qu, Jia; Wu, Jinyu; Jin, Zi-Bing

2014-01-01

Inherited retinal degeneration (IRD), a leading cause of human blindness worldwide, is exceptionally heterogeneous with clinical heterogeneity and genetic variety. During the past decades, tremendous efforts have been made to explore the complex heterogeneity, and massive mutations have been identified in different genes underlying IRD with the significant advancement of sequencing technology. In this study, we developed a comprehensive database, 'RetinoGenetics', which contains informative knowledge about all known IRD-related genes and mutations for IRD. 'RetinoGenetics' currently contains 4270 mutations in 186 genes, with detailed information associated with 164 phenotypes from 934 publications and various types of functional annotations. Then extensive annotations were performed to each gene using various resources, including Gene Ontology, KEGG pathways, protein-protein interaction, mutational annotations and gene-disease network. Furthermore, by using the search functions, convenient browsing ways and intuitive graphical displays, 'RetinoGenetics' could serve as a valuable resource for unveiling the genetic basis of IRD. Taken together, 'RetinoGenetics' is an integrative, informative and updatable resource for IRD-related genetic predispositions. Database URL: http://www.retinogenetics.org/. © The Author(s) 2014. Published by Oxford University Press.

DSSTOX STRUCTURE-SEARCHABLE PUBLIC TOXICITY DATABASE NETWORK: CURRENT PROGRESS AND NEW INITIATIVES TO IMPROVE CHEMO-BIOINFORMATICS CAPABILITIES

EPA Science Inventory

The EPA DSSTox website (http://www/epa.gov/nheerl/dsstox) publishes standardized, structure-annotated toxicity databases, covering a broad range of toxicity disciplines. Each DSSTox database features documentation written in collaboration with the source authors and toxicity expe...

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...

Chado Controller: advanced annotation management with a community annotation system

PubMed Central

Guignon, Valentin; Droc, Gaëtan; Alaux, Michael; Baurens, Franc-Christophe; Garsmeur, Olivier; Poiron, Claire; Carver, Tim; Rouard, Mathieu; Bocs, Stéphanie

2012-01-01

Summary: We developed a controller that is compliant with the Chado database schema, GBrowse and genome annotation-editing tools such as Artemis and Apollo. It enables the management of public and private data, monitors manual annotation (with controlled vocabularies, structural and functional annotation controls) and stores versions of annotation for all modified features. The Chado controller uses PostgreSQL and Perl. Availability: The Chado Controller package is available for download at http://www.gnpannot.org/content/chado-controller and runs on any Unix-like operating system, and documentation is available at http://www.gnpannot.org/content/chado-controller-doc The system can be tested using the GNPAnnot Sandbox at http://www.gnpannot.org/content/gnpannot-sandbox-form Contact: valentin.guignon@cirad.fr; stephanie.sidibe-bocs@cirad.fr Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22285827

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

MetaboLyzer: A Novel Statistical Workflow for Analyzing Post-Processed LC/MS Metabolomics Data

PubMed Central

Mak, Tytus D.; Laiakis, Evagelia C.; Goudarzi, Maryam; Fornace, Albert J.

2014-01-01

Metabolomics, the global study of small molecules in a particular system, has in the last few years risen to become a primary –omics platform for the study of metabolic processes. With the ever-increasing pool of quantitative data yielded from metabolomic research, specialized methods and tools with which to analyze and extract meaningful conclusions from these data are becoming more and more crucial. Furthermore, the depth of knowledge and expertise required to undertake a metabolomics oriented study is a daunting obstacle to investigators new to the field. As such, we have created a new statistical analysis workflow, MetaboLyzer, which aims to both simplify analysis for investigators new to metabolomics, as well as provide experienced investigators the flexibility to conduct sophisticated analysis. MetaboLyzer’s workflow is specifically tailored to the unique characteristics and idiosyncrasies of postprocessed liquid chromatography/mass spectrometry (LC/MS) based metabolomic datasets. It utilizes a wide gamut of statistical tests, procedures, and methodologies that belong to classical biostatistics, as well as several novel statistical techniques that we have developed specifically for metabolomics data. Furthermore, MetaboLyzer conducts rapid putative ion identification and putative biologically relevant analysis via incorporation of four major small molecule databases: KEGG, HMDB, Lipid Maps, and BioCyc. MetaboLyzer incorporates these aspects into a comprehensive workflow that outputs easy to understand statistically significant and potentially biologically relevant information in the form of heatmaps, volcano plots, 3D visualization plots, correlation maps, and metabolic pathway hit histograms. For demonstration purposes, a urine metabolomics data set from a previously reported radiobiology study in which samples were collected from mice exposed to gamma radiation was analyzed. MetaboLyzer was able to identify 243 statistically significant ions out of a total of 1942. Numerous putative metabolites and pathways were found to be biologically significant from the putative ion identification workflow. PMID:24266674

Untargeted mass spectrometry-based metabolomic profiling of pleural effusions: fatty acids as novel cancer biomarkers for malignant pleural effusions.

PubMed

Lam, Ching-Wan; Law, Chun-Yiu

2014-09-05

Untargeted mass spectrometry-based metabolomic profiling is a powerful analytical method used for broad-spectrum identification and quantification of metabolites in biofluids in human health and disease states. In this study, we exploit metabolomic profiling for cancer biomarker discovery for diagnosis of malignant pleural effusions. We envisage the result will be clinically useful since currently there are no cancer biomarkers that are accurate enough for the diagnosis of malignant pleural effusions. Metabolomes of 32 malignant pleural effusions from lung cancer patients and 18 benign effusions from patients with pulmonary tuberculosis were analyzed using reversed-phase liquid chromatography tandem mass spectrometry (LC-MS/MS) using AB SCIEX TripleTOF 5600. MS spectra were analyzed using XCMS, PeakView, and LipidView. Metabolome-Wide Association Study (MWAS) was performed by Receiver Operating Characteristic Curve Explorer and Tester (ROCCET). Insignificant markers were filtered out using a metabolome-wide significance level (MWSL) with p-value < 2 × 10(-5) for t test. Only compounds in Human Metabolome Database (HMDB) will be used as cancer biomarkers. ROCCET analysis of ESI positive and negative MS spectra revealed free fatty acid (FFA) 18:1 (oleic acid) had the largest area-under-ROC of 0.96 (95% CI = 0.87-1.00) in malignant pleural effusions. Using a ratio of FFA 18:1-to-ceramide (d18:1/16:0), the area-under-ROC was further increased to 0.99 (95% CI = 0.91-1.00) with sensitivity 93.8% and specificity 100.0%. Using untargeted metabolomic profiling, the diagnostic cancer biomarker with the largest area-under-ROC can be determined objectively. This lipogenic phenotype could be explained by overexpression of fatty acid synthase (FASN) in cancer cells. The diagnostic performance of FFA 18:1-to-ceramide (d18:1/16:0) ratio supports its use for diagnosis of malignant pleural effusions.

A guide to best practices for Gene Ontology (GO) manual annotation

PubMed Central

Balakrishnan, Rama; Harris, Midori A.; Huntley, Rachael; Van Auken, Kimberly; Cherry, J. Michael

2013-01-01

The Gene Ontology Consortium (GOC) is a community-based bioinformatics project that classifies gene product function through the use of structured controlled vocabularies. A fundamental application of the Gene Ontology (GO) is in the creation of gene product annotations, evidence-based associations between GO definitions and experimental or sequence-based analysis. Currently, the GOC disseminates 126 million annotations covering >374 000 species including all the kingdoms of life. This number includes two classes of GO annotations: those created manually by experienced biocurators reviewing the literature or by examination of biological data (1.1 million annotations covering 2226 species) and those generated computationally via automated methods. As manual annotations are often used to propagate functional predictions between related proteins within and between genomes, it is critical to provide accurate consistent manual annotations. Toward this goal, we present here the conventions defined by the GOC for the creation of manual annotation. This guide represents the best practices for manual annotation as established by the GOC project over the past 12 years. We hope this guide will encourage research communities to annotate gene products of their interest to enhance the corpus of GO annotations available to all. Database URL: http://www.geneontology.org PMID:23842463

PAZAR: a framework for collection and dissemination of cis-regulatory sequence annotation

PubMed Central

Portales-Casamar, Elodie; Kirov, Stefan; Lim, Jonathan; Lithwick, Stuart; Swanson, Magdalena I; Ticoll, Amy; Snoddy, Jay; Wasserman, Wyeth W

2007-01-01

PAZAR is an open-access and open-source database of transcription factor and regulatory sequence annotation with associated web interface and programming tools for data submission and extraction. Curated boutique data collections can be maintained and disseminated through the unified schema of the mall-like PAZAR repository. The Pleiades Promoter Project collection of brain-linked regulatory sequences is introduced to demonstrate the depth of annotation possible within PAZAR. PAZAR, located at , is open for business. PMID:17916232

PAZAR: a framework for collection and dissemination of cis-regulatory sequence annotation.

PubMed

Portales-Casamar, Elodie; Kirov, Stefan; Lim, Jonathan; Lithwick, Stuart; Swanson, Magdalena I; Ticoll, Amy; Snoddy, Jay; Wasserman, Wyeth W

2007-01-01

PAZAR is an open-access and open-source database of transcription factor and regulatory sequence annotation with associated web interface and programming tools for data submission and extraction. Curated boutique data collections can be maintained and disseminated through the unified schema of the mall-like PAZAR repository. The Pleiades Promoter Project collection of brain-linked regulatory sequences is introduced to demonstrate the depth of annotation possible within PAZAR. PAZAR, located at http://www.pazar.info, is open for business.

Content-Based Management of Image Databases in the Internet Age

ERIC Educational Resources Information Center

Kleban, James Theodore

2010-01-01

The Internet Age has seen the emergence of richly annotated image data collections numbering in the billions of items. This work makes contributions in three primary areas which aid the management of this data: image representation, efficient retrieval, and annotation based on content and metadata. The contributions are as follows. First,…

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.

pGenN, a gene normalization tool for plant genes and proteins in scientific literature.

PubMed

Ding, Ruoyao; Arighi, Cecilia N; Lee, Jung-Youn; Wu, Cathy H; Vijay-Shanker, K

2015-01-01

Automatically detecting gene/protein names in the literature and connecting them to databases records, also known as gene normalization, provides a means to structure the information buried in free-text literature. Gene normalization is critical for improving the coverage of annotation in the databases, and is an essential component of many text mining systems and database curation pipelines. In this manuscript, we describe a gene normalization system specifically tailored for plant species, called pGenN (pivot-based Gene Normalization). The system consists of three steps: dictionary-based gene mention detection, species assignment, and intra species normalization. We have developed new heuristics to improve each of these phases. We evaluated the performance of pGenN on an in-house expertly annotated corpus consisting of 104 plant relevant abstracts. Our system achieved an F-value of 88.9% (Precision 90.9% and Recall 87.2%) on this corpus, outperforming state-of-art systems presented in BioCreative III. We have processed over 440,000 plant-related Medline abstracts using pGenN. The gene normalization results are stored in a local database for direct query from the pGenN web interface (proteininformationresource.org/pgenn/). The annotated literature corpus is also publicly available through the PIR text mining portal (proteininformationresource.org/iprolink/).

GDR (Genome Database for Rosaceae): integrated web-database for Rosaceae genomics and genetics data

PubMed Central

Jung, Sook; Staton, Margaret; Lee, Taein; Blenda, Anna; Svancara, Randall; Abbott, Albert; Main, Dorrie

2008-01-01

The Genome Database for Rosaceae (GDR) is a central repository of curated and integrated genetics and genomics data of Rosaceae, an economically important family which includes apple, cherry, peach, pear, raspberry, rose and strawberry. GDR contains annotated databases of all publicly available Rosaceae ESTs, the genetically anchored peach physical map, Rosaceae genetic maps and comprehensively annotated markers and traits. The ESTs are assembled to produce unigene sets of each genus and the entire Rosaceae. Other annotations include putative function, microsatellites, open reading frames, single nucleotide polymorphisms, gene ontology terms and anchored map position where applicable. Most of the published Rosaceae genetic maps can be viewed and compared through CMap, the comparative map viewer. The peach physical map can be viewed using WebFPC/WebChrom, and also through our integrated GDR map viewer, which serves as a portal to the combined genetic, transcriptome and physical mapping information. ESTs, BACs, markers and traits can be queried by various categories and the search result sites are linked to the mapping visualization tools. GDR also provides online analysis tools such as a batch BLAST/FASTA server for the GDR datasets, a sequence assembly server and microsatellite and primer detection tools. GDR is available at http://www.rosaceae.org. PMID:17932055

Bioinformatics for spermatogenesis: annotation of male reproduction based on proteomics

PubMed Central

Zhou, Tao; Zhou, Zuo-Min; Guo, Xue-Jiang

2013-01-01

Proteomics strategies have been widely used in the field of male reproduction, both in basic and clinical research. Bioinformatics methods are indispensable in proteomics-based studies and are used for data presentation, database construction and functional annotation. In the present review, we focus on the functional annotation of gene lists obtained through qualitative or quantitative methods, summarizing the common and male reproduction specialized proteomics databases. We introduce several integrated tools used to find the hidden biological significance from the data obtained. We further describe in detail the information on male reproduction derived from Gene Ontology analyses, pathway analyses and biomedical analyses. We provide an overview of bioinformatics annotations in spermatogenesis, from gene function to biological function and from biological function to clinical application. On the basis of recently published proteomics studies and associated data, we show that bioinformatics methods help us to discover drug targets for sperm motility and to scan for cancer-testis genes. In addition, we summarize the online resources relevant to male reproduction research for the exploration of the regulation of spermatogenesis. PMID:23852026

UniProtKB/Swiss-Prot, the Manually Annotated Section of the UniProt KnowledgeBase: How to Use the Entry View.

PubMed

Boutet, Emmanuel; Lieberherr, Damien; Tognolli, Michael; Schneider, Michel; Bansal, Parit; Bridge, Alan J; Poux, Sylvain; Bougueleret, Lydie; Xenarios, Ioannis

2016-01-01

The Universal Protein Resource (UniProt, http://www.uniprot.org ) consortium is an initiative of the SIB Swiss Institute of Bioinformatics (SIB), the European Bioinformatics Institute (EBI) and the Protein Information Resource (PIR) to provide the scientific community with a central resource for protein sequences and functional information. The UniProt consortium maintains the UniProt KnowledgeBase (UniProtKB), updated every 4 weeks, and several supplementary databases including the UniProt Reference Clusters (UniRef) and the UniProt Archive (UniParc).The Swiss-Prot section of the UniProt KnowledgeBase (UniProtKB/Swiss-Prot) contains publicly available expertly manually annotated protein sequences obtained from a broad spectrum of organisms. Plant protein entries are produced in the frame of the Plant Proteome Annotation Program (PPAP), with an emphasis on characterized proteins of Arabidopsis thaliana and Oryza sativa. High level annotations provided by UniProtKB/Swiss-Prot are widely used to predict annotation of newly available proteins through automatic pipelines.The purpose of this chapter is to present a guided tour of a UniProtKB/Swiss-Prot entry. We will also present some of the tools and databases that are linked to each entry.

NCBI-compliant genome submissions: tips and tricks to save time and money.

PubMed

Pirovano, Walter; Boetzer, Marten; Derks, Martijn F L; Smit, Sandra

2017-03-01

Genome sequences nowadays play a central role in molecular biology and bioinformatics. These sequences are shared with the scientific community through sequence databases. The sequence repositories of the International Nucleotide Sequence Database Collaboration (INSDC, comprising GenBank, ENA and DDBJ) are the largest in the world. Preparing an annotated sequence in such a way that it will be accepted by the database is challenging because many validation criteria apply. In our opinion, it is an undesirable situation that researchers who want to submit their sequence need either a lot of experience or help from partners to get the job done. To save valuable time and money, we list a number of recommendations for people who want to submit an annotated genome to a sequence database, as well as for tool developers, who could help to ease the process. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Benchmarking database performance for genomic data.

PubMed

Khushi, Matloob

2015-06-01

Genomic regions represent features such as gene annotations, transcription factor binding sites and epigenetic modifications. Performing various genomic operations such as identifying overlapping/non-overlapping regions or nearest gene annotations are common research needs. The data can be saved in a database system for easy management, however, there is no comprehensive database built-in algorithm at present to identify overlapping regions. Therefore I have developed a novel region-mapping (RegMap) SQL-based algorithm to perform genomic operations and have benchmarked the performance of different databases. Benchmarking identified that PostgreSQL extracts overlapping regions much faster than MySQL. Insertion and data uploads in PostgreSQL were also better, although general searching capability of both databases was almost equivalent. In addition, using the algorithm pair-wise, overlaps of >1000 datasets of transcription factor binding sites and histone marks, collected from previous publications, were reported and it was found that HNF4G significantly co-locates with cohesin subunit STAG1 (SA1).Inc. © 2015 Wiley Periodicals, Inc.

Database citation in full text biomedical articles.

PubMed

Kafkas, Şenay; Kim, Jee-Hyub; McEntyre, Johanna R

2013-01-01

Molecular biology and literature databases represent essential infrastructure for life science research. Effective integration of these data resources requires that there are structured cross-references at the level of individual articles and biological records. Here, we describe the current patterns of how database entries are cited in research articles, based on analysis of the full text Open Access articles available from Europe PMC. Focusing on citation of entries in the European Nucleotide Archive (ENA), UniProt and Protein Data Bank, Europe (PDBe), we demonstrate that text mining doubles the number of structured annotations of database record citations supplied in journal articles by publishers. Many thousands of new literature-database relationships are found by text mining, since these relationships are also not present in the set of articles cited by database records. We recommend that structured annotation of database records in articles is extended to other databases, such as ArrayExpress and Pfam, entries from which are also cited widely in the literature. The very high precision and high-throughput of this text-mining pipeline makes this activity possible both accurately and at low cost, which will allow the development of new integrated data services.

Database Citation in Full Text Biomedical Articles

PubMed Central

Kafkas, Şenay; Kim, Jee-Hyub; McEntyre, Johanna R.

2013-01-01

Molecular biology and literature databases represent essential infrastructure for life science research. Effective integration of these data resources requires that there are structured cross-references at the level of individual articles and biological records. Here, we describe the current patterns of how database entries are cited in research articles, based on analysis of the full text Open Access articles available from Europe PMC. Focusing on citation of entries in the European Nucleotide Archive (ENA), UniProt and Protein Data Bank, Europe (PDBe), we demonstrate that text mining doubles the number of structured annotations of database record citations supplied in journal articles by publishers. Many thousands of new literature-database relationships are found by text mining, since these relationships are also not present in the set of articles cited by database records. We recommend that structured annotation of database records in articles is extended to other databases, such as ArrayExpress and Pfam, entries from which are also cited widely in the literature. The very high precision and high-throughput of this text-mining pipeline makes this activity possible both accurately and at low cost, which will allow the development of new integrated data services. PMID:23734176

PIGD: a database for intronless genes in the Poaceae.

PubMed

Yan, Hanwei; Jiang, Cuiping; Li, Xiaoyu; Sheng, Lei; Dong, Qing; Peng, Xiaojian; Li, Qian; Zhao, Yang; Jiang, Haiyang; Cheng, Beijiu

2014-10-01

Intronless genes are a feature of prokaryotes; however, they are widespread and unequally distributed among eukaryotes and represent an important resource to study the evolution of gene architecture. Although many databases on exons and introns exist, there is currently no cohesive database that collects intronless genes in plants into a single database. In this study, we present the Poaceae Intronless Genes Database (PIGD), a user-friendly web interface to explore information on intronless genes from different plants. Five Poaceae species, Sorghum bicolor, Zea mays, Setaria italica, Panicum virgatum and Brachypodium distachyon, are included in the current release of PIGD. Gene annotations and sequence data were collected and integrated from different databases. The primary focus of this study was to provide gene descriptions and gene product records. In addition, functional annotations, subcellular localization prediction and taxonomic distribution are reported. PIGD allows users to readily browse, search and download data. BLAST and comparative analyses are also provided through this online database, which is available at http://pigd.ahau.edu.cn/. PIGD provides a solid platform for the collection, integration and analysis of intronless genes in the Poaceae. As such, this database will be useful for subsequent bio-computational analysis in comparative genomics and evolutionary studies.

KEGG orthology-based annotation of the predicted proteome of Acropora digitifera: ZoophyteBase - an open access and searchable database of a coral genome

PubMed Central

2013-01-01

Background Contemporary coral reef research has firmly established that a genomic approach is urgently needed to better understand the effects of anthropogenic environmental stress and global climate change on coral holobiont interactions. Here we present KEGG orthology-based annotation of the complete genome sequence of the scleractinian coral Acropora digitifera and provide the first comprehensive view of the genome of a reef-building coral by applying advanced bioinformatics. Description Sequences from the KEGG database of protein function were used to construct hidden Markov models. These models were used to search the predicted proteome of A. digitifera to establish complete genomic annotation. The annotated dataset is published in ZoophyteBase, an open access format with different options for searching the data. A particularly useful feature is the ability to use a Google-like search engine that links query words to protein attributes. We present features of the annotation that underpin the molecular structure of key processes of coral physiology that include (1) regulatory proteins of symbiosis, (2) planula and early developmental proteins, (3) neural messengers, receptors and sensory proteins, (4) calcification and Ca2+-signalling proteins, (5) plant-derived proteins, (6) proteins of nitrogen metabolism, (7) DNA repair proteins, (8) stress response proteins, (9) antioxidant and redox-protective proteins, (10) proteins of cellular apoptosis, (11) microbial symbioses and pathogenicity proteins, (12) proteins of viral pathogenicity, (13) toxins and venom, (14) proteins of the chemical defensome and (15) coral epigenetics. Conclusions We advocate that providing annotation in an open-access searchable database available to the public domain will give an unprecedented foundation to interrogate the fundamental molecular structure and interactions of coral symbiosis and allow critical questions to be addressed at the genomic level based on combined aspects of evolutionary, developmental, metabolic, and environmental perspectives. PMID:23889801

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

Alga-PrAS (Algal Protein Annotation Suite): A Database of Comprehensive Annotation in Algal Proteomes

PubMed Central

Kurotani, Atsushi; Yamada, Yutaka

2017-01-01

Algae are smaller organisms than land plants and offer clear advantages in research over terrestrial species in terms of rapid production, short generation time and varied commercial applications. Thus, studies investigating the practical development of effective algal production are important and will improve our understanding of both aquatic and terrestrial plants. In this study we estimated multiple physicochemical and secondary structural properties of protein sequences, the predicted presence of post-translational modification (PTM) sites, and subcellular localization using a total of 510,123 protein sequences from the proteomes of 31 algal and three plant species. Algal species were broadly selected from green and red algae, glaucophytes, oomycetes, diatoms and other microalgal groups. The results were deposited in the Algal Protein Annotation Suite database (Alga-PrAS; http://alga-pras.riken.jp/), which can be freely accessed online. PMID:28069893

STINGRAY: system for integrated genomic resources and analysis.

PubMed

Wagner, Glauber; Jardim, Rodrigo; Tschoeke, Diogo A; Loureiro, Daniel R; Ocaña, Kary A C S; Ribeiro, Antonio C B; Emmel, Vanessa E; Probst, Christian M; Pitaluga, André N; Grisard, Edmundo C; Cavalcanti, Maria C; Campos, Maria L M; Mattoso, Marta; Dávila, Alberto M R

2014-03-07

The STINGRAY system has been conceived to ease the tasks of integrating, analyzing, annotating and presenting genomic and expression data from Sanger and Next Generation Sequencing (NGS) platforms. STINGRAY includes: (a) a complete and integrated workflow (more than 20 bioinformatics tools) ranging from functional annotation to phylogeny; (b) a MySQL database schema, suitable for data integration and user access control; and (c) a user-friendly graphical web-based interface that makes the system intuitive, facilitating the tasks of data analysis and annotation. STINGRAY showed to be an easy to use and complete system for analyzing sequencing data. While both Sanger and NGS platforms are supported, the system could be faster using Sanger data, since the large NGS datasets could potentially slow down the MySQL database usage. STINGRAY is available at http://stingray.biowebdb.org and the open source code at http://sourceforge.net/projects/stingray-biowebdb/.

STINGRAY: system for integrated genomic resources and analysis

PubMed Central

2014-01-01

Background The STINGRAY system has been conceived to ease the tasks of integrating, analyzing, annotating and presenting genomic and expression data from Sanger and Next Generation Sequencing (NGS) platforms. Findings STINGRAY includes: (a) a complete and integrated workflow (more than 20 bioinformatics tools) ranging from functional annotation to phylogeny; (b) a MySQL database schema, suitable for data integration and user access control; and (c) a user-friendly graphical web-based interface that makes the system intuitive, facilitating the tasks of data analysis and annotation. Conclusion STINGRAY showed to be an easy to use and complete system for analyzing sequencing data. While both Sanger and NGS platforms are supported, the system could be faster using Sanger data, since the large NGS datasets could potentially slow down the MySQL database usage. STINGRAY is available at http://stingray.biowebdb.org and the open source code at http://sourceforge.net/projects/stingray-biowebdb/. PMID:24606808

Metabolomics as a tool in the identification of dietary biomarkers.

PubMed

Gibbons, Helena; Brennan, Lorraine

2017-02-01

Current dietary assessment methods including FFQ, 24-h recalls and weighed food diaries are associated with many measurement errors. In an attempt to overcome some of these errors, dietary biomarkers have emerged as a complementary approach to these traditional methods. Metabolomics has developed as a key technology for the identification of new dietary biomarkers and to date, metabolomic-based approaches have led to the identification of a number of putative biomarkers. The three approaches generally employed when using metabolomics in dietary biomarker discovery are: (i) acute interventions where participants consume specific amounts of a test food, (ii) cohort studies where metabolic profiles are compared between consumers and non-consumers of a specific food and (iii) the analysis of dietary patterns and metabolic profiles to identify nutritypes and biomarkers. The present review critiques the current literature in terms of the approaches used for dietary biomarker discovery and gives a detailed overview of the currently proposed biomarkers, highlighting steps needed for their full validation. Furthermore, the present review also evaluates areas such as current databases and software tools, which are needed to advance the interpretation of results and therefore enhance the utility of dietary biomarkers in nutrition research.

Application of Stable Isotope-Assisted Metabolomics for Cell Metabolism Studies

PubMed Central

You, Le; Zhang, Baichen; Tang, Yinjie J.

2014-01-01

The applications of stable isotopes in metabolomics have facilitated the study of cell metabolisms. Stable isotope-assisted metabolomics requires: (1) properly designed tracer experiments; (2) stringent sampling and quenching protocols to minimize isotopic alternations; (3) efficient metabolite separations; (4) high resolution mass spectrometry to resolve overlapping peaks and background noises; and (5) data analysis methods and databases to decipher isotopic clusters over a broad m/z range (mass-to-charge ratio). This paper overviews mass spectrometry based techniques for precise determination of metabolites and their isotopologues. It also discusses applications of isotopic approaches to track substrate utilization, identify unknown metabolites and their chemical formulas, measure metabolite concentrations, determine putative metabolic pathways, and investigate microbial community populations and their carbon assimilation patterns. In addition, 13C-metabolite fingerprinting and metabolic models can be integrated to quantify carbon fluxes (enzyme reaction rates). The fluxome, in combination with other “omics” analyses, may give systems-level insights into regulatory mechanisms underlying gene functions. More importantly, 13C-tracer experiments significantly improve the potential of low-resolution gas chromatography-mass spectrometry (GC-MS) for broad-scope metabolism studies. We foresee the isotope-assisted metabolomics to be an indispensable tool in industrial biotechnology, environmental microbiology, and medical research. PMID:24957020

Metabolomic biomarkers in diabetic kidney diseases--A systematic review.

PubMed

Zhang, Yumin; Zhang, Siwen; Wang, Guixia

2015-01-01

Diabetic kidney disease (DKD) is generally characterized by increasing albuminuria in diabetic patients; however, few biomarkers are available to facilitate early diagnosis of this disease. The application of metabolomics has shown promises addressing this need. In this review, we conducted a search about metabolomic biomarkers in DKD patients through MEDLINE, EMBASE, and Cochrane Database up to the end of March, 2015. 12 eligible studies were selected and evaluated subsequently through the use of QUADOMICS, a quality assessment tool. 7 of the 12 included studies were classified as 'high quality'. We also recorded specific study characteristics including participants' characteristics, metabolomic techniques, sample types, and significantly altered metabolites between DKD and control groups. Products of lipid metabolisms including esterified and non-esterified fatty acids, carnitines, phospholipids and metabolites involved in branch-chained amino acids and aromatic amino acids metabolisms were frequently affected biomarkers of DKD. Other differential metabolites were also found, while some of their associations with DKD were unclear. Further more studies are required to test these findings in larger, diverse ethnic populations with elaborate study designs, and finally we could translate them into the benefits of DKD patients. Copyright © 2015 Elsevier Inc. All rights reserved.

AnnotCompute: annotation-based exploration and meta-analysis of genomics experiments

PubMed Central

Zheng, Jie; Stoyanovich, Julia; Manduchi, Elisabetta; Liu, Junmin; Stoeckert, Christian J.

2011-01-01

The ever-increasing scale of biological data sets, particularly those arising in the context of high-throughput technologies, requires the development of rich data exploration tools. In this article, we present AnnotCompute, an information discovery platform for repositories of functional genomics experiments such as ArrayExpress. Our system leverages semantic annotations of functional genomics experiments with controlled vocabulary and ontology terms, such as those from the MGED Ontology, to compute conceptual dissimilarities between pairs of experiments. These dissimilarities are then used to support two types of exploratory analysis—clustering and query-by-example. We show that our proposed dissimilarity measures correspond to a user's intuition about conceptual dissimilarity, and can be used to support effective query-by-example. We also evaluate the quality of clustering based on these measures. While AnnotCompute can support a richer data exploration experience, its effectiveness is limited in some cases, due to the quality of available annotations. Nonetheless, tools such as AnnotCompute may provide an incentive for richer annotations of experiments. Code is available for download at http://www.cbil.upenn.edu/downloads/AnnotCompute. Database URL: http://www.cbil.upenn.edu/annotCompute/ PMID:22190598

ReprOlive: a database with linked data for the olive tree (Olea europaea L.) reproductive transcriptome

PubMed Central

Carmona, Rosario; Zafra, Adoración; Seoane, Pedro; Castro, Antonio J.; Guerrero-Fernández, Darío; Castillo-Castillo, Trinidad; Medina-García, Ana; Cánovas, Francisco M.; Aldana-Montes, José F.; Navas-Delgado, Ismael; Alché, Juan de Dios; Claros, M. Gonzalo

2015-01-01

Plant reproductive transcriptomes have been analyzed in different species due to the agronomical and biotechnological importance of plant reproduction. Here we presented an olive tree reproductive transcriptome database with samples from pollen and pistil at different developmental stages, and leaf and root as control vegetative tissues http://reprolive.eez.csic.es). It was developed from 2,077,309 raw reads to 1,549 Sanger sequences. Using a pre-defined workflow based on open-source tools, sequences were pre-processed, assembled, mapped, and annotated with expression data, descriptions, GO terms, InterPro signatures, EC numbers, KEGG pathways, ORFs, and SSRs. Tentative transcripts (TTs) were also annotated with the corresponding orthologs in Arabidopsis thaliana from TAIR and RefSeq databases to enable Linked Data integration. It results in a reproductive transcriptome comprising 72,846 contigs with average length of 686 bp, of which 63,965 (87.8%) included at least one functional annotation, and 55,356 (75.9%) had an ortholog. A minimum of 23,568 different TTs was identified and 5,835 of them contain a complete ORF. The representative reproductive transcriptome can be reduced to 28,972 TTs for further gene expression studies. Partial transcriptomes from pollen, pistil, and vegetative tissues as control were also constructed. ReprOlive provides free access and download capability to these results. Retrieval mechanisms for sequences and transcript annotations are provided. Graphical localization of annotated enzymes into KEGG pathways is also possible. Finally, ReprOlive has included a semantic conceptualisation by means of a Resource Description Framework (RDF) allowing a Linked Data search for extracting the most updated information related to enzymes, interactions, allergens, structures, and reactive oxygen species. PMID:26322066

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...

The volatile compound BinBase mass spectral database.

PubMed

Skogerson, Kirsten; Wohlgemuth, Gert; Barupal, Dinesh K; Fiehn, Oliver

2011-08-04

Volatile compounds comprise diverse chemical groups with wide-ranging sources and functions. These compounds originate from major pathways of secondary metabolism in many organisms and play essential roles in chemical ecology in both plant and animal kingdoms. In past decades, sampling methods and instrumentation for the analysis of complex volatile mixtures have improved; however, design and implementation of database tools to process and store the complex datasets have lagged behind. The volatile compound BinBase (vocBinBase) is an automated peak annotation and database system developed for the analysis of GC-TOF-MS data derived from complex volatile mixtures. The vocBinBase DB is an extension of the previously reported metabolite BinBase software developed to track and identify derivatized metabolites. The BinBase algorithm uses deconvoluted spectra and peak metadata (retention index, unique ion, spectral similarity, peak signal-to-noise ratio, and peak purity) from the Leco ChromaTOF software, and annotates peaks using a multi-tiered filtering system with stringent thresholds. The vocBinBase algorithm assigns the identity of compounds existing in the database. Volatile compound assignments are supported by the Adams mass spectral-retention index library, which contains over 2,000 plant-derived volatile compounds. Novel molecules that are not found within vocBinBase are automatically added using strict mass spectral and experimental criteria. Users obtain fully annotated data sheets with quantitative information for all volatile compounds for studies that may consist of thousands of chromatograms. The vocBinBase database may also be queried across different studies, comprising currently 1,537 unique mass spectra generated from 1.7 million deconvoluted mass spectra of 3,435 samples (18 species). Mass spectra with retention indices and volatile profiles are available as free download under the CC-BY agreement (http://vocbinbase.fiehnlab.ucdavis.edu). The BinBase database algorithms have been successfully modified to allow for tracking and identification of volatile compounds in complex mixtures. The database is capable of annotating large datasets (hundreds to thousands of samples) and is well-suited for between-study comparisons such as chemotaxonomy investigations. This novel volatile compound database tool is applicable to research fields spanning chemical ecology to human health. The BinBase source code is freely available at http://binbase.sourceforge.net/ under the LGPL 2.0 license agreement.

The volatile compound BinBase mass spectral database

PubMed Central

2011-01-01

Background Volatile compounds comprise diverse chemical groups with wide-ranging sources and functions. These compounds originate from major pathways of secondary metabolism in many organisms and play essential roles in chemical ecology in both plant and animal kingdoms. In past decades, sampling methods and instrumentation for the analysis of complex volatile mixtures have improved; however, design and implementation of database tools to process and store the complex datasets have lagged behind. Description The volatile compound BinBase (vocBinBase) is an automated peak annotation and database system developed for the analysis of GC-TOF-MS data derived from complex volatile mixtures. The vocBinBase DB is an extension of the previously reported metabolite BinBase software developed to track and identify derivatized metabolites. The BinBase algorithm uses deconvoluted spectra and peak metadata (retention index, unique ion, spectral similarity, peak signal-to-noise ratio, and peak purity) from the Leco ChromaTOF software, and annotates peaks using a multi-tiered filtering system with stringent thresholds. The vocBinBase algorithm assigns the identity of compounds existing in the database. Volatile compound assignments are supported by the Adams mass spectral-retention index library, which contains over 2,000 plant-derived volatile compounds. Novel molecules that are not found within vocBinBase are automatically added using strict mass spectral and experimental criteria. Users obtain fully annotated data sheets with quantitative information for all volatile compounds for studies that may consist of thousands of chromatograms. The vocBinBase database may also be queried across different studies, comprising currently 1,537 unique mass spectra generated from 1.7 million deconvoluted mass spectra of 3,435 samples (18 species). Mass spectra with retention indices and volatile profiles are available as free download under the CC-BY agreement (http://vocbinbase.fiehnlab.ucdavis.edu). Conclusions The BinBase database algorithms have been successfully modified to allow for tracking and identification of volatile compounds in complex mixtures. The database is capable of annotating large datasets (hundreds to thousands of samples) and is well-suited for between-study comparisons such as chemotaxonomy investigations. This novel volatile compound database tool is applicable to research fields spanning chemical ecology to human health. The BinBase source code is freely available at http://binbase.sourceforge.net/ under the LGPL 2.0 license agreement. PMID:21816034

Improving Microbial Genome Annotations in an Integrated Database Context

PubMed Central

Chen, I-Min A.; Markowitz, Victor M.; Chu, Ken; Anderson, Iain; Mavromatis, Konstantinos; Kyrpides, Nikos C.; Ivanova, Natalia N.

2013-01-01

Effective comparative analysis of microbial genomes requires a consistent and complete view of biological data. Consistency regards the biological coherence of annotations, while completeness regards the extent and coverage of functional characterization for genomes. We have developed tools that allow scientists to assess and improve the consistency and completeness of microbial genome annotations in the context of the Integrated Microbial Genomes (IMG) family of systems. All publicly available microbial genomes are characterized in IMG using different functional annotation and pathway resources, thus providing a comprehensive framework for identifying and resolving annotation discrepancies. A rule based system for predicting phenotypes in IMG provides a powerful mechanism for validating functional annotations, whereby the phenotypic traits of an organism are inferred based on the presence of certain metabolic reactions and pathways and compared to experimentally observed phenotypes. The IMG family of systems are available at http://img.jgi.doe.gov/. PMID:23424620

The standard operating procedure of the DOE-JGI Metagenome Annotation Pipeline (MAP v.4)

DOE PAGES

Huntemann, Marcel; Ivanova, Natalia N.; Mavromatis, Konstantinos; ...

2016-02-24

The DOE-JGI Metagenome Annotation Pipeline (MAP v.4) performs structural and functional annotation for metagenomic sequences that are submitted to the Integrated Microbial Genomes with Microbiomes (IMG/M) system for comparative analysis. The pipeline runs on nucleotide sequences provide d via the IMG submission site. Users must first define their analysis projects in GOLD and then submit the associated sequence datasets consisting of scaffolds/contigs with optional coverage information and/or unassembled reads in fasta and fastq file formats. The MAP processing consists of feature prediction including identification of protein-coding genes, non-coding RNAs and regulatory RNAs, as well as CRISPR elements. Structural annotation ismore » followed by functional annotation including assignment of protein product names and connection to various protein family databases.« less

The standard operating procedure of the DOE-JGI Metagenome Annotation Pipeline (MAP v.4)

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

Huntemann, Marcel; Ivanova, Natalia N.; Mavromatis, Konstantinos

The DOE-JGI Metagenome Annotation Pipeline (MAP v.4) performs structural and functional annotation for metagenomic sequences that are submitted to the Integrated Microbial Genomes with Microbiomes (IMG/M) system for comparative analysis. The pipeline runs on nucleotide sequences provide d via the IMG submission site. Users must first define their analysis projects in GOLD and then submit the associated sequence datasets consisting of scaffolds/contigs with optional coverage information and/or unassembled reads in fasta and fastq file formats. The MAP processing consists of feature prediction including identification of protein-coding genes, non-coding RNAs and regulatory RNAs, as well as CRISPR elements. Structural annotation ismore » followed by functional annotation including assignment of protein product names and connection to various protein family databases.« less

Defining and Detecting Complex Peak Relationships in Mass Spectral Data: The Mz.unity Algorithm.

PubMed

Mahieu, Nathaniel G; Spalding, Jonathan L; Gelman, Susan J; Patti, Gary J

2016-09-20

Analysis of a single analyte by mass spectrometry can result in the detection of more than 100 degenerate peaks. These degenerate peaks complicate spectral interpretation and are challenging to annotate. In mass spectrometry-based metabolomics, this degeneracy leads to inflated false discovery rates, data sets containing an order of magnitude more features than analytes, and an inefficient use of resources during data analysis. Although software has been introduced to annotate spectral degeneracy, current approaches are unable to represent several important classes of peak relationships. These include heterodimers and higher complex adducts, distal fragments, relationships between peaks in different polarities, and complex adducts between features and background peaks. Here we outline sources of peak degeneracy in mass spectra that are not annotated by current approaches and introduce a software package called mz.unity to detect these relationships in accurate mass data. Using mz.unity, we find that data sets contain many more complex relationships than we anticipated. Examples include the adduct of glutamate and nicotinamide adenine dinucleotide (NAD), fragments of NAD detected in the same or opposite polarities, and the adduct of glutamate and a background peak. Further, the complex relationships we identify show that several assumptions commonly made when interpreting mass spectral degeneracy do not hold in general. These contributions provide new tools and insight to aid in the annotation of complex spectral relationships and provide a foundation for improved data set identification. Mz.unity is an R package and is freely available at https://github.com/nathaniel-mahieu/mz.unity as well as our laboratory Web site http://pattilab.wustl.edu/software/ .

GenomewidePDB 2.0: A Newly Upgraded Versatile Proteogenomic Database for the Chromosome-Centric Human Proteome Project.

PubMed

Jeong, Seul-Ki; Hancock, William S; Paik, Young-Ki

2015-09-04

Since the launch of the Chromosome-centric Human Proteome Project (C-HPP) in 2012, the number of "missing" proteins has fallen to 2932, down from ∼5932 since the number was first counted in 2011. We compared the characteristics of missing proteins with those of already annotated proteins with respect to transcriptional expression pattern and the time periods in which newly identified proteins were annotated. We learned that missing proteins commonly exhibit lower levels of transcriptional expression and less tissue-specific expression compared with already annotated proteins. This makes it more difficult to identify missing proteins as time goes on. One of the C-HPP goals is to identify alternative spliced product of proteins (ASPs), which are usually difficult to find by shot-gun proteomic methods due to their sequence similarities with the representative proteins. To resolve this problem, it may be necessary to use a targeted proteomics approach (e.g., selected and multiple reaction monitoring [S/MRM] assays) and an innovative bioinformatics platform that enables the selection of target peptides for rarely expressed missing proteins or ASPs. Given that the success of efforts to identify missing proteins may rely on more informative public databases, it was necessary to upgrade the available integrative databases. To this end, we attempted to improve the features and utility of GenomewidePDB by integrating transcriptomic information (e.g., alternatively spliced transcripts), annotated peptide information, and an advanced search interface that can find proteins of interest when applying a targeted proteomics strategy. This upgraded version of the database, GenomewidePDB 2.0, may not only expedite identification of the remaining missing proteins but also enhance the exchange of information among the proteome community. GenomewidePDB 2.0 is available publicly at http://genomewidepdb.proteomix.org/.

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:.

Methods for eliciting, annotating, and analyzing databases for child speech development.

PubMed

Beckman, Mary E; Plummer, Andrew R; Munson, Benjamin; Reidy, Patrick F

2017-09-01

Methods from automatic speech recognition (ASR), such as segmentation and forced alignment, have facilitated the rapid annotation and analysis of very large adult speech databases and databases of caregiver-infant interaction, enabling advances in speech science that were unimaginable just a few decades ago. This paper centers on two main problems that must be addressed in order to have analogous resources for developing and exploiting databases of young children's speech. The first problem is to understand and appreciate the differences between adult and child speech that cause ASR models developed for adult speech to fail when applied to child speech. These differences include the fact that children's vocal tracts are smaller than those of adult males and also changing rapidly in size and shape over the course of development, leading to between-talker variability across age groups that dwarfs the between-talker differences between adult men and women. Moreover, children do not achieve fully adult-like speech motor control until they are young adults, and their vocabularies and phonological proficiency are developing as well, leading to considerably more within-talker variability as well as more between-talker variability. The second problem then is to determine what annotation schemas and analysis techniques can most usefully capture relevant aspects of this variability. Indeed, standard acoustic characterizations applied to child speech reveal that adult-centered annotation schemas fail to capture phenomena such as the emergence of covert contrasts in children's developing phonological systems, while also revealing children's nonuniform progression toward community speech norms as they acquire the phonological systems of their native languages. Both problems point to the need for more basic research into the growth and development of the articulatory system (as well as of the lexicon and phonological system) that is oriented explicitly toward the construction of age-appropriate computational models.

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.

PANNZER2: a rapid functional annotation web server.

PubMed

Törönen, Petri; Medlar, Alan; Holm, Liisa

2018-05-08

The unprecedented growth of high-throughput sequencing has led to an ever-widening annotation gap in protein databases. While computational prediction methods are available to make up the shortfall, a majority of public web servers are hindered by practical limitations and poor performance. Here, we introduce PANNZER2 (Protein ANNotation with Z-scoRE), a fast functional annotation web server that provides both Gene Ontology (GO) annotations and free text description predictions. PANNZER2 uses SANSparallel to perform high-performance homology searches, making bulk annotation based on sequence similarity practical. PANNZER2 can output GO annotations from multiple scoring functions, enabling users to see which predictions are robust across predictors. Finally, PANNZER2 predictions scored within the top 10 methods for molecular function and biological process in the CAFA2 NK-full benchmark. The PANNZER2 web server is updated on a monthly schedule and is accessible at http://ekhidna2.biocenter.helsinki.fi/sanspanz/. The source code is available under the GNU Public Licence v3.

MAKER-P: a tool-kit for the creation, management, and quality control of plant genome annotations

USDA-ARS?s Scientific Manuscript database

We have optimized and extended the widely used annotation-engine MAKER for use on plant genomes. We have benchmarked the resulting software, MAKER-P, using the A. thaliana genome and the TAIR10 gene models. Here we demonstrate the ability of the MAKER-P toolkit to generate de novo repeat databases, ...

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

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

Database Constraints Applied to Metabolic Pathway Reconstruction Tools

PubMed Central

Vilaplana, Jordi; Solsona, Francesc; Teixido, Ivan; Usié, Anabel; Karathia, Hiren; Alves, Rui; Mateo, Jordi

2014-01-01

Our group developed two biological applications, Biblio-MetReS and Homol-MetReS, accessing the same database of organisms with annotated genes. Biblio-MetReS is a data-mining application that facilitates the reconstruction of molecular networks based on automated text-mining analysis of published scientific literature. Homol-MetReS allows functional (re)annotation of proteomes, to properly identify both the individual proteins involved in the process(es) of interest and their function. It also enables the sets of proteins involved in the process(es) in different organisms to be compared directly. The efficiency of these biological applications is directly related to the design of the shared database. We classified and analyzed the different kinds of access to the database. Based on this study, we tried to adjust and tune the configurable parameters of the database server to reach the best performance of the communication data link to/from the database system. Different database technologies were analyzed. We started the study with a public relational SQL database, MySQL. Then, the same database was implemented by a MapReduce-based database named HBase. The results indicated that the standard configuration of MySQL gives an acceptable performance for low or medium size databases. Nevertheless, tuning database parameters can greatly improve the performance and lead to very competitive runtimes. PMID:25202745

The language of gene ontology: a Zipf's law analysis.

PubMed

Kalankesh, Leila Ranandeh; Stevens, Robert; Brass, Andy

2012-06-07

Most major genome projects and sequence databases provide a GO annotation of their data, either automatically or through human annotators, creating a large corpus of data written in the language of GO. Texts written in natural language show a statistical power law behaviour, Zipf's law, the exponent of which can provide useful information on the nature of the language being used. We have therefore explored the hypothesis that collections of GO annotations will show similar statistical behaviours to natural language. Annotations from the Gene Ontology Annotation project were found to follow Zipf's law. Surprisingly, the measured power law exponents were consistently different between annotation captured using the three GO sub-ontologies in the corpora (function, process and component). On filtering the corpora using GO evidence codes we found that the value of the measured power law exponent responded in a predictable way as a function of the evidence codes used to support the annotation. Techniques from computational linguistics can provide new insights into the annotation process. GO annotations show similar statistical behaviours to those seen in natural language with measured exponents that provide a signal which correlates with the nature of the evidence codes used to support the annotations, suggesting that the measured exponent might provide a signal regarding the information content of the annotation.

SEED Servers: High-Performance Access to the SEED Genomes, Annotations, and Metabolic Models

PubMed Central

Aziz, Ramy K.; Devoid, Scott; Disz, Terrence; Edwards, Robert A.; Henry, Christopher S.; Olsen, Gary J.; Olson, Robert; Overbeek, Ross; Parrello, Bruce; Pusch, Gordon D.; Stevens, Rick L.; Vonstein, Veronika; Xia, Fangfang

2012-01-01

The remarkable advance in sequencing technology and the rising interest in medical and environmental microbiology, biotechnology, and synthetic biology resulted in a deluge of published microbial genomes. Yet, genome annotation, comparison, and modeling remain a major bottleneck to the translation of sequence information into biological knowledge, hence computational analysis tools are continuously being developed for rapid genome annotation and interpretation. Among the earliest, most comprehensive resources for prokaryotic genome analysis, the SEED project, initiated in 2003 as an integration of genomic data and analysis tools, now contains >5,000 complete genomes, a constantly updated set of curated annotations embodied in a large and growing collection of encoded subsystems, a derived set of protein families, and hundreds of genome-scale metabolic models. Until recently, however, maintaining current copies of the SEED code and data at remote locations has been a pressing issue. To allow high-performance remote access to the SEED database, we developed the SEED Servers (http://www.theseed.org/servers): four network-based servers intended to expose the data in the underlying relational database, support basic annotation services, offer programmatic access to the capabilities of the RAST annotation server, and provide access to a growing collection of metabolic models that support flux balance analysis. The SEED servers offer open access to regularly updated data, the ability to annotate prokaryotic genomes, the ability to create metabolic reconstructions and detailed models of metabolism, and access to hundreds of existing metabolic models. This work offers and supports a framework upon which other groups can build independent research efforts. Large integrations of genomic data represent one of the major intellectual resources driving research in biology, and programmatic access to the SEED data will provide significant utility to a broad collection of potential users. PMID:23110173

Untargeted metabolomic analysis using liquid chromatography quadrupole time-of-flight mass spectrometry for non-volatile profiling of wines.

PubMed

Arbulu, M; Sampedro, M C; Gómez-Caballero, A; Goicolea, M A; Barrio, R J

2015-02-09

The current study presents a method for comprehensive untargeted metabolomic fingerprinting of the non-volatile profile of the Graciano Vitis vinifera wine variety, using liquid chromatography/electrospray ionization time of flight mass spectrometry (LC-ESI-QTOF). Pre-treatment of samples, chromatographic columns, mobile phases, elution gradients and ionization sources, were evaluated for the extraction of the maximum number of metabolites in red wine. Putative compounds were extracted from the raw data using the extraction algorithm, molecular feature extractor (MFE). For the metabolite identification the WinMet database was designed based on electronic databases and literature research and includes only the putative metabolites reported to be present in oenological matrices. The results from WinMet were compared with those in the METLIN database to evaluate how much the databases overlap for performing identifications. The reproducibility of the analysis was assessed using manual processing following replicate injections of Vitis vinifera cv. Graciano wine spiked with external standards. In the present work, 411 different metabolites in Graciano Vitis vinifera red wine were identified, including primary wine metabolites such as sugars (4%), amino acids (23%), biogenic amines (4%), fatty acids (2%), and organic acids (32%) and secondary metabolites such as phenols (27%) and esters (8%). Significant differences between varieties Tempranillo and Graciano were related to the presence of fifteen specific compounds. Copyright © 2014 Elsevier B.V. All rights reserved.

Scripps Genome ADVISER: Annotation and Distributed Variant Interpretation SERver

PubMed Central

Pham, Phillip H.; Shipman, William J.; Erikson, Galina A.; Schork, Nicholas J.; Torkamani, Ali

2015-01-01

Interpretation of human genomes is a major challenge. We present the Scripps Genome ADVISER (SG-ADVISER) suite, which aims to fill the gap between data generation and genome interpretation by performing holistic, in-depth, annotations and functional predictions on all variant types and effects. The SG-ADVISER suite includes a de-identification tool, a variant annotation web-server, and a user interface for inheritance and annotation-based filtration. SG-ADVISER allows users with no bioinformatics expertise to manipulate large volumes of variant data with ease – without the need to download large reference databases, install software, or use a command line interface. SG-ADVISER is freely available at genomics.scripps.edu/ADVISER. PMID:25706643

AphidBase: A centralized bioinformatic resource for annotation of the pea aphid genome

PubMed Central

Legeai, Fabrice; Shigenobu, Shuji; Gauthier, Jean-Pierre; Colbourne, John; Rispe, Claude; Collin, Olivier; Richards, Stephen; Wilson, Alex C. C.; Tagu, Denis

2015-01-01

AphidBase is a centralized bioinformatic resource that was developed to facilitate community annotation of the pea aphid genome by the International Aphid Genomics Consortium (IAGC). The AphidBase Information System designed to organize and distribute genomic data and annotations for a large international community was constructed using open source software tools from the Generic Model Organism Database (GMOD). The system includes Apollo and GBrowse utilities as well as a wiki, blast search capabilities and a full text search engine. AphidBase strongly supported community cooperation and coordination in the curation of gene models during community annotation of the pea aphid genome. AphidBase can be accessed at http://www.aphidbase.com. PMID:20482635

DWARF – a data warehouse system for analyzing protein families

PubMed Central

Fischer, Markus; Thai, Quan K; Grieb, Melanie; Pleiss, Jürgen

2006-01-01

Background The emerging field of integrative bioinformatics provides the tools to organize and systematically analyze vast amounts of highly diverse biological data and thus allows to gain a novel understanding of complex biological systems. The data warehouse DWARF applies integrative bioinformatics approaches to the analysis of large protein families. Description The data warehouse system DWARF integrates data on sequence, structure, and functional annotation for protein fold families. The underlying relational data model consists of three major sections representing entities related to the protein (biochemical function, source organism, classification to homologous families and superfamilies), the protein sequence (position-specific annotation, mutant information), and the protein structure (secondary structure information, superimposed tertiary structure). Tools for extracting, transforming and loading data from public available resources (ExPDB, GenBank, DSSP) are provided to populate the database. The data can be accessed by an interface for searching and browsing, and by analysis tools that operate on annotation, sequence, or structure. We applied DWARF to the family of α/β-hydrolases to host the Lipase Engineering database. Release 2.3 contains 6138 sequences and 167 experimentally determined protein structures, which are assigned to 37 superfamilies 103 homologous families. Conclusion DWARF has been designed for constructing databases of large structurally related protein families and for evaluating their sequence-structure-function relationships by a systematic analysis of sequence, structure and functional annotation. It has been applied to predict biochemical properties from sequence, and serves as a valuable tool for protein engineering. PMID:17094801

Djeen (Database for Joomla!'s Extensible Engine): a research information management system for flexible multi-technology project administration.

PubMed

Stahl, Olivier; Duvergey, Hugo; Guille, Arnaud; Blondin, Fanny; Vecchio, Alexandre Del; Finetti, Pascal; Granjeaud, Samuel; Vigy, Oana; Bidaut, Ghislain

2013-06-06

With the advance of post-genomic technologies, the need for tools to manage large scale data in biology becomes more pressing. This involves annotating and storing data securely, as well as granting permissions flexibly with several technologies (all array types, flow cytometry, proteomics) for collaborative work and data sharing. This task is not easily achieved with most systems available today. We developed Djeen (Database for Joomla!'s Extensible Engine), a new Research Information Management System (RIMS) for collaborative projects. Djeen is a user-friendly application, designed to streamline data storage and annotation collaboratively. Its database model, kept simple, is compliant with most technologies and allows storing and managing of heterogeneous data with the same system. Advanced permissions are managed through different roles. Templates allow Minimum Information (MI) compliance. Djeen allows managing project associated with heterogeneous data types while enforcing annotation integrity and minimum information. Projects are managed within a hierarchy and user permissions are finely-grained for each project, user and group.Djeen Component source code (version 1.5.1) and installation documentation are available under CeCILL license from http://sourceforge.net/projects/djeen/files and supplementary material.

Lightweight genome viewer: portable software for browsing genomics data in its chromosomal context

PubMed Central

Faith, Jeremiah J; Olson, Andrew J; Gardner, Timothy S; Sachidanandam, Ravi

2007-01-01

Background Lightweight genome viewer (lwgv) is a web-based tool for visualization of sequence annotations in their chromosomal context. It performs most of the functions of larger genome browsers, while relying on standard flat-file formats and bypassing the database needs of most visualization tools. Visualization as an aide to discovery requires display of novel data in conjunction with static annotations in their chromosomal context. With database-based systems, displaying dynamic results requires temporary tables that need to be tracked for removal. Results lwgv simplifies the visualization of user-generated results on a local computer. The dynamic results of these analyses are written to transient files, which can import static content from a more permanent file. lwgv is currently used in many different applications, from whole genome browsers to single-gene RNAi design visualization, demonstrating its applicability in a large variety of contexts and scales. Conclusion lwgv provides a lightweight alternative to large genome browsers for visualizing biological annotations and dynamic analyses in their chromosomal context. It is particularly suited for applications ranging from short sequences to medium-sized genomes when the creation and maintenance of a large software and database infrastructure is not necessary or desired. PMID:17877794

PedAM: a database for Pediatric Disease Annotation and Medicine.

PubMed

Jia, Jinmeng; An, Zhongxin; Ming, Yue; Guo, Yongli; Li, Wei; Li, Xin; Liang, Yunxiang; Guo, Dongming; Tai, Jun; Chen, Geng; Jin, Yaqiong; Liu, Zhimei; Ni, Xin; Shi, Tieliu

2018-01-04

There is a significant number of children around the world suffering from the consequence of the misdiagnosis and ineffective treatment for various diseases. To facilitate the precision medicine in pediatrics, a database namely the Pediatric Disease Annotations & Medicines (PedAM) has been built to standardize and classify pediatric diseases. The PedAM integrates both biomedical resources and clinical data from Electronic Medical Records to support the development of computational tools, by which enables robust data analysis and integration. It also uses disease-manifestation (D-M) integrated from existing biomedical ontologies as prior knowledge to automatically recognize text-mined, D-M-specific syntactic patterns from 774 514 full-text articles and 8 848 796 abstracts in MEDLINE. Additionally, disease connections based on phenotypes or genes can be visualized on the web page of PedAM. Currently, the PedAM contains standardized 8528 pediatric disease terms (4542 unique disease concepts and 3986 synonyms) with eight annotation fields for each disease, including definition synonyms, gene, symptom, cross-reference (Xref), human phenotypes and its corresponding phenotypes in the mouse. The database PedAM is freely accessible at http://www.unimd.org/pedam/. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Djeen (Database for Joomla!’s Extensible Engine): a research information management system for flexible multi-technology project administration

PubMed Central

2013-01-01

Background With the advance of post-genomic technologies, the need for tools to manage large scale data in biology becomes more pressing. This involves annotating and storing data securely, as well as granting permissions flexibly with several technologies (all array types, flow cytometry, proteomics) for collaborative work and data sharing. This task is not easily achieved with most systems available today. Findings We developed Djeen (Database for Joomla!’s Extensible Engine), a new Research Information Management System (RIMS) for collaborative projects. Djeen is a user-friendly application, designed to streamline data storage and annotation collaboratively. Its database model, kept simple, is compliant with most technologies and allows storing and managing of heterogeneous data with the same system. Advanced permissions are managed through different roles. Templates allow Minimum Information (MI) compliance. Conclusion Djeen allows managing project associated with heterogeneous data types while enforcing annotation integrity and minimum information. Projects are managed within a hierarchy and user permissions are finely-grained for each project, user and group. Djeen Component source code (version 1.5.1) and installation documentation are available under CeCILL license from http://sourceforge.net/projects/djeen/files and supplementary material. PMID:23742665

Lightweight genome viewer: portable software for browsing genomics data in its chromosomal context.

PubMed

Faith, Jeremiah J; Olson, Andrew J; Gardner, Timothy S; Sachidanandam, Ravi

2007-09-18

Lightweight genome viewer (lwgv) is a web-based tool for visualization of sequence annotations in their chromosomal context. It performs most of the functions of larger genome browsers, while relying on standard flat-file formats and bypassing the database needs of most visualization tools. Visualization as an aide to discovery requires display of novel data in conjunction with static annotations in their chromosomal context. With database-based systems, displaying dynamic results requires temporary tables that need to be tracked for removal. lwgv simplifies the visualization of user-generated results on a local computer. The dynamic results of these analyses are written to transient files, which can import static content from a more permanent file. lwgv is currently used in many different applications, from whole genome browsers to single-gene RNAi design visualization, demonstrating its applicability in a large variety of contexts and scales. lwgv provides a lightweight alternative to large genome browsers for visualizing biological annotations and dynamic analyses in their chromosomal context. It is particularly suited for applications ranging from short sequences to medium-sized genomes when the creation and maintenance of a large software and database infrastructure is not necessary or desired.

In silico analysis of expressed sequence tags from Trichostrongylus vitrinus (Nematoda): comparison of the automated ESTExplorer workflow platform with conventional database searches.

PubMed

Nagaraj, Shivashankar H; Gasser, Robin B; Nisbet, Alasdair J; Ranganathan, Shoba

2008-01-01

The analysis of expressed sequence tags (EST) offers a rapid and cost effective approach to elucidate the transcriptome of an organism, but requires several computational methods for assembly and annotation. Researchers frequently analyse each step manually, which is laborious and time consuming. We have recently developed ESTExplorer, a semi-automated computational workflow system, in order to achieve the rapid analysis of EST datasets. In this study, we evaluated EST data analysis for the parasitic nematode Trichostrongylus vitrinus (order Strongylida) using ESTExplorer, compared with database matching alone. We functionally annotated 1776 ESTs obtained via suppressive-subtractive hybridisation from T. vitrinus, an important parasitic trichostrongylid of small ruminants. Cluster and comparative genomic analyses of the transcripts using ESTExplorer indicated that 290 (41%) sequences had homologues in Caenorhabditis elegans, 329 (42%) in parasitic nematodes, 202 (28%) in organisms other than nematodes, and 218 (31%) had no significant match to any sequence in the current databases. Of the C. elegans homologues, 90 were associated with 'non-wildtype' double-stranded RNA interference (RNAi) phenotypes, including embryonic lethality, maternal sterility, sterile progeny, larval arrest and slow growth. We could functionally classify 267 (38%) sequences using the Gene Ontologies (GO) and establish pathway associations for 230 (33%) sequences using the Kyoto Encyclopedia of Genes and Genomes (KEGG). Further examination of this EST dataset revealed a number of signalling molecules, proteases, protease inhibitors, enzymes, ion channels and immune-related genes. In addition, we identified 40 putative secreted proteins that could represent potential candidates for developing novel anthelmintics or vaccines. We further compared the automated EST sequence annotations, using ESTExplorer, with database search results for individual T. vitrinus ESTs. ESTExplorer reliably and rapidly annotated 301 ESTs, with pathway and GO information, eliminating 60 low quality hits from database searches. We evaluated the efficacy of ESTExplorer in analysing EST data, and demonstrate that computational tools can be used to accelerate the process of gene discovery in EST sequencing projects. The present study has elucidated sets of relatively conserved and potentially novel genes for biological investigation, and the annotated EST set provides further insight into the molecular biology of T. vitrinus, towards the identification of novel drug targets.

Using comparative genome analysis to identify problems in annotated microbial genomes.

PubMed

Poptsova, Maria S; Gogarten, J Peter

2010-07-01

Genome annotation is a tedious task that is mostly done by automated methods; however, the accuracy of these approaches has been questioned since the beginning of the sequencing era. Genome annotation is a multilevel process, and errors can emerge at different stages: during sequencing, as a result of gene-calling procedures, and in the process of assigning gene functions. Missed or wrongly annotated genes differentially impact different types of analyses. Here we discuss and demonstrate how the methods of comparative genome analysis can refine annotations by locating missing orthologues. We also discuss possible reasons for errors and show that the second-generation annotation systems, which combine multiple gene-calling programs with similarity-based methods, perform much better than the first annotation tools. Since old errors may propagate to the newly sequenced genomes, we emphasize that the problem of continuously updating popular public databases is an urgent and unresolved one. Due to the progress in genome-sequencing technologies, automated annotation techniques will remain the main approach in the future. Researchers need to be aware of the existing errors in the annotation of even well-studied genomes, such as Escherichia coli, and consider additional quality control for their results.

Aspergillus flavus Blast2GO gene ontology database: elevated growth temperature alters amino acid metabolism

USDA-ARS?s Scientific Manuscript database

The availability of a representative gene ontology (GO) database is a prerequisite for a successful functional genomics study. Using online Blast2GO resources we constructed a GO database of Aspergillus flavus. Of the predicted total 13,485 A. flavus genes 8,987 were annotated with GO terms. The mea...

GrTEdb: the first web-based database of transposable elements in cotton (Gossypium raimondii).

PubMed

Xu, Zhenzhen; Liu, Jing; Ni, Wanchao; Peng, Zhen; Guo, Yue; Ye, Wuwei; Huang, Fang; Zhang, Xianggui; Xu, Peng; Guo, Qi; Shen, Xinlian; Du, Jianchang

2017-01-01

Although several diploid and tetroploid Gossypium species genomes have been sequenced, the well annotated web-based transposable elements (TEs) database is lacking. To better understand the roles of TEs in structural, functional and evolutionary dynamics of the cotton genome, a comprehensive, specific, and user-friendly web-based database, Gossypium raimondii transposable elements database (GrTEdb), was constructed. A total of 14 332 TEs were structurally annotated and clearly categorized in G. raimondii genome, and these elements have been classified into seven distinct superfamilies based on the order of protein-coding domains, structures and/or sequence similarity, including 2929 Copia-like elements, 10 368 Gypsy-like elements, 299 L1 , 12 Mutators , 435 PIF-Harbingers , 275 CACTAs and 14 Helitrons . Meanwhile, the web-based sequence browsing, searching, downloading and blast tool were implemented to help users easily and effectively to annotate the TEs or TE fragments in genomic sequences from G. raimondii and other closely related Gossypium species. GrTEdb provides resources and information related with TEs in G. raimondii , and will facilitate gene and genome analyses within or across Gossypium species, evaluating the impact of TEs on their host genomes, and investigating the potential interaction between TEs and protein-coding genes in Gossypium species. http://www.grtedb.org/. © The Author(s) 2017. Published by Oxford University Press.

pGenN, a Gene Normalization Tool for Plant Genes and Proteins in Scientific Literature

PubMed Central

Ding, Ruoyao; Arighi, Cecilia N.; Lee, Jung-Youn; Wu, Cathy H.; Vijay-Shanker, K.

2015-01-01

Background Automatically detecting gene/protein names in the literature and connecting them to databases records, also known as gene normalization, provides a means to structure the information buried in free-text literature. Gene normalization is critical for improving the coverage of annotation in the databases, and is an essential component of many text mining systems and database curation pipelines. Methods In this manuscript, we describe a gene normalization system specifically tailored for plant species, called pGenN (pivot-based Gene Normalization). The system consists of three steps: dictionary-based gene mention detection, species assignment, and intra species normalization. We have developed new heuristics to improve each of these phases. Results We evaluated the performance of pGenN on an in-house expertly annotated corpus consisting of 104 plant relevant abstracts. Our system achieved an F-value of 88.9% (Precision 90.9% and Recall 87.2%) on this corpus, outperforming state-of-art systems presented in BioCreative III. We have processed over 440,000 plant-related Medline abstracts using pGenN. The gene normalization results are stored in a local database for direct query from the pGenN web interface (proteininformationresource.org/pgenn/). The annotated literature corpus is also publicly available through the PIR text mining portal (proteininformationresource.org/iprolink/). PMID:26258475

Evaluation of web-based annotation of ophthalmic images for multicentric clinical trials.

PubMed

Chalam, K V; Jain, P; Shah, V A; Shah, Gaurav Y

2006-06-01

An Internet browser-based annotation system can be used to identify and describe features in digitalized retinal images, in multicentric clinical trials, in real time. In this web-based annotation system, the user employs a mouse to draw and create annotations on a transparent layer, that encapsulates the observations and interpretations of a specific image. Multiple annotation layers may be overlaid on a single image. These layers may correspond to annotations by different users on the same image or annotations of a temporal sequence of images of a disease process, over a period of time. In addition, geometrical properties of annotated figures may be computed and measured. The annotations are stored in a central repository database on a server, which can be retrieved by multiple users in real time. This system facilitates objective evaluation of digital images and comparison of double-blind readings of digital photographs, with an identifiable audit trail. Annotation of ophthalmic images allowed clinically feasible and useful interpretation to track properties of an area of fundus pathology. This provided an objective method to monitor properties of pathologies over time, an essential component of multicentric clinical trials. The annotation system also allowed users to view stereoscopic images that are stereo pairs. This web-based annotation system is useful and valuable in monitoring patient care, in multicentric clinical trials, telemedicine, teaching and routine clinical settings.

Seshat: A Web service for accurate annotation, validation, and analysis of TP53 variants generated by conventional and next-generation sequencing.

PubMed

Tikkanen, Tuomas; Leroy, Bernard; Fournier, Jean Louis; Risques, Rosa Ana; Malcikova, Jitka; Soussi, Thierry

2018-07-01

Accurate annotation of genomic variants in human diseases is essential to allow personalized medicine. Assessment of somatic and germline TP53 alterations has now reached the clinic and is required in several circumstances such as the identification of the most effective cancer therapy for patients with chronic lymphocytic leukemia (CLL). Here, we present Seshat, a Web service for annotating TP53 information derived from sequencing data. A flexible framework allows the use of standard file formats such as Mutation Annotation Format (MAF) or Variant Call Format (VCF), as well as common TXT files. Seshat performs accurate variant annotations using the Human Genome Variation Society (HGVS) nomenclature and the stable TP53 genomic reference provided by the Locus Reference Genomic (LRG). In addition, using the 2017 release of the UMD_TP53 database, Seshat provides multiple statistical information for each TP53 variant including database frequency, functional activity, or pathogenicity. The information is delivered in standardized output tables that minimize errors and facilitate comparison of mutational data across studies. Seshat is a beneficial tool to interpret the ever-growing TP53 sequencing data generated by multiple sequencing platforms and it is freely available via the TP53 Website, http://p53.fr or directly at http://vps338341.ovh.net/. © 2018 Wiley Periodicals, Inc.

Diversity and association of phenotypic and metabolomic traits in the close model grasses Brachypodium distachyon, B. stacei and B. hybridum

PubMed Central

López-Álvarez, Diana; Zubair, Hassan; Beckmann, Manfred; Draper, John

2017-01-01

Abstract Background and Aims Morphological traits in combination with metabolite fingerprinting were used to investigate inter- and intraspecies diversity within the model annual grasses Brachypodium distachyon, Brachypodium stacei and Brachypodium hybridum. Methods Phenotypic variation of 15 morphological characters and 2219 nominal mass (m/z) signals generated using flow infusion electrospray ionization–mass spectrometry (FIE–MS) were evaluated in individuals from a total of 174 wild populations and six inbred lines, and 12 lines, of the three species, respectively. Basic statistics and multivariate principal component analysis and discriminant analysis were used to differentiate inter- and intraspecific variability of the two types of variable, and their association was assayed with the rcorr function. Key Results Basic statistics and analysis of variance detected eight phenotypic characters [(stomata) leaf guard cell length, pollen grain length, (plant) height, second leaf width, inflorescence length, number of spikelets per inflorescence, lemma length, awn length] and 434 tentatively annotated metabolite signals that significantly discriminated the three species. Three phenotypic traits (pollen grain length, spikelet length, number of flowers per inflorescence) might be genetically fixed. The three species showed different metabolomic profiles. Discriminant analysis significantly discriminated the three taxa with both morphometric and metabolome traits and the intraspecific phenotypic diversity within B. distachyon and B. stacei. The populations of B. hybridum were considerably less differentiated. Conclusions Highly explanatory metabolite signals together with morphological characters revealed concordant patterns of differentiation of the three taxa. Intraspecific phenotypic diversity was observed between northern and southern Iberian populations of B. distachyon and between eastern Mediterranean/south-western Asian and western Mediterranean populations of B. stacei. Significant association was found for pollen grain length and lemma length and ten and six metabolomic signals, respectively. These results would guide the selection of new germplasm lines of the three model grasses in ongoing genome-wide association studies. PMID:28040672

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

PubMed Central

Liu, Xiaojing; Ser, Zheng; Cluntun, Ahmad A.; Mentch, Samantha J.; Locasale, Jason W.

2014-01-01

Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. However, current platforms have different limiting factors, such as labor intensive sample preparations, low detection limits, slow scan speeds, intensive method optimization for each metabolite, and the inability to measure both positively and negatively charged ions in single experiments. Therefore, a novel metabolomics protocol could advance metabolomics studies. Amide-based hydrophilic chromatography enables polar metabolite analysis without any chemical derivatization. High resolution MS using the Q-Exactive (QE-MS) has improved ion optics, increased scan speeds (256 msec at resolution 70,000), and has the capability of carrying out positive/negative switching. Using a cold methanol extraction strategy, and coupling an amide column with QE-MS enables robust detection of 168 targeted polar metabolites and thousands of additional features simultaneously.  Data processing is carried out with commercially available software in a highly efficient way, and unknown features extracted from the mass spectra can be queried in databases. PMID:24894601

Metabolomic-Guided Isolation of Bioactive Natural Products from Curvularia sp., an Endophytic Fungus of Terminalia laxiflora.

PubMed

Tawfike, Ahmed F; Abbott, Grainne; Young, Louise; Edrada-Ebel, RuAngelie

2018-02-01

Endophytic fungi associated with medicinal plants are a potential source of novel chemistry and biology. Metabolomic tools were successfully employed to compare the metabolite fingerprints of solid and liquid culture extracts of endophyte Curvularia sp. isolated from the leaves of Terminalia laxiflora . Natural product databases were used to dereplicate metabolites in order to determine known compounds and the presence of new natural products. Multivariate analysis highlighted the putative metabolites responsible for the bioactivity of the fungal extract and its fractions on NF- κ B and the myelogenous leukemia cell line K562. Metabolomic tools and dereplication studies using high-resolution electrospray ionization mass spectrometry directed the fractionation and isolation of the bioactive components from the fungal extracts. This resulted in the isolation of N -acetylphenylalanine (1: ) and two linear peptide congeners of 1: : dipeptide N -acetylphenylalanyl-L-phenylalanine (2: ) and tripeptide N -acetylphenylalanyl-L-phenylalanyl-L-leucine (3: ). Georg Thieme Verlag KG Stuttgart · New York.

Adding Value to Large Multimedia Collections through Annotation Technologies and Tools: Serving Communities of Interest.

ERIC Educational Resources Information Center

Shabajee, Paul; Miller, Libby; Dingley, Andy

A group of research projects based at HP-Labs Bristol, the University of Bristol (England) and ARKive (a new large multimedia database project focused on the worlds biodiversity based in the United Kingdom) are working to develop a flexible model for the indexing of multimedia collections that allows users to annotate content utilizing extensible…

Measuring and Enhancing Organizational Productivity: An Annotated Bibliography. Interim Report, April 2, 1980 through June 30, 1980.

ERIC Educational Resources Information Center

Tuttle, Thomas C.; And Others

This report resulted from visits to over 50 organizations in the Air Force, Army, Navy, and in the civilian sector, automated and manual searches of journals, and computerized databases. This report is a comprehensive annotated bibliography of the literature on productivity measurement and enhancement. The report is organized into four sections:…

Proteomics and Metabolomics: Two Emerging Areas for Legume Improvement

PubMed Central

Ramalingam, Abirami; Kudapa, Himabindu; Pazhamala, Lekha T.; Weckwerth, Wolfram; Varshney, Rajeev K.

2015-01-01

The crop legumes such as chickpea, common bean, cowpea, peanut, pigeonpea, soybean, etc. are important sources of nutrition and contribute to a significant amount of biological nitrogen fixation (>20 million tons of fixed nitrogen) in agriculture. However, the production of legumes is constrained due to abiotic and biotic stresses. It is therefore imperative to understand the molecular mechanisms of plant response to different stresses and identify key candidate genes regulating tolerance which can be deployed in breeding programs. The information obtained from transcriptomics has facilitated the identification of candidate genes for the given trait of interest and utilizing them in crop breeding programs to improve stress tolerance. However, the mechanisms of stress tolerance are complex due to the influence of multi-genes and post-transcriptional regulations. Furthermore, stress conditions greatly affect gene expression which in turn causes modifications in the composition of plant proteomes and metabolomes. Therefore, functional genomics involving various proteomics and metabolomics approaches have been obligatory for understanding plant stress tolerance. These approaches have also been found useful to unravel different pathways related to plant and seed development as well as symbiosis. Proteome and metabolome profiling using high-throughput based systems have been extensively applied in the model legume species, Medicago truncatula and Lotus japonicus, as well as in the model crop legume, soybean, to examine stress signaling pathways, cellular and developmental processes and nodule symbiosis. Moreover, the availability of protein reference maps as well as proteomics and metabolomics databases greatly support research and understanding of various biological processes in legumes. Protein-protein interaction techniques, particularly the yeast two-hybrid system have been advantageous for studying symbiosis and stress signaling in legumes. In this review, several studies on proteomics and metabolomics in model and crop legumes have been discussed. Additionally, applications of advanced proteomics and metabolomics approaches have also been included in this review for future applications in legume research. The integration of these “omics” approaches will greatly support the identification of accurate biomarkers in legume smart breeding programs. PMID:26734026

Ion Mobility Derived Collision Cross Sections to Support Metabolomics Applications

PubMed Central

2015-01-01

Metabolomics is a rapidly evolving analytical approach in life and health sciences. The structural elucidation of the metabolites of interest remains a major analytical challenge in the metabolomics workflow. Here, we investigate the use of ion mobility as a tool to aid metabolite identification. Ion mobility allows for the measurement of the rotationally averaged collision cross-section (CCS), which gives information about the ionic shape of a molecule in the gas phase. We measured the CCSs of 125 common metabolites using traveling-wave ion mobility-mass spectrometry (TW-IM-MS). CCS measurements were highly reproducible on instruments located in three independent laboratories (RSD < 5% for 99%). We also determined the reproducibility of CCS measurements in various biological matrixes including urine, plasma, platelets, and red blood cells using ultra performance liquid chromatography (UPLC) coupled with TW-IM-MS. The mean RSD was < 2% for 97% of the CCS values, compared to 80% of retention times. Finally, as proof of concept, we used UPLC–TW-IM-MS to compare the cellular metabolome of epithelial and mesenchymal cells, an in vitro model used to study cancer development. Experimentally determined and computationally derived CCS values were used as orthogonal analytical parameters in combination with retention time and accurate mass information to confirm the identity of key metabolites potentially involved in cancer. Thus, our results indicate that adding CCS data to searchable databases and to routine metabolomics workflows will increase the identification confidence compared to traditional analytical approaches. PMID:24640936

From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics

PubMed Central

Perez de Souza, Leonardo; Naake, Thomas; Tohge, Takayuki; Fernie, Alisdair R

2017-01-01

Abstract The grand challenge currently facing metabolomics is the expansion of the coverage of the metabolome from a minor percentage of the metabolic complement of the cell toward the level of coverage afforded by other post-genomic technologies such as transcriptomics and proteomics. In plants, this problem is exacerbated by the sheer diversity of chemicals that constitute the metabolome, with the number of metabolites in the plant kingdom generally considered to be in excess of 200 000. In this review, we focus on web resources that can be exploited in order to improve analyte and ultimately metabolite identification and quantification. There is a wide range of available software that not only aids in this but also in the related area of peak alignment; however, for the uninitiated, choosing which program to use is a daunting task. For this reason, we provide an overview of the pros and cons of the software as well as comments regarding the level of programing skills required to effectively exploit their basic functions. In addition, the torrent of available genome and transcriptome sequences that followed the advent of next-generation sequencing has opened up further valuable resources for metabolite identification. All things considered, we posit that only via a continued communal sharing of information such as that deposited in the databases described within the article are we likely to be able to make significant headway toward improving our coverage of the plant metabolome. PMID:28520864

Generation of comprehensive thoracic oncology database--tool for translational research.

PubMed

Surati, Mosmi; Robinson, Matthew; Nandi, Suvobroto; Faoro, Leonardo; Demchuk, Carley; Kanteti, Rajani; Ferguson, Benjamin; Gangadhar, Tara; Hensing, Thomas; Hasina, Rifat; Husain, Aliya; Ferguson, Mark; Karrison, Theodore; Salgia, Ravi

2011-01-22

The Thoracic Oncology Program Database Project was created to serve as a comprehensive, verified, and accessible repository for well-annotated cancer specimens and clinical data to be available to researchers within the Thoracic Oncology Research Program. This database also captures a large volume of genomic and proteomic data obtained from various tumor tissue studies. A team of clinical and basic science researchers, a biostatistician, and a bioinformatics expert was convened to design the database. Variables of interest were clearly defined and their descriptions were written within a standard operating manual to ensure consistency of data annotation. Using a protocol for prospective tissue banking and another protocol for retrospective banking, tumor and normal tissue samples from patients consented to these protocols were collected. Clinical information such as demographics, cancer characterization, and treatment plans for these patients were abstracted and entered into an Access database. Proteomic and genomic data have been included in the database and have been linked to clinical information for patients described within the database. The data from each table were linked using the relationships function in Microsoft Access to allow the database manager to connect clinical and laboratory information during a query. The queried data can then be exported for statistical analysis and hypothesis generation.

E-MSD: an integrated data resource for bioinformatics.

PubMed

Velankar, S; McNeil, P; Mittard-Runte, V; Suarez, A; Barrell, D; Apweiler, R; Henrick, K

2005-01-01

The Macromolecular Structure Database (MSD) group (http://www.ebi.ac.uk/msd/) continues to enhance the quality and consistency of macromolecular structure data in the worldwide Protein Data Bank (wwPDB) and to work towards the integration of various bioinformatics data resources. One of the major obstacles to the improved integration of structural databases such as MSD and sequence databases like UniProt is the absence of up to date and well-maintained mapping between corresponding entries. We have worked closely with the UniProt group at the EBI to clean up the taxonomy and sequence cross-reference information in the MSD and UniProt databases. This information is vital for the reliable integration of the sequence family databases such as Pfam and Interpro with the structure-oriented databases of SCOP and CATH. This information has been made available to the eFamily group (http://www.efamily.org.uk/) and now forms the basis of the regular interchange of information between the member databases (MSD, UniProt, Pfam, Interpro, SCOP and CATH). This exchange of annotation information has enriched the structural information in the MSD database with annotation from wider sequence-oriented resources. This work was carried out under the 'Structure Integration with Function, Taxonomy and Sequences (SIFTS)' initiative (http://www.ebi.ac.uk/msd-srv/docs/sifts) in the MSD group.

The Chinchilla Research Resource Database: resource for an otolaryngology disease model

PubMed Central

Shimoyama, Mary; Smith, Jennifer R.; De Pons, Jeff; Tutaj, Marek; Khampang, Pawjai; Hong, Wenzhou; Erbe, Christy B.; Ehrlich, Garth D.; Bakaletz, Lauren O.; Kerschner, Joseph E.

2016-01-01

The long-tailed chinchilla (Chinchilla lanigera) is an established animal model for diseases of the inner and middle ear, among others. In particular, chinchilla is commonly used to study diseases involving viral and bacterial pathogens and polymicrobial infections of the upper respiratory tract and the ear, such as otitis media. The value of the chinchilla as a model for human diseases prompted the sequencing of its genome in 2012 and the more recent development of the Chinchilla Research Resource Database (http://crrd.mcw.edu) to provide investigators with easy access to relevant datasets and software tools to enhance their research. The Chinchilla Research Resource Database contains a complete catalog of genes for chinchilla and, for comparative purposes, human. Chinchilla genes can be viewed in the context of their genomic scaffold positions using the JBrowse genome browser. In contrast to the corresponding records at NCBI, individual gene reports at CRRD include functional annotations for Disease, Gene Ontology (GO) Biological Process, GO Molecular Function, GO Cellular Component and Pathway assigned to chinchilla genes based on annotations from the corresponding human orthologs. Data can be retrieved via keyword and gene-specific searches. Lists of genes with similar functional attributes can be assembled by leveraging the hierarchical structure of the Disease, GO and Pathway vocabularies through the Ontology Search and Browser tool. Such lists can then be further analyzed for commonalities using the Gene Annotator (GA) Tool. All data in the Chinchilla Research Resource Database is freely accessible and downloadable via the CRRD FTP site or using the download functions available in the search and analysis tools. The Chinchilla Research Resource Database is a rich resource for researchers using, or considering the use of, chinchilla as a model for human disease. Database URL: http://crrd.mcw.edu PMID:27173523

The Plant Structure Ontology, a Unified Vocabulary of Anatomy and Morphology of a Flowering Plant1[W][OA

PubMed Central

Ilic, Katica; Kellogg, Elizabeth A.; Jaiswal, Pankaj; Zapata, Felipe; Stevens, Peter F.; Vincent, Leszek P.; Avraham, Shulamit; Reiser, Leonore; Pujar, Anuradha; Sachs, Martin M.; Whitman, Noah T.; McCouch, Susan R.; Schaeffer, Mary L.; Ware, Doreen H.; Stein, Lincoln D.; Rhee, Seung Y.

2007-01-01

Formal description of plant phenotypes and standardized annotation of gene expression and protein localization data require uniform terminology that accurately describes plant anatomy and morphology. This facilitates cross species comparative studies and quantitative comparison of phenotypes and expression patterns. A major drawback is variable terminology that is used to describe plant anatomy and morphology in publications and genomic databases for different species. The same terms are sometimes applied to different plant structures in different taxonomic groups. Conversely, similar structures are named by their species-specific terms. To address this problem, we created the Plant Structure Ontology (PSO), the first generic ontological representation of anatomy and morphology of a flowering plant. The PSO is intended for a broad plant research community, including bench scientists, curators in genomic databases, and bioinformaticians. The initial releases of the PSO integrated existing ontologies for Arabidopsis (Arabidopsis thaliana), maize (Zea mays), and rice (Oryza sativa); more recent versions of the ontology encompass terms relevant to Fabaceae, Solanaceae, additional cereal crops, and poplar (Populus spp.). Databases such as The Arabidopsis Information Resource, Nottingham Arabidopsis Stock Centre, Gramene, MaizeGDB, and SOL Genomics Network are using the PSO to describe expression patterns of genes and phenotypes of mutants and natural variants and are regularly contributing new annotations to the Plant Ontology database. The PSO is also used in specialized public databases, such as BRENDA, GENEVESTIGATOR, NASCArrays, and others. Over 10,000 gene annotations and phenotype descriptions from participating databases can be queried and retrieved using the Plant Ontology browser. The PSO, as well as contributed gene associations, can be obtained at www.plantontology.org. PMID:17142475

Linking microarray reporters with protein functions.

PubMed

Gaj, Stan; van Erk, Arie; van Haaften, Rachel I M; Evelo, Chris T A

2007-09-26

The analysis of microarray experiments requires accurate and up-to-date functional annotation of the microarray reporters to optimize the interpretation of the biological processes involved. Pathway visualization tools are used to connect gene expression data with existing biological pathways by using specific database identifiers that link reporters with elements in the pathways. This paper proposes a novel method that aims to improve microarray reporter annotation by BLASTing the original reporter sequences against a species-specific EMBL subset, that was derived from and crosslinked back to the highly curated UniProt database. The resulting alignments were filtered using high quality alignment criteria and further compared with the outcome of a more traditional approach, where reporter sequences were BLASTed against EnsEMBL followed by locating the corresponding protein (UniProt) entry for the high quality hits. Combining the results of both methods resulted in successful annotation of > 58% of all reporter sequences with UniProt IDs on two commercial array platforms, increasing the amount of Incyte reporters that could be coupled to Gene Ontology terms from 32.7% to 58.3% and to a local GenMAPP pathway from 9.6% to 16.7%. For Agilent, 35.3% of the total reporters are now linked towards GO nodes and 7.1% on local pathways. Our methods increased the annotation quality of microarray reporter sequences and allowed us to visualize more reporters using pathway visualization tools. Even in cases where the original reporter annotation showed the correct description the new identifiers often allowed improved pathway and Gene Ontology linking. These methods are freely available at http://www.bigcat.unimaas.nl/public/publications/Gaj_Annotation/.

Knowns and unknowns in metabolomics identified by multidimensional NMR and hybrid MS/NMR methods

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

Bingol, Kerem; Brüschweiler, Rafael

Metabolomics continues to make rapid progress through the development of new and better methods and their applications to gain insight into the metabolism of a wide range of different biological systems from a systems biology perspective. Customization of NMR databases and search tools allows the faster and more accurate identification of known metabolites, whereas the identification of unknowns, without a need for extensive purification, requires new strategies to integrate NMR with mass spectrometry, cheminformatics, and computational methods. For some applications, the use of covalent and non-covalent attachments in the form of labeled tags or nanoparticles can significantly reduce the complexitymore » of these tasks.« less

Discovery of A-type procyanidin dimers in yellow raspberries by untargeted metabolomics and correlation based data analysis.

PubMed

Carvalho, Elisabete; Franceschi, Pietro; Feller, Antje; Herrera, Lorena; Palmieri, Luisa; Arapitsas, Panagiotis; Riccadonna, Samantha; Martens, Stefan

2016-01-01

Raspberries are becoming increasingly popular due to their reported health beneficial properties. Despite the presence of only trace amounts of anthocyanins, yellow varieties seems to show similar or better effects in comparison to conventional raspberries. The aim of this work is to characterize the metabolic differences between red and yellow berries, focussing on the compounds showing a higher concentration in yellow varieties. The metabolomic profile of 13 red and 12 yellow raspberries (of different varieties, locations and collection dates) was determined by UPLC-TOF-MS. A novel approach based on Pearson correlation on the extracted ion chromatograms was implemented to extract the pseudospectra of the most relevant biomarkers from high energy LC-MS runs. The raw data will be made publicly available on MetaboLights (MTBLS333). Among the metabolites showing higher concentration in yellow raspberries it was possible to identify a series of compounds showing a pseudospectrum similar to that of A-type procyanidin polymers. The annotation of this group of compounds was confirmed by specific MS/MS experiments and performing standard injections. In berries lacking anthocyanins the polyphenol metabolism might be shifted to the formation of a novel class of A-type procyanidin polymers.

Prognostic Metabolite Biomarkers for Soft Tissue Sarcomas Discovered by Mass Spectrometry Imaging

NASA Astrophysics Data System (ADS)

Lou, Sha; Balluff, Benjamin; Cleven, Arjen H. G.; Bovée, Judith V. M. G.; McDonnell, Liam A.

2017-02-01

Metabolites can be an important read-out of disease. The identification and validation of biomarkers in the cancer metabolome that can stratify high-risk patients is one of the main current research aspects. Mass spectrometry has become the technique of choice for metabolomics studies, and mass spectrometry imaging (MSI) enables their visualization in patient tissues. In this study, we used MSI to identify prognostic metabolite biomarkers in high grade sarcomas; 33 high grade sarcoma patients, comprising osteosarcoma, leiomyosarcoma, myxofibrosarcoma, and undifferentiated pleomorphic sarcoma were analyzed. Metabolite MSI data were obtained from sections of fresh frozen tissue specimens with matrix-assisted laser/desorption ionization (MALDI) MSI in negative polarity using 9-aminoarcridine as matrix. Subsequent annotation of tumor regions by expert pathologists resulted in tumor-specific metabolite signatures, which were then tested for association with patient survival. Metabolite signals with significant clinical value were further validated and identified by high mass resolution Fourier transform ion cyclotron resonance (FTICR) MSI. Three metabolite signals were found to correlate with overall survival ( m/z 180.9436 and 241.0118) and metastasis-free survival ( m/z 160.8417). FTICR-MSI identified m/z 241.0118 as inositol cyclic phosphate and m/z 160.8417 as carnitine.

GARNET--gene set analysis with exploration of annotation relations.

PubMed

Rho, Kyoohyoung; Kim, Bumjin; Jang, Youngjun; Lee, Sanghyun; Bae, Taejeong; Seo, Jihae; Seo, Chaehwa; Lee, Jihyun; Kang, Hyunjung; Yu, Ungsik; Kim, Sunghoon; Lee, Sanghyuk; Kim, Wan Kyu

2011-02-15

Gene set analysis is a powerful method of deducing biological meaning for an a priori defined set of genes. Numerous tools have been developed to test statistical enrichment or depletion in specific pathways or gene ontology (GO) terms. Major difficulties towards biological interpretation are integrating diverse types of annotation categories and exploring the relationships between annotation terms of similar information. GARNET (Gene Annotation Relationship NEtwork Tools) is an integrative platform for gene set analysis with many novel features. It includes tools for retrieval of genes from annotation database, statistical analysis & visualization of annotation relationships, and managing gene sets. In an effort to allow access to a full spectrum of amassed biological knowledge, we have integrated a variety of annotation data that include the GO, domain, disease, drug, chromosomal location, and custom-defined annotations. Diverse types of molecular networks (pathways, transcription and microRNA regulations, protein-protein interaction) are also included. The pair-wise relationship between annotation gene sets was calculated using kappa statistics. GARNET consists of three modules--gene set manager, gene set analysis and gene set retrieval, which are tightly integrated to provide virtually automatic analysis for gene sets. A dedicated viewer for annotation network has been developed to facilitate exploration of the related annotations. GARNET (gene annotation relationship network tools) is an integrative platform for diverse types of gene set analysis, where complex relationships among gene annotations can be easily explored with an intuitive network visualization tool (http://garnet.isysbio.org/ or http://ercsb.ewha.ac.kr/garnet/).

PlantTFDB: a comprehensive plant transcription factor database

PubMed Central

Guo, An-Yuan; Chen, Xin; Gao, Ge; Zhang, He; Zhu, Qi-Hui; Liu, Xiao-Chuan; Zhong, Ying-Fu; Gu, Xiaocheng; He, Kun; Luo, Jingchu

2008-01-01

Transcription factors (TFs) play key roles in controlling gene expression. Systematic identification and annotation of TFs, followed by construction of TF databases may serve as useful resources for studying the function and evolution of transcription factors. We developed a comprehensive plant transcription factor database PlantTFDB (http://planttfdb.cbi.pku.edu.cn), which contains 26 402 TFs predicted from 22 species, including five model organisms with available whole genome sequence and 17 plants with available EST sequences. To provide comprehensive information for those putative TFs, we made extensive annotation at both family and gene levels. A brief introduction and key references were presented for each family. Functional domain information and cross-references to various well-known public databases were available for each identified TF. In addition, we predicted putative orthologs of those TFs among the 22 species. PlantTFDB has a simple interface to allow users to search the database by IDs or free texts, to make sequence similarity search against TFs of all or individual species, and to download TF sequences for local analysis. PMID:17933783

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

REDIdb: an upgraded bioinformatics resource for organellar RNA editing sites.

PubMed

Picardi, Ernesto; Regina, Teresa M R; Verbitskiy, Daniil; Brennicke, Axel; Quagliariello, Carla

2011-03-01

RNA editing is a post-transcriptional molecular process whereby the information in a genetic message is modified from that in the corresponding DNA template by means of nucleotide substitutions, insertions and/or deletions. It occurs mostly in organelles by clade-specific diverse and unrelated biochemical mechanisms. RNA editing events have been annotated in primary databases as GenBank and at more sophisticated level in the specialized databases REDIdb, dbRES and EdRNA. At present, REDIdb is the only freely available database that focuses on the organellar RNA editing process and annotates each editing modification in its biological context. Here we present an updated and upgraded release of REDIdb with a web-interface refurbished with graphical and computational facilities that improve RNA editing investigations. Details of the REDIdb features and novelties are illustrated and compared to other RNA editing databases. REDIdb is freely queried at http://biologia.unical.it/py_script/REDIdb/. Copyright © 2010 Elsevier B.V. and Mitochondria Research Society. All rights reserved.

GenomeHubs: simple containerized setup of a custom Ensembl database and web server for any species

PubMed Central

Kumar, Sujai; Stevens, Lewis; Blaxter, Mark

2017-01-01

Abstract As the generation and use of genomic datasets is becoming increasingly common in all areas of biology, the need for resources to collate, analyse and present data from one or more genome projects is becoming more pressing. The Ensembl platform is a powerful tool to make genome data and cross-species analyses easily accessible through a web interface and a comprehensive application programming interface. Here we introduce GenomeHubs, which provide a containerized environment to facilitate the setup and hosting of custom Ensembl genome browsers. This simplifies mirroring of existing content and import of new genomic data into the Ensembl database schema. GenomeHubs also provide a set of analysis containers to decorate imported genomes with results of standard analyses and functional annotations and support export to flat files, including EMBL format for submission of assemblies and annotations to International Nucleotide Sequence Database Collaboration. Database URL: http://GenomeHubs.org PMID:28605774

Relax with CouchDB - Into the non-relational DBMS era of Bioinformatics

PubMed Central

Manyam, Ganiraju; Payton, Michelle A.; Roth, Jack A.; Abruzzo, Lynne V.; Coombes, Kevin R.

2012-01-01

With the proliferation of high-throughput technologies, genome-level data analysis has become common in molecular biology. Bioinformaticians are developing extensive resources to annotate and mine biological features from high-throughput data. The underlying database management systems for most bioinformatics software are based on a relational model. Modern non-relational databases offer an alternative that has flexibility, scalability, and a non-rigid design schema. Moreover, with an accelerated development pace, non-relational databases like CouchDB can be ideal tools to construct bioinformatics utilities. We describe CouchDB by presenting three new bioinformatics resources: (a) geneSmash, which collates data from bioinformatics resources and provides automated gene-centric annotations, (b) drugBase, a database of drug-target interactions with a web interface powered by geneSmash, and (c) HapMap-CN, which provides a web interface to query copy number variations from three SNP-chip HapMap datasets. In addition to the web sites, all three systems can be accessed programmatically via web services. PMID:22609849

Computational Metabolomics: A Framework for the Million Metabolome

PubMed Central

Uppal, Karan; Walker, Douglas I.; Liu, Ken; Li, Shuzhao; Go, Young-Mi; Jones, Dean P.

2017-01-01

“Sola dosis facit venenum.” These words of Paracelsus, “the dose makes the poison”, can lead to a cavalier attitude concerning potential toxicities of the vast array of low abundance environmental chemicals to which humans are exposed. Exposome research teaches that 80–85% of human disease is linked to environmental exposures. The human exposome is estimated to include >400,000 environmental chemicals, most of which are uncharacterized with regard to human health. In fact, mass spectrometry measures >200,000 m/z features (ions) in microliter volumes derived from human samples; most are unidentified. This crystallizes a grand challenge for chemical research in toxicology: to develop reliable and affordable analytical methods to understand health impacts of the extensive human chemical experience. To this end, there appears to be no choice but to abandon the limitations of measuring one chemical at a time. The present review looks at progress in computational metabolomics to provide probability based annotation linking ions to known chemicals and serve as a foundation for unambiguous designation of unidentified ions for toxicologic study. We review methods to characterize ions in terms of accurate mass m/z, chromatographic retention time, correlation of adduct, isotopic and fragment forms, association with metabolic pathways and measurement of collision-induced dissociation products, collision cross section, and chirality. Such information can support a largely unambiguous system for documenting unidentified ions in environmental surveillance and human biomonitoring. Assembly of this data would provide a resource to characterize and understand health risks of the array of low-abundance chemicals to which humans are exposed. PMID:27629808

Mastitomics, the integrated omics of bovine milk in an experimental model of Streptococcus uberis mastitis: 3. Untargeted metabolomics.

PubMed

Thomas, Funmilola Clara; Mudaliar, Manikhandan; Tassi, Riccardo; McNeilly, Tom N; Burchmore, Richard; Burgess, Karl; Herzyk, Pawel; Zadoks, Ruth N; Eckersall, P David

2016-08-16

Intramammary infection leading to bovine mastitis is the leading disease problem affecting dairy cows and has marked effects on the milk produced by infected udder quarters. An experimental model of Streptococcus uberis mastitis has previously been investigated for clinical, immunological and pathophysiological alteration in milk, and has been the subject of peptidomic and quantitative proteomic investigation. The same sample set has now been investigated with a metabolomics approach using liquid chromatography and mass spectrometry. The analysis revealed over 3000 chromatographic peaks, of which 690 were putatively annotated with a metabolite. Hierarchical clustering analysis and principal component analysis demonstrated that metabolite changes due to S. uberis infection were maximal at 81 hours post challenge with metabolites in the milk from the resolution phase at 312 hours post challenge being closest to the pre-challenge samples. Metabolic pathway analysis revealed that the majority of the metabolites mapped to carbohydrate and nucleotide metabolism show a decreasing trend in concentration up to 81 hours post-challenge whereas an increasing trend was found in lipid metabolites and di-, tri- and tetra-peptides up to the same time point. The increase in these peptides coincides with an increase in larger peptides found in the previous peptidomic analysis and is likely to be due to protease degradation of milk proteins. Components of bile acid metabolism, linked to the FXR pathway regulating inflammation, were also increased. Metabolomic analysis of the response in milk during mastitis provides an essential component to the full understanding of the mammary gland's response to infection.

Illuminating a plant’s tissue-specific metabolic diversity using computational metabolomics and information theory

PubMed Central

Li, Dapeng; Heiling, Sven; Baldwin, Ian T.

2016-01-01

Secondary metabolite diversity is considered an important fitness determinant for plants’ biotic and abiotic interactions in nature. This diversity can be examined in two dimensions. The first one considers metabolite diversity across plant species. A second way of looking at this diversity is by considering the tissue-specific localization of pathways underlying secondary metabolism within a plant. Although these cross-tissue metabolite variations are increasingly regarded as important readouts of tissue-level gene function and regulatory processes, they have rarely been comprehensively explored by nontargeted metabolomics. As such, important questions have remained superficially addressed. For instance, which tissues exhibit prevalent signatures of metabolic specialization? Reciprocally, which metabolites contribute most to this tissue specialization in contrast to those metabolites exhibiting housekeeping characteristics? Here, we explore tissue-level metabolic specialization in Nicotiana attenuata, an ecological model with rich secondary metabolism, by combining tissue-wide nontargeted mass spectral data acquisition, information theory analysis, and tandem MS (MS/MS) molecular networks. This analysis was conducted for two different methanolic extracts of 14 tissues and deconvoluted 895 nonredundant MS/MS spectra. Using information theory analysis, anthers were found to harbor the most specialized metabolome, and most unique metabolites of anthers and other tissues were annotated through MS/MS molecular networks. Tissue–metabolite association maps were used to predict tissue-specific gene functions. Predictions for the function of two UDP-glycosyltransferases in flavonoid metabolism were confirmed by virus-induced gene silencing. The present workflow allows biologists to amortize the vast amount of data produced by modern MS instrumentation in their quest to understand gene function. PMID:27821729

Digging Deeper: Development and evaluation of an untargeted metabolomics approach to identify biogeochemical hotspots with depth and by vegetation type in Arctic tundra soils

NASA Astrophysics Data System (ADS)

Ladd, M.; Wullschleger, S.; Hettich, R.

2017-12-01

Elucidating the chemical composition of low molecular weight (LMW) dissolved organic matter (DOM), and monitoring how this bioavailable pool varies over space and time, is critical to understanding the controlling mechanisms that underlie carbon release and storage in Arctic systems. Due to analytical challenges however, relatively little is known about how this complex mixture of small molecules varies with soil depth or how it may be influenced by vegetation. In this study, we evaluated an untargeted metabolomics approach for the characterization of LMW DOM in water extracts, and applied this approach in soil cores (10-cm diam., 30-cm depth), obtained near Barrow, Alaska (71° 16' N) from the organic-rich active layer where the aboveground vegetation was primarily either Carex aquatilis or Eriophorum angustifolium, two species commonly found in tundra systems. We hypothesized that by using a discovery-based approach, spatial patterns of chemical diversity could be identified, enabling the detection of biogeochemical hotspots across scales. LMW DOM profiles from triplicate water extracts were characterized using dual-separation, nano-liquid chromatography (LC) coupled to an electrospray Orbitrap mass spectrometer in positive and negative ion modes. Both LC separations—reversed-phase and hydrophilic interaction chromatography—were achieved with gradient elutions in 15 minutes. Using a precursor and fragment mass measurement accuracy of <5 ppm for singly charged ions, unique features not observed in the blank or control were compared across all samples. Statistically significant differences with depth and between vegetation were determined, and the resulting list of features was matched to online databases. Annotated classes of LMW DOM included plant and microbial metabolites, organic acids, osmolytes, sugars, and simple peptides. Based on the chemical profile, we were able to distinguish between samples at each depth and between vegetation types, suggesting that a molecularly-resolved, data-driven approach could allow for more reliable predictions of how biogeochemical processes occurring at the molecular-scale (e.g. plant-microbial competition for organic nutrients) impact carbon fluxes in the Arctic at the landscape-scale.

Biomarker Discovery Using New Metabolomics Software for Automated Processing of High Resolution LC-MS Data

PubMed Central

Hnatyshyn, S.; Reily, M.; Shipkova, P.; McClure, T.; Sanders, M.; Peake, D.

2011-01-01

Robust biomarkers of target engagement and efficacy are required in different stages of drug discovery. Liquid chromatography coupled to high resolution mass spectrometry provides sensitivity, accuracy and wide dynamic range required for identification of endogenous metabolites in biological matrices. LCMS is widely-used tool for biomarker identification and validation. Typical high resolution LCMS profiles from biological samples may contain greater than a million mass spectral peaks corresponding to several thousand endogenous metabolites. Reduction of the total number of peaks, component identification and statistical comparison across sample groups remains to be a difficult and time consuming challenge. Blood samples from four groups of rats (male vs. female, fully satiated and food deprived) were analyzed using high resolution accurate mass (HRAM) LCMS. All samples were separated using a 15 minute reversed-phase C18 LC gradient and analyzed in both positive and negative ion modes. Data was acquired using 15K resolution and 5ppm mass measurement accuracy. The entire data set was analyzed using software developed in collaboration between Bristol Meyers Squibb and Thermo Fisher Scientific to determine the metabolic effects of food deprivation on rats. Metabolomic LC-MS data files are extraordinarily complex and appropriate reduction of the number of spectral peaks via identification of related peaks and background removal is essential. A single component such as hippuric acid generates more than 20 related peaks including isotopic clusters, adducts and dimers. Plasma and urine may contain 500-1500 unique quantifiable metabolites. Noise filtering approaches including blank subtraction were used to reduce the number of irrelevant peaks. By grouping related signals such as isotopic peaks and alkali adducts, data processing was greatly simplified by reducing the total number of components by 10-fold. The software processes 48 samples in under 60minutes. Principle Component Analysis showed substantial differences in endogenous metabolites levels between the animal groups. Annotation of components was accomplished via searching the ChemSpider database. Tentative assignments made using accurate mass need further verification by comparison with the retention time of authentic standards.

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.

Phenol-Explorer: an online comprehensive database on polyphenol contents in foods.

PubMed

Neveu, V; Perez-Jiménez, J; Vos, F; Crespy, V; du Chaffaut, L; Mennen, L; Knox, C; Eisner, R; Cruz, J; Wishart, D; Scalbert, A

2010-01-01

A number of databases on the plant metabolome describe the chemistry and biosynthesis of plant chemicals. However, no such database is specifically focused on foods and more precisely on polyphenols, one of the major classes of phytochemicals. As antioxidants, polyphenols influence human health and may play a role in the prevention of a number of chronic diseases such as cardiovascular diseases, some cancers or type 2 diabetes. To determine polyphenol intake in populations and study their association with health, it is essential to have detailed information on their content in foods. However this information is not easily collected due to the variety of their chemical structures and the variability of their content in a given food. Phenol-Explorer is the first comprehensive web-based database on polyphenol content in foods. It contains more than 37,000 original data points collected from 638 scientific articles published in peer-reviewed journals. The quality of these data has been evaluated before they were aggregated to produce final representative mean content values for 502 polyphenols in 452 foods. The web interface allows making various queries on the aggregated data to identify foods containing a given polyphenol or polyphenols present in a given food. For each mean content value, it is possible to trace all original content values and their literature sources. Phenol-Explorer is a major step forward in the development of databases on food constituents and the food metabolome. It should help researchers to better understand the role of phytochemicals in the technical and nutritional quality of food, and food manufacturers to develop tailor-made healthy foods. Database URL: http://www.phenol-explorer.eu.

Phenol-Explorer: an online comprehensive database on polyphenol contents in foods

PubMed Central

Neveu, V.; Perez-Jiménez, J.; Vos, F.; Crespy, V.; du Chaffaut, L.; Mennen, L.; Knox, C.; Eisner, R.; Cruz, J.; Wishart, D.; Scalbert, A.

2010-01-01

A number of databases on the plant metabolome describe the chemistry and biosynthesis of plant chemicals. However, no such database is specifically focused on foods and more precisely on polyphenols, one of the major classes of phytochemicals. As antoxidants, polyphenols influence human health and may play a role in the prevention of a number of chronic diseases such as cardiovascular diseases, some cancers or type 2 diabetes. To determine polyphenol intake in populations and study their association with health, it is essential to have detailed information on their content in foods. However this information is not easily collected due to the variety of their chemical structures and the variability of their content in a given food. Phenol-Explorer is the first comprehensive web-based database on polyphenol content in foods. It contains more than 37 000 original data points collected from 638 scientific articles published in peer-reviewed journals. The quality of these data has been evaluated before they were aggregated to produce final representative mean content values for 502 polyphenols in 452 foods. The web interface allows making various queries on the aggregated data to identify foods containing a given polyphenol or polyphenols present in a given food. For each mean content value, it is possible to trace all original content values and their literature sources. Phenol-Explorer is a major step forward in the development of databases on food constituents and the food metabolome. It should help researchers to better understand the role of phytochemicals in the technical and nutritional quality of food, and food manufacturers to develop tailor-made healthy foods. Database URL: http://www.phenol-explorer.eu PMID:20428313

AIM: a comprehensive Arabidopsis interactome module database and related interologs in plants.

PubMed

Wang, Yi; Thilmony, Roger; Zhao, Yunjun; Chen, Guoping; Gu, Yong Q

2014-01-01

Systems biology analysis of protein modules is important for understanding the functional relationships between proteins in the interactome. Here, we present a comprehensive database named AIM for Arabidopsis (Arabidopsis thaliana) interactome modules. The database contains almost 250,000 modules that were generated using multiple analysis methods and integration of microarray expression data. All the modules in AIM are well annotated using multiple gene function knowledge databases. AIM provides a user-friendly interface for different types of searches and offers a powerful graphical viewer for displaying module networks linked to the enrichment annotation terms. Both interactive Venn diagram and power graph viewer are integrated into the database for easy comparison of modules. In addition, predicted interologs from other plant species (homologous proteins from different species that share a conserved interaction module) are available for each Arabidopsis module. AIM is a powerful systems biology platform for obtaining valuable insights into the function of proteins in Arabidopsis and other plants using the modules of the Arabidopsis interactome. Database URL:http://probes.pw.usda.gov/AIM Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.

Machine learning applications in genetics and genomics.

PubMed

Libbrecht, Maxwell W; Noble, William Stafford

2015-06-01

The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.

LCGbase: A Comprehensive Database for Lineage-Based Co-regulated Genes.

PubMed

Wang, Dapeng; Zhang, Yubin; Fan, Zhonghua; Liu, Guiming; Yu, Jun

2012-01-01

Animal genes of different lineages, such as vertebrates and arthropods, are well-organized and blended into dynamic chromosomal structures that represent a primary regulatory mechanism for body development and cellular differentiation. The majority of genes in a genome are actually clustered, which are evolutionarily stable to different extents and biologically meaningful when evaluated among genomes within and across lineages. Until now, many questions concerning gene organization, such as what is the minimal number of genes in a cluster and what is the driving force leading to gene co-regulation, remain to be addressed. Here, we provide a user-friendly database-LCGbase (a comprehensive database for lineage-based co-regulated genes)-hosting information on evolutionary dynamics of gene clustering and ordering within animal kingdoms in two different lineages: vertebrates and arthropods. The database is constructed on a web-based Linux-Apache-MySQL-PHP framework and effective interactive user-inquiry service. Compared to other gene annotation databases with similar purposes, our database has three comprehensible advantages. First, our database is inclusive, including all high-quality genome assemblies of vertebrates and representative arthropod species. Second, it is human-centric since we map all gene clusters from other genomes in an order of lineage-ranks (such as primates, mammals, warm-blooded, and reptiles) onto human genome and start the database from well-defined gene pairs (a minimal cluster where the two adjacent genes are oriented as co-directional, convergent, and divergent pairs) to large gene clusters. Furthermore, users can search for any adjacent genes and their detailed annotations. Third, the database provides flexible parameter definitions, such as the distance of transcription start sites between two adjacent genes, which is extendable to genes that flanking the cluster across species. We also provide useful tools for sequence alignment, gene ontology (GO) annotation, promoter identification, gene expression (co-expression), and evolutionary analysis. This database not only provides a way to define lineage-specific and species-specific gene clusters but also facilitates future studies on gene co-regulation, epigenetic control of gene expression (DNA methylation and histone marks), and chromosomal structures in a context of gene clusters and species evolution. LCGbase is freely available at http://lcgbase.big.ac.cn/LCGbase.

Smartphone Analytics: Mobilizing the Lab into the Cloud for Omic-Scale Analyses.

PubMed

Montenegro-Burke, J Rafael; Phommavongsay, Thiery; Aisporna, Aries E; Huan, Tao; Rinehart, Duane; Forsberg, Erica; Poole, Farris L; Thorgersen, Michael P; Adams, Michael W W; Krantz, Gregory; Fields, Matthew W; Northen, Trent R; Robbins, Paul D; Niedernhofer, Laura J; Lairson, Luke; Benton, H Paul; Siuzdak, Gary

2016-10-04

Active data screening is an integral part of many scientific activities, and mobile technologies have greatly facilitated this process by minimizing the reliance on large hardware instrumentation. In order to meet with the increasingly growing field of metabolomics and heavy workload of data processing, we designed the first remote metabolomic data screening platform for mobile devices. Two mobile applications (apps), XCMS Mobile and METLIN Mobile, facilitate access to XCMS and METLIN, which are the most important components in the computer-based XCMS Online platforms. These mobile apps allow for the visualization and analysis of metabolic data throughout the entire analytical process. Specifically, XCMS Mobile and METLIN Mobile provide the capabilities for remote monitoring of data processing, real time notifications for the data processing, visualization and interactive analysis of processed data (e.g., cloud plots, principle component analysis, box-plots, extracted ion chromatograms, and hierarchical cluster analysis), and database searching for metabolite identification. These apps, available on Apple iOS and Google Android operating systems, allow for the migration of metabolomic research onto mobile devices for better accessibility beyond direct instrument operation. The utility of XCMS Mobile and METLIN Mobile functionalities was developed and is demonstrated here through the metabolomic LC-MS analyses of stem cells, colon cancer, aging, and bacterial metabolism.

Bioengineering Silicon Quantum Dot Theranostics using a Network Analysis of Metabolomic and Proteomic Data in Cardiac Ischemia

PubMed Central

Erogbogbo, Folarin; May, Jasmine; Swihart, Mark; Prasad, Paras N.; Smart, Katie; Jack, Seif El; Korcyk, Dariusz; Webster, Mark; Stewart, Ralph; Zeng, Irene; Jullig, Mia; Bakeev, Katherine; Jamieson, Michelle; Kasabov, Nikolas; Gopalan, Banu; Liang, Linda; Hu, Raphael; Schliebs, Stefan; Villas-Boas, Silas; Gladding, Patrick

2013-01-01

Metabolomic profiling is ideally suited for the analysis of cardiac metabolism in healthy and diseased states. Here, we show that systematic discovery of biomarkers of ischemic preconditioning using metabolomics can be translated to potential nanotheranostics. Thirty-three patients underwent percutaneous coronary intervention (PCI) after myocardial infarction. Blood was sampled from catheters in the coronary sinus, aorta and femoral vein before coronary occlusion and 20 minutes after one minute of coronary occlusion. Plasma was analysed using GC-MS metabolomics and iTRAQ LC-MS/MS proteomics. Proteins and metabolites were mapped into the Metacore network database (GeneGo, MI, USA) to establish functional relevance. Expression of 13 proteins was significantly different (p<0.05) as a result of PCI. Included amongst these was CD44, a cell surface marker of reperfusion injury. Thirty-eight metabolites were identified using a targeted approach. Using PCA, 42% of their variance was accounted for by 21 metabolites. Multiple metabolic pathways and potential biomarkers of cardiac ischemia, reperfusion and preconditioning were identified. CD44, a marker of reperfusion injury, and myristic acid, a potential preconditioning agent, were incorporated into a nanotheranostic that may be useful for cardiovascular applications. Integrating biomarker discovery techniques into rationally designed nanoconstructs may lead to improvements in disease-specific diagnosis and treatment. PMID:24019856

Smartphone Analytics: Mobilizing the Lab into the Cloud for Omic-Scale Analyses

PubMed Central

2016-01-01

Active data screening is an integral part of many scientific activities, and mobile technologies have greatly facilitated this process by minimizing the reliance on large hardware instrumentation. In order to meet with the increasingly growing field of metabolomics and heavy workload of data processing, we designed the first remote metabolomic data screening platform for mobile devices. Two mobile applications (apps), XCMS Mobile and METLIN Mobile, facilitate access to XCMS and METLIN, which are the most important components in the computer-based XCMS Online platforms. These mobile apps allow for the visualization and analysis of metabolic data throughout the entire analytical process. Specifically, XCMS Mobile and METLIN Mobile provide the capabilities for remote monitoring of data processing, real time notifications for the data processing, visualization and interactive analysis of processed data (e.g., cloud plots, principle component analysis, box-plots, extracted ion chromatograms, and hierarchical cluster analysis), and database searching for metabolite identification. These apps, available on Apple iOS and Google Android operating systems, allow for the migration of metabolomic research onto mobile devices for better accessibility beyond direct instrument operation. The utility of XCMS Mobile and METLIN Mobile functionalities was developed and is demonstrated here through the metabolomic LC-MS analyses of stem cells, colon cancer, aging, and bacterial metabolism. PMID:27560777

Smartphone Analytics: Mobilizing the Lab into the Cloud for Omic-Scale Analyses

DOE PAGES

Montenegro-Burke, J. Rafael; Phommavongsay, Thiery; Aisporna, Aries E.; ...

2016-08-25

Active data screening is an integral part of many scientific activities, and mobile technologies have greatly facilitated this process by minimizing the reliance on large hardware instrumentation. In order to meet with the increasingly growing field of metabolomics and heavy workload of data processing, we designed the first remote metabolomic data screening platform for mobile devices. Two mobile applications (apps), XCMS Mobile and METLIN Mobile, facilitate access to XCMS and METLIN, which are the most important components in the computer-based XCMS Online platforms. These mobile apps allow for the visualization and analysis of metabolic data throughout the entire analytical process.more » Specifically, XCMS Mobile and METLIN Mobile provide the capabilities for remote monitoring of data processing, real time notifications for the data processing, visualization and interactive analysis of processed data (e.g., cloud plots, principle component analysis, box-plots, extracted ion chromatograms, and hierarchical cluster analysis), and database searching for metabolite identification. These apps, available on Apple iOS and Google Android operating systems, allow for the migration of metabolomic research onto mobile devices for better accessibility beyond direct instrument operation. The utility of XCMS Mobile and METLIN Mobile functionalities was developed and is demonstrated here through the metabolomic LC-MS analyses of stem cells, colon cancer, aging, and bacterial metabolism.« less

Smartphone Analytics: Mobilizing the Lab into the Cloud for Omic-Scale Analyses

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

Montenegro-Burke, J. Rafael; Phommavongsay, Thiery; Aisporna, Aries E.

Active data screening is an integral part of many scientific activities, and mobile technologies have greatly facilitated this process by minimizing the reliance on large hardware instrumentation. In order to meet with the increasingly growing field of metabolomics and heavy workload of data processing, we designed the first remote metabolomic data screening platform for mobile devices. Two mobile applications (apps), XCMS Mobile and METLIN Mobile, facilitate access to XCMS and METLIN, which are the most important components in the computer-based XCMS Online platforms. These mobile apps allow for the visualization and analysis of metabolic data throughout the entire analytical process.more » Specifically, XCMS Mobile and METLIN Mobile provide the capabilities for remote monitoring of data processing, real time notifications for the data processing, visualization and interactive analysis of processed data (e.g., cloud plots, principle component analysis, box-plots, extracted ion chromatograms, and hierarchical cluster analysis), and database searching for metabolite identification. These apps, available on Apple iOS and Google Android operating systems, allow for the migration of metabolomic research onto mobile devices for better accessibility beyond direct instrument operation. The utility of XCMS Mobile and METLIN Mobile functionalities was developed and is demonstrated here through the metabolomic LC-MS analyses of stem cells, colon cancer, aging, and bacterial metabolism.« less

Metabolomics-Based Elucidation of Active Metabolic Pathways in Erythrocytes and HSC-Derived Reticulocytes.

PubMed

Srivastava, Anubhav; Evans, Krystal J; Sexton, Anna E; Schofield, Louis; Creek, Darren J

2017-04-07

A detailed analysis of the metabolic state of human-stem-cell-derived erythrocytes allowed us to characterize the existence of active metabolic pathways in younger reticulocytes and compare them to mature erythrocytes. Using high-resolution LC-MS-based untargeted metabolomics, we found that reticulocytes had a comparatively much richer repertoire of metabolites, which spanned a range of metabolite classes. An untargeted metabolomics analysis using stable-isotope-labeled glucose showed that only glycolysis and the pentose phosphate pathway actively contributed to the biosynthesis of metabolites in erythrocytes, and these pathways were upregulated in reticulocytes. Most metabolite species found to be enriched in reticulocytes were residual pools of metabolites produced by earlier erythropoietic processes, and their systematic depletion in mature erythrocytes aligns with the simplification process, which is also seen at the cellular and the structural level. Our work shows that high-resolution LC-MS-based untargeted metabolomics provides a global coverage of the biochemical species that are present in erythrocytes. However, the incorporation of stable isotope labeling provides a more accurate description of the active metabolic processes that occur in each developmental stage. To our knowledge, this is the first detailed characterization of the active metabolic pathways of the erythroid lineage, and it provides a rich database for understanding the physiology of the maturation of reticulocytes into mature erythrocytes.

Chemical annotation of small and peptide-like molecules at the Protein Data Bank

PubMed Central

Young, Jasmine Y.; Feng, Zukang; Dimitropoulos, Dimitris; Sala, Raul; Westbrook, John; Zhuravleva, Marina; Shao, Chenghua; Quesada, Martha; Peisach, Ezra; Berman, Helen M.

2013-01-01

Over the past decade, the number of polymers and their complexes with small molecules in the Protein Data Bank archive (PDB) has continued to increase significantly. To support scientific advancements and ensure the best quality and completeness of the data files over the next 10 years and beyond, the Worldwide PDB partnership that manages the PDB archive is developing a new deposition and annotation system. This system focuses on efficient data capture across all supported experimental methods. The new deposition and annotation system is composed of four major modules that together support all of the processing requirements for a PDB entry. In this article, we describe one such module called the Chemical Component Annotation Tool. This tool uses information from both the Chemical Component Dictionary and Biologically Interesting molecule Reference Dictionary to aid in annotation. Benchmark studies have shown that the Chemical Component Annotation Tool provides significant improvements in processing efficiency and data quality. Database URL: http://wwpdb.org PMID:24291661

APPRIS: annotation of principal and alternative splice isoforms

PubMed Central

Rodriguez, Jose Manuel; Maietta, Paolo; Ezkurdia, Iakes; Pietrelli, Alessandro; Wesselink, Jan-Jaap; Lopez, Gonzalo; Valencia, Alfonso; Tress, Michael L.

2013-01-01

Here, we present APPRIS (http://appris.bioinfo.cnio.es), a database that houses annotations of human splice isoforms. APPRIS has been designed to provide value to manual annotations of the human genome by adding reliable protein structural and functional data and information from cross-species conservation. The visual representation of the annotations provided by APPRIS for each gene allows annotators and researchers alike to easily identify functional changes brought about by splicing events. In addition to collecting, integrating and analyzing reliable predictions of the effect of splicing events, APPRIS also selects a single reference sequence for each gene, here termed the principal isoform, based on the annotations of structure, function and conservation for each transcript. APPRIS identifies a principal isoform for 85% of the protein-coding genes in the GENCODE 7 release for ENSEMBL. Analysis of the APPRIS data shows that at least 70% of the alternative (non-principal) variants would lose important functional or structural information relative to the principal isoform. PMID:23161672

Chemical annotation of small and peptide-like molecules at the Protein Data Bank.

PubMed

Young, Jasmine Y; Feng, Zukang; Dimitropoulos, Dimitris; Sala, Raul; Westbrook, John; Zhuravleva, Marina; Shao, Chenghua; Quesada, Martha; Peisach, Ezra; Berman, Helen M

2013-01-01

Over the past decade, the number of polymers and their complexes with small molecules in the Protein Data Bank archive (PDB) has continued to increase significantly. To support scientific advancements and ensure the best quality and completeness of the data files over the next 10 years and beyond, the Worldwide PDB partnership that manages the PDB archive is developing a new deposition and annotation system. This system focuses on efficient data capture across all supported experimental methods. The new deposition and annotation system is composed of four major modules that together support all of the processing requirements for a PDB entry. In this article, we describe one such module called the Chemical Component Annotation Tool. This tool uses information from both the Chemical Component Dictionary and Biologically Interesting molecule Reference Dictionary to aid in annotation. Benchmark studies have shown that the Chemical Component Annotation Tool provides significant improvements in processing efficiency and data quality. Database URL: http://wwpdb.org.

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

Semantic Annotations and Querying of Web Data Sources

NASA Astrophysics Data System (ADS)

Hornung, Thomas; May, Wolfgang

A large part of the Web, actually holding a significant portion of the useful information throughout the Web, consists of views on hidden databases, provided by numerous heterogeneous interfaces that are partly human-oriented via Web forms ("Deep Web"), and partly based on Web Services (only machine accessible). In this paper we present an approach for annotating these sources in a way that makes them citizens of the Semantic Web. We illustrate how queries can be stated in terms of the ontology, and how the annotations are used to selected and access appropriate sources and to answer the queries.

Fuzzy Emotional Semantic Analysis and Automated Annotation of Scene Images

PubMed Central

Cao, Jianfang; Chen, Lichao

2015-01-01

With the advances in electronic and imaging techniques, the production of digital images has rapidly increased, and the extraction and automated annotation of emotional semantics implied by images have become issues that must be urgently addressed. To better simulate human subjectivity and ambiguity for understanding scene images, the current study proposes an emotional semantic annotation method for scene images based on fuzzy set theory. A fuzzy membership degree was calculated to describe the emotional degree of a scene image and was implemented using the Adaboost algorithm and a back-propagation (BP) neural network. The automated annotation method was trained and tested using scene images from the SUN Database. The annotation results were then compared with those based on artificial annotation. Our method showed an annotation accuracy rate of 91.2% for basic emotional values and 82.4% after extended emotional values were added, which correspond to increases of 5.5% and 8.9%, respectively, compared with the results from using a single BP neural network algorithm. Furthermore, the retrieval accuracy rate based on our method reached approximately 89%. This study attempts to lay a solid foundation for the automated emotional semantic annotation of more types of images and therefore is of practical significance. PMID:25838818

PipeOnline 2.0: automated EST processing and functional data sorting.

PubMed

Ayoubi, Patricia; Jin, Xiaojing; Leite, Saul; Liu, Xianghui; Martajaja, Jeson; Abduraham, Abdurashid; Wan, Qiaolan; Yan, Wei; Misawa, Eduardo; Prade, Rolf A

2002-11-01

Expressed sequence tags (ESTs) are generated and deposited in the public domain, as redundant, unannotated, single-pass reactions, with virtually no biological content. PipeOnline automatically analyses and transforms large collections of raw DNA-sequence data from chromatograms or FASTA files by calling the quality of bases, screening and removing vector sequences, assembling and rewriting consensus sequences of redundant input files into a unigene EST data set and finally through translation, amino acid sequence similarity searches, annotation of public databases and functional data. PipeOnline generates an annotated database, retaining the processed unigene sequence, clone/file history, alignments with similar sequences, and proposed functional classification, if available. Functional annotation is automatic and based on a novel method that relies on homology of amino acid sequence multiplicity within GenBank records. Records are examined through a function ordered browser or keyword queries with automated export of results. PipeOnline offers customization for individual projects (MyPipeOnline), automated updating and alert service. PipeOnline is available at http://stress-genomics.org.

Chemotext: A Publicly Available Web Server for Mining Drug-Target-Disease Relationships in PubMed.

PubMed

Capuzzi, Stephen J; Thornton, Thomas E; Liu, Kammy; Baker, Nancy; Lam, Wai In; O'Banion, Colin P; Muratov, Eugene N; Pozefsky, Diane; Tropsha, Alexander

2018-02-26

Elucidation of the mechanistic relationships between drugs, their targets, and diseases is at the core of modern drug discovery research. Thousands of studies relevant to the drug-target-disease (DTD) triangle have been published and annotated in the Medline/PubMed database. Mining this database affords rapid identification of all published studies that confirm connections between vertices of this triangle or enable new inferences of such connections. To this end, we describe the development of Chemotext, a publicly available Web server that mines the entire compendium of published literature in PubMed annotated by Medline Subject Heading (MeSH) terms. The goal of Chemotext is to identify all known DTD relationships and infer missing links between vertices of the DTD triangle. As a proof-of-concept, we show that Chemotext could be instrumental in generating new drug repurposing hypotheses or annotating clinical outcomes pathways for known drugs. The Chemotext Web server is freely available at http://chemotext.mml.unc.edu .

The Proteome Folding Project: Proteome-scale prediction of structure and function

PubMed Central

Drew, Kevin; Winters, Patrick; Butterfoss, Glenn L.; Berstis, Viktors; Uplinger, Keith; Armstrong, Jonathan; Riffle, Michael; Schweighofer, Erik; Bovermann, Bill; Goodlett, David R.; Davis, Trisha N.; Shasha, Dennis; Malmström, Lars; Bonneau, Richard

2011-01-01

The incompleteness of proteome structure and function annotation is a critical problem for biologists and, in particular, severely limits interpretation of high-throughput and next-generation experiments. We have developed a proteome annotation pipeline based on structure prediction, where function and structure annotations are generated using an integration of sequence comparison, fold recognition, and grid-computing-enabled de novo structure prediction. We predict protein domain boundaries and three-dimensional (3D) structures for protein domains from 94 genomes (including human, Arabidopsis, rice, mouse, fly, yeast, Escherichia coli, and worm). De novo structure predictions were distributed on a grid of more than 1.5 million CPUs worldwide (World Community Grid). We generated significant numbers of new confident fold annotations (9% of domains that are otherwise unannotated in these genomes). We demonstrate that predicted structures can be combined with annotations from the Gene Ontology database to predict new and more specific molecular functions. PMID:21824995

The history of the CATH structural classification of protein domains.

PubMed

Sillitoe, Ian; Dawson, Natalie; Thornton, Janet; Orengo, Christine

2015-12-01

This article presents a historical review of the protein structure classification database CATH. Together with the SCOP database, CATH remains comprehensive and reasonably up-to-date with the now more than 100,000 protein structures in the PDB. We review the expansion of the CATH and SCOP resources to capture predicted domain structures in the genome sequence data and to provide information on the likely functions of proteins mediated by their constituent domains. The establishment of comprehensive function annotation resources has also meant that domain families can be functionally annotated allowing insights into functional divergence and evolution within protein families. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

EUCLID: automatic classification of proteins in functional classes by their database annotations.

PubMed

Tamames, J; Ouzounis, C; Casari, G; Sander, C; Valencia, A

1998-01-01

A tool is described for the automatic classification of sequences in functional classes using their database annotations. The Euclid system is based on a simple learning procedure from examples provided by human experts. Euclid is freely available for academics at http://www.gredos.cnb.uam.es/EUCLID, with the corresponding dictionaries for the generation of three, eight and 14 functional classes. E-mail: valencia@cnb.uam.es The results of the EUCLID classification of different genomes are available at http://www.sander.ebi.ac. uk/genequiz/. A detailed description of the different applications mentioned in the text is available at http://www.gredos.cnb.uam. es/EUCLID/Full_Paper

Cloning, analysis and functional annotation of expressed sequence tags from the Earthworm Eisenia fetida

PubMed Central

Pirooznia, Mehdi; Gong, Ping; Guan, Xin; Inouye, Laura S; Yang, Kuan; Perkins, Edward J; Deng, Youping

2007-01-01

Background Eisenia fetida, commonly known as red wiggler or compost worm, belongs to the Lumbricidae family of the Annelida phylum. Little is known about its genome sequence although it has been extensively used as a test organism in terrestrial ecotoxicology. In order to understand its gene expression response to environmental contaminants, we cloned 4032 cDNAs or expressed sequence tags (ESTs) from two E. fetida libraries enriched with genes responsive to ten ordnance related compounds using suppressive subtractive hybridization-PCR. Results A total of 3144 good quality ESTs (GenBank dbEST accession number EH669363–EH672369 and EL515444–EL515580) were obtained from the raw clone sequences after cleaning. Clustering analysis yielded 2231 unique sequences including 448 contigs (from 1361 ESTs) and 1783 singletons. Comparative genomic analysis showed that 743 or 33% of the unique sequences shared high similarity with existing genes in the GenBank nr database. Provisional function annotation assigned 830 Gene Ontology terms to 517 unique sequences based on their homology with the annotated genomes of four model organisms Drosophila melanogaster, Mus musculus, Saccharomyces cerevisiae, and Caenorhabditis elegans. Seven percent of the unique sequences were further mapped to 99 Kyoto Encyclopedia of Genes and Genomes pathways based on their matching Enzyme Commission numbers. All the information is stored and retrievable at a highly performed, web-based and user-friendly relational database called EST model database or ESTMD version 2. Conclusion The ESTMD containing the sequence and annotation information of 4032 E. fetida ESTs is publicly accessible at . PMID:18047730

Basic level scene understanding: categories, attributes and structures

PubMed Central

Xiao, Jianxiong; Hays, James; Russell, Bryan C.; Patterson, Genevieve; Ehinger, Krista A.; Torralba, Antonio; Oliva, Aude

2013-01-01

A longstanding goal of computer vision is to build a system that can automatically understand a 3D scene from a single image. This requires extracting semantic concepts and 3D information from 2D images which can depict an enormous variety of environments that comprise our visual world. This paper summarizes our recent efforts toward these goals. First, we describe the richly annotated SUN database which is a collection of annotated images spanning 908 different scene categories with object, attribute, and geometric labels for many scenes. This database allows us to systematically study the space of scenes and to establish a benchmark for scene and object recognition. We augment the categorical SUN database with 102 scene attributes for every image and explore attribute recognition. Finally, we present an integrated system to extract the 3D structure of the scene and objects depicted in an image. PMID:24009590

Canis mtDNA HV1 database: a web-based tool for collecting and surveying Canis mtDNA HV1 haplotype in public database.

PubMed

Thai, Quan Ke; Chung, Dung Anh; Tran, Hoang-Dung

2017-06-26

Canine and wolf mitochondrial DNA haplotypes, which can be used for forensic or phylogenetic analyses, have been defined in various schemes depending on the region analyzed. In recent studies, the 582 bp fragment of the HV1 region is most commonly used. 317 different canine HV1 haplotypes have been reported in the rapidly growing public database GenBank. These reported haplotypes contain several inconsistencies in their haplotype information. To overcome this issue, we have developed a Canis mtDNA HV1 database. This database collects data on the HV1 582 bp region in dog mitochondrial DNA from the GenBank to screen and correct the inconsistencies. It also supports users in detection of new novel mutation profiles and assignment of new haplotypes. The Canis mtDNA HV1 database (CHD) contains 5567 nucleotide entries originating from 15 subspecies in the species Canis lupus. Of these entries, 3646 were haplotypes and grouped into 804 distinct sequences. 319 sequences were recognized as previously assigned haplotypes, while the remaining 485 sequences had new mutation profiles and were marked as new haplotype candidates awaiting further analysis for haplotype assignment. Of the 3646 nucleotide entries, only 414 were annotated with correct haplotype information, while 3232 had insufficient or lacked haplotype information and were corrected or modified before storing in the CHD. The CHD can be accessed at http://chd.vnbiology.com . It provides sequences, haplotype information, and a web-based tool for mtDNA HV1 haplotyping. The CHD is updated monthly and supplies all data for download. The Canis mtDNA HV1 database contains information about canine mitochondrial DNA HV1 sequences with reconciled annotation. It serves as a tool for detection of inconsistencies in GenBank and helps identifying new HV1 haplotypes. Thus, it supports the scientific community in naming new HV1 haplotypes and to reconcile existing annotation of HV1 582 bp sequences.