Modelling the structure of a ceRNA-theoretical, bipartite microRNA-mRNA interaction network regulating intestinal epithelial cellular pathways using R programming.

PubMed

Robinson, J M; Henderson, W A

2018-01-12

We report a method using functional-molecular databases and network modelling to identify hypothetical mRNA-miRNA interaction networks regulating intestinal epithelial barrier function. The model forms a data-analysis component of our cell culture experiments, which produce RNA expression data from Nanostring Technologies nCounter ® system. The epithelial tight-junction (TJ) and actin cytoskeleton interact as molecular components of the intestinal epithelial barrier. Upstream regulation of TJ-cytoskeleton interaction is effected by the Rac/Rock/Rho signaling pathway and other associated pathways which may be activated or suppressed by extracellular signaling from growth factors, hormones, and immune receptors. Pathway activations affect epithelial homeostasis, contributing to degradation of the epithelial barrier associated with osmotic dysregulation, inflammation, and tumor development. The complexity underlying miRNA-mRNA interaction networks represents a roadblock for prediction and validation of competing-endogenous RNA network function. We developed a network model to identify hypothetical co-regulatory motifs in a miRNA-mRNA interaction network related to epithelial function. A mRNA-miRNA interaction list was generated using KEGG and miRWalk2.0 databases. R-code was developed to quantify and visualize inherent network structures. We identified a sub-network with a high number of shared, targeting miRNAs, of genes associated with cellular proliferation and cancer, including c-MYC and Cyclin D.

Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae

PubMed Central

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

2006-01-01

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

TranscriptomeBrowser 3.0: introducing a new compendium of molecular interactions and a new visualization tool for the study of gene regulatory networks.

PubMed

Lepoivre, Cyrille; Bergon, Aurélie; Lopez, Fabrice; Perumal, Narayanan B; Nguyen, Catherine; Imbert, Jean; Puthier, Denis

2012-01-31

Deciphering gene regulatory networks by in silico approaches is a crucial step in the study of the molecular perturbations that occur in diseases. The development of regulatory maps is a tedious process requiring the comprehensive integration of various evidences scattered over biological databases. Thus, the research community would greatly benefit from having a unified database storing known and predicted molecular interactions. Furthermore, given the intrinsic complexity of the data, the development of new tools offering integrated and meaningful visualizations of molecular interactions is necessary to help users drawing new hypotheses without being overwhelmed by the density of the subsequent graph. We extend the previously developed TranscriptomeBrowser database with a set of tables containing 1,594,978 human and mouse molecular interactions. The database includes: (i) predicted regulatory interactions (computed by scanning vertebrate alignments with a set of 1,213 position weight matrices), (ii) potential regulatory interactions inferred from systematic analysis of ChIP-seq experiments, (iii) regulatory interactions curated from the literature, (iv) predicted post-transcriptional regulation by micro-RNA, (v) protein kinase-substrate interactions and (vi) physical protein-protein interactions. In order to easily retrieve and efficiently analyze these interactions, we developed In-teractomeBrowser, a graph-based knowledge browser that comes as a plug-in for Transcriptome-Browser. The first objective of InteractomeBrowser is to provide a user-friendly tool to get new insight into any gene list by providing a context-specific display of putative regulatory and physical interactions. To achieve this, InteractomeBrowser relies on a "cell compartments-based layout" that makes use of a subset of the Gene Ontology to map gene products onto relevant cell compartments. This layout is particularly powerful for visual integration of heterogeneous biological information and is a productive avenue in generating new hypotheses. The second objective of InteractomeBrowser is to fill the gap between interaction databases and dynamic modeling. It is thus compatible with the network analysis software Cytoscape and with the Gene Interaction Network simulation software (GINsim). We provide examples underlying the benefits of this visualization tool for large gene set analysis related to thymocyte differentiation. The InteractomeBrowser plugin is a powerful tool to get quick access to a knowledge database that includes both predicted and validated molecular interactions. InteractomeBrowser is available through the TranscriptomeBrowser framework and can be found at: http://tagc.univ-mrs.fr/tbrowser/. Our database is updated on a regular basis.

Protein-protein interaction networks: unraveling the wiring of molecular machines within the cell.

PubMed

De Las Rivas, Javier; Fontanillo, Celia

2012-11-01

Mapping and understanding of the protein interaction networks with their key modules and hubs can provide deeper insights into the molecular machinery underlying complex phenotypes. In this article, we present the basic characteristics and definitions of protein networks, starting with a distinction of the different types of associations between proteins. We focus the review on protein-protein interactions (PPIs), a subset of associations defined as physical contacts between proteins that occur by selective molecular docking in a particular biological context. We present such definition as opposed to other types of protein associations derived from regulatory, genetic, structural or functional relations. To determine PPIs, a variety of binary and co-complex methods exist; however, not all the technologies provide the same information and data quality. A way of increasing confidence in a given protein interaction is to integrate orthogonal experimental evidences. The use of several complementary methods testing each single interaction assesses the accuracy of PPI data and tries to minimize the occurrence of false interactions. Following this approach there have been important efforts to unify primary databases of experimentally proven PPIs into integrated databases. These meta-databases provide a measure of the confidence of interactions based on the number of experimental proofs that report them. As a conclusion, we can state that integrated information allows the building of more reliable interaction networks. Identification of communities, cliques, modules and hubs by analysing the topological parameters and graph properties of the protein networks allows the discovery of central/critical nodes, which are candidates to regulate cellular flux and dynamics.

HBVPathDB: a database of HBV infection-related molecular interaction network.

PubMed

Zhang, Yi; Bo, Xiao-Chen; Yang, Jing; Wang, Sheng-Qi

2005-03-21

To describe molecules or genes interaction between hepatitis B viruses (HBV) and host, for understanding how virus' and host's genes and molecules are networked to form a biological system and for perceiving mechanism of HBV infection. The knowledge of HBV infection-related reactions was organized into various kinds of pathways with carefully drawn graphs in HBVPathDB. Pathway information is stored with relational database management system (DBMS), which is currently the most efficient way to manage large amounts of data and query is implemented with powerful Structured Query Language (SQL). The search engine is written using Personal Home Page (PHP) with SQL embedded and web retrieval interface is developed for searching with Hypertext Markup Language (HTML). We present the first version of HBVPathDB, which is a HBV infection-related molecular interaction network database composed of 306 pathways with 1 050 molecules involved. With carefully drawn graphs, pathway information stored in HBVPathDB can be browsed in an intuitive way. We develop an easy-to-use interface for flexible accesses to the details of database. Convenient software is implemented to query and browse the pathway information of HBVPathDB. Four search page layout options-category search, gene search, description search, unitized search-are supported by the search engine of the database. The database is freely available at http://www.bio-inf.net/HBVPathDB/HBV/. The conventional perspective HBVPathDB have already contained a considerable amount of pathway information with HBV infection related, which is suitable for in-depth analysis of molecular interaction network of virus and host. HBVPathDB integrates pathway data-sets with convenient software for query, browsing, visualization, that provides users more opportunity to identify regulatory key molecules as potential drug targets and to explore the possible mechanism of HBV infection based on gene expression datasets.

Text mining for metabolic pathways, signaling cascades, and protein networks.

PubMed

Hoffmann, Robert; Krallinger, Martin; Andres, Eduardo; Tamames, Javier; Blaschke, Christian; Valencia, Alfonso

2005-05-10

The complexity of the information stored in databases and publications on metabolic and signaling pathways, the high throughput of experimental data, and the growing number of publications make it imperative to provide systems to help the researcher navigate through these interrelated information resources. Text-mining methods have started to play a key role in the creation and maintenance of links between the information stored in biological databases and its original sources in the literature. These links will be extremely useful for database updating and curation, especially if a number of technical problems can be solved satisfactorily, including the identification of protein and gene names (entities in general) and the characterization of their types of interactions. The first generation of openly accessible text-mining systems, such as iHOP (Information Hyperlinked over Proteins), provides additional functions to facilitate the reconstruction of protein interaction networks, combine database and text information, and support the scientist in the formulation of novel hypotheses. The next challenge is the generation of comprehensive information regarding the general function of signaling pathways and protein interaction networks.

Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing.

PubMed

Udrescu, Lucreţia; Sbârcea, Laura; Topîrceanu, Alexandru; Iovanovici, Alexandru; Kurunczi, Ludovic; Bogdan, Paul; Udrescu, Mihai

2016-09-07

Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications.

Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing

PubMed Central

Udrescu, Lucreţia; Sbârcea, Laura; Topîrceanu, Alexandru; Iovanovici, Alexandru; Kurunczi, Ludovic; Bogdan, Paul; Udrescu, Mihai

2016-01-01

Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications. PMID:27599720

SpirPro: A Spirulina proteome database and web-based tools for the analysis of protein-protein interactions at the metabolic level in Spirulina (Arthrospira) platensis C1.

PubMed

Senachak, Jittisak; Cheevadhanarak, Supapon; Hongsthong, Apiradee

2015-07-29

Spirulina (Arthrospira) platensis is the only cyanobacterium that in addition to being studied at the molecular level and subjected to gene manipulation, can also be mass cultivated in outdoor ponds for commercial use as a food supplement. Thus, encountering environmental changes, including temperature stresses, is common during the mass production of Spirulina. The use of cyanobacteria as an experimental platform, especially for photosynthetic gene manipulation in plants and bacteria, is becoming increasingly important. Understanding the mechanisms and protein-protein interaction networks that underlie low- and high-temperature responses is relevant to Spirulina mass production. To accomplish this goal, high-throughput techniques such as OMICs analyses are used. Thus, large datasets must be collected, managed and subjected to information extraction. Therefore, databases including (i) proteomic analysis and protein-protein interaction (PPI) data and (ii) domain/motif visualization tools are required for potential use in temperature response models for plant chloroplasts and photosynthetic bacteria. A web-based repository was developed including an embedded database, SpirPro, and tools for network visualization. Proteome data were analyzed integrated with protein-protein interactions and/or metabolic pathways from KEGG. The repository provides various information, ranging from raw data (2D-gel images) to associated results, such as data from interaction and/or pathway analyses. This integration allows in silico analyses of protein-protein interactions affected at the metabolic level and, particularly, analyses of interactions between and within the affected metabolic pathways under temperature stresses for comparative proteomic analysis. The developed tool, which is coded in HTML with CSS/JavaScript and depicted in Scalable Vector Graphics (SVG), is designed for interactive analysis and exploration of the constructed network. SpirPro is publicly available on the web at http://spirpro.sbi.kmutt.ac.th . SpirPro is an analysis platform containing an integrated proteome and PPI database that provides the most comprehensive data on this cyanobacterium at the systematic level. As an integrated database, SpirPro can be applied in various analyses, such as temperature stress response networking analysis in cyanobacterial models and interacting domain-domain analysis between proteins of interest.

DIMA 3.0: Domain Interaction Map.

PubMed

Luo, Qibin; Pagel, Philipp; Vilne, Baiba; Frishman, Dmitrij

2011-01-01

Domain Interaction MAp (DIMA, available at http://webclu.bio.wzw.tum.de/dima) is a database of predicted and known interactions between protein domains. It integrates 5807 structurally known interactions imported from the iPfam and 3did databases and 46,900 domain interactions predicted by four computational methods: domain phylogenetic profiling, domain pair exclusion algorithm correlated mutations and domain interaction prediction in a discriminative way. Additionally predictions are filtered to exclude those domain pairs that are reported as non-interacting by the Negatome database. The DIMA Web site allows to calculate domain interaction networks either for a domain of interest or for entire organisms, and to explore them interactively using the Flash-based Cytoscape Web software.

Molecular Interaction Map of the Mammalian Cell Cycle Control and DNA Repair Systems

PubMed Central

Kohn, Kurt W.

1999-01-01

Eventually to understand the integrated function of the cell cycle regulatory network, we must organize the known interactions in the form of a diagram, map, and/or database. A diagram convention was designed capable of unambiguous representation of networks containing multiprotein complexes, protein modifications, and enzymes that are substrates of other enzymes. To facilitate linkage to a database, each molecular species is symbolically represented only once in each diagram. Molecular species can be located on the map by means of indexed grid coordinates. Each interaction is referenced to an annotation list where pertinent information and references can be found. Parts of the network are grouped into functional subsystems. The map shows how multiprotein complexes could assemble and function at gene promoter sites and at sites of DNA damage. It also portrays the richness of connections between the p53-Mdm2 subsystem and other parts of the network. PMID:10436023

Integrated web visualizations for protein-protein interaction databases.

PubMed

Jeanquartier, Fleur; Jean-Quartier, Claire; Holzinger, Andreas

2015-06-16

Understanding living systems is crucial for curing diseases. To achieve this task we have to understand biological networks based on protein-protein interactions. Bioinformatics has come up with a great amount of databases and tools that support analysts in exploring protein-protein interactions on an integrated level for knowledge discovery. They provide predictions and correlations, indicate possibilities for future experimental research and fill the gaps to complete the picture of biochemical processes. There are numerous and huge databases of protein-protein interactions used to gain insights into answering some of the many questions of systems biology. Many computational resources integrate interaction data with additional information on molecular background. However, the vast number of diverse Bioinformatics resources poses an obstacle to the goal of understanding. We present a survey of databases that enable the visual analysis of protein networks. We selected M=10 out of N=53 resources supporting visualization, and we tested against the following set of criteria: interoperability, data integration, quantity of possible interactions, data visualization quality and data coverage. The study reveals differences in usability, visualization features and quality as well as the quantity of interactions. StringDB is the recommended first choice. CPDB presents a comprehensive dataset and IntAct lets the user change the network layout. A comprehensive comparison table is available via web. The supplementary table can be accessed on http://tinyurl.com/PPI-DB-Comparison-2015. Only some web resources featuring graph visualization can be successfully applied to interactive visual analysis of protein-protein interaction. Study results underline the necessity for further enhancements of visualization integration in biochemical analysis tools. Identified challenges are data comprehensiveness, confidence, interactive feature and visualization maturing.

Merging in-silico and in vitro salivary protein complex partners using the STRING database: A tutorial.

PubMed

Crosara, Karla Tonelli Bicalho; Moffa, Eduardo Buozi; Xiao, Yizhi; Siqueira, Walter Luiz

2018-01-16

Protein-protein interaction is a common physiological mechanism for protection and actions of proteins in an organism. The identification and characterization of protein-protein interactions in different organisms is necessary to better understand their physiology and to determine their efficacy. In a previous in vitro study using mass spectrometry, we identified 43 proteins that interact with histatin 1. Six previously documented interactors were confirmed and 37 novel partners were identified. In this tutorial, we aimed to demonstrate the usefulness of the STRING database for studying protein-protein interactions. We used an in-silico approach along with the STRING database (http://string-db.org/) and successfully performed a fast simulation of a novel constructed histatin 1 protein-protein network, including both the previously known and the predicted interactors, along with our newly identified interactors. Our study highlights the advantages and importance of applying bioinformatics tools to merge in-silico tactics with experimental in vitro findings for rapid advancement of our knowledge about protein-protein interactions. Our findings also indicate that bioinformatics tools such as the STRING protein network database can help predict potential interactions between proteins and thus serve as a guide for future steps in our exploration of the Human Interactome. Our study highlights the usefulness of the STRING protein database for studying protein-protein interactions. The STRING database can collect and integrate data about known and predicted protein-protein associations from many organisms, including both direct (physical) and indirect (functional) interactions, in an easy-to-use interface. Copyright © 2017 Elsevier B.V. All rights reserved.

CerebralWeb: a Cytoscape.js plug-in to visualize networks stratified by subcellular localization.

PubMed

Frias, Silvia; Bryan, Kenneth; Brinkman, Fiona S L; Lynn, David J

2015-01-01

CerebralWeb is a light-weight JavaScript plug-in that extends Cytoscape.js to enable fast and interactive visualization of molecular interaction networks stratified based on subcellular localization or other user-supplied annotation. The application is designed to be easily integrated into any website and is configurable to support customized network visualization. CerebralWeb also supports the automatic retrieval of Cerebral-compatible localizations for human, mouse and bovine genes via a web service and enables the automated parsing of Cytoscape compatible XGMML network files. CerebralWeb currently supports embedded network visualization on the InnateDB (www.innatedb.com) and Allergy and Asthma Portal (allergen.innatedb.com) database and analysis resources. Database tool URL: http://www.innatedb.com/CerebralWeb © The Author(s) 2015. Published by Oxford University Press.

An online database for informing ecological network models: http://kelpforest.ucsc.edu.

PubMed

Beas-Luna, Rodrigo; Novak, Mark; Carr, Mark H; Tinker, Martin T; Black, August; Caselle, Jennifer E; Hoban, Michael; Malone, Dan; Iles, Alison

2014-01-01

Ecological network models and analyses are recognized as valuable tools for understanding the dynamics and resiliency of ecosystems, and for informing ecosystem-based approaches to management. However, few databases exist that can provide the life history, demographic and species interaction information necessary to parameterize ecological network models. Faced with the difficulty of synthesizing the information required to construct models for kelp forest ecosystems along the West Coast of North America, we developed an online database (http://kelpforest.ucsc.edu/) to facilitate the collation and dissemination of such information. Many of the database's attributes are novel yet the structure is applicable and adaptable to other ecosystem modeling efforts. Information for each taxonomic unit includes stage-specific life history, demography, and body-size allometries. Species interactions include trophic, competitive, facilitative, and parasitic forms. Each data entry is temporally and spatially explicit. The online data entry interface allows researchers anywhere to contribute and access information. Quality control is facilitated by attributing each entry to unique contributor identities and source citations. The database has proven useful as an archive of species and ecosystem-specific information in the development of several ecological network models, for informing management actions, and for education purposes (e.g., undergraduate and graduate training). To facilitate adaptation of the database by other researches for other ecosystems, the code and technical details on how to customize this database and apply it to other ecosystems are freely available and located at the following link (https://github.com/kelpforest-cameo/databaseui).

An Online Database for Informing Ecological Network Models: http://kelpforest.ucsc.edu

PubMed Central

Beas-Luna, Rodrigo; Novak, Mark; Carr, Mark H.; Tinker, Martin T.; Black, August; Caselle, Jennifer E.; Hoban, Michael; Malone, Dan; Iles, Alison

2014-01-01

Ecological network models and analyses are recognized as valuable tools for understanding the dynamics and resiliency of ecosystems, and for informing ecosystem-based approaches to management. However, few databases exist that can provide the life history, demographic and species interaction information necessary to parameterize ecological network models. Faced with the difficulty of synthesizing the information required to construct models for kelp forest ecosystems along the West Coast of North America, we developed an online database (http://kelpforest.ucsc.edu/) to facilitate the collation and dissemination of such information. Many of the database's attributes are novel yet the structure is applicable and adaptable to other ecosystem modeling efforts. Information for each taxonomic unit includes stage-specific life history, demography, and body-size allometries. Species interactions include trophic, competitive, facilitative, and parasitic forms. Each data entry is temporally and spatially explicit. The online data entry interface allows researchers anywhere to contribute and access information. Quality control is facilitated by attributing each entry to unique contributor identities and source citations. The database has proven useful as an archive of species and ecosystem-specific information in the development of several ecological network models, for informing management actions, and for education purposes (e.g., undergraduate and graduate training). To facilitate adaptation of the database by other researches for other ecosystems, the code and technical details on how to customize this database and apply it to other ecosystems are freely available and located at the following link (https://github.com/kelpforest-cameo/databaseui). PMID:25343723

An online database for informing ecological network models: http://kelpforest.ucsc.edu

USGS Publications Warehouse

Beas-Luna, Rodrigo; Tinker, M. Tim; Novak, Mark; Carr, Mark H.; Black, August; Caselle, Jennifer E.; Hoban, Michael; Malone, Dan; Iles, Alison C.

2014-01-01

Ecological network models and analyses are recognized as valuable tools for understanding the dynamics and resiliency of ecosystems, and for informing ecosystem-based approaches to management. However, few databases exist that can provide the life history, demographic and species interaction information necessary to parameterize ecological network models. Faced with the difficulty of synthesizing the information required to construct models for kelp forest ecosystems along the West Coast of North America, we developed an online database (http://kelpforest.ucsc.edu/) to facilitate the collation and dissemination of such information. Many of the database's attributes are novel yet the structure is applicable and adaptable to other ecosystem modeling efforts. Information for each taxonomic unit includes stage-specific life history, demography, and body-size allometries. Species interactions include trophic, competitive, facilitative, and parasitic forms. Each data entry is temporally and spatially explicit. The online data entry interface allows researchers anywhere to contribute and access information. Quality control is facilitated by attributing each entry to unique contributor identities and source citations. The database has proven useful as an archive of species and ecosystem-specific information in the development of several ecological network models, for informing management actions, and for education purposes (e.g., undergraduate and graduate training). To facilitate adaptation of the database by other researches for other ecosystems, the code and technical details on how to customize this database and apply it to other ecosystems are freely available and located at the following link (https://github.com/kelpforest-cameo/data​baseui).

Differential C3NET reveals disease networks of direct physical interactions

PubMed Central

2011-01-01

Background Genes might have different gene interactions in different cell conditions, which might be mapped into different networks. Differential analysis of gene networks allows spotting condition-specific interactions that, for instance, form disease networks if the conditions are a disease, such as cancer, and normal. This could potentially allow developing better and subtly targeted drugs to cure cancer. Differential network analysis with direct physical gene interactions needs to be explored in this endeavour. Results C3NET is a recently introduced information theory based gene network inference algorithm that infers direct physical gene interactions from expression data, which was shown to give consistently higher inference performances over various networks than its competitors. In this paper, we present, DC3net, an approach to employ C3NET in inferring disease networks. We apply DC3net on a synthetic and real prostate cancer datasets, which show promising results. With loose cutoffs, we predicted 18583 interactions from tumor and normal samples in total. Although there are no reference interactions databases for the specific conditions of our samples in the literature, we found verifications for 54 of our predicted direct physical interactions from only four of the biological interaction databases. As an example, we predicted that RAD50 with TRF2 have prostate cancer specific interaction that turned out to be having validation from the literature. It is known that RAD50 complex associates with TRF2 in the S phase of cell cycle, which suggests that this predicted interaction may promote telomere maintenance in tumor cells in order to allow tumor cells to divide indefinitely. Our enrichment analysis suggests that the identified tumor specific gene interactions may be potentially important in driving the growth in prostate cancer. Additionally, we found that the highest connected subnetwork of our predicted tumor specific network is enriched for all proliferation genes, which further suggests that the genes in this network may serve in the process of oncogenesis. Conclusions Our approach reveals disease specific interactions. It may help to make experimental follow-up studies more cost and time efficient by prioritizing disease relevant parts of the global gene network. PMID:21777411

Building a glaucoma interaction network using a text mining approach.

PubMed

Soliman, Maha; Nasraoui, Olfa; Cooper, Nigel G F

2016-01-01

The volume of biomedical literature and its underlying knowledge base is rapidly expanding, making it beyond the ability of a single human being to read through all the literature. Several automated methods have been developed to help make sense of this dilemma. The present study reports on the results of a text mining approach to extract gene interactions from the data warehouse of published experimental results which are then used to benchmark an interaction network associated with glaucoma. To the best of our knowledge, there is, as yet, no glaucoma interaction network derived solely from text mining approaches. The presence of such a network could provide a useful summative knowledge base to complement other forms of clinical information related to this disease. A glaucoma corpus was constructed from PubMed Central and a text mining approach was applied to extract genes and their relations from this corpus. The extracted relations between genes were checked using reference interaction databases and classified generally as known or new relations. The extracted genes and relations were then used to construct a glaucoma interaction network. Analysis of the resulting network indicated that it bears the characteristics of a small world interaction network. Our analysis showed the presence of seven glaucoma linked genes that defined the network modularity. A web-based system for browsing and visualizing the extracted glaucoma related interaction networks is made available at http://neurogene.spd.louisville.edu/GlaucomaINViewer/Form1.aspx. This study has reported the first version of a glaucoma interaction network using a text mining approach. The power of such an approach is in its ability to cover a wide range of glaucoma related studies published over many years. Hence, a bigger picture of the disease can be established. To the best of our knowledge, this is the first glaucoma interaction network to summarize the known literature. The major findings were a set of relations that could not be found in existing interaction databases and that were found to be new, in addition to a smaller subnetwork consisting of interconnected clusters of seven glaucoma genes. Future improvements can be applied towards obtaining a better version of this network.

Reconstruction of a Functional Human Gene Network, with an Application for Prioritizing Positional Candidate Genes

PubMed Central

Franke, Lude; Bakel, Harm van; Fokkens, Like; de Jong, Edwin D.; Egmont-Petersen, Michael; Wijmenga, Cisca

2006-01-01

Most common genetic disorders have a complex inheritance and may result from variants in many genes, each contributing only weak effects to the disease. Pinpointing these disease genes within the myriad of susceptibility loci identified in linkage studies is difficult because these loci may contain hundreds of genes. However, in any disorder, most of the disease genes will be involved in only a few different molecular pathways. If we know something about the relationships between the genes, we can assess whether some genes (which may reside in different loci) functionally interact with each other, indicating a joint basis for the disease etiology. There are various repositories of information on pathway relationships. To consolidate this information, we developed a functional human gene network that integrates information on genes and the functional relationships between genes, based on data from the Kyoto Encyclopedia of Genes and Genomes, the Biomolecular Interaction Network Database, Reactome, the Human Protein Reference Database, the Gene Ontology database, predicted protein-protein interactions, human yeast two-hybrid interactions, and microarray coexpressions. We applied this network to interrelate positional candidate genes from different disease loci and then tested 96 heritable disorders for which the Online Mendelian Inheritance in Man database reported at least three disease genes. Artificial susceptibility loci, each containing 100 genes, were constructed around each disease gene, and we used the network to rank these genes on the basis of their functional interactions. By following up the top five genes per artificial locus, we were able to detect at least one known disease gene in 54% of the loci studied, representing a 2.8-fold increase over random selection. This suggests that our method can significantly reduce the cost and effort of pinpointing true disease genes in analyses of disorders for which numerous loci have been reported but for which most of the genes are unknown. PMID:16685651

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).

Video Discs in Libraries.

ERIC Educational Resources Information Center

Barker, Philip

1986-01-01

Discussion of developments in information storage technology likely to have significant impact upon library utilization focuses on hardware (videodisc technology) and software developments (knowledge databases; computer networks; database management systems; interactive video, computer, and multimedia user interfaces). Three generic computer-based…

Social inertia and diversity in collaboration networks

NASA Astrophysics Data System (ADS)

Ramasco, J. J.

2007-04-01

Random graphs are useful tools to study social interactions. In particular, the use of weighted random graphs allows to handle a high level of information concerning which agents interact and in which degree the interactions take place. Taking advantage of this representation, we recently defined a magnitude, the Social Inertia, that measures the eagerness of agents to keep ties with previous partners. To study this magnitude, we used collaboration networks that are specially appropriate to obtain valid statitical results due to the large size of publically available databases. In this work, I study the Social Inertia in two of these empirical networks, IMDB movie database and condmat. More specifically, I focus on how the Inertia relates to other properties of the graphs, and show that the Inertia provides information on how the weight of neighboring edges correlates. A social interpretation of this effect is also offered.

Revealing the potential pathogenesis of glioma by utilizing a glioma associated protein-protein interaction network.

PubMed

Pan, Weiran; Li, Gang; Yang, Xiaoxiao; Miao, Jinming

2015-04-01

This study aims to explore the potential mechanism of glioma through bioinformatic approaches. The gene expression profile (GSE4290) of glioma tumor and non-tumor samples was downloaded from Gene Expression Omnibus database. A total of 180 samples were available, including 23 non-tumor and 157 tumor samples. Then the raw data were preprocessed using robust multiarray analysis, and 8,890 differentially expressed genes (DEGs) were identified by using t-test (false discovery rate < 0.0005). Furthermore, 16 known glioma related genes were abstracted from Genetic Association Database. After mapping 8,890 DEGs and 16 known glioma related genes to Human Protein Reference Database, a glioma associated protein-protein interaction network (GAPN) was constructed. In addition, 51 sub-networks in GAPN were screened out through Molecular Complex Detection (score ≥ 1), and sub-network 1 was found to have the closest interaction (score = 3). What' more, for the top 10 sub-networks, Gene Ontology (GO) enrichment analysis (p value < 0.05) was performed, and DEGs involved in sub-network 1 and 2, such as BRMS1L and CCNA1, were predicted to regulate cell growth, cell cycle, and DNA replication via interacting with known glioma related genes. Finally, the overlaps of DEGs and human essential, housekeeping, tissue-specific genes were calculated (p value = 1.0, 1.0, and 0.00014, respectively) and visualized by Venn Diagram package in R. About 61% of human tissue-specific genes were DEGs as well. This research shed new light on the pathogenesis of glioma based on DEGs and GAPN, and our findings might provide potential targets for clinical glioma treatment.

Including the Group Quarters Population in the US Synthesized Population Database

PubMed Central

Chasteen, Bernadette M.; Wheaton, William D.; Cooley, Philip C.; Ganapathi, Laxminarayana; Wagener, Diane K.

2011-01-01

In 2005, RTI International researchers developed methods to generate synthesized population data on US households for the US Synthesized Population Database. These data are used in agent-based modeling, which simulates large-scale social networks to test how changes in the behaviors of individuals affect the overall network. Group quarters are residences where individuals live in close proximity and interact frequently. Although the Synthesized Population Database represents the population living in households, data for the nation’s group quarters residents are not easily quantified because of US Census Bureau reporting methods designed to protect individuals’ privacy. Including group quarters population data can be an important factor in agent-based modeling because the number of residents and the frequency of their interactions are variables that directly affect modeling results. Particularly with infectious disease modeling, the increased frequency of agent interaction may increase the probability of infectious disease transmission between individuals and the probability of disease outbreaks. This report reviews our methods to synthesize data on group quarters residents to match US Census Bureau data. Our goal in developing the Group Quarters Population Database was to enable its use with RTI’s US Synthesized Population Database in the Modeling of Infectious Diseases Agent Study. PMID:21841972

SoyFN: a knowledge database of soybean functional networks.

PubMed

Xu, Yungang; Guo, Maozu; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang

2014-01-01

Many databases for soybean genomic analysis have been built and made publicly available, but few of them contain knowledge specifically targeting the omics-level gene-gene, gene-microRNA (miRNA) and miRNA-miRNA interactions. Here, we present SoyFN, a knowledge database of soybean functional gene networks and miRNA functional networks. SoyFN provides user-friendly interfaces to retrieve, visualize, analyze and download the functional networks of soybean genes and miRNAs. In addition, it incorporates much information about KEGG pathways, gene ontology annotations and 3'-UTR sequences as well as many useful tools including SoySearch, ID mapping, Genome Browser, eFP Browser and promoter motif scan. SoyFN is a schema-free database that can be accessed as a Web service from any modern programming language using a simple Hypertext Transfer Protocol call. The Web site is implemented in Java, JavaScript, PHP, HTML and Apache, with all major browsers supported. We anticipate that this database will be useful for members of research communities both in soybean experimental science and bioinformatics. Database URL: http://nclab.hit.edu.cn/SoyFN.

Investigation of candidate genes for osteoarthritis based on gene expression profiles.

PubMed

Dong, Shuanghai; Xia, Tian; Wang, Lei; Zhao, Qinghua; Tian, Jiwei

2016-12-01

To explore the mechanism of osteoarthritis (OA) and provide valid biological information for further investigation. Gene expression profile of GSE46750 was downloaded from Gene Expression Omnibus database. The Linear Models for Microarray Data (limma) package (Bioconductor project, http://www.bioconductor.org/packages/release/bioc/html/limma.html) was used to identify differentially expressed genes (DEGs) in inflamed OA samples. Gene Ontology function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis of DEGs were performed based on Database for Annotation, Visualization and Integrated Discovery data, and protein-protein interaction (PPI) network was constructed based on the Search Tool for the Retrieval of Interacting Genes/Proteins database. Regulatory network was screened based on Encyclopedia of DNA Elements. Molecular Complex Detection was used for sub-network screening. Two sub-networks with highest node degree were integrated with transcriptional regulatory network and KEGG functional enrichment analysis was processed for 2 modules. In total, 401 up- and 196 down-regulated DEGs were obtained. Up-regulated DEGs were involved in inflammatory response, while down-regulated DEGs were involved in cell cycle. PPI network with 2392 protein interactions was constructed. Moreover, 10 genes including Interleukin 6 (IL6) and Aurora B kinase (AURKB) were found to be outstanding in PPI network. There are 214 up- and 8 down-regulated transcription factor (TF)-target pairs in the TF regulatory network. Module 1 had TFs including SPI1, PRDM1, and FOS, while module 2 contained FOSL1. The nodes in module 1 were enriched in chemokine signaling pathway, while the nodes in module 2 were mainly enriched in cell cycle. The screened DEGs including IL6, AGT, and AURKB might be potential biomarkers for gene therapy for OA by being regulated by TFs such as FOS and SPI1, and participating in the cell cycle and cytokine-cytokine receptor interaction pathway. Copyright © 2016 Turkish Association of Orthopaedics and Traumatology. Production and hosting by Elsevier B.V. All rights reserved.

ProteoLens: a visual analytic tool for multi-scale database-driven biological network data mining.

PubMed

Huan, Tianxiao; Sivachenko, Andrey Y; Harrison, Scott H; Chen, Jake Y

2008-08-12

New systems biology studies require researchers to understand how interplay among myriads of biomolecular entities is orchestrated in order to achieve high-level cellular and physiological functions. Many software tools have been developed in the past decade to help researchers visually navigate large networks of biomolecular interactions with built-in template-based query capabilities. To further advance researchers' ability to interrogate global physiological states of cells through multi-scale visual network explorations, new visualization software tools still need to be developed to empower the analysis. A robust visual data analysis platform driven by database management systems to perform bi-directional data processing-to-visualizations with declarative querying capabilities is needed. We developed ProteoLens as a JAVA-based visual analytic software tool for creating, annotating and exploring multi-scale biological networks. It supports direct database connectivity to either Oracle or PostgreSQL database tables/views, on which SQL statements using both Data Definition Languages (DDL) and Data Manipulation languages (DML) may be specified. The robust query languages embedded directly within the visualization software help users to bring their network data into a visualization context for annotation and exploration. ProteoLens supports graph/network represented data in standard Graph Modeling Language (GML) formats, and this enables interoperation with a wide range of other visual layout tools. The architectural design of ProteoLens enables the de-coupling of complex network data visualization tasks into two distinct phases: 1) creating network data association rules, which are mapping rules between network node IDs or edge IDs and data attributes such as functional annotations, expression levels, scores, synonyms, descriptions etc; 2) applying network data association rules to build the network and perform the visual annotation of graph nodes and edges according to associated data values. We demonstrated the advantages of these new capabilities through three biological network visualization case studies: human disease association network, drug-target interaction network and protein-peptide mapping network. The architectural design of ProteoLens makes it suitable for bioinformatics expert data analysts who are experienced with relational database management to perform large-scale integrated network visual explorations. ProteoLens is a promising visual analytic platform that will facilitate knowledge discoveries in future network and systems biology studies.

MicroRNA Gene Regulatory Networks in Peripheral Nerve Sheath Tumors

DTIC Science & Technology

2013-09-01

3.0 hierarchical clustering of both the X and the Y-axis using Centroid linkage. The resulting clustered matrixes were visualized using Java Treeview...To score potential ceRNA interactions, the 54979 human interactions were loaded into a mySQL database and when the user selects a given mRNA all...on the fly using PHP interactions with mySQL in a similar fashion as previously described in our publicly available databases such as sarcoma

Role for protein–protein interaction databases in human genetics

PubMed Central

Pattin, Kristine A; Moore, Jason H

2010-01-01

Proteomics and the study of protein–protein interactions are becoming increasingly important in our effort to understand human diseases on a system-wide level. Thanks to the development and curation of protein-interaction databases, up-to-date information on these interaction networks is accessible and publicly available to the scientific community. As our knowledge of protein–protein interactions increases, it is important to give thought to the different ways that these resources can impact biomedical research. In this article, we highlight the importance of protein–protein interactions in human genetics and genetic epidemiology. Since protein–protein interactions demonstrate one of the strongest functional relationships between genes, combining genomic data with available proteomic data may provide us with a more in-depth understanding of common human diseases. In this review, we will discuss some of the fundamentals of protein interactions, the databases that are publicly available and how information from these databases can be used to facilitate genome-wide genetic studies. PMID:19929610

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

PubMed

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

2013-01-01

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

Protein interaction networks from literature mining

NASA Astrophysics Data System (ADS)

Ihara, Sigeo

2005-03-01

The ability to accurately predict and understand physiological changes in the biological network system in response to disease or drug therapeutics is of crucial importance in life science. The extensive amount of gene expression data generated from even a single microarray experiment often proves difficult to fully interpret and comprehend the biological significance. An increasing knowledge of protein interactions stored in the PubMed database, as well as the advancement of natural language processing, however, makes it possible to construct protein interaction networks from the gene expression information that are essential for understanding the biological meaning. From the in house literature mining system we have developed, the protein interaction network for humans was constructed. By analysis based on the graph-theoretical characterization of the total interaction network in literature, we found that the network is scale-free and semantic long-ranged interactions (i.e. inhibit, induce) between proteins dominate in the total interaction network, reducing the degree exponent. Interaction networks generated based on scientific text in which the interaction event is ambiguously described result in disconnected networks. In contrast interaction networks based on text in which the interaction events are clearly stated result in strongly connected networks. The results of protein-protein interaction networks obtained in real applications from microarray experiments are discussed: For example, comparisons of the gene expression data indicative of either a good or a poor prognosis for acute lymphoblastic leukemia with MLL rearrangements, using our system, showed newly discovered signaling cross-talk.

Experiments and Analysis on a Computer Interface to an Information-Retrieval Network.

ERIC Educational Resources Information Center

Marcus, Richard S.; Reintjes, J. Francis

A primary goal of this project was to develop an interface that would provide direct access for inexperienced users to existing online bibliographic information retrieval networks. The experiment tested the concept of a virtual-system mode of access to a network of heterogeneous interactive retrieval systems and databases. An experimental…

atBioNet--an integrated network analysis tool for genomics and biomarker discovery.

PubMed

Ding, Yijun; Chen, Minjun; Liu, Zhichao; Ding, Don; Ye, Yanbin; Zhang, Min; Kelly, Reagan; Guo, Li; Su, Zhenqiang; Harris, Stephen C; Qian, Feng; Ge, Weigong; Fang, Hong; Xu, Xiaowei; Tong, Weida

2012-07-20

Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity. atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis. atBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm.

DOMMINO 2.0: integrating structurally resolved protein-, RNA-, and DNA-mediated macromolecular interactions

PubMed Central

Kuang, Xingyan; Dhroso, Andi; Han, Jing Ginger; Shyu, Chi-Ren; Korkin, Dmitry

2016-01-01

Macromolecular interactions are formed between proteins, DNA and RNA molecules. Being a principle building block in macromolecular assemblies and pathways, the interactions underlie most of cellular functions. Malfunctioning of macromolecular interactions is also linked to a number of diseases. Structural knowledge of the macromolecular interaction allows one to understand the interaction’s mechanism, determine its functional implications and characterize the effects of genetic variations, such as single nucleotide polymorphisms, on the interaction. Unfortunately, until now the interactions mediated by different types of macromolecules, e.g. protein–protein interactions or protein–DNA interactions, are collected into individual and unrelated structural databases. This presents a significant obstacle in the analysis of macromolecular interactions. For instance, the homogeneous structural interaction databases prevent scientists from studying structural interactions of different types but occurring in the same macromolecular complex. Here, we introduce DOMMINO 2.0, a structural Database Of Macro-Molecular INteractiOns. Compared to DOMMINO 1.0, a comprehensive database on protein-protein interactions, DOMMINO 2.0 includes the interactions between all three basic types of macromolecules extracted from PDB files. DOMMINO 2.0 is automatically updated on a weekly basis. It currently includes ∼1 040 000 interactions between two polypeptide subunits (e.g. domains, peptides, termini and interdomain linkers), ∼43 000 RNA-mediated interactions, and ∼12 000 DNA-mediated interactions. All protein structures in the database are annotated using SCOP and SUPERFAMILY family annotation. As a result, protein-mediated interactions involving protein domains, interdomain linkers, C- and N- termini, and peptides are identified. Our database provides an intuitive web interface, allowing one to investigate interactions at three different resolution levels: whole subunit network, binary interaction and interaction interface. Database URL: http://dommino.org PMID:26827237

Updates on drug-target network; facilitating polypharmacology and data integration by growth of DrugBank database.

PubMed

Barneh, Farnaz; Jafari, Mohieddin; Mirzaie, Mehdi

2016-11-01

Network pharmacology elucidates the relationship between drugs and targets. As the identified targets for each drug increases, the corresponding drug-target network (DTN) evolves from solely reflection of the pharmaceutical industry trend to a portrait of polypharmacology. The aim of this study was to evaluate the potentials of DrugBank database in advancing systems pharmacology. We constructed and analyzed DTN from drugs and targets associations in the DrugBank 4.0 database. Our results showed that in bipartite DTN, increased ratio of identified targets for drugs augmented density and connectivity of drugs and targets and decreased modular structure. To clear up the details in the network structure, the DTNs were projected into two networks namely, drug similarity network (DSN) and target similarity network (TSN). In DSN, various classes of Food and Drug Administration-approved drugs with distinct therapeutic categories were linked together based on shared targets. Projected TSN also showed complexity because of promiscuity of the drugs. By including investigational drugs that are currently being tested in clinical trials, the networks manifested more connectivity and pictured the upcoming pharmacological space in the future years. Diverse biological processes and protein-protein interactions were manipulated by new drugs, which can extend possible target combinations. We conclude that network-based organization of DrugBank 4.0 data not only reveals the potential for repurposing of existing drugs, also allows generating novel predictions about drugs off-targets, drug-drug interactions and their side effects. Our results also encourage further effort for high-throughput identification of targets to build networks that can be integrated into disease networks. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Gene essentiality and the topology of protein interaction networks

PubMed Central

Coulomb, Stéphane; Bauer, Michel; Bernard, Denis; Marsolier-Kergoat, Marie-Claude

2005-01-01

The mechanistic bases for gene essentiality and for cell mutational resistance have long been disputed. The recent availability of large protein interaction databases has fuelled the analysis of protein interaction networks and several authors have proposed that gene dispensability could be strongly related to some topological parameters of these networks. However, many results were based on protein interaction data whose biases were not taken into account. In this article, we show that the essentiality of a gene in yeast is poorly related to the number of interactants (or degree) of the corresponding protein and that the physiological consequences of gene deletions are unrelated to several other properties of proteins in the interaction networks, such as the average degrees of their nearest neighbours, their clustering coefficients or their relative distances. We also found that yeast protein interaction networks lack degree correlation, i.e. a propensity for their vertices to associate according to their degrees. Gene essentiality and more generally cell resistance against mutations thus seem largely unrelated to many parameters of protein network topology. PMID:16087428

CoryneRegNet 4.0 – A reference database for corynebacterial gene regulatory networks

PubMed Central

Baumbach, Jan

2007-01-01

Background Detailed information on DNA-binding transcription factors (the key players in the regulation of gene expression) and on transcriptional regulatory interactions of microorganisms deduced from literature-derived knowledge, computer predictions and global DNA microarray hybridization experiments, has opened the way for the genome-wide analysis of transcriptional regulatory networks. The large-scale reconstruction of these networks allows the in silico analysis of cell behavior in response to changing environmental conditions. We previously published CoryneRegNet, an ontology-based data warehouse of corynebacterial transcription factors and regulatory networks. Initially, it was designed to provide methods for the analysis and visualization of the gene regulatory network of Corynebacterium glutamicum. Results Now we introduce CoryneRegNet release 4.0, which integrates data on the gene regulatory networks of 4 corynebacteria, 2 mycobacteria and the model organism Escherichia coli K12. As the previous versions, CoryneRegNet provides a web-based user interface to access the database content, to allow various queries, and to support the reconstruction, analysis and visualization of regulatory networks at different hierarchical levels. In this article, we present the further improved database content of CoryneRegNet along with novel analysis features. The network visualization feature GraphVis now allows the inter-species comparisons of reconstructed gene regulatory networks and the projection of gene expression levels onto that networks. Therefore, we added stimulon data directly into the database, but also provide Web Service access to the DNA microarray analysis platform EMMA. Additionally, CoryneRegNet now provides a SOAP based Web Service server, which can easily be consumed by other bioinformatics software systems. Stimulons (imported from the database, or uploaded by the user) can be analyzed in the context of known transcriptional regulatory networks to predict putative contradictions or further gene regulatory interactions. Furthermore, it integrates protein clusters by means of heuristically solving the weighted graph cluster editing problem. In addition, it provides Web Service based access to up to date gene annotation data from GenDB. Conclusion The release 4.0 of CoryneRegNet is a comprehensive system for the integrated analysis of procaryotic gene regulatory networks. It is a versatile systems biology platform to support the efficient and large-scale analysis of transcriptional regulation of gene expression in microorganisms. It is publicly available at . PMID:17986320

Reconstruction of metabolic pathways by combining probabilistic graphical model-based and knowledge-based methods

PubMed Central

2014-01-01

Automatic reconstruction of metabolic pathways for an organism from genomics and transcriptomics data has been a challenging and important problem in bioinformatics. Traditionally, known reference pathways can be mapped into an organism-specific ones based on its genome annotation and protein homology. However, this simple knowledge-based mapping method might produce incomplete pathways and generally cannot predict unknown new relations and reactions. In contrast, ab initio metabolic network construction methods can predict novel reactions and interactions, but its accuracy tends to be low leading to a lot of false positives. Here we combine existing pathway knowledge and a new ab initio Bayesian probabilistic graphical model together in a novel fashion to improve automatic reconstruction of metabolic networks. Specifically, we built a knowledge database containing known, individual gene / protein interactions and metabolic reactions extracted from existing reference pathways. Known reactions and interactions were then used as constraints for Bayesian network learning methods to predict metabolic pathways. Using individual reactions and interactions extracted from different pathways of many organisms to guide pathway construction is new and improves both the coverage and accuracy of metabolic pathway construction. We applied this probabilistic knowledge-based approach to construct the metabolic networks from yeast gene expression data and compared its results with 62 known metabolic networks in the KEGG database. The experiment showed that the method improved the coverage of metabolic network construction over the traditional reference pathway mapping method and was more accurate than pure ab initio methods. PMID:25374614

Urban Mobility and Location-Based Social Networks: Social, Economic and Environmental Incentives

ERIC Educational Resources Information Center

Zhang, Ke

2016-01-01

Location-based social networks (LBSNs) have recently attracted the interest of millions of users who can now not only connect and interact with their friends--as it also happens in traditional online social networks--but can also voluntarily share their whereabouts in real time. A location database is the backbone of a location-based social…

FoodMicrobionet: A database for the visualisation and exploration of food bacterial communities based on network analysis.

PubMed

Parente, Eugenio; Cocolin, Luca; De Filippis, Francesca; Zotta, Teresa; Ferrocino, Ilario; O'Sullivan, Orla; Neviani, Erasmo; De Angelis, Maria; Cotter, Paul D; Ercolini, Danilo

2016-02-16

Amplicon targeted high-throughput sequencing has become a popular tool for the culture-independent analysis of microbial communities. Although the data obtained with this approach are portable and the number of sequences available in public databases is increasing, no tool has been developed yet for the analysis and presentation of data obtained in different studies. This work describes an approach for the development of a database for the rapid exploration and analysis of data on food microbial communities. Data from seventeen studies investigating the structure of bacterial communities in dairy, meat, sourdough and fermented vegetable products, obtained by 16S rRNA gene targeted high-throughput sequencing, were collated and analysed using Gephi, a network analysis software. The resulting database, which we named FoodMicrobionet, was used to analyse nodes and network properties and to build an interactive web-based visualisation. The latter allows the visual exploration of the relationships between Operational Taxonomic Units (OTUs) and samples and the identification of core- and sample-specific bacterial communities. It also provides additional search tools and hyperlinks for the rapid selection of food groups and OTUs and for rapid access to external resources (NCBI taxonomy, digital versions of the original articles). Microbial interaction network analysis was carried out using CoNet on datasets extracted from FoodMicrobionet: the complexity of interaction networks was much lower than that found for other bacterial communities (human microbiome, soil and other environments). This may reflect both a bias in the dataset (which was dominated by fermented foods and starter cultures) and the lower complexity of food bacterial communities. Although some technical challenges exist, and are discussed here, the net result is a valuable tool for the exploration of food bacterial communities by the scientific community and food industry. Copyright © 2015. Published by Elsevier B.V.

Network pharmacology-based prediction of active compounds and molecular targets in Yijin-Tang acting on hyperlipidaemia and atherosclerosis.

PubMed

Lee, A Yeong; Park, Won; Kang, Tae-Wook; Cha, Min Ho; Chun, Jin Mi

2018-07-15

Yijin-Tang (YJT) is a traditional prescription for the treatment of hyperlipidaemia, atherosclerosis and other ailments related to dampness phlegm, a typical pathological symptom of abnormal body fluid metabolism in Traditional Korean Medicine. However, a holistic network pharmacology approach to understanding the therapeutic mechanisms underlying hyperlipidaemia and atherosclerosis has not been pursued. To examine the network pharmacological potential effects of YJT on hyperlipidaemia and atherosclerosis, we analysed components, performed target prediction and network analysis, and investigated interacting pathways using a network pharmacology approach. Information on compounds in herbal medicines was obtained from public databases, and oral bioavailability and drug-likeness was screened using absorption, distribution, metabolism, and excretion (ADME) criteria. Correlations between compounds and genes were linked using the STITCH database, and genes related to hyperlipidaemia and atherosclerosis were gathered using the GeneCards database. Human genes were identified and subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Network analysis identified 447 compounds in five herbal medicines that were subjected to ADME screening, and 21 compounds and 57 genes formed the main pathways linked to hyperlipidaemia and atherosclerosis. Among them, 10 compounds (naringenin, nobiletin, hesperidin, galangin, glycyrrhizin, homogentisic acid, stigmasterol, 6-gingerol, quercetin and glabridin) were linked to more than four genes, and are bioactive compounds and key chemicals. Core genes in this network were CASP3, CYP1A1, CYP1A2, MMP2 and MMP9. The compound-target gene network revealed close interactions between multiple components and multiple targets, and facilitates a better understanding of the potential therapeutic effects of YJT. Pharmacological network analysis can help to explain the potential effects of YJT for treating dampness phlegm-related diseases such as hyperlipidaemia and atherosclerosis. Copyright © 2018 Elsevier B.V. All rights reserved.

TheCellMap.org: A Web-Accessible Database for Visualizing and Mining the Global Yeast Genetic Interaction Network

PubMed Central

Usaj, Matej; Tan, Yizhao; Wang, Wen; VanderSluis, Benjamin; Zou, Albert; Myers, Chad L.; Costanzo, Michael; Andrews, Brenda; Boone, Charles

2017-01-01

Providing access to quantitative genomic data is key to ensure large-scale data validation and promote new discoveries. TheCellMap.org serves as a central repository for storing and analyzing quantitative genetic interaction data produced by genome-scale Synthetic Genetic Array (SGA) experiments with the budding yeast Saccharomyces cerevisiae. In particular, TheCellMap.org allows users to easily access, visualize, explore, and functionally annotate genetic interactions, or to extract and reorganize subnetworks, using data-driven network layouts in an intuitive and interactive manner. PMID:28325812

TheCellMap.org: A Web-Accessible Database for Visualizing and Mining the Global Yeast Genetic Interaction Network.

PubMed

Usaj, Matej; Tan, Yizhao; Wang, Wen; VanderSluis, Benjamin; Zou, Albert; Myers, Chad L; Costanzo, Michael; Andrews, Brenda; Boone, Charles

2017-05-05

Providing access to quantitative genomic data is key to ensure large-scale data validation and promote new discoveries. TheCellMap.org serves as a central repository for storing and analyzing quantitative genetic interaction data produced by genome-scale Synthetic Genetic Array (SGA) experiments with the budding yeast Saccharomyces cerevisiae In particular, TheCellMap.org allows users to easily access, visualize, explore, and functionally annotate genetic interactions, or to extract and reorganize subnetworks, using data-driven network layouts in an intuitive and interactive manner. Copyright © 2017 Usaj et al.

PharmDB-K: Integrated Bio-Pharmacological Network Database for Traditional Korean Medicine

PubMed Central

Lee, Ji-Hyun; Park, Kyoung Mii; Han, Dong-Jin; Bang, Nam Young; Kim, Do-Hee; Na, Hyeongjin; Lim, Semi; Kim, Tae Bum; Kim, Dae Gyu; Kim, Hyun-Jung; Chung, Yeonseok; Sung, Sang Hyun; Surh, Young-Joon; Kim, Sunghoon; Han, Byung Woo

2015-01-01

Despite the growing attention given to Traditional Medicine (TM) worldwide, there is no well-known, publicly available, integrated bio-pharmacological Traditional Korean Medicine (TKM) database for researchers in drug discovery. In this study, we have constructed PharmDB-K, which offers comprehensive information relating to TKM-associated drugs (compound), disease indication, and protein relationships. To explore the underlying molecular interaction of TKM, we integrated fourteen different databases, six Pharmacopoeias, and literature, and established a massive bio-pharmacological network for TKM and experimentally validated some cases predicted from the PharmDB-K analyses. Currently, PharmDB-K contains information about 262 TKMs, 7,815 drugs, 3,721 diseases, 32,373 proteins, and 1,887 side effects. One of the unique sets of information in PharmDB-K includes 400 indicator compounds used for standardization of herbal medicine. Furthermore, we are operating PharmDB-K via phExplorer (a network visualization software) and BioMart (a data federation framework) for convenient search and analysis of the TKM network. Database URL: http://pharmdb-k.org, http://biomart.i-pharm.org. PMID:26555441

MetNetAPI: A flexible method to access and manipulate biological network data from MetNet

PubMed Central

2010-01-01

Background Convenient programmatic access to different biological databases allows automated integration of scientific knowledge. Many databases support a function to download files or data snapshots, or a webservice that offers "live" data. However, the functionality that a database offers cannot be represented in a static data download file, and webservices may consume considerable computational resources from the host server. Results MetNetAPI is a versatile Application Programming Interface (API) to the MetNetDB database. It abstracts, captures and retains operations away from a biological network repository and website. A range of database functions, previously only available online, can be immediately (and independently from the website) applied to a dataset of interest. Data is available in four layers: molecular entities, localized entities (linked to a specific organelle), interactions, and pathways. Navigation between these layers is intuitive (e.g. one can request the molecular entities in a pathway, as well as request in what pathways a specific entity participates). Data retrieval can be customized: Network objects allow the construction of new and integration of existing pathways and interactions, which can be uploaded back to our server. In contrast to webservices, the computational demand on the host server is limited to processing data-related queries only. Conclusions An API provides several advantages to a systems biology software platform. MetNetAPI illustrates an interface with a central repository of data that represents the complex interrelationships of a metabolic and regulatory network. As an alternative to data-dumps and webservices, it allows access to a current and "live" database and exposes analytical functions to application developers. Yet it only requires limited resources on the server-side (thin server/fat client setup). The API is available for Java, Microsoft.NET and R programming environments and offers flexible query and broad data- retrieval methods. Data retrieval can be customized to client needs and the API offers a framework to construct and manipulate user-defined networks. The design principles can be used as a template to build programmable interfaces for other biological databases. The API software and tutorials are available at http://www.metnetonline.org/api. PMID:21083943

Comprehensive Reconstruction and Visualization of Non-Coding Regulatory Networks in Human

PubMed Central

Bonnici, Vincenzo; Russo, Francesco; Bombieri, Nicola; Pulvirenti, Alfredo; Giugno, Rosalba

2014-01-01

Research attention has been powered to understand the functional roles of non-coding RNAs (ncRNAs). Many studies have demonstrated their deregulation in cancer and other human disorders. ncRNAs are also present in extracellular human body fluids such as serum and plasma, giving them a great potential as non-invasive biomarkers. However, non-coding RNAs have been relatively recently discovered and a comprehensive database including all of them is still missing. Reconstructing and visualizing the network of ncRNAs interactions are important steps to understand their regulatory mechanism in complex systems. This work presents ncRNA-DB, a NoSQL database that integrates ncRNAs data interactions from a large number of well established on-line repositories. The interactions involve RNA, DNA, proteins, and diseases. ncRNA-DB is available at http://ncrnadb.scienze.univr.it/ncrnadb/. It is equipped with three interfaces: web based, command-line, and a Cytoscape app called ncINetView. By accessing only one resource, users can search for ncRNAs and their interactions, build a network annotated with all known ncRNAs and associated diseases, and use all visual and mining features available in Cytoscape. PMID:25540777

Comprehensive reconstruction and visualization of non-coding regulatory networks in human.

PubMed

Bonnici, Vincenzo; Russo, Francesco; Bombieri, Nicola; Pulvirenti, Alfredo; Giugno, Rosalba

2014-01-01

Research attention has been powered to understand the functional roles of non-coding RNAs (ncRNAs). Many studies have demonstrated their deregulation in cancer and other human disorders. ncRNAs are also present in extracellular human body fluids such as serum and plasma, giving them a great potential as non-invasive biomarkers. However, non-coding RNAs have been relatively recently discovered and a comprehensive database including all of them is still missing. Reconstructing and visualizing the network of ncRNAs interactions are important steps to understand their regulatory mechanism in complex systems. This work presents ncRNA-DB, a NoSQL database that integrates ncRNAs data interactions from a large number of well established on-line repositories. The interactions involve RNA, DNA, proteins, and diseases. ncRNA-DB is available at http://ncrnadb.scienze.univr.it/ncrnadb/. It is equipped with three interfaces: web based, command-line, and a Cytoscape app called ncINetView. By accessing only one resource, users can search for ncRNAs and their interactions, build a network annotated with all known ncRNAs and associated diseases, and use all visual and mining features available in Cytoscape.

Contextualization of drug-mediator relations using evidence networks.

PubMed

Tran, Hai Joey; Speyer, Gil; Kiefer, Jeff; Kim, Seungchan

2017-05-31

Genomic analysis of drug response can provide unique insights into therapies that can be used to match the "right drug to the right patient." However, the process of discovering such therapeutic insights using genomic data is not straightforward and represents an area of active investigation. EDDY (Evaluation of Differential DependencY), a statistical test to detect differential statistical dependencies, is one method that leverages genomic data to identify differential genetic dependencies. EDDY has been used in conjunction with the Cancer Therapeutics Response Portal (CTRP), a dataset with drug-response measurements for more than 400 small molecules, and RNAseq data of cell lines in the Cancer Cell Line Encyclopedia (CCLE) to find potential drug-mediator pairs. Mediators were identified as genes that showed significant change in genetic statistical dependencies within annotated pathways between drug sensitive and drug non-sensitive cell lines, and the results are presented as a public web-portal (EDDY-CTRP). However, the interpretability of drug-mediator pairs currently hinders further exploration of these potentially valuable results. In this study, we address this challenge by constructing evidence networks built with protein and drug interactions from the STITCH and STRING interaction databases. STITCH and STRING are sister databases that catalog known and predicted drug-protein interactions and protein-protein interactions, respectively. Using these two databases, we have developed a method to construct evidence networks to "explain" the relation between a drug and a mediator.  RESULTS: We applied this approach to drug-mediator relations discovered in EDDY-CTRP analysis and identified evidence networks for ~70% of drug-mediator pairs where most mediators were not known direct targets for the drug. Constructed evidence networks enable researchers to contextualize the drug-mediator pair with current research and knowledge. Using evidence networks, we were able to improve the interpretability of the EDDY-CTRP results by linking the drugs and mediators with genes associated with both the drug and the mediator. We anticipate that these evidence networks will help inform EDDY-CTRP results and enhance the generation of important insights to drug sensitivity that will lead to improved precision medicine applications.

cisPath: an R/Bioconductor package for cloud users for visualization and management of functional protein interaction networks.

PubMed

Wang, Likun; Yang, Luhe; Peng, Zuohan; Lu, Dan; Jin, Yan; McNutt, Michael; Yin, Yuxin

2015-01-01

With the burgeoning development of cloud technology and services, there are an increasing number of users who prefer cloud to run their applications. All software and associated data are hosted on the cloud, allowing users to access them via a web browser from any computer, anywhere. This paper presents cisPath, an R/Bioconductor package deployed on cloud servers for client users to visualize, manage, and share functional protein interaction networks. With this R package, users can easily integrate downloaded protein-protein interaction information from different online databases with private data to construct new and personalized interaction networks. Additional functions allow users to generate specific networks based on private databases. Since the results produced with the use of this package are in the form of web pages, cloud users can easily view and edit the network graphs via the browser, using a mouse or touch screen, without the need to download them to a local computer. This package can also be installed and run on a local desktop computer. Depending on user preference, results can be publicized or shared by uploading to a web server or cloud driver, allowing other users to directly access results via a web browser. This package can be installed and run on a variety of platforms. Since all network views are shown in web pages, such package is particularly useful for cloud users. The easy installation and operation is an attractive quality for R beginners and users with no previous experience with cloud services.

cisPath: an R/Bioconductor package for cloud users for visualization and management of functional protein interaction networks

PubMed Central

2015-01-01

Background With the burgeoning development of cloud technology and services, there are an increasing number of users who prefer cloud to run their applications. All software and associated data are hosted on the cloud, allowing users to access them via a web browser from any computer, anywhere. This paper presents cisPath, an R/Bioconductor package deployed on cloud servers for client users to visualize, manage, and share functional protein interaction networks. Results With this R package, users can easily integrate downloaded protein-protein interaction information from different online databases with private data to construct new and personalized interaction networks. Additional functions allow users to generate specific networks based on private databases. Since the results produced with the use of this package are in the form of web pages, cloud users can easily view and edit the network graphs via the browser, using a mouse or touch screen, without the need to download them to a local computer. This package can also be installed and run on a local desktop computer. Depending on user preference, results can be publicized or shared by uploading to a web server or cloud driver, allowing other users to directly access results via a web browser. Conclusions This package can be installed and run on a variety of platforms. Since all network views are shown in web pages, such package is particularly useful for cloud users. The easy installation and operation is an attractive quality for R beginners and users with no previous experience with cloud services. PMID:25708840

Semantic integration to identify overlapping functional modules in protein interaction networks

PubMed Central

Cho, Young-Rae; Hwang, Woochang; Ramanathan, Murali; Zhang, Aidong

2007-01-01

Background The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms. Results We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches. Conclusion The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification. PMID:17650343

A novel method to identify pathways associated with renal cell carcinoma based on a gene co-expression network

PubMed Central

RUAN, XIYUN; LI, HONGYUN; LIU, BO; CHEN, JIE; ZHANG, SHIBAO; SUN, ZEQIANG; LIU, SHUANGQING; SUN, FAHAI; LIU, QINGYONG

2015-01-01

The aim of the present study was to develop a novel method for identifying pathways associated with renal cell carcinoma (RCC) based on a gene co-expression network. A framework was established where a co-expression network was derived from the database as well as various co-expression approaches. First, the backbone of the network based on differentially expressed (DE) genes between RCC patients and normal controls was constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The differentially co-expressed links were detected by Pearson’s correlation, the empirical Bayesian (EB) approach and Weighted Gene Co-expression Network Analysis (WGCNA). The co-expressed gene pairs were merged by a rank-based algorithm. We obtained 842; 371; 2,883 and 1,595 co-expressed gene pairs from the co-expression networks of the STRING database, Pearson’s correlation EB method and WGCNA, respectively. Two hundred and eighty-one differentially co-expressed (DC) gene pairs were obtained from the merged network using this novel method. Pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the network enrichment analysis (NEA) method were performed to verify feasibility of the merged method. Results of the KEGG and NEA pathway analyses showed that the network was associated with RCC. The suggested method was computationally efficient to identify pathways associated with RCC and has been identified as a useful complement to traditional co-expression analysis. PMID:26058425

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

Automatic reconstruction of a bacterial regulatory network using Natural Language Processing

PubMed Central

Rodríguez-Penagos, Carlos; Salgado, Heladia; Martínez-Flores, Irma; Collado-Vides, Julio

2007-01-01

Background Manual curation of biological databases, an expensive and labor-intensive process, is essential for high quality integrated data. In this paper we report the implementation of a state-of-the-art Natural Language Processing system that creates computer-readable networks of regulatory interactions directly from different collections of abstracts and full-text papers. Our major aim is to understand how automatic annotation using Text-Mining techniques can complement manual curation of biological databases. We implemented a rule-based system to generate networks from different sets of documents dealing with regulation in Escherichia coli K-12. Results Performance evaluation is based on the most comprehensive transcriptional regulation database for any organism, the manually-curated RegulonDB, 45% of which we were able to recreate automatically. From our automated analysis we were also able to find some new interactions from papers not already curated, or that were missed in the manual filtering and review of the literature. We also put forward a novel Regulatory Interaction Markup Language better suited than SBML for simultaneously representing data of interest for biologists and text miners. Conclusion Manual curation of the output of automatic processing of text is a good way to complement a more detailed review of the literature, either for validating the results of what has been already annotated, or for discovering facts and information that might have been overlooked at the triage or curation stages. PMID:17683642

Network portal: a database for storage, analysis and visualization of biological networks

PubMed Central

Turkarslan, Serdar; Wurtmann, Elisabeth J.; Wu, Wei-Ju; Jiang, Ning; Bare, J. Christopher; Foley, Karen; Reiss, David J.; Novichkov, Pavel; Baliga, Nitin S.

2014-01-01

The ease of generating high-throughput data has enabled investigations into organismal complexity at the systems level through the inference of networks of interactions among the various cellular components (genes, RNAs, proteins and metabolites). The wider scientific community, however, currently has limited access to tools for network inference, visualization and analysis because these tasks often require advanced computational knowledge and expensive computing resources. We have designed the network portal (http://networks.systemsbiology.net) to serve as a modular database for the integration of user uploaded and public data, with inference algorithms and tools for the storage, visualization and analysis of biological networks. The portal is fully integrated into the Gaggle framework to seamlessly exchange data with desktop and web applications and to allow the user to create, save and modify workspaces, and it includes social networking capabilities for collaborative projects. While the current release of the database contains networks for 13 prokaryotic organisms from diverse phylogenetic clades (4678 co-regulated gene modules, 3466 regulators and 9291 cis-regulatory motifs), it will be rapidly populated with prokaryotic and eukaryotic organisms as relevant data become available in public repositories and through user input. The modular architecture, simple data formats and open API support community development of the portal. PMID:24271392

The Knowledge-Integrated Network Biomarkers Discovery for Major Adverse Cardiac Events

PubMed Central

Jin, Guangxu; Zhou, Xiaobo; Wang, Honghui; Zhao, Hong; Cui, Kemi; Zhang, Xiang-Sun; Chen, Luonan; Hazen, Stanley L.; Li, King; Wong, Stephen T. C.

2010-01-01

The mass spectrometry (MS) technology in clinical proteomics is very promising for discovery of new biomarkers for diseases management. To overcome the obstacles of data noises in MS analysis, we proposed a new approach of knowledge-integrated biomarker discovery using data from Major Adverse Cardiac Events (MACE) patients. We first built up a cardiovascular-related network based on protein information coming from protein annotations in Uniprot, protein–protein interaction (PPI), and signal transduction database. Distinct from the previous machine learning methods in MS data processing, we then used statistical methods to discover biomarkers in cardiovascular-related network. Through the tradeoff between known protein information and data noises in mass spectrometry data, we finally could firmly identify those high-confident biomarkers. Most importantly, aided by protein–protein interaction network, that is, cardiovascular-related network, we proposed a new type of biomarkers, that is, network biomarkers, composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify the two groups of patients more accurately than current single ones without consideration of biological molecular interaction. PMID:18665624

Modeling and simulating networks of interdependent protein interactions.

PubMed

Stöcker, Bianca K; Köster, Johannes; Zamir, Eli; Rahmann, Sven

2018-05-21

Protein interactions are fundamental building blocks of biochemical reaction systems underlying cellular functions. The complexity and functionality of these systems emerge not only from the protein interactions themselves but also from the dependencies between these interactions, as generated by allosteric effects or mutual exclusion due to steric hindrance. Therefore, formal models for integrating and utilizing information about interaction dependencies are of high interest. Here, we describe an approach for endowing protein networks with interaction dependencies using propositional logic, thereby obtaining constrained protein interaction networks ("constrained networks"). The construction of these networks is based on public interaction databases as well as text-mined information about interaction dependencies. We present an efficient data structure and algorithm to simulate protein complex formation in constrained networks. The efficiency of the model allows fast simulation and facilitates the analysis of many proteins in large networks. In addition, this approach enables the simulation of perturbation effects, such as knockout of single or multiple proteins and changes of protein concentrations. We illustrate how our model can be used to analyze a constrained human adhesome protein network, which is responsible for the formation of diverse and dynamic cell-matrix adhesion sites. By comparing protein complex formation under known interaction dependencies versus without dependencies, we investigate how these dependencies shape the resulting repertoire of protein complexes. Furthermore, our model enables investigating how the interplay of network topology with interaction dependencies influences the propagation of perturbation effects across a large biochemical system. Our simulation software CPINSim (for Constrained Protein Interaction Network Simulator) is available under the MIT license at http://github.com/BiancaStoecker/cpinsim and as a Bioconda package (https://bioconda.github.io).

A bioinformatics analysis of Lamin-A regulatory network: a perspective on epigenetic involvement in Hutchinson-Gilford progeria syndrome.

PubMed

Arancio, Walter

2012-04-01

Hutchinson-Gilford progeria syndrome (HGPS) is a rare human genetic disease that leads to premature aging. HGPS is caused by mutation in the Lamin-A (LMNA) gene that leads, in affected young individuals, to the accumulation of the progerin protein, usually present only in aging differentiated cells. Bioinformatics analyses of the network of interactions of the LMNA gene and transcripts are presented. The LMNA gene network has been analyzed using the BioGRID database (http://thebiogrid.org/) and related analysis tools such as Osprey (http://biodata.mshri.on.ca/osprey/servlet/Index) and GeneMANIA ( http://genemania.org/). The network of interaction of LMNA transcripts has been further analyzed following the competing endogenous (ceRNA) hypotheses (RNA cross-talk via microRNAs [miRNAs]) and using the miRWalk database and tools (www.ma.uni-heidelberg.de/apps/zmf/mirwalk/). These analyses suggest particular relevance of epigenetic modifiers (via acetylase complexes and specifically HTATIP histone acetylase) and adenosine triphosphate (ATP)-dependent chromatin remodelers (via pBAF, BAF, and SWI/SNF complexes).

Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology

PubMed Central

Paley, Suzanne M.; Krummenacker, Markus; Latendresse, Mario; Dale, Joseph M.; Lee, Thomas J.; Kaipa, Pallavi; Gilham, Fred; Spaulding, Aaron; Popescu, Liviu; Altman, Tomer; Paulsen, Ian; Keseler, Ingrid M.; Caspi, Ron

2010-01-01

Pathway Tools is a production-quality software environment for creating a type of model-organism database called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc integrates the evolving understanding of the genes, proteins, metabolic network and regulatory network of an organism. This article provides an overview of Pathway Tools capabilities. The software performs multiple computational inferences including prediction of metabolic pathways, prediction of metabolic pathway hole fillers and prediction of operons. It enables interactive editing of PGDBs by DB curators. It supports web publishing of PGDBs, and provides a large number of query and visualization tools. The software also supports comparative analyses of PGDBs, and provides several systems biology analyses of PGDBs including reachability analysis of metabolic networks, and interactive tracing of metabolites through a metabolic network. More than 800 PGDBs have been created using Pathway Tools by scientists around the world, many of which are curated DBs for important model organisms. Those PGDBs can be exchanged using a peer-to-peer DB sharing system called the PGDB Registry. PMID:19955237

Implementation of medical monitor system based on networks

NASA Astrophysics Data System (ADS)

Yu, Hui; Cao, Yuzhen; Zhang, Lixin; Ding, Mingshi

2006-11-01

In this paper, the development trend of medical monitor system is analyzed and portable trend and network function become more and more popular among all kinds of medical monitor devices. The architecture of medical network monitor system solution is provided and design and implementation details of medical monitor terminal, monitor center software, distributed medical database and two kind of medical information terminal are especially discussed. Rabbit3000 system is used in medical monitor terminal to implement security administration of data transfer on network, human-machine interface, power management and DSP interface while DSP chip TMS5402 is used in signal analysis and data compression. Distributed medical database is designed for hospital center according to DICOM information model and HL7 standard. Pocket medical information terminal based on ARM9 embedded platform is also developed to interactive with center database on networks. Two kernels based on WINCE are customized and corresponding terminal software are developed for nurse's routine care and doctor's auxiliary diagnosis. Now invention patent of the monitor terminal is approved and manufacture and clinic test plans are scheduled. Applications for invention patent are also arranged for two medical information terminals.

Time Allocation in Social Networks: Correlation Between Social Structure and Human Communication Dynamics

NASA Astrophysics Data System (ADS)

Miritello, Giovanna; Lara, Rubén; Moro, Esteban

Recent research has shown the deep impact of the dynamics of human interactions (or temporal social networks) on the spreading of information, opinion formation, etc. In general, the bursty nature of human interactions lowers the interaction between people to the extent that both the speed and reach of information diffusion are diminished. Using a large database of 20 million users of mobile phone calls we show evidence this effect is not homogeneous in the social network but in fact, there is a large correlation between this effect and the social topological structure around a given individual. In particular, we show that social relations of hubs in a network are relatively weaker from the dynamical point than those that are poorer connected in the information diffusion process. Our results show the importance of the temporal patterns of communication when analyzing and modeling dynamical process on social networks.

Identifying essential proteins based on sub-network partition and prioritization by integrating subcellular localization information.

PubMed

Li, Min; Li, Wenkai; Wu, Fang-Xiang; Pan, Yi; Wang, Jianxin

2018-06-14

Essential proteins are important participants in various life activities and play a vital role in the survival and reproduction of living organisms. Identification of essential proteins from protein-protein interaction (PPI) networks has great significance to facilitate the study of human complex diseases, the design of drugs and the development of bioinformatics and computational science. Studies have shown that highly connected proteins in a PPI network tend to be essential. A series of computational methods have been proposed to identify essential proteins by analyzing topological structures of PPI networks. However, the high noise in the PPI data can degrade the accuracy of essential protein prediction. Moreover, proteins must be located in the appropriate subcellular localization to perform their functions, and only when the proteins are located in the same subcellular localization, it is possible that they can interact with each other. In this paper, we propose a new network-based essential protein discovery method based on sub-network partition and prioritization by integrating subcellular localization information, named SPP. The proposed method SPP was tested on two different yeast PPI networks obtained from DIP database and BioGRID database. The experimental results show that SPP can effectively reduce the effect of false positives in PPI networks and predict essential proteins more accurately compared with other existing computational methods DC, BC, CC, SC, EC, IC, NC. Copyright © 2018 Elsevier Ltd. All rights reserved.

ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci

PubMed Central

2010-01-01

Background Growing interest and burgeoning technology for discovering genetic mechanisms that influence disease processes have ushered in a flood of genetic association studies over the last decade, yet little heritability in highly studied complex traits has been explained by genetic variation. Non-additive gene-gene interactions, which are not often explored, are thought to be one source of this "missing" heritability. Methods Stochastic methods employing evolutionary algorithms have demonstrated promise in being able to detect and model gene-gene and gene-environment interactions that influence human traits. Here we demonstrate modifications to a neural network algorithm in ATHENA (the Analysis Tool for Heritable and Environmental Network Associations) resulting in clear performance improvements for discovering gene-gene interactions that influence human traits. We employed an alternative tree-based crossover, backpropagation for locally fitting neural network weights, and incorporation of domain knowledge obtainable from publicly accessible biological databases for initializing the search for gene-gene interactions. We tested these modifications in silico using simulated datasets. Results We show that the alternative tree-based crossover modification resulted in a modest increase in the sensitivity of the ATHENA algorithm for discovering gene-gene interactions. The performance increase was highly statistically significant when backpropagation was used to locally fit NN weights. We also demonstrate that using domain knowledge to initialize the search for gene-gene interactions results in a large performance increase, especially when the search space is larger than the search coverage. Conclusions We show that a hybrid optimization procedure, alternative crossover strategies, and incorporation of domain knowledge from publicly available biological databases can result in marked increases in sensitivity and performance of the ATHENA algorithm for detecting and modelling gene-gene interactions that influence a complex human trait. PMID:20875103

Massive Scale Cyber Traffic Analysis: A Driver for Graph Database Research

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

Joslyn, Cliff A.; Choudhury, S.; Haglin, David J.

2013-06-19

We describe the significance and prominence of network traffic analysis (TA) as a graph- and network-theoretical domain for advancing research in graph database systems. TA involves observing and analyzing the connections between clients, servers, hosts, and actors within IP networks, both at particular times and as extended over times. Towards that end, NetFlow (or more generically, IPFLOW) data are available from routers and servers which summarize coherent groups of IP packets flowing through the network. IPFLOW databases are routinely interrogated statistically and visualized for suspicious patterns. But the ability to cast IPFLOW data as a massive graph and query itmore » interactively, in order to e.g.\\ identify connectivity patterns, is less well advanced, due to a number of factors including scaling, and their hybrid nature combining graph connectivity and quantitative attributes. In this paper, we outline requirements and opportunities for graph-structured IPFLOW analytics based on our experience with real IPFLOW databases. Specifically, we describe real use cases from the security domain, cast them as graph patterns, show how to express them in two graph-oriented query languages SPARQL and Datalog, and use these examples to motivate a new class of "hybrid" graph-relational systems.« less

ImmunemiR - A Database of Prioritized Immune miRNA Disease Associations and its Interactome.

PubMed

Prabahar, Archana; Natarajan, Jeyakumar

2017-01-01

MicroRNAs are the key regulators of gene expression and their abnormal expression in the immune system may be associated with several human diseases such as inflammation, cancer and autoimmune diseases. Elucidation of miRNA disease association through the interactome will deepen the understanding of its disease mechanisms. A specialized database for immune miRNAs is highly desirable to demonstrate the immune miRNA disease associations in the interactome. miRNAs specific to immune related diseases were retrieved from curated databases such as HMDD, miR2disease and PubMed literature based on MeSH classification of immune system diseases. The additional data such as miRNA target genes, genes coding protein-protein interaction information were compiled from related resources. Further, miRNAs were prioritized to specific immune diseases using random walk ranking algorithm. In total 245 immune miRNAs associated with 92 OMIM disease categories were identified from external databases. The resultant data were compiled as ImmunemiR, a database of prioritized immune miRNA disease associations. This database provides both text based annotation information and network visualization of its interactome. To our knowledge, ImmunemiR is the first available database to provide a comprehensive repository of human immune disease associated miRNAs with network visualization options of its target genes, protein-protein interactions (PPI) and its disease associations. It is freely available at http://www.biominingbu.org/immunemir/. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

ModuleRole: a tool for modulization, role determination and visualization in protein-protein interaction networks.

PubMed

Li, Guipeng; Li, Ming; Zhang, Yiwei; Wang, Dong; Li, Rong; Guimerà, Roger; Gao, Juntao Tony; Zhang, Michael Q

2014-01-01

Rapidly increasing amounts of (physical and genetic) protein-protein interaction (PPI) data are produced by various high-throughput techniques, and interpretation of these data remains a major challenge. In order to gain insight into the organization and structure of the resultant large complex networks formed by interacting molecules, using simulated annealing, a method based on the node connectivity, we developed ModuleRole, a user-friendly web server tool which finds modules in PPI network and defines the roles for every node, and produces files for visualization in Cytoscape and Pajek. For given proteins, it analyzes the PPI network from BioGRID database, finds and visualizes the modules these proteins form, and then defines the role every node plays in this network, based on two topological parameters Participation Coefficient and Z-score. This is the first program which provides interactive and very friendly interface for biologists to find and visualize modules and roles of proteins in PPI network. It can be tested online at the website http://www.bioinfo.org/modulerole/index.php, which is free and open to all users and there is no login requirement, with demo data provided by "User Guide" in the menu Help. Non-server application of this program is considered for high-throughput data with more than 200 nodes or user's own interaction datasets. Users are able to bookmark the web link to the result page and access at a later time. As an interactive and highly customizable application, ModuleRole requires no expert knowledge in graph theory on the user side and can be used in both Linux and Windows system, thus a very useful tool for biologist to analyze and visualize PPI networks from databases such as BioGRID. ModuleRole is implemented in Java and C, and is freely available at http://www.bioinfo.org/modulerole/index.php. Supplementary information (user guide, demo data) is also available at this website. API for ModuleRole used for this program can be obtained upon request.

A Systems Biology Approach to the Coordination of Defensive and Offensive Molecular Mechanisms in the Innate and Adaptive Host–Pathogen Interaction Networks

PubMed Central

Wu, Chia-Chou; Chen, Bor-Sen

2016-01-01

Infected zebrafish coordinates defensive and offensive molecular mechanisms in response to Candida albicans infections, and invasive C. albicans coordinates corresponding molecular mechanisms to interact with the host. However, knowledge of the ensuing infection-activated signaling networks in both host and pathogen and their interspecific crosstalk during the innate and adaptive phases of the infection processes remains incomplete. In the present study, dynamic network modeling, protein interaction databases, and dual transcriptome data from zebrafish and C. albicans during infection were used to infer infection-activated host–pathogen dynamic interaction networks. The consideration of host–pathogen dynamic interaction systems as innate and adaptive loops and subsequent comparisons of inferred innate and adaptive networks indicated previously unrecognized crosstalk between known pathways and suggested roles of immunological memory in the coordination of host defensive and offensive molecular mechanisms to achieve specific and powerful defense against pathogens. Moreover, pathogens enhance intraspecific crosstalk and abrogate host apoptosis to accommodate enhanced host defense mechanisms during the adaptive phase. Accordingly, links between physiological phenomena and changes in the coordination of defensive and offensive molecular mechanisms highlight the importance of host–pathogen molecular interaction networks, and consequent inferences of the host–pathogen relationship could be translated into biomedical applications. PMID:26881892

A Systems Biology Approach to the Coordination of Defensive and Offensive Molecular Mechanisms in the Innate and Adaptive Host-Pathogen Interaction Networks.

PubMed

Wu, Chia-Chou; Chen, Bor-Sen

2016-01-01

Infected zebrafish coordinates defensive and offensive molecular mechanisms in response to Candida albicans infections, and invasive C. albicans coordinates corresponding molecular mechanisms to interact with the host. However, knowledge of the ensuing infection-activated signaling networks in both host and pathogen and their interspecific crosstalk during the innate and adaptive phases of the infection processes remains incomplete. In the present study, dynamic network modeling, protein interaction databases, and dual transcriptome data from zebrafish and C. albicans during infection were used to infer infection-activated host-pathogen dynamic interaction networks. The consideration of host-pathogen dynamic interaction systems as innate and adaptive loops and subsequent comparisons of inferred innate and adaptive networks indicated previously unrecognized crosstalk between known pathways and suggested roles of immunological memory in the coordination of host defensive and offensive molecular mechanisms to achieve specific and powerful defense against pathogens. Moreover, pathogens enhance intraspecific crosstalk and abrogate host apoptosis to accommodate enhanced host defense mechanisms during the adaptive phase. Accordingly, links between physiological phenomena and changes in the coordination of defensive and offensive molecular mechanisms highlight the importance of host-pathogen molecular interaction networks, and consequent inferences of the host-pathogen relationship could be translated into biomedical applications.

ARACNe-based inference, using curated microarray data, of Arabidopsis thaliana root transcriptional regulatory networks

PubMed Central

2014-01-01

Background Uncovering the complex transcriptional regulatory networks (TRNs) that underlie plant and animal development remains a challenge. However, a vast amount of data from public microarray experiments is available, which can be subject to inference algorithms in order to recover reliable TRN architectures. Results In this study we present a simple bioinformatics methodology that uses public, carefully curated microarray data and the mutual information algorithm ARACNe in order to obtain a database of transcriptional interactions. We used data from Arabidopsis thaliana root samples to show that the transcriptional regulatory networks derived from this database successfully recover previously identified root transcriptional modules and to propose new transcription factors for the SHORT ROOT/SCARECROW and PLETHORA pathways. We further show that these networks are a powerful tool to integrate and analyze high-throughput expression data, as exemplified by our analysis of a SHORT ROOT induction time-course microarray dataset, and are a reliable source for the prediction of novel root gene functions. In particular, we used our database to predict novel genes involved in root secondary cell-wall synthesis and identified the MADS-box TF XAL1/AGL12 as an unexpected participant in this process. Conclusions This study demonstrates that network inference using carefully curated microarray data yields reliable TRN architectures. In contrast to previous efforts to obtain root TRNs, that have focused on particular functional modules or tissues, our root transcriptional interactions provide an overview of the transcriptional pathways present in Arabidopsis thaliana roots and will likely yield a plethora of novel hypotheses to be tested experimentally. PMID:24739361

A Bayesian network approach to the database search problem in criminal proceedings

PubMed Central

2012-01-01

Background The ‘database search problem’, that is, the strengthening of a case - in terms of probative value - against an individual who is found as a result of a database search, has been approached during the last two decades with substantial mathematical analyses, accompanied by lively debate and centrally opposing conclusions. This represents a challenging obstacle in teaching but also hinders a balanced and coherent discussion of the topic within the wider scientific and legal community. This paper revisits and tracks the associated mathematical analyses in terms of Bayesian networks. Their derivation and discussion for capturing probabilistic arguments that explain the database search problem are outlined in detail. The resulting Bayesian networks offer a distinct view on the main debated issues, along with further clarity. Methods As a general framework for representing and analyzing formal arguments in probabilistic reasoning about uncertain target propositions (that is, whether or not a given individual is the source of a crime stain), this paper relies on graphical probability models, in particular, Bayesian networks. This graphical probability modeling approach is used to capture, within a single model, a series of key variables, such as the number of individuals in a database, the size of the population of potential crime stain sources, and the rarity of the corresponding analytical characteristics in a relevant population. Results This paper demonstrates the feasibility of deriving Bayesian network structures for analyzing, representing, and tracking the database search problem. The output of the proposed models can be shown to agree with existing but exclusively formulaic approaches. Conclusions The proposed Bayesian networks allow one to capture and analyze the currently most well-supported but reputedly counter-intuitive and difficult solution to the database search problem in a way that goes beyond the traditional, purely formulaic expressions. The method’s graphical environment, along with its computational and probabilistic architectures, represents a rich package that offers analysts and discussants with additional modes of interaction, concise representation, and coherent communication. PMID:22849390

QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks.

PubMed

Thibodeau, Asa; Márquez, Eladio J; Luo, Oscar; Ruan, Yijun; Menghi, Francesca; Shin, Dong-Guk; Stitzel, Michael L; Vera-Licona, Paola; Ucar, Duygu

2016-06-01

Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions. QuIN's web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub: https://github.com/UcarLab/QuIN/.

Incorporation of spatial interactions in location networks to identify critical geo-referenced routes for assessing disease control measures on a large-scale campus.

PubMed

Wen, Tzai-Hung; Chin, Wei Chien Benny

2015-04-14

Respiratory diseases mainly spread through interpersonal contact. Class suspension is the most direct strategy to prevent the spread of disease through elementary or secondary schools by blocking the contact network. However, as university students usually attend courses in different buildings, the daily contact patterns on a university campus are complicated, and once disease clusters have occurred, suspending classes is far from an efficient strategy to control disease spread. The purpose of this study is to propose a methodological framework for generating campus location networks from a routine administration database, analyzing the community structure of the network, and identifying the critical links and nodes for blocking respiratory disease transmission. The data comes from the student enrollment records of a major comprehensive university in Taiwan. We combined the social network analysis and spatial interaction model to establish a geo-referenced community structure among the classroom buildings. We also identified the critical links among the communities that were acting as contact bridges and explored the changes in the location network after the sequential removal of the high-risk buildings. Instead of conducting a questionnaire survey, the study established a standard procedure for constructing a location network on a large-scale campus from a routine curriculum database. We also present how a location network structure at a campus could function to target the high-risk buildings as the bridges connecting communities for blocking disease transmission.

Mining for recurrent long-range interactions in RNA structures reveals embedded hierarchies in network families.

PubMed

Reinharz, Vladimir; Soulé, Antoine; Westhof, Eric; Waldispühl, Jérôme; Denise, Alain

2018-05-04

The wealth of the combinatorics of nucleotide base pairs enables RNA molecules to assemble into sophisticated interaction networks, which are used to create complex 3D substructures. These interaction networks are essential to shape the 3D architecture of the molecule, and also to provide the key elements to carry molecular functions such as protein or ligand binding. They are made of organised sets of long-range tertiary interactions which connect distinct secondary structure elements in 3D structures. Here, we present a de novo data-driven approach to extract automatically from large data sets of full RNA 3D structures the recurrent interaction networks (RINs). Our methodology enables us for the first time to detect the interaction networks connecting distinct components of the RNA structure, highlighting their diversity and conservation through non-related functional RNAs. We use a graphical model to perform pairwise comparisons of all RNA structures available and to extract RINs and modules. Our analysis yields a complete catalog of RNA 3D structures available in the Protein Data Bank and reveals the intricate hierarchical organization of the RNA interaction networks and modules. We assembled our results in an online database (http://carnaval.lri.fr) which will be regularly updated. Within the site, a tool allows users with a novel RNA structure to detect automatically whether the novel structure contains previously observed RINs.

An automated method for finding molecular complexes in large protein interaction networks

PubMed Central

Bader, Gary D; Hogue, Christopher WV

2003-01-01

Background Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial mapping efforts have already produced a wealth of data. As the size of the interaction set increases, databases and computational methods will be required to store, visualize and analyze the information in order to effectively aid in knowledge discovery. Results This paper describes a novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction and complex information from the yeast Saccharomyces cerevisiae was used for evaluation. Conclusion Dense regions of protein interaction networks can be found, based solely on connectivity data, many of which correspond to known protein complexes. The algorithm is not affected by a known high rate of false positives in data from high-throughput interaction techniques. The program is available from . PMID:12525261

rSNPBase 3.0: an updated database of SNP-related regulatory elements, element-gene pairs and SNP-based gene regulatory networks.

PubMed

Guo, Liyuan; Wang, Jing

2018-01-04

Here, we present the updated rSNPBase 3.0 database (http://rsnp3.psych.ac.cn), which provides human SNP-related regulatory elements, element-gene pairs and SNP-based regulatory networks. This database is the updated version of the SNP regulatory annotation database rSNPBase and rVarBase. In comparison to the last two versions, there are both structural and data adjustments in rSNPBase 3.0: (i) The most significant new feature is the expansion of analysis scope from SNP-related regulatory elements to include regulatory element-target gene pairs (E-G pairs), therefore it can provide SNP-based gene regulatory networks. (ii) Web function was modified according to data content and a new network search module is provided in the rSNPBase 3.0 in addition to the previous regulatory SNP (rSNP) search module. The two search modules support data query for detailed information (related-elements, element-gene pairs, and other extended annotations) on specific SNPs and SNP-related graphic networks constructed by interacting transcription factors (TFs), miRNAs and genes. (3) The type of regulatory elements was modified and enriched. To our best knowledge, the updated rSNPBase 3.0 is the first data tool supports SNP functional analysis from a regulatory network prospective, it will provide both a comprehensive understanding and concrete guidance for SNP-related regulatory studies. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

rSNPBase 3.0: an updated database of SNP-related regulatory elements, element-gene pairs and SNP-based gene regulatory networks

PubMed Central

2018-01-01

Abstract Here, we present the updated rSNPBase 3.0 database (http://rsnp3.psych.ac.cn), which provides human SNP-related regulatory elements, element-gene pairs and SNP-based regulatory networks. This database is the updated version of the SNP regulatory annotation database rSNPBase and rVarBase. In comparison to the last two versions, there are both structural and data adjustments in rSNPBase 3.0: (i) The most significant new feature is the expansion of analysis scope from SNP-related regulatory elements to include regulatory element–target gene pairs (E–G pairs), therefore it can provide SNP-based gene regulatory networks. (ii) Web function was modified according to data content and a new network search module is provided in the rSNPBase 3.0 in addition to the previous regulatory SNP (rSNP) search module. The two search modules support data query for detailed information (related-elements, element-gene pairs, and other extended annotations) on specific SNPs and SNP-related graphic networks constructed by interacting transcription factors (TFs), miRNAs and genes. (3) The type of regulatory elements was modified and enriched. To our best knowledge, the updated rSNPBase 3.0 is the first data tool supports SNP functional analysis from a regulatory network prospective, it will provide both a comprehensive understanding and concrete guidance for SNP-related regulatory studies. PMID:29140525

PodNet, a protein-protein interaction network of the podocyte.

PubMed

Warsow, Gregor; Endlich, Nicole; Schordan, Eric; Schordan, Sandra; Chilukoti, Ravi K; Homuth, Georg; Moeller, Marcus J; Fuellen, Georg; Endlich, Karlhans

2013-07-01

Interactions between proteins crucially determine cellular structure and function. Differential analysis of the interactome may help elucidate molecular mechanisms during disease development; however, this analysis necessitates mapping of expression data on protein-protein interaction networks. These networks do not exist for the podocyte; therefore, we built PodNet, a literature-based mouse podocyte network in Cytoscape format. Using database protein-protein interactions, we expanded PodNet to XPodNet with enhanced connectivity. In order to test the performance of XPodNet in differential interactome analysis, we examined podocyte developmental differentiation and the effect of cell culture. Transcriptomes of podocytes in 10 different states were mapped on XPodNet and analyzed with the Cytoscape plugin ExprEssence, based on the law of mass action. Interactions between slit diaphragm proteins are most significantly upregulated during podocyte development and most significantly downregulated in culture. On the other hand, our analysis revealed that interactions lost during podocyte differentiation are not regained in culture, suggesting a loss rather than a reversal of differentiation for podocytes in culture. Thus, we have developed PodNet as a valuable tool for differential interactome analysis in podocytes, and we have identified established and unexplored regulated interactions in developing and cultured podocytes.

SANSparallel: interactive homology search against Uniprot

PubMed Central

Somervuo, Panu; Holm, Liisa

2015-01-01

Proteins evolve by mutations and natural selection. The network of sequence similarities is a rich source for mining homologous relationships that inform on protein structure and function. There are many servers available to browse the network of homology relationships but one has to wait up to a minute for results. The SANSparallel webserver provides protein sequence database searches with immediate response and professional alignment visualization by third-party software. The output is a list, pairwise alignment or stacked alignment of sequence-similar proteins from Uniprot, UniRef90/50, Swissprot or Protein Data Bank. The stacked alignments are viewed in Jalview or as sequence logos. The database search uses the suffix array neighborhood search (SANS) method, which has been re-implemented as a client-server, improved and parallelized. The method is extremely fast and as sensitive as BLAST above 50% sequence identity. Benchmarks show that the method is highly competitive compared to previously published fast database search programs: UBLAST, DIAMOND, LAST, LAMBDA, RAPSEARCH2 and BLAT. The web server can be accessed interactively or programmatically at http://ekhidna2.biocenter.helsinki.fi/cgi-bin/sans/sans.cgi. It can be used to make protein functional annotation pipelines more efficient, and it is useful in interactive exploration of the detailed evidence supporting the annotation of particular proteins of interest. PMID:25855811

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

PubMed

López, Yosvany; Nakai, Kenta; Patil, Ashwini

2015-01-01

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

Complex network theory for the identification and assessment of candidate protein targets.

PubMed

McGarry, Ken; McDonald, Sharon

2018-06-01

In this work we use complex network theory to provide a statistical model of the connectivity patterns of human proteins and their interaction partners. Our intention is to identify important proteins that may be predisposed to be potential candidates as drug targets for therapeutic interventions. Target proteins usually have more interaction partners than non-target proteins, but there are no hard-and-fast rules for defining the actual number of interactions. We devise a statistical measure for identifying hub proteins, we score our target proteins with gene ontology annotations. The important druggable protein targets are likely to have similar biological functions that can be assessed for their potential therapeutic value. Our system provides a statistical analysis of the local and distant neighborhood protein interactions of the potential targets using complex network measures. This approach builds a more accurate model of drug-to-target activity and therefore the likely impact on treating diseases. We integrate high quality protein interaction data from the HINT database and disease associated proteins from the DrugTarget database. Other sources include biological knowledge from Gene Ontology and drug information from DrugBank. The problem is a very challenging one since the data is highly imbalanced between target proteins and the more numerous nontargets. We use undersampling on the training data and build Random Forest classifier models which are used to identify previously unclassified target proteins. We validate and corroborate these findings from the available literature. Copyright © 2018 Elsevier Ltd. All rights reserved.

Key genes and pathways in measles and their interaction with environmental chemicals.

PubMed

Zhang, Rongqiang; Jiang, Hualin; Li, Fengying; Su, Ning; Ding, Yi; Mao, Xiang; Ren, Dan; Wang, Jing

2018-06-01

The aim of the present study was to explore key genes that may have a role in the pathology of measles virus infection and to clarify the interaction networks between environmental factors and differentially expressed genes (DEGs). After screening the database of the Gene Expression Omnibus of the National Center for Biotechnology Information, the dataset GSE5808 was downloaded and analyzed. A global normalization method was performed to minimize data inconsistencies and heterogeneity. DEGs during different stages of measles virus infection were explored using R software (v3.4.0). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the DEGs were performed using Cytoscape 3.4.0 software. A protein-protein interaction (PPI) network of the DEGs was obtained from the STRING database v9.05. A total of 43 DEGs were obtained from four analyzed sample groups, including 10 highly expressed genes and 33 genes with decreased expression. The most enriched pathways based on KEGG analysis were fatty acid elongation, cytokine-cytokine receptor interaction and RNA degradation. The genes mentioned in the PPI network were mainly associated with protein binding and chemokine activity. A total of 219 chemicals were identified that may, jointly or on their own, interact with the 6 DEGs between the control group and patients with measles (at hospital entry), including benzo(a)pyrene (BaP) and tetrachlorodibenzodioxin (TCDD). In conclusion, the present study revealed that chemokines and environmental chemicals, e.g. BaP and TCDD, may affect the development of measles.

The BioGRID Interaction Database: 2011 update

PubMed Central

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

2011-01-01

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

PyPathway: Python Package for Biological Network Analysis and Visualization.

PubMed

Xu, Yang; Luo, Xiao-Chun

2018-05-01

Life science studies represent one of the biggest generators of large data sets, mainly because of rapid sequencing technological advances. Biological networks including interactive networks and human curated pathways are essential to understand these high-throughput data sets. Biological network analysis offers a method to explore systematically not only the molecular complexity of a particular disease but also the molecular relationships among apparently distinct phenotypes. Currently, several packages for Python community have been developed, such as BioPython and Goatools. However, tools to perform comprehensive network analysis and visualization are still needed. Here, we have developed PyPathway, an extensible free and open source Python package for functional enrichment analysis, network modeling, and network visualization. The network process module supports various interaction network and pathway databases such as Reactome, WikiPathway, STRING, and BioGRID. The network analysis module implements overrepresentation analysis, gene set enrichment analysis, network-based enrichment, and de novo network modeling. Finally, the visualization and data publishing modules enable users to share their analysis by using an easy web application. For package availability, see the first Reference.

Social disadvantage and borderline personality disorder: A study of social networks.

PubMed

Beeney, Joseph E; Hallquist, Michael N; Clifton, Allan D; Lazarus, Sophie A; Pilkonis, Paul A

2018-01-01

Examining differences in social integration, social support, and relationship characteristics in social networks may be critical for understanding the character and costs of the social difficulties experienced of borderline personality disorder (BPD). We conducted an ego-based (self-reported, individual) social network analysis of 142 participants recruited from clinical and community sources. Each participant listed the 30 most significant people (called alters) in their social network, then rated each alter in terms of amount of contact, social support, attachment strength and negative interactions. In addition, measures of social integration were determined using participant's report of the connection between people in their networks. BPD was associated with poorer social support, more frequent negative interactions, and less social integration. Examination of alter-by-BPD interactions indicated that whereas participants with low BPD symptoms had close relationships with people with high centrality within their networks, participants with high BPD symptoms had their closest relationships with people less central to their networks. The results suggest that individuals with BPD are at a social disadvantage: Those with whom they are most closely linked (including romantic partners) are less socially connected (i.e., less central) within their social network. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

STARNET 2: a web-based tool for accelerating discovery of gene regulatory networks using microarray co-expression data

PubMed Central

Jupiter, Daniel; Chen, Hailin; VanBuren, Vincent

2009-01-01

Background Although expression microarrays have become a standard tool used by biologists, analysis of data produced by microarray experiments may still present challenges. Comparison of data from different platforms, organisms, and labs may involve complicated data processing, and inferring relationships between genes remains difficult. Results STARNET 2 is a new web-based tool that allows post hoc visual analysis of correlations that are derived from expression microarray data. STARNET 2 facilitates user discovery of putative gene regulatory networks in a variety of species (human, rat, mouse, chicken, zebrafish, Drosophila, C. elegans, S. cerevisiae, Arabidopsis and rice) by graphing networks of genes that are closely co-expressed across a large heterogeneous set of preselected microarray experiments. For each of the represented organisms, raw microarray data were retrieved from NCBI's Gene Expression Omnibus for a selected Affymetrix platform. All pairwise Pearson correlation coefficients were computed for expression profiles measured on each platform, respectively. These precompiled results were stored in a MySQL database, and supplemented by additional data retrieved from NCBI. A web-based tool allows user-specified queries of the database, centered at a gene of interest. The result of a query includes graphs of correlation networks, graphs of known interactions involving genes and gene products that are present in the correlation networks, and initial statistical analyses. Two analyses may be performed in parallel to compare networks, which is facilitated by the new HEATSEEKER module. Conclusion STARNET 2 is a useful tool for developing new hypotheses about regulatory relationships between genes and gene products, and has coverage for 10 species. Interpretation of the correlation networks is supported with a database of previously documented interactions, a test for enrichment of Gene Ontology terms, and heat maps of correlation distances that may be used to compare two networks. The list of genes in a STARNET network may be useful in developing a list of candidate genes to use for the inference of causal networks. The tool is freely available at , and does not require user registration. PMID:19828039

Integrated inference and evaluation of host–fungi interaction networks

PubMed Central

Remmele, Christian W.; Luther, Christian H.; Balkenhol, Johannes; Dandekar, Thomas; Müller, Tobias; Dittrich, Marcus T.

2015-01-01

Fungal microorganisms frequently lead to life-threatening infections. Within this group of pathogens, the commensal Candida albicans and the filamentous fungus Aspergillus fumigatus are by far the most important causes of invasive mycoses in Europe. A key capability for host invasion and immune response evasion are specific molecular interactions between the fungal pathogen and its human host. Experimentally validated knowledge about these crucial interactions is rare in literature and even specialized host–pathogen databases mainly focus on bacterial and viral interactions whereas information on fungi is still sparse. To establish large-scale host–fungi interaction networks on a systems biology scale, we develop an extended inference approach based on protein orthology and data on gene functions. Using human and yeast intraspecies networks as template, we derive a large network of pathogen–host interactions (PHI). Rigorous filtering and refinement steps based on cellular localization and pathogenicity information of predicted interactors yield a primary scaffold of fungi–human and fungi–mouse interaction networks. Specific enrichment of known pathogenicity-relevant genes indicates the biological relevance of the predicted PHI. A detailed inspection of functionally relevant subnetworks reveals novel host–fungal interaction candidates such as the Candida virulence factor PLB1 and the anti-fungal host protein APP. Our results demonstrate the applicability of interolog-based prediction methods for host–fungi interactions and underline the importance of filtering and refinement steps to attain biologically more relevant interactions. This integrated network framework can serve as a basis for future analyses of high-throughput host–fungi transcriptome and proteome data. PMID:26300851

QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks

PubMed Central

Thibodeau, Asa; Márquez, Eladio J.; Luo, Oscar; Ruan, Yijun; Shin, Dong-Guk; Stitzel, Michael L.; Ucar, Duygu

2016-01-01

Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions. AVAILABILITY: QuIN’s web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub: https://github.com/UcarLab/QuIN/. PMID:27336171

Exploring of the molecular mechanism of rhinitis via bioinformatics methods

PubMed Central

Song, Yufen; Yan, Zhaohui

2018-01-01

The aim of this study was to analyze gene expression profiles for exploring the function and regulatory network of differentially expressed genes (DEGs) in pathogenesis of rhinitis by a bioinformatics method. The gene expression profile of GSE43523 was downloaded from the Gene Expression Omnibus database. The dataset contained 7 seasonal allergic rhinitis samples and 5 non-allergic normal samples. DEGs between rhinitis samples and normal samples were identified via the limma package of R. The webGestal database was used to identify enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the DEGs. The differentially co-expressed pairs of the DEGs were identified via the DCGL package in R, and the differential co-expression network was constructed based on these pairs. A protein-protein interaction (PPI) network of the DEGs was constructed based on the Search Tool for the Retrieval of Interacting Genes database. A total of 263 DEGs were identified in rhinitis samples compared with normal samples, including 125 downregulated ones and 138 upregulated ones. The DEGs were enriched in 7 KEGG pathways. 308 differential co-expression gene pairs were obtained. A differential co-expression network was constructed, containing 212 nodes. In total, 148 PPI pairs of the DEGs were identified, and a PPI network was constructed based on these pairs. Bioinformatics methods could help us identify significant genes and pathways related to the pathogenesis of rhinitis. Steroid biosynthesis pathway and metabolic pathways might play important roles in the development of allergic rhinitis (AR). Genes such as CDC42 effector protein 5, solute carrier family 39 member A11 and PR/SET domain 10 might be also associated with the pathogenesis of AR, which provided references for the molecular mechanisms of AR. PMID:29257233

RISE: a database of RNA interactome from sequencing experiments

PubMed Central

Gong, Jing; Shao, Di; Xu, Kui

2018-01-01

Abstract We present RISE (http://rise.zhanglab.net), a database of RNA Interactome from Sequencing Experiments. RNA-RNA interactions (RRIs) are essential for RNA regulation and function. RISE provides a comprehensive collection of RRIs that mainly come from recent transcriptome-wide sequencing-based experiments like PARIS, SPLASH, LIGR-seq, and MARIO, as well as targeted studies like RIA-seq, RAP-RNA and CLASH. It also includes interactions aggregated from other primary databases and publications. The RISE database currently contains 328,811 RNA-RNA interactions mainly in human, mouse and yeast. While most existing RNA databases mainly contain interactions of miRNA targeting, notably, more than half of the RRIs in RISE are among mRNA and long non-coding RNAs. We compared different RRI datasets in RISE and found limited overlaps in interactions resolved by different techniques and in different cell lines. It may suggest technology preference and also dynamic natures of RRIs. We also analyzed the basic features of the human and mouse RRI networks and found that they tend to be scale-free, small-world, hierarchical and modular. The analysis may nominate important RNAs or RRIs for further investigation. Finally, RISE provides a Circos plot and several table views for integrative visualization, with extensive molecular and functional annotations to facilitate exploration of biological functions for any RRI of interest. PMID:29040625

Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs.

PubMed

Le, Duc-Hau; Verbeke, Lieven; Son, Le Hoang; Chu, Dinh-Toi; Pham, Van-Huy

2017-11-14

MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks.

Major component analysis of dynamic networks of physiologic organ interactions

NASA Astrophysics Data System (ADS)

Liu, Kang K. L.; Bartsch, Ronny P.; Ma, Qianli D. Y.; Ivanov, Plamen Ch

2015-09-01

The human organism is a complex network of interconnected organ systems, where the behavior of one system affects the dynamics of other systems. Identifying and quantifying dynamical networks of diverse physiologic systems under varied conditions is a challenge due to the complexity in the output dynamics of the individual systems and the transient and nonlinear characteristics of their coupling. We introduce a novel computational method based on the concept of time delay stability and major component analysis to investigate how organ systems interact as a network to coordinate their functions. We analyze a large database of continuously recorded multi-channel physiologic signals from healthy young subjects during night-time sleep. We identify a network of dynamic interactions between key physiologic systems in the human organism. Further, we find that each physiologic state is characterized by a distinct network structure with different relative contribution from individual organ systems to the global network dynamics. Specifically, we observe a gradual decrease in the strength of coupling of heart and respiration to the rest of the network with transition from wake to deep sleep, and in contrast, an increased relative contribution to network dynamics from chin and leg muscle tone and eye movement, demonstrating a robust association between network topology and physiologic function.

Heard it through the grapevine: indirect networks and employee creativity.

PubMed

Hirst, Giles; Van Knippenberg, Daan; Zhou, Jing; Quintane, Eric; Zhu, Cherrie

2015-03-01

Social networks can be important sources of information and insights that may spark employee creativity. The cross-fertilization of ideas depends not just on access to information and insights through one's direct network-the people one actually interacts with--but at least as much on access to the indirect network one's direct ties connect one to (i.e., people one does not interact with directly, but with whom one's direct ties interact). We propose that the reach efficiency of this indirect network--its nonredundancy in terms of interconnections--is positively related to individual creativity. To help specify the boundaries of this positive influence of the indirect network, we also explore how many steps removed the indirect network still adds to creativity. In addition, we propose that the efficiency (nonredundancy) of one's direct network is important here, because more efficient direct networks give one access to indirect networks with greater reach efficiency. Our hypotheses were supported in a multilevel analysis of multisource survey data from 223 sales representatives nested within 11 divisions of a Chinese pharmaceutical company. This analysis also showed that the creative benefits of reach efficiency were evident for 3 and 4 degrees of separation but were greatest for indirect ties that depend only on one's direct ties. PsycINFO Database Record (c) 2015 APA, all rights reserved.

Identification of GRB2 and GAB1 Coexpression as an Unfavorable Prognostic Factor for Hepatocellular Carcinoma by a Combination of Expression Profile and Network Analysis

PubMed Central

Yang, Mei; Wang, Danhua; Yu, Lingxiang; Guo, Chaonan; Guo, Xiaodong; Lin, Na

2013-01-01

Aim To screen novel markers for hepatocellular carcinoma (HCC) by a combination of expression profile, interaction network analysis and clinical validation. Methods HCC significant molecules which are differentially expressed or had genetic variations in HCC tissues were obtained from five existing HCC related databases (OncoDB.HCC, HCC.net, dbHCCvar, EHCO and Liverome). Then, the protein-protein interaction (PPI) network of these molecules was constructed. Three topological features of the network ('Degree', 'Betweenness', and 'Closeness') and the k-core algorithm were used to screen candidate HCC markers which play crucial roles in tumorigenesis of HCC. Furthermore, the clinical significance of two candidate HCC markers growth factor receptor-bound 2 (GRB2) and GRB2-associated-binding protein 1 (GAB1) was validated. Results In total, 6179 HCC significant genes and 977 HCC significant proteins were collected from existing HCC related databases. After network analysis, 331 candidate HCC markers were identified. Especially, GAB1 has the highest k-coreness suggesting its central localization in HCC related network, and the interaction between GRB2 and GAB1 has the largest edge-betweenness implying it may be biologically important to the function of HCC related network. As the results of clinical validation, the expression levels of both GRB2 and GAB1 proteins were significantly higher in HCC tissues than those in their adjacent nonneoplastic tissues. More importantly, the combined GRB2 and GAB1 protein expression was significantly associated with aggressive tumor progression and poor prognosis in patients with HCC. Conclusion This study provided an integrative analysis by combining expression profile and interaction network analysis to identify a list of biologically significant HCC related markers and pathways. Further experimental validation indicated that the aberrant expression of GRB2 and GAB1 proteins may be strongly related to tumor progression and prognosis in patients with HCC. The overexpression of GRB2 in combination with upregulation of GAB1 may be an unfavorable prognostic factor for HCC. PMID:24391994

Abasy Atlas: a comprehensive inventory of systems, global network properties and systems-level elements across bacteria

PubMed Central

Ibarra-Arellano, Miguel A.; Campos-González, Adrián I.; Treviño-Quintanilla, Luis G.; Tauch, Andreas; Freyre-González, Julio A.

2016-01-01

The availability of databases electronically encoding curated regulatory networks and of high-throughput technologies and methods to discover regulatory interactions provides an invaluable source of data to understand the principles underpinning the organization and evolution of these networks responsible for cellular regulation. Nevertheless, data on these sources never goes beyond the regulon level despite the fact that regulatory networks are complex hierarchical-modular structures still challenging our understanding. This brings the necessity for an inventory of systems across a large range of organisms, a key step to rendering feasible comparative systems biology approaches. In this work, we take the first step towards a global understanding of the regulatory networks organization by making a cartography of the functional architectures of diverse bacteria. Abasy (Across-bacteria systems) Atlas provides a comprehensive inventory of annotated functional systems, global network properties and systems-level elements (global regulators, modular genes shaping functional systems, basal machinery genes and intermodular genes) predicted by the natural decomposition approach for reconstructed and meta-curated regulatory networks across a large range of bacteria, including pathogenically and biotechnologically relevant organisms. The meta-curation of regulatory datasets provides the most complete and reliable set of regulatory interactions currently available, which can even be projected into subsets by considering the force or weight of evidence supporting them or the systems that they belong to. Besides, Abasy Atlas provides data enabling large-scale comparative systems biology studies aimed at understanding the common principles and particular lifestyle adaptions of systems across bacteria. Abasy Atlas contains systems and system-level elements for 50 regulatory networks comprising 78 649 regulatory interactions covering 42 bacteria in nine taxa, containing 3708 regulons and 1776 systems. All this brings together a large corpus of data that will surely inspire studies to generate hypothesis regarding the principles governing the evolution and organization of systems and the functional architectures controlling them. Database URL: http://abasy.ccg.unam.mx PMID:27242034

BNDB - the Biochemical Network Database.

PubMed

Küntzer, Jan; Backes, Christina; Blum, Torsten; Gerasch, Andreas; Kaufmann, Michael; Kohlbacher, Oliver; Lenhof, Hans-Peter

2007-10-02

Technological advances in high-throughput techniques and efficient data acquisition methods have resulted in a massive amount of life science data. The data is stored in numerous databases that have been established over the last decades and are essential resources for scientists nowadays. However, the diversity of the databases and the underlying data models make it difficult to combine this information for solving complex problems in systems biology. Currently, researchers typically have to browse several, often highly focused, databases to obtain the required information. Hence, there is a pressing need for more efficient systems for integrating, analyzing, and interpreting these data. The standardization and virtual consolidation of the databases is a major challenge resulting in a unified access to a variety of data sources. We present the Biochemical Network Database (BNDB), a powerful relational database platform, allowing a complete semantic integration of an extensive collection of external databases. BNDB is built upon a comprehensive and extensible object model called BioCore, which is powerful enough to model most known biochemical processes and at the same time easily extensible to be adapted to new biological concepts. Besides a web interface for the search and curation of the data, a Java-based viewer (BiNA) provides a powerful platform-independent visualization and navigation of the data. BiNA uses sophisticated graph layout algorithms for an interactive visualization and navigation of BNDB. BNDB allows a simple, unified access to a variety of external data sources. Its tight integration with the biochemical network library BN++ offers the possibility for import, integration, analysis, and visualization of the data. BNDB is freely accessible at http://www.bndb.org.

The Deep Impact Network Experiment Operations Center Monitor and Control System

NASA Technical Reports Server (NTRS)

Wang, Shin-Ywan (Cindy); Torgerson, J. Leigh; Schoolcraft, Joshua; Brenman, Yan

2009-01-01

The Interplanetary Overlay Network (ION) software at JPL is an implementation of Delay/Disruption Tolerant Networking (DTN) which has been proposed as an interplanetary protocol to support space communication. The JPL Deep Impact Network (DINET) is a technology development experiment intended to increase the technical readiness of the JPL implemented ION suite. The DINET Experiment Operations Center (EOC) developed by JPL's Protocol Technology Lab (PTL) was critical in accomplishing the experiment. EOC, containing all end nodes of simulated spaces and one administrative node, exercised publish and subscribe functions for payload data among all end nodes to verify the effectiveness of data exchange over ION protocol stacks. A Monitor and Control System was created and installed on the administrative node as a multi-tiered internet-based Web application to support the Deep Impact Network Experiment by allowing monitoring and analysis of the data delivery and statistics from ION. This Monitor and Control System includes the capability of receiving protocol status messages, classifying and storing status messages into a database from the ION simulation network, and providing web interfaces for viewing the live results in addition to interactive database queries.

Rhizoma Dioscoreae extract protects against alveolar bone loss by regulating the cell cycle: A predictive study based on the protein‑protein interaction network.

PubMed

Zhang, Zhi-Guo; Song, Chang-Heng; Zhang, Fang-Zhen; Chen, Yan-Jing; Xiang, Li-Hua; Xiao, Gary Guishan; Ju, Da-Hong

2016-06-01

Rhizoma Dioscoreae extract (RDE) exhibits a protective effect on alveolar bone loss in ovariectomized (OVX) rats. The aim of this study was to predict the pathways or targets that are regulated by RDE, by re‑assessing our previously reported data and conducting a protein‑protein interaction (PPI) network analysis. In total, 383 differentially expressed genes (≥3‑fold) between alveolar bone samples from the RDE and OVX group rats were identified, and a PPI network was constructed based on these genes. Furthermore, four molecular clusters (A‑D) in the PPI network with the smallest P‑values were detected by molecular complex detection (MCODE) algorithm. Using Database for Annotation, Visualization and Integrated Discovery (DAVID) and Ingenuity Pathway Analysis (IPA) tools, two molecular clusters (A and B) were enriched for biological process in Gene Ontology (GO). Only cluster A was associated with biological pathways in the IPA database. GO and pathway analysis results showed that cluster A, associated with cell cycle regulation, was the most important molecular cluster in the PPI network. In addition, cyclin‑dependent kinase 1 (CDK1) may be a key molecule achieving the cell‑cycle‑regulatory function of cluster A. From the PPI network analysis, it was predicted that delayed cell cycle progression in excessive alveolar bone remodeling via downregulation of CDK1 may be another mechanism underling the anti‑osteopenic effect of RDE on alveolar bone.

A Novel Biclustering Approach to Association Rule Mining for Predicting HIV-1–Human Protein Interactions

PubMed Central

Mukhopadhyay, Anirban; Maulik, Ujjwal; Bandyopadhyay, Sanghamitra

2012-01-01

Identification of potential viral-host protein interactions is a vital and useful approach towards development of new drugs targeting those interactions. In recent days, computational tools are being utilized for predicting viral-host interactions. Recently a database containing records of experimentally validated interactions between a set of HIV-1 proteins and a set of human proteins has been published. The problem of predicting new interactions based on this database is usually posed as a classification problem. However, posing the problem as a classification one suffers from the lack of biologically validated negative interactions. Therefore it will be beneficial to use the existing database for predicting new viral-host interactions without the need of negative samples. Motivated by this, in this article, the HIV-1–human protein interaction database has been analyzed using association rule mining. The main objective is to identify a set of association rules both among the HIV-1 proteins and among the human proteins, and use these rules for predicting new interactions. In this regard, a novel association rule mining technique based on biclustering has been proposed for discovering frequent closed itemsets followed by the association rules from the adjacency matrix of the HIV-1–human interaction network. Novel HIV-1–human interactions have been predicted based on the discovered association rules and tested for biological significance. For validation of the predicted new interactions, gene ontology-based and pathway-based studies have been performed. These studies show that the human proteins which are predicted to interact with a particular viral protein share many common biological activities. Moreover, literature survey has been used for validation purpose to identify some predicted interactions that are already validated experimentally but not present in the database. Comparison with other prediction methods is also discussed. PMID:22539940

Topological and organizational properties of the products of house-keeping and tissue-specific genes in protein-protein interaction networks.

PubMed

Lin, Wen-Hsien; Liu, Wei-Chung; Hwang, Ming-Jing

2009-03-11

Human cells of various tissue types differ greatly in morphology despite having the same set of genetic information. Some genes are expressed in all cell types to perform house-keeping functions, while some are selectively expressed to perform tissue-specific functions. In this study, we wished to elucidate how proteins encoded by human house-keeping genes and tissue-specific genes are organized in human protein-protein interaction networks. We constructed protein-protein interaction networks for different tissue types using two gene expression datasets and one protein-protein interaction database. We then calculated three network indices of topological importance, the degree, closeness, and betweenness centralities, to measure the network position of proteins encoded by house-keeping and tissue-specific genes, and quantified their local connectivity structure. Compared to a random selection of proteins, house-keeping gene-encoded proteins tended to have a greater number of directly interacting neighbors and occupy network positions in several shortest paths of interaction between protein pairs, whereas tissue-specific gene-encoded proteins did not. In addition, house-keeping gene-encoded proteins tended to connect with other house-keeping gene-encoded proteins in all tissue types, whereas tissue-specific gene-encoded proteins also tended to connect with other tissue-specific gene-encoded proteins, but only in approximately half of the tissue types examined. Our analysis showed that house-keeping gene-encoded proteins tend to occupy important network positions, while those encoded by tissue-specific genes do not. The biological implications of our findings were discussed and we proposed a hypothesis regarding how cells organize their protein tools in protein-protein interaction networks. Our results led us to speculate that house-keeping gene-encoded proteins might form a core in human protein-protein interaction networks, while clusters of tissue-specific gene-encoded proteins are attached to the core at more peripheral positions of the networks.

A network-based training environment: a medical image processing paradigm.

PubMed

Costaridou, L; Panayiotakis, G; Sakellaropoulos, P; Cavouras, D; Dimopoulos, J

1998-01-01

The capability of interactive multimedia and Internet technologies is investigated with respect to the implementation of a distance learning environment. The system is built according to a client-server architecture, based on the Internet infrastructure, composed of server nodes conceptually modelled as WWW sites. Sites are implemented by customization of available components. The environment integrates network-delivered interactive multimedia courses, network-based tutoring, SIG support, information databases of professional interest, as well as course and tutoring management. This capability has been demonstrated by means of an implemented system, validated with digital image processing content, specifically image enhancement. Image enhancement methods are theoretically described and applied to mammograms. Emphasis is given to the interactive presentation of the effects of algorithm parameters on images. The system end-user access depends on available bandwidth, so high-speed access can be achieved via LAN or local ISDN connections. Network based training offers new means of improved access and sharing of learning resources and expertise, as promising supplements in training.

Pharmacovigilance of drug allergy and hypersensitivity using the ENDA-DAHD database and the GALEN platform. The Galenda project.

PubMed

Bousquet, P-J; Demoly, P; Romano, A; Aberer, W; Bircher, A; Blanca, M; Brockow, K; Pichler, W; Torres, M J; Terreehorst, I; Arnoux, B; Atanaskovic-Markovic, M; Barbaud, A; Bijl, A; Bonadonna, P; Burney, P G; Caimmi, S; Canonica, G W; Cernadas, J; Dahlen, B; Daures, J-P; Fernandez, J; Gomes, E; Gueant, J-L; Kowalski, M L; Kvedariene, V; Mertes, P-M; Martins, P; Nizankowska-Mogilnicka, E; Papadopoulos, N; Ponvert, C; Pirmohamed, M; Ring, J; Salapatas, M; Sanz, M L; Szczeklik, A; Van Ganse, E; De Weck, A L; Zuberbier, T; Merk, H F; Sachs, B; Sidoroff, A

2009-02-01

Nonallergic hypersensitivity and allergic reactions are part of the many different types of adverse drug reactions (ADRs). Databases exist for the collection of ADRs. Spontaneous reporting makes up the core data-generating system of pharmacovigilance, but there is a large under-estimation of allergy/hypersensitivity drug reactions. A specific database is therefore required for drug allergy and hypersensitivity using standard operating procedures (SOPs), as the diagnosis of drug allergy/hypersensitivity is difficult and current pharmacovigilance algorithms are insufficient. Although difficult, the diagnosis of drug allergy/hypersensitivity has been standardized by the European Network for Drug Allergy (ENDA) under the aegis of the European Academy of Allergology and Clinical Immunology and SOPs have been published. Based on ENDA and Global Allergy and Asthma European Network (GA(2)LEN, EU Framework Programme 6) SOPs, a Drug Allergy and Hypersensitivity Database (DAHD((R))) has been established under FileMaker((R)) Pro 9. It is already available online in many different languages and can be accessed using a personal login. GA(2)LEN is a European network of 27 partners (16 countries) and 59 collaborating centres (26 countries), which can coordinate and implement the DAHD across Europe. The GA(2)LEN-ENDA-DAHD platform interacting with a pharmacovigilance network appears to be of great interest for the reporting of allergy/hypersensitivity ADRs in conjunction with other pharmacovigilance instruments.

A Generic Data Harmonization Process for Cross-linked Research and Network Interaction. Construction and Application for the Lung Cancer Phenotype Database of the German Center for Lung Research.

PubMed

Firnkorn, D; Ganzinger, M; Muley, T; Thomas, M; Knaup, P

2015-01-01

Joint data analysis is a key requirement in medical research networks. Data are available in heterogeneous formats at each network partner and their harmonization is often rather complex. The objective of our paper is to provide a generic approach for the harmonization process in research networks. We applied the process when harmonizing data from three sites for the Lung Cancer Phenotype Database within the German Center for Lung Research. We developed a spreadsheet-based solution as tool to support the harmonization process for lung cancer data and a data integration procedure based on Talend Open Studio. The harmonization process consists of eight steps describing a systematic approach for defining and reviewing source data elements and standardizing common data elements. The steps for defining common data elements and harmonizing them with local data definitions are repeated until consensus is reached. Application of this process for building the phenotype database led to a common basic data set on lung cancer with 285 structured parameters. The Lung Cancer Phenotype Database was realized as an i2b2 research data warehouse. Data harmonization is a challenging task requiring informatics skills as well as domain knowledge. Our approach facilitates data harmonization by providing guidance through a uniform process that can be applied in a wide range of projects.

First Lessons From The Biarritz Trial Network [1

NASA Astrophysics Data System (ADS)

Touyarot, P.; Marc, B.; de Panafieu, A.

1986-07-01

Opened for commercial operation in 1984, the trial optical fiber network at Biarritz in south-west France gives 1,500 subscribers access to a whole range of broadband services - videophony, audiovisual databases, TV and stereo sound program distribution, and an on-line TV program library - in addition to conventional narrow-band services like telephony and videotex. The Biarritz network is an outstanding technology and engineering testbed. It is also a sociological testing ground for new services, unique in the world, with results of particular relevance to the interactive cable TV and visual communications networks of the future.

Creative benefits from well-connected leaders: leader social network ties as facilitators of employee radical creativity.

PubMed

Venkataramani, Vijaya; Richter, Andreas W; Clarke, Ronald

2014-09-01

Employee radical creativity critically depends on substantive informational resources from others across the wider organization. We propose that the social network ties of employees' immediate leaders assume a central role in garnering these resources, thereby fostering their employees' radical creativity both independent of and interactively with employees' own network ties. Drawing on data from 214 employees working in 30 teams of a public technology and environmental services organization, we find that team leaders' betweenness centrality in the idea network within their teams as well as among their peer leaders provides creative benefits beyond employees' own internal and external ties. Further, employees' and leaders' ties within and external to the team interactively predict employee radical creativity. Implications for theory and practice are discussed. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Key genes and pathways in measles and their interaction with environmental chemicals

PubMed Central

Zhang, Rongqiang; Jiang, Hualin; Li, Fengying; Su, Ning; Ding, Yi; Mao, Xiang; Ren, Dan; Wang, Jing

2018-01-01

The aim of the present study was to explore key genes that may have a role in the pathology of measles virus infection and to clarify the interaction networks between environmental factors and differentially expressed genes (DEGs). After screening the database of the Gene Expression Omnibus of the National Center for Biotechnology Information, the dataset GSE5808 was downloaded and analyzed. A global normalization method was performed to minimize data inconsistencies and heterogeneity. DEGs during different stages of measles virus infection were explored using R software (v3.4.0). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the DEGs were performed using Cytoscape 3.4.0 software. A protein-protein interaction (PPI) network of the DEGs was obtained from the STRING database v9.05. A total of 43 DEGs were obtained from four analyzed sample groups, including 10 highly expressed genes and 33 genes with decreased expression. The most enriched pathways based on KEGG analysis were fatty acid elongation, cytokine-cytokine receptor interaction and RNA degradation. The genes mentioned in the PPI network were mainly associated with protein binding and chemokine activity. A total of 219 chemicals were identified that may, jointly or on their own, interact with the 6 DEGs between the control group and patients with measles (at hospital entry), including benzo(a)pyrene (BaP) and tetrachlorodibenzodioxin (TCDD). In conclusion, the present study revealed that chemokines and environmental chemicals, e.g. BaP and TCDD, may affect the development of measles. PMID:29805511

SANSparallel: interactive homology search against Uniprot.

PubMed

Somervuo, Panu; Holm, Liisa

2015-07-01

Proteins evolve by mutations and natural selection. The network of sequence similarities is a rich source for mining homologous relationships that inform on protein structure and function. There are many servers available to browse the network of homology relationships but one has to wait up to a minute for results. The SANSparallel webserver provides protein sequence database searches with immediate response and professional alignment visualization by third-party software. The output is a list, pairwise alignment or stacked alignment of sequence-similar proteins from Uniprot, UniRef90/50, Swissprot or Protein Data Bank. The stacked alignments are viewed in Jalview or as sequence logos. The database search uses the suffix array neighborhood search (SANS) method, which has been re-implemented as a client-server, improved and parallelized. The method is extremely fast and as sensitive as BLAST above 50% sequence identity. Benchmarks show that the method is highly competitive compared to previously published fast database search programs: UBLAST, DIAMOND, LAST, LAMBDA, RAPSEARCH2 and BLAT. The web server can be accessed interactively or programmatically at http://ekhidna2.biocenter.helsinki.fi/cgi-bin/sans/sans.cgi. It can be used to make protein functional annotation pipelines more efficient, and it is useful in interactive exploration of the detailed evidence supporting the annotation of particular proteins of interest. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

A gene network bioinformatics analysis for pemphigoid autoimmune blistering diseases.

PubMed

Barone, Antonio; Toti, Paolo; Giuca, Maria Rita; Derchi, Giacomo; Covani, Ugo

2015-07-01

In this theoretical study, a text mining search and clustering analysis of data related to genes potentially involved in human pemphigoid autoimmune blistering diseases (PAIBD) was performed using web tools to create a gene/protein interaction network. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was employed to identify a final set of PAIBD-involved genes and to calculate the overall significant interactions among genes: for each gene, the weighted number of links, or WNL, was registered and a clustering procedure was performed using the WNL analysis. Genes were ranked in class (leader, B, C, D and so on, up to orphans). An ontological analysis was performed for the set of 'leader' genes. Using the above-mentioned data network, 115 genes represented the final set; leader genes numbered 7 (intercellular adhesion molecule 1 (ICAM-1), interferon gamma (IFNG), interleukin (IL)-2, IL-4, IL-6, IL-8 and tumour necrosis factor (TNF)), class B genes were 13, whereas the orphans were 24. The ontological analysis attested that the molecular action was focused on extracellular space and cell surface, whereas the activation and regulation of the immunity system was widely involved. Despite the limited knowledge of the present pathologic phenomenon, attested by the presence of 24 genes revealing no protein-protein direct or indirect interactions, the network showed significant pathways gathered in several subgroups: cellular components, molecular functions, biological processes and the pathologic phenomenon obtained from the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database. The molecular basis for PAIBD was summarised and expanded, which will perhaps give researchers promising directions for the identification of new therapeutic targets.

DenHunt - A Comprehensive Database of the Intricate Network of Dengue-Human Interactions

PubMed Central

Arjunan, Selvam; Sastri, Narayan P.; Chandra, Nagasuma

2016-01-01

Dengue virus (DENV) is a human pathogen and its etiology has been widely established. There are many interactions between DENV and human proteins that have been reported in literature. However, no publicly accessible resource for efficiently retrieving the information is yet available. In this study, we mined all publicly available dengue–human interactions that have been reported in the literature into a database called DenHunt. We retrieved 682 direct interactions of human proteins with dengue viral components, 382 indirect interactions and 4120 differentially expressed human genes in dengue infected cell lines and patients. We have illustrated the importance of DenHunt by mapping the dengue–human interactions on to the host interactome and observed that the virus targets multiple host functional complexes of important cellular processes such as metabolism, immune system and signaling pathways suggesting a potential role of these interactions in viral pathogenesis. We also observed that 7 percent of the dengue virus interacting human proteins are also associated with other infectious and non-infectious diseases. Finally, the understanding that comes from such analyses could be used to design better strategies to counteract the diseases caused by dengue virus. The whole dataset has been catalogued in a searchable database, called DenHunt (http://proline.biochem.iisc.ernet.in/DenHunt/). PMID:27618709

DenHunt - A Comprehensive Database of the Intricate Network of Dengue-Human Interactions.

PubMed

Karyala, Prashanthi; Metri, Rahul; Bathula, Christopher; Yelamanchi, Syam K; Sahoo, Lipika; Arjunan, Selvam; Sastri, Narayan P; Chandra, Nagasuma

2016-09-01

Dengue virus (DENV) is a human pathogen and its etiology has been widely established. There are many interactions between DENV and human proteins that have been reported in literature. However, no publicly accessible resource for efficiently retrieving the information is yet available. In this study, we mined all publicly available dengue-human interactions that have been reported in the literature into a database called DenHunt. We retrieved 682 direct interactions of human proteins with dengue viral components, 382 indirect interactions and 4120 differentially expressed human genes in dengue infected cell lines and patients. We have illustrated the importance of DenHunt by mapping the dengue-human interactions on to the host interactome and observed that the virus targets multiple host functional complexes of important cellular processes such as metabolism, immune system and signaling pathways suggesting a potential role of these interactions in viral pathogenesis. We also observed that 7 percent of the dengue virus interacting human proteins are also associated with other infectious and non-infectious diseases. Finally, the understanding that comes from such analyses could be used to design better strategies to counteract the diseases caused by dengue virus. The whole dataset has been catalogued in a searchable database, called DenHunt (http://proline.biochem.iisc.ernet.in/DenHunt/).

Mining disease genes using integrated protein-protein interaction and gene-gene co-regulation information.

PubMed

Li, Jin; Wang, Limei; Guo, Maozu; Zhang, Ruijie; Dai, Qiguo; Liu, Xiaoyan; Wang, Chunyu; Teng, Zhixia; Xuan, Ping; Zhang, Mingming

2015-01-01

In humans, despite the rapid increase in disease-associated gene discovery, a large proportion of disease-associated genes are still unknown. Many network-based approaches have been used to prioritize disease genes. Many networks, such as the protein-protein interaction (PPI), KEGG, and gene co-expression networks, have been used. Expression quantitative trait loci (eQTLs) have been successfully applied for the determination of genes associated with several diseases. In this study, we constructed an eQTL-based gene-gene co-regulation network (GGCRN) and used it to mine for disease genes. We adopted the random walk with restart (RWR) algorithm to mine for genes associated with Alzheimer disease. Compared to the Human Protein Reference Database (HPRD) PPI network alone, the integrated HPRD PPI and GGCRN networks provided faster convergence and revealed new disease-related genes. Therefore, using the RWR algorithm for integrated PPI and GGCRN is an effective method for disease-associated gene mining.

Comprehensive Analysis of Interaction Networks of Telomerase Reverse Transcriptase with Multiple Bioinformatic Approaches: Deep Mining the Potential Functions of Telomere and Telomerase.

PubMed

Hou, Chunyu; Wang, Fei; Liu, Xuewen; Chang, Guangming; Wang, Feng; Geng, Xin

2017-08-01

Telomerase reverse transcriptase (TERT) is the protein component of telomerase complex. Evidence has accumulated showing that the nontelomeric functions of TERT are independent of telomere elongation. However, the mechanisms governing the interaction between TERT and its target genes are not clearly revealed. The biological functions of TERT are not fully elucidated and have thus far been underestimated. To further explore these functions, we investigated TERT interaction networks using multiple bioinformatic databases, including BioGRID, STRING, DAVID, GeneCards, GeneMANIA, PANTHER, miRWalk, mirTarBase, miRNet, miRDB, and TargetScan. In addition, network diagrams were built using Cytoscape software. As competing endogenous RNAs (ceRNAs) are endogenous transcripts that compete for the binding of microRNAs (miRNAs) by using shared miRNA recognition elements, they are involved in creating widespread regulatory networks. Therefore, the ceRNA regulatory networks of TERT were also investigated in this study. Interestingly, we found that the three genes PABPC1, SLC7A11, and TP53 were present in both TERT interaction networks and ceRNAs target genes. It was predicted that TERT might play nontelomeric roles in the generation or development of some rare diseases, such as Rift Valley fever and dyscalculia. Thus, our data will help to decipher the interaction networks of TERT and reveal the unknown functions of telomerase in cancer and aging-related diseases.

Virtual Interactomics of Proteins from Biochemical Standpoint

PubMed Central

Kubrycht, Jaroslav; Sigler, Karel; Souček, Pavel

2012-01-01

Virtual interactomics represents a rapidly developing scientific area on the boundary line of bioinformatics and interactomics. Protein-related virtual interactomics then comprises instrumental tools for prediction, simulation, and networking of the majority of interactions important for structural and individual reproduction, differentiation, recognition, signaling, regulation, and metabolic pathways of cells and organisms. Here, we describe the main areas of virtual protein interactomics, that is, structurally based comparative analysis and prediction of functionally important interacting sites, mimotope-assisted and combined epitope prediction, molecular (protein) docking studies, and investigation of protein interaction networks. Detailed information about some interesting methodological approaches and online accessible programs or databases is displayed in our tables. Considerable part of the text deals with the searches for common conserved or functionally convergent protein regions and subgraphs of conserved interaction networks, new outstanding trends and clinically interesting results. In agreement with the presented data and relationships, virtual interactomic tools improve our scientific knowledge, help us to formulate working hypotheses, and they frequently also mediate variously important in silico simulations. PMID:22928109

The Pathway Tools software.

PubMed

Karp, Peter D; Paley, Suzanne; Romero, Pedro

2002-01-01

Bioinformatics requires reusable software tools for creating model-organism databases (MODs). The Pathway Tools is a reusable, production-quality software environment for creating a type of MOD called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc (see http://ecocyc.org) integrates our evolving understanding of the genes, proteins, metabolic network, and genetic network of an organism. This paper provides an overview of the four main components of the Pathway Tools: The PathoLogic component supports creation of new PGDBs from the annotated genome of an organism. The Pathway/Genome Navigator provides query, visualization, and Web-publishing services for PGDBs. The Pathway/Genome Editors support interactive updating of PGDBs. The Pathway Tools ontology defines the schema of PGDBs. The Pathway Tools makes use of the Ocelot object database system for data management services for PGDBs. The Pathway Tools has been used to build PGDBs for 13 organisms within SRI and by external users.

Identification of potential crucial genes associated with steroid-induced necrosis of femoral head based on gene expression profile.

PubMed

Lin, Zhe; Lin, Yongsheng

2017-09-05

The aim of this study was to explore potential crucial genes associated with the steroid-induced necrosis of femoral head (SINFH) and to provide valid biological information for further investigation of SINFH. Gene expression profile of GSE26316, generated from 3 SINFH rat samples and 3 normal rat samples were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using LIMMA package. After functional enrichment analyses of DEGs, protein-protein interaction (PPI) network and sub-PPI network analyses were conducted based on the STRING database and cytoscape. In total, 59 up-regulated DEGs and 156 downregulated DEGs were identified. The up-regulated DEGs were mainly involved in functions about immunity (e.g. Fcer1A and Il7R), and the downregulated DEGs were mainly enriched in muscle system process (e.g. Tnni2, Mylpf and Myl1). The PPI network of DEGs consisted of 123 nodes and 300 interactions. Tnni2, Mylpf, and Myl1 were the top 3 outstanding genes based on both subgraph centrality and degree centrality evaluation. These three genes interacted with each other in the network. Furthermore, the significant network module was composed of 22 downregulated genes (e.g. Tnni2, Mylpf and Myl1). These genes were mainly enriched in functions like muscle system process. The DEGs related to the regulation of immune system process (e.g. Fcer1A and Il7R), and DEGs correlated with muscle system process (e.g. Tnni2, Mylpf and Myl1) may be closely associated with the progress of SINFH, which is still needed to be confirmed by experiments. Copyright © 2017 Elsevier B.V. All rights reserved.

Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo.

PubMed

Uhlirova, Hana; Tian, Peifang; Kılıç, Kıvılcım; Thunemann, Martin; Sridhar, Vishnu B; Chmelik, Radim; Bartsch, Hauke; Dale, Anders M; Devor, Anna; Saisan, Payam A

2018-05-04

The importance of sharing experimental data in neuroscience grows with the amount and complexity of data acquired and various techniques used to obtain and process these data. However, the majority of experimental data, especially from individual studies of regular-sized laboratories never reach wider research community. A graphical user interface (GUI) engine called Neurovascular Network Explorer 2.0 (NNE 2.0) has been created as a tool for simple and low-cost sharing and exploring of vascular imaging data. NNE 2.0 interacts with a database containing optogenetically-evoked dilation/constriction time-courses of individual vessels measured in mice somatosensory cortex in vivo by 2-photon microscopy. NNE 2.0 enables selection and display of the time-courses based on different criteria (subject, branching order, cortical depth, vessel diameter, arteriolar tree) as well as simple mathematical manipulation (e.g. averaging, peak-normalization) and data export. It supports visualization of the vascular network in 3D and enables localization of the individual functional vessel diameter measurements within vascular trees. NNE 2.0, its source code, and the corresponding database are freely downloadable from UCSD Neurovascular Imaging Laboratory website 1 . The source code can be utilized by the users to explore the associated database or as a template for databasing and sharing their own experimental results provided the appropriate format.

ActiveDriverDB: human disease mutations and genome variation in post-translational modification sites of proteins

PubMed Central

Krassowski, Michal; Paczkowska, Marta; Cullion, Kim; Huang, Tina; Dzneladze, Irakli; Ouellette, B F Francis; Yamada, Joseph T; Fradet-Turcotte, Amelie

2018-01-01

Abstract Interpretation of genetic variation is needed for deciphering genotype-phenotype associations, mechanisms of inherited disease, and cancer driver mutations. Millions of single nucleotide variants (SNVs) in human genomes are known and thousands are associated with disease. An estimated 21% of disease-associated amino acid substitutions corresponding to missense SNVs are located in protein sites of post-translational modifications (PTMs), chemical modifications of amino acids that extend protein function. ActiveDriverDB is a comprehensive human proteo-genomics database that annotates disease mutations and population variants through the lens of PTMs. We integrated >385,000 published PTM sites with ∼3.6 million substitutions from The Cancer Genome Atlas (TCGA), the ClinVar database of disease genes, and human genome sequencing projects. The database includes site-specific interaction networks of proteins, upstream enzymes such as kinases, and drugs targeting these enzymes. We also predicted network-rewiring impact of mutations by analyzing gains and losses of kinase-bound sequence motifs. ActiveDriverDB provides detailed visualization, filtering, browsing and searching options for studying PTM-associated mutations. Users can upload mutation datasets interactively and use our application programming interface in pipelines. Integrative analysis of mutations and PTMs may help decipher molecular mechanisms of phenotypes and disease, as exemplified by case studies of TP53, BRCA2 and VHL. The open-source database is available at https://www.ActiveDriverDB.org. PMID:29126202

CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components.

PubMed

Heberle, Henry; Carazzolle, Marcelo Falsarella; Telles, Guilherme P; Meirelles, Gabriela Vaz; Minghim, Rosane

2017-09-13

The advent of "omics" science has brought new perspectives in contemporary biology through the high-throughput analyses of molecular interactions, providing new clues in protein/gene function and in the organization of biological pathways. Biomolecular interaction networks, or graphs, are simple abstract representations where the components of a cell (e.g. proteins, metabolites etc.) are represented by nodes and their interactions are represented by edges. An appropriate visualization of data is crucial for understanding such networks, since pathways are related to functions that occur in specific regions of the cell. The force-directed layout is an important and widely used technique to draw networks according to their topologies. Placing the networks into cellular compartments helps to quickly identify where network elements are located and, more specifically, concentrated. Currently, only a few tools provide the capability of visually organizing networks by cellular compartments. Most of them cannot handle large and dense networks. Even for small networks with hundreds of nodes the available tools are not able to reposition the network while the user is interacting, limiting the visual exploration capability. Here we propose CellNetVis, a web tool to easily display biological networks in a cell diagram employing a constrained force-directed layout algorithm. The tool is freely available and open-source. It was originally designed for networks generated by the Integrated Interactome System and can be used with networks from others databases, like InnateDB. CellNetVis has demonstrated to be applicable for dynamic investigation of complex networks over a consistent representation of a cell on the Web, with capabilities not matched elsewhere.

Prediction of Oncogenic Interactions and Cancer-Related Signaling Networks Based on Network Topology

PubMed Central

Acencio, Marcio Luis; Bovolenta, Luiz Augusto; Camilo, Esther; Lemke, Ney

2013-01-01

Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI). This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved in cancer research interested in detecting signaling networks most prone to contribute with the emergence of malignant phenotype. PMID:24204854

Comprehensive data resources and analytical tools for pathological association of aminoacyl tRNA synthetases with cancer

PubMed Central

Lee, Ji-Hyun; You, Sungyong; Hyeon, Do Young; Kang, Byeongsoo; Kim, Hyerim; Park, Kyoung Mii; Han, Byungwoo; Hwang, Daehee; Kim, Sunghoon

2015-01-01

Mammalian cells have cytoplasmic and mitochondrial aminoacyl-tRNA synthetases (ARSs) that catalyze aminoacylation of tRNAs during protein synthesis. Despite their housekeeping functions in protein synthesis, recently, ARSs and ARS-interacting multifunctional proteins (AIMPs) have been shown to play important roles in disease pathogenesis through their interactions with disease-related molecules. However, there are lacks of data resources and analytical tools that can be used to examine disease associations of ARS/AIMPs. Here, we developed an Integrated Database for ARSs (IDA), a resource database including cancer genomic/proteomic and interaction data of ARS/AIMPs. IDA includes mRNA expression, somatic mutation, copy number variation and phosphorylation data of ARS/AIMPs and their interacting proteins in various cancers. IDA further includes an array of analytical tools for exploration of disease association of ARS/AIMPs, identification of disease-associated ARS/AIMP interactors and reconstruction of ARS-dependent disease-perturbed network models. Therefore, IDA provides both comprehensive data resources and analytical tools for understanding potential roles of ARS/AIMPs in cancers. Database URL: http://ida.biocon.re.kr/, http://ars.biocon.re.kr/ PMID:25824651

Active subnetwork recovery with a mechanism-dependent scoring function; with application to angiogenesis and organogenesis studies

PubMed Central

2013-01-01

Background The learning active subnetworks problem involves finding subnetworks of a bio-molecular network that are active in a particular condition. Many approaches integrate observation data (e.g., gene expression) with the network topology to find candidate subnetworks. Increasingly, pathway databases contain additional annotation information that can be mined to improve prediction accuracy, e.g., interaction mechanism (e.g., transcription, microRNA, cleavage) annotations. We introduce a mechanism-based approach to active subnetwork recovery which exploits such annotations. We suggest that neighboring interactions in a network tend to be co-activated in a way that depends on the “correlation” of their mechanism annotations. e.g., neighboring phosphorylation and de-phosphorylation interactions may be more likely to be co-activated than neighboring phosphorylation and covalent bonding interactions. Results Our method iteratively learns the mechanism correlations and finds the most likely active subnetwork. We use a probabilistic graphical model with a Markov Random Field component which creates dependencies between the states (active or non-active) of neighboring interactions, that incorporates a mechanism-based component to the function. We apply a heuristic-based EM-based algorithm suitable for the problem. We validated our method’s performance using simulated data in networks downloaded from GeneGO against the same approach without the mechanism-based component, and two other existing methods. We validated our methods performance in correctly recovering (1) the true interaction states, and (2) global network properties of the original network against these other methods. We applied our method to networks generated from time-course gene expression studies in angiogenesis and lung organogenesis and validated the findings from a biological perspective against current literature. Conclusions The advantage of our mechanism-based approach is best seen in networks composed of connected regions with a large number of interactions annotated with a subset of mechanisms, e.g., a regulatory region of transcription interactions, or a cleavage cascade region. When applied to real datasets, our method recovered novel and biologically meaningful putative interactions, e.g., interactions from an integrin signaling pathway using the angiogenesis dataset, and a group of regulatory microRNA interactions in an organogenesis network. PMID:23432934

CellMap visualizes protein-protein interactions and subcellular localization

PubMed Central

Dallago, Christian; Goldberg, Tatyana; Andrade-Navarro, Miguel Angel; Alanis-Lobato, Gregorio; Rost, Burkhard

2018-01-01

Many tools visualize protein-protein interaction (PPI) networks. The tool introduced here, CellMap, adds one crucial novelty by visualizing PPI networks in the context of subcellular localization, i.e. the location in the cell or cellular component in which a PPI happens. Users can upload images of cells and define areas of interest against which PPIs for selected proteins are displayed (by default on a cartoon of a cell). Annotations of localization are provided by the user or through our in-house database. The visualizer and server are written in JavaScript, making CellMap easy to customize and to extend by researchers and developers. PMID:29497493

Protein annotation from protein interaction networks and Gene Ontology.

PubMed

Nguyen, Cao D; Gardiner, Katheleen J; Cios, Krzysztof J

2011-10-01

We introduce a novel method for annotating protein function that combines Naïve Bayes and association rules, and takes advantage of the underlying topology in protein interaction networks and the structure of graphs in the Gene Ontology. We apply our method to proteins from the Human Protein Reference Database (HPRD) and show that, in comparison with other approaches, it predicts protein functions with significantly higher recall with no loss of precision. Specifically, it achieves 51% precision and 60% recall versus 45% and 26% for Majority and 24% and 61% for χ²-statistics, respectively. Copyright © 2011 Elsevier Inc. All rights reserved.

Uncovering Trophic Interactions in Arthropod Predators through DNA Shotgun-Sequencing of Gut Contents

PubMed Central

Paula, Débora P.; Linard, Benjamin; Crampton-Platt, Alex; Srivathsan, Amrita; Timmermans, Martijn J. T. N.; Sujii, Edison R.; Pires, Carmen S. S.; Souza, Lucas M.; Andow, David A.; Vogler, Alfried P.

2016-01-01

Characterizing trophic networks is fundamental to many questions in ecology, but this typically requires painstaking efforts, especially to identify the diet of small generalist predators. Several attempts have been devoted to develop suitable molecular tools to determine predatory trophic interactions through gut content analysis, and the challenge has been to achieve simultaneously high taxonomic breadth and resolution. General and practical methods are still needed, preferably independent of PCR amplification of barcodes, to recover a broader range of interactions. Here we applied shotgun-sequencing of the DNA from arthropod predator gut contents, extracted from four common coccinellid and dermapteran predators co-occurring in an agroecosystem in Brazil. By matching unassembled reads against six DNA reference databases obtained from public databases and newly assembled mitogenomes, and filtering for high overlap length and identity, we identified prey and other foreign DNA in the predator guts. Good taxonomic breadth and resolution was achieved (93% of prey identified to species or genus), but with low recovery of matching reads. Two to nine trophic interactions were found for these predators, some of which were only inferred by the presence of parasitoids and components of the microbiome known to be associated with aphid prey. Intraguild predation was also found, including among closely related ladybird species. Uncertainty arises from the lack of comprehensive reference databases and reliance on low numbers of matching reads accentuating the risk of false positives. We discuss caveats and some future prospects that could improve the use of direct DNA shotgun-sequencing to characterize arthropod trophic networks. PMID:27622637

VTCdb: a gene co-expression database for the crop species Vitis vinifera (grapevine).

PubMed

Wong, Darren C J; Sweetman, Crystal; Drew, Damian P; Ford, Christopher M

2013-12-16

Gene expression datasets in model plants such as Arabidopsis have contributed to our understanding of gene function and how a single underlying biological process can be governed by a diverse network of genes. The accumulation of publicly available microarray data encompassing a wide range of biological and environmental conditions has enabled the development of additional capabilities including gene co-expression analysis (GCA). GCA is based on the understanding that genes encoding proteins involved in similar and/or related biological processes may exhibit comparable expression patterns over a range of experimental conditions, developmental stages and tissues. We present an open access database for the investigation of gene co-expression networks within the cultivated grapevine, Vitis vinifera. The new gene co-expression database, VTCdb (http://vtcdb.adelaide.edu.au/Home.aspx), offers an online platform for transcriptional regulatory inference in the cultivated grapevine. Using condition-independent and condition-dependent approaches, grapevine co-expression networks were constructed using the latest publicly available microarray datasets from diverse experimental series, utilising the Affymetrix Vitis vinifera GeneChip (16 K) and the NimbleGen Grape Whole-genome microarray chip (29 K), thus making it possible to profile approximately 29,000 genes (95% of the predicted grapevine transcriptome). Applications available with the online platform include the use of gene names, probesets, modules or biological processes to query the co-expression networks, with the option to choose between Affymetrix or Nimblegen datasets and between multiple co-expression measures. Alternatively, the user can browse existing network modules using interactive network visualisation and analysis via CytoscapeWeb. To demonstrate the utility of the database, we present examples from three fundamental biological processes (berry development, photosynthesis and flavonoid biosynthesis) whereby the recovered sub-networks reconfirm established plant gene functions and also identify novel associations. Together, we present valuable insights into grapevine transcriptional regulation by developing network models applicable to researchers in their prioritisation of gene candidates, for on-going study of biological processes related to grapevine development, metabolism and stress responses.

Redrawing the map of Great Britain from a network of human interactions.

PubMed

Ratti, Carlo; Sobolevsky, Stanislav; Calabrese, Francesco; Andris, Clio; Reades, Jonathan; Martino, Mauro; Claxton, Rob; Strogatz, Steven H

2010-12-08

Do regional boundaries defined by governments respect the more natural ways that people interact across space? This paper proposes a novel, fine-grained approach to regional delineation, based on analyzing networks of billions of individual human transactions. Given a geographical area and some measure of the strength of links between its inhabitants, we show how to partition the area into smaller, non-overlapping regions while minimizing the disruption to each person's links. We tested our method on the largest non-Internet human network, inferred from a large telecommunications database in Great Britain. Our partitioning algorithm yields geographically cohesive regions that correspond remarkably well with administrative regions, while unveiling unexpected spatial structures that had previously only been hypothesized in the literature. We also quantify the effects of partitioning, showing for instance that the effects of a possible secession of Wales from Great Britain would be twice as disruptive for the human network than that of Scotland.

SPIKE – a database, visualization and analysis tool of cellular signaling pathways

PubMed Central

Elkon, Ran; Vesterman, Rita; Amit, Nira; Ulitsky, Igor; Zohar, Idan; Weisz, Mali; Mass, Gilad; Orlev, Nir; Sternberg, Giora; Blekhman, Ran; Assa, Jackie; Shiloh, Yosef; Shamir, Ron

2008-01-01

Background Biological signaling pathways that govern cellular physiology form an intricate web of tightly regulated interlocking processes. Data on these regulatory networks are accumulating at an unprecedented pace. The assimilation, visualization and interpretation of these data have become a major challenge in biological research, and once met, will greatly boost our ability to understand cell functioning on a systems level. Results To cope with this challenge, we are developing the SPIKE knowledge-base of signaling pathways. SPIKE contains three main software components: 1) A database (DB) of biological signaling pathways. Carefully curated information from the literature and data from large public sources constitute distinct tiers of the DB. 2) A visualization package that allows interactive graphic representations of regulatory interactions stored in the DB and superposition of functional genomic and proteomic data on the maps. 3) An algorithmic inference engine that analyzes the networks for novel functional interplays between network components. SPIKE is designed and implemented as a community tool and therefore provides a user-friendly interface that allows registered users to upload data to SPIKE DB. Our vision is that the DB will be populated by a distributed and highly collaborative effort undertaken by multiple groups in the research community, where each group contributes data in its field of expertise. Conclusion The integrated capabilities of SPIKE make it a powerful platform for the analysis of signaling networks and the integration of knowledge on such networks with omics data. PMID:18289391

Network Analysis Tools: from biological networks to clusters and pathways.

PubMed

Brohée, Sylvain; Faust, Karoline; Lima-Mendez, Gipsi; Vanderstocken, Gilles; van Helden, Jacques

2008-01-01

Network Analysis Tools (NeAT) is a suite of computer tools that integrate various algorithms for the analysis of biological networks: comparison between graphs, between clusters, or between graphs and clusters; network randomization; analysis of degree distribution; network-based clustering and path finding. The tools are interconnected to enable a stepwise analysis of the network through a complete analytical workflow. In this protocol, we present a typical case of utilization, where the tasks above are combined to decipher a protein-protein interaction network retrieved from the STRING database. The results returned by NeAT are typically subnetworks, networks enriched with additional information (i.e., clusters or paths) or tables displaying statistics. Typical networks comprising several thousands of nodes and arcs can be analyzed within a few minutes. The complete protocol can be read and executed in approximately 1 h.

EcoCyc: a comprehensive database resource for Escherichia coli

PubMed Central

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

2005-01-01

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

Development of a chemotherapy regimen interaction database for the mobile internet: detecting interactions with psychotropics through OncoRx-MI.

PubMed

Yap, Kevin Yi-Lwern; Chui, Wai Keung; Chan, Alexandre

2011-09-01

Cancer patients are at high risks of drug-drug interactions (DDIs). Clinicians need to know the magnitude of DDIs so as to better manage their patients' drug therapies. We have previously created a novel interaction database for oncology prescriptions (OncoRx). In this project, we leverage on 3G networks to further develop this database into an iPhone-specific application for the mobile internet (OncoRx-MI). Data on anticancer drugs (ACDs), chemotherapy regimens (CRegs) and DDIs with psychotropics were compiled from various hardcopy and online resources, and published articles from PubMed, Scopus and Science Direct. The database and iPhone web documents were designed using Adobe Dreamweaver CS4 and associated with a combination of open-source programming scripts. OncoRx-MI currently detects over 5000 DDIs (69.3% pharmacokinetic, 30.7% pharmacodynamic) between 256 single-agent and combination CRegs with 51 psychotropic drugs. OncoRx-MI fits the iPhone screen configuration, and displays information regarding the regimen, pharmacokinetics of the drugs and detected DDIs in tabular format for improved usability. OncoRx-MI is the first mobile DDI application of its kind which detects interactions for combination CRegs. Future versions will include DDIs with other drug categories. Usability studies on its impact in clinical practice will also be carried out.

Prior knowledge based mining functional modules from Yeast PPI networks with gene ontology

PubMed Central

2010-01-01

Background In the literature, there are fruitful algorithmic approaches for identification functional modules in protein-protein interactions (PPI) networks. Because of accumulation of large-scale interaction data on multiple organisms and non-recording interaction data in the existing PPI database, it is still emergent to design novel computational techniques that can be able to correctly and scalably analyze interaction data sets. Indeed there are a number of large scale biological data sets providing indirect evidence for protein-protein interaction relationships. Results The main aim of this paper is to present a prior knowledge based mining strategy to identify functional modules from PPI networks with the aid of Gene Ontology. Higher similarity value in Gene Ontology means that two gene products are more functionally related to each other, so it is better to group such gene products into one functional module. We study (i) to encode the functional pairs into the existing PPI networks; and (ii) to use these functional pairs as pairwise constraints to supervise the existing functional module identification algorithms. Topology-based modularity metric and complex annotation in MIPs will be used to evaluate the identified functional modules by these two approaches. Conclusions The experimental results on Yeast PPI networks and GO have shown that the prior knowledge based learning methods perform better than the existing algorithms. PMID:21172053

Amyloid precursor protein interaction network in human testis: sentinel proteins for male reproduction.

PubMed

Silva, Joana Vieira; Yoon, Sooyeon; Domingues, Sara; Guimarães, Sofia; Goltsev, Alexander V; da Cruz E Silva, Edgar Figueiredo; Mendes, José Fernando F; da Cruz E Silva, Odete Abreu Beirão; Fardilha, Margarida

2015-01-16

Amyloid precursor protein (APP) is widely recognized for playing a central role in Alzheimer's disease pathogenesis. Although APP is expressed in several tissues outside the human central nervous system, the functions of APP and its family members in other tissues are still poorly understood. APP is involved in several biological functions which might be potentially important for male fertility, such as cell adhesion, cell motility, signaling, and apoptosis. Furthermore, APP superfamily members are known to be associated with fertility. Knowledge on the protein networks of APP in human testis and spermatozoa will shed light on the function of APP in the male reproductive system. We performed a Yeast Two-Hybrid screen and a database search to study the interaction network of APP in human testis and sperm. To gain insights into the role of APP superfamily members in fertility, the study was extended to APP-like protein 2 (APLP2). We analyzed several topological properties of the APP interaction network and the biological and physiological properties of the proteins in the APP interaction network were also specified by gene ontologyand pathways analyses. We classified significant features related to the human male reproduction for the APP interacting proteins and identified modules of proteins with similar functional roles which may show cooperative behavior for male fertility. The present work provides the first report on the APP interactome in human testis. Our approach allowed the identification of novel interactions and recognition of key APP interacting proteins for male reproduction, particularly in sperm-oocyte interaction.

An attempt to understand glioma stem cell biology through centrality analysis of a protein interaction network.

PubMed

Mallik, Mrinmay Kumar

2018-02-07

Biological networks can be analyzed using "Centrality Analysis" to identify the more influential nodes and interactions in the network. This study was undertaken to create and visualize a biological network comprising of protein-protein interactions (PPIs) amongst proteins which are preferentially over-expressed in glioma cancer stem cell component (GCSC) of glioblastomas as compared to the glioma non-stem cancer cell (GNSC) component and then to analyze this network through centrality analyses (CA) in order to identify the essential proteins in this network and their interactions. In addition, this study proposes a new centrality analysis method pertaining exclusively to transcription factors (TFs) and interactions amongst them. Moreover the relevant molecular functions, biological processes and biochemical pathways amongst these proteins were sought through enrichment analysis. A protein interaction network was created using a list of proteins which have been shown to be preferentially expressed or over-expressed in GCSCs isolated from glioblastomas as compared to the GNSCs. This list comprising of 38 proteins, created using manual literature mining, was submitted to the Reactome FIViz tool, a web based application integrated into Cytoscape, an open source software platform for visualizing and analyzing molecular interaction networks and biological pathways to produce the network. This network was subjected to centrality analyses utilizing ranked lists of six centrality measures using the FIViz application and (for the first time) a dedicated centrality analysis plug-in ; CytoNCA. The interactions exclusively amongst the transcription factors were nalyzed through a newly proposed centrality analysis method called "Gene Expression Associated Degree Centrality Analysis (GEADCA)". Enrichment analysis was performed using the "network function analysis" tool on Reactome. The CA was able to identify a small set of proteins with consistently high centrality ranks that is indicative of their strong influence in the protein protein interaction network. Similarly the newly proposed GEADCA helped identify the transcription factors with high centrality values indicative of their key roles in transcriptional regulation. The enrichment studies provided a list of molecular functions, biological processes and biochemical pathways associated with the constructed network. The study shows how pathway based databases may be used to create and analyze a relevant protein interaction network in glioma cancer stem cells and identify the essential elements within it to gather insights into the molecular interactions that regulate the properties of glioma stem cells. How these insights may be utilized to help the development of future research towards formulation of new management strategies have been discussed from a theoretical standpoint. Copyright © 2017 Elsevier Ltd. All rights reserved.

Integration of multiple biological features yields high confidence human protein interactome.

PubMed

Karagoz, Kubra; Sevimoglu, Tuba; Arga, Kazim Yalcin

2016-08-21

The biological function of a protein is usually determined by its physical interaction with other proteins. Protein-protein interactions (PPIs) are identified through various experimental methods and are stored in curated databases. The noisiness of the existing PPI data is evident, and it is essential that a more reliable data is generated. Furthermore, the selection of a set of PPIs at different confidence levels might be necessary for many studies. Although different methodologies were introduced to evaluate the confidence scores for binary interactions, a highly reliable, almost complete PPI network of Homo sapiens is not proposed yet. The quality and coverage of human protein interactome need to be improved to be used in various disciplines, especially in biomedicine. In the present work, we propose an unsupervised statistical approach to assign confidence scores to PPIs of H. sapiens. To achieve this goal PPI data from six different databases were collected and a total of 295,288 non-redundant interactions between 15,950 proteins were acquired. The present scoring system included the context information that was assigned to PPIs derived from eight biological attributes. A high confidence network, which included 147,923 binary interactions between 13,213 proteins, had scores greater than the cutoff value of 0.80, for which sensitivity, specificity, and coverage were 94.5%, 80.9%, and 82.8%, respectively. We compared the present scoring method with others for evaluation. Reducing the noise inherent in experimental PPIs via our scoring scheme increased the accuracy significantly. As it was demonstrated through the assessment of process and cancer subnetworks, this study allows researchers to construct and analyze context-specific networks via valid PPI sets and one can easily achieve subnetworks around proteins of interest at a specified confidence level. Copyright © 2016 Elsevier Ltd. All rights reserved.

A novel method to identify hub pathways of rheumatoid arthritis based on differential pathway networks.

PubMed

Wei, Shi-Tong; Sun, Yong-Hua; Zong, Shi-Hua

2017-09-01

The aim of the current study was to identify hub pathways of rheumatoid arthritis (RA) using a novel method based on differential pathway network (DPN) analysis. The present study proposed a DPN where protein‑protein interaction (PPI) network was integrated with pathway‑pathway interactions. Pathway data was obtained from background PPI network and the Reactome pathway database. Subsequently, pathway interactions were extracted from the pathway data by building randomized gene‑gene interactions and a weight value was assigned to each pathway interaction using Spearman correlation coefficient (SCC) to identify differential pathway interactions. Differential pathway interactions were visualized using Cytoscape to construct a DPN. Topological analysis was conducted to identify hub pathways that possessed the top 5% degree distribution of DPN. Modules of DPN were mined according to ClusterONE. A total of 855 pathways were selected to build pathway interactions. By filtrating pathway interactions of weight values >0.7, a DPN with 312 nodes and 791 edges was obtained. Topological degree analysis revealed 15 hub pathways, such as heparan sulfate/heparin‑glycosaminoglycan (HS‑GAG) degradation, HS‑GAG metabolism and keratan sulfate degradation for RA based on DPN. Furthermore, hub pathways were also important in modules, which validated the significance of hub pathways. In conclusion, the proposed method is a computationally efficient way to identify hub pathways of RA, which identified 15 hub pathways that may be potential biomarkers and provide insight to future investigation and treatment of RA.

Protein-protein interaction network of gene expression in the hydrocortisone-treated keloid.

PubMed

Chen, Rui; Zhang, Zhiliang; Xue, Zhujia; Wang, Lin; Fu, Mingang; Lu, Yi; Bai, Ling; Zhang, Ping; Fan, Zhihong

2015-01-01

In order to explore the molecular mechanism of hydrocortisone in keloid tissue, the gene expression profiles of keloid samples treated with hydrocortisone were subjected to bioinformatics analysis. Firstly, the gene expression profiles (GSE7890) of five samples of keloid treated with hydrocortisone and five untreated keloid samples were downloaded from the Gene Expression Omnibus (GEO) database. Secondly, data were preprocessed using packages in R language and differentially expressed genes (DEGs) were screened using a significance analysis of microarrays (SAM) protocol. Thirdly, the DEGs were subjected to gene ontology (GO) function and KEGG pathway enrichment analysis. Finally, the interactions of DEGs in samples of keloid treated with hydrocortisone were explored in a human protein-protein interaction (PPI) network, and sub-modules of the DEGs interaction network were analyzed using Cytoscape software. Based on the analysis, 572 DEGs in the hydrocortisone-treated samples were screened; most of these were involved in the signal transduction and cell cycle. Furthermore, three critical genes in the module, including COL1A1, NID1, and PRELP, were screened in the PPI network analysis. These findings enhance understanding of the pathogenesis of the keloid and provide references for keloid therapy. © 2015 The International Society of Dermatology.

Databases and coordinated research projects at the IAEA on atomic processes in plasmas

NASA Astrophysics Data System (ADS)

Braams, Bastiaan J.; Chung, Hyun-Kyung

2012-05-01

The Atomic and Molecular Data Unit at the IAEA works with a network of national data centres to encourage and coordinate production and dissemination of fundamental data for atomic, molecular and plasma-material interaction (A+M/PMI) processes that are relevant to the realization of fusion energy. The Unit maintains numerical and bibliographical databases and has started a Wiki-style knowledge base. The Unit also contributes to A+M database interface standards and provides a search engine that offers a common interface to multiple numerical A+M/PMI databases. Coordinated Research Projects (CRPs) bring together fusion energy researchers and atomic, molecular and surface physicists for joint work towards the development of new data and new methods. The databases and current CRPs on A+M/PMI processes are briefly described here.

Neurovascular Network Explorer 1.0: a database of 2-photon single-vessel diameter measurements with MATLAB(®) graphical user interface.

PubMed

Sridhar, Vishnu B; Tian, Peifang; Dale, Anders M; Devor, Anna; Saisan, Payam A

2014-01-01

We present a database client software-Neurovascular Network Explorer 1.0 (NNE 1.0)-that uses MATLAB(®) based Graphical User Interface (GUI) for interaction with a database of 2-photon single-vessel diameter measurements from our previous publication (Tian et al., 2010). These data are of particular interest for modeling the hemodynamic response. NNE 1.0 is downloaded by the user and then runs either as a MATLAB script or as a standalone program on a Windows platform. The GUI allows browsing the database according to parameters specified by the user, simple manipulation and visualization of the retrieved records (such as averaging and peak-normalization), and export of the results. Further, we provide NNE 1.0 source code. With this source code, the user can database their own experimental results, given the appropriate data structure and naming conventions, and thus share their data in a user-friendly format with other investigators. NNE 1.0 provides an example of seamless and low-cost solution for sharing of experimental data by a regular size neuroscience laboratory and may serve as a general template, facilitating dissemination of biological results and accelerating data-driven modeling approaches.

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.

Novel dynamic Bayesian networks for facial action element recognition and understanding

NASA Astrophysics Data System (ADS)

Zhao, Wei; Park, Jeong-Seon; Choi, Dong-You; Lee, Sang-Woong

2011-12-01

In daily life, language is an important tool of communication between people. Besides language, facial action can also provide a great amount of information. Therefore, facial action recognition has become a popular research topic in the field of human-computer interaction (HCI). However, facial action recognition is quite a challenging task due to its complexity. In a literal sense, there are thousands of facial muscular movements, many of which have very subtle differences. Moreover, muscular movements always occur simultaneously when the pose is changed. To address this problem, we first build a fully automatic facial points detection system based on a local Gabor filter bank and principal component analysis. Then, novel dynamic Bayesian networks are proposed to perform facial action recognition using the junction tree algorithm over a limited number of feature points. In order to evaluate the proposed method, we have used the Korean face database for model training. For testing, we used the CUbiC FacePix, facial expressions and emotion database, Japanese female facial expression database, and our own database. Our experimental results clearly demonstrate the feasibility of the proposed approach.

Sig2BioPAX: Java tool for converting flat files to BioPAX Level 3 format.

PubMed

Webb, Ryan L; Ma'ayan, Avi

2011-03-21

The World Wide Web plays a critical role in enabling molecular, cell, systems and computational biologists to exchange, search, visualize, integrate, and analyze experimental data. Such efforts can be further enhanced through the development of semantic web concepts. The semantic web idea is to enable machines to understand data through the development of protocol free data exchange formats such as Resource Description Framework (RDF) and the Web Ontology Language (OWL). These standards provide formal descriptors of objects, object properties and their relationships within a specific knowledge domain. However, the overhead of converting datasets typically stored in data tables such as Excel, text or PDF into RDF or OWL formats is not trivial for non-specialists and as such produces a barrier to seamless data exchange between researchers, databases and analysis tools. This problem is particularly of importance in the field of network systems biology where biochemical interactions between genes and their protein products are abstracted to networks. For the purpose of converting biochemical interactions into the BioPAX format, which is the leading standard developed by the computational systems biology community, we developed an open-source command line tool that takes as input tabular data describing different types of molecular biochemical interactions. The tool converts such interactions into the BioPAX level 3 OWL format. We used the tool to convert several existing and new mammalian networks of protein interactions, signalling pathways, and transcriptional regulatory networks into BioPAX. Some of these networks were deposited into PathwayCommons, a repository for consolidating and organizing biochemical networks. The software tool Sig2BioPAX is a resource that enables experimental and computational systems biologists to contribute their identified networks and pathways of molecular interactions for integration and reuse with the rest of the research community.

Information Retrieval Using ADABAS-NATURAL (with Applications for Television and Radio).

ERIC Educational Resources Information Center

Silbergeld, I.; Kutok, P.

1984-01-01

Describes use of the software ADABAS (general purpose database management system) and NATURAL (interactive programing language) in development and implementation of an information retrieval system for the National Television and Radio Network of Israel. General design considerations, files contained in each archive, search strategies, and keywords…

Expert Systems the Old Fashioned Way: Person to Person.

ERIC Educational Resources Information Center

McCleary, Hunter; Mayer, William J.

1988-01-01

Describes the services of Teltech, Inc., which mimic the desirable attributes of artificial intelligence and expert systems via a "database" of 5,000 experts in technical areas and interactive literature searches executed by staff. Advantages and shortcomings of the network are exemplified by sample searches. Several sample menus and…

MOOsburg: Multi-User Domain Support for a Community Network.

ERIC Educational Resources Information Center

Carroll, John M.; Rosson, Mary Beth; Isenhour, Philip L.; Van Metre, Christina; Schafer, Wendy A.; Ganoe, Craig H.

2001-01-01

Explains MOOsburg, a community-oriented MOO that models the geography of the town of Blacksburg, Virginia and is designed to be used by local residents. Highlights include the software architecture; client-server communication; spatial database; user interface; interaction; map-based navigation; application development; and future plans. (LRW)

Web application for detailed real-time database transaction monitoring for CMS condition data

NASA Astrophysics Data System (ADS)

de Gruttola, Michele; Di Guida, Salvatore; Innocente, Vincenzo; Pierro, Antonio

2012-12-01

In the upcoming LHC era, database have become an essential part for the experiments collecting data from LHC, in order to safely store, and consistently retrieve, a wide amount of data, which are produced by different sources. In the CMS experiment at CERN, all this information is stored in ORACLE databases, allocated in several servers, both inside and outside the CERN network. In this scenario, the task of monitoring different databases is a crucial database administration issue, since different information may be required depending on different users' tasks such as data transfer, inspection, planning and security issues. We present here a web application based on Python web framework and Python modules for data mining purposes. To customize the GUI we record traces of user interactions that are used to build use case models. In addition the application detects errors in database transactions (for example identify any mistake made by user, application failure, unexpected network shutdown or Structured Query Language (SQL) statement error) and provides warning messages from the different users' perspectives. Finally, in order to fullfill the requirements of the CMS experiment community, and to meet the new development in many Web client tools, our application was further developed, and new features were deployed.

Abasy Atlas: a comprehensive inventory of systems, global network properties and systems-level elements across bacteria.

PubMed

Ibarra-Arellano, Miguel A; Campos-González, Adrián I; Treviño-Quintanilla, Luis G; Tauch, Andreas; Freyre-González, Julio A

2016-01-01

The availability of databases electronically encoding curated regulatory networks and of high-throughput technologies and methods to discover regulatory interactions provides an invaluable source of data to understand the principles underpinning the organization and evolution of these networks responsible for cellular regulation. Nevertheless, data on these sources never goes beyond the regulon level despite the fact that regulatory networks are complex hierarchical-modular structures still challenging our understanding. This brings the necessity for an inventory of systems across a large range of organisms, a key step to rendering feasible comparative systems biology approaches. In this work, we take the first step towards a global understanding of the regulatory networks organization by making a cartography of the functional architectures of diverse bacteria. Abasy ( A: cross- BA: cteria SY: stems) Atlas provides a comprehensive inventory of annotated functional systems, global network properties and systems-level elements (global regulators, modular genes shaping functional systems, basal machinery genes and intermodular genes) predicted by the natural decomposition approach for reconstructed and meta-curated regulatory networks across a large range of bacteria, including pathogenically and biotechnologically relevant organisms. The meta-curation of regulatory datasets provides the most complete and reliable set of regulatory interactions currently available, which can even be projected into subsets by considering the force or weight of evidence supporting them or the systems that they belong to. Besides, Abasy Atlas provides data enabling large-scale comparative systems biology studies aimed at understanding the common principles and particular lifestyle adaptions of systems across bacteria. Abasy Atlas contains systems and system-level elements for 50 regulatory networks comprising 78 649 regulatory interactions covering 42 bacteria in nine taxa, containing 3708 regulons and 1776 systems. All this brings together a large corpus of data that will surely inspire studies to generate hypothesis regarding the principles governing the evolution and organization of systems and the functional architectures controlling them.Database URL: http://abasy.ccg.unam.mx. © The Author(s) 2016. Published by Oxford University Press.

Predicting drug-target interactions using restricted Boltzmann machines.

PubMed

Wang, Yuhao; Zeng, Jianyang

2013-07-01

In silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action. We propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action. We cast the new DTI prediction problem into a two-layer graphical model, called restricted Boltzmann machine, and apply a practical learning algorithm to train our model and make predictions. Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6. In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type. Further tests show that our approach can infer a high fraction of novel DTIs that has been validated by known experiments in the literature or other databases. These results indicate that our approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process. Software and datasets are available on request. Supplementary data are available at Bioinformatics online.

Visualisation and graph-theoretic analysis of a large-scale protein structural interactome

PubMed Central

Bolser, Dan; Dafas, Panos; Harrington, Richard; Park, Jong; Schroeder, Michael

2003-01-01

Background Large-scale protein interaction maps provide a new, global perspective with which to analyse protein function. PSIMAP, the Protein Structural Interactome Map, is a database of all the structurally observed interactions between superfamilies of protein domains with known three-dimensional structure in the PDB. PSIMAP incorporates both functional and evolutionary information into a single network. Results We present a global analysis of PSIMAP using several distinct network measures relating to centrality, interactivity, fault-tolerance, and taxonomic diversity. We found the following results: Centrality: we show that the center and barycenter of PSIMAP do not coincide, and that the superfamilies forming the barycenter relate to very general functions, while those constituting the center relate to enzymatic activity. Interactivity: we identify the P-loop and immunoglobulin superfamilies as the most highly interactive. We successfully use connectivity and cluster index, which characterise the connectivity of a superfamily's neighbourhood, to discover superfamilies of complex I and II. This is particularly significant as the structure of complex I is not yet solved. Taxonomic diversity: we found that highly interactive superfamilies are in general taxonomically very diverse and are thus amongst the oldest. Fault-tolerance: we found that the network is very robust as for the majority of superfamilies removal from the network will not break up the network. Conclusions Overall, we can single out the P-loop containing nucleotide triphosphate hydrolases superfamily as it is the most highly connected and has the highest taxonomic diversity. In addition, this superfamily has the highest interaction rank, is the barycenter of the network (it has the shortest average path to every other superfamily in the network), and is an articulation vertex, whose removal will disconnect the network. More generally, we conclude that the graph-theoretic and taxonomic analysis of PSIMAP is an important step towards the understanding of protein function and could be an important tool for tracing the evolution of life at the molecular level. PMID:14531933

PceRBase: a database of plant competing endogenous RNA.

PubMed

Yuan, Chunhui; Meng, Xianwen; Li, Xue; Illing, Nicola; Ingle, Robert A; Wang, Jingjing; Chen, Ming

2017-01-04

Competition for microRNA (miRNA) binding between RNA molecules has emerged as a novel mechanism for the regulation of eukaryotic gene expression. Competing endogenous RNA (ceRNA) can act as decoys for miRNA binding, thereby forming a ceRNA network by regulating the abundance of other RNA transcripts which share the same or similar microRNA response elements. Although this type of RNA cross talk was first described in Arabidopsis, and was subsequently shown to be active in animal models, there is no database collecting potential ceRNA data for plants. We have developed a Plant ceRNA database (PceRBase, http://bis.zju.edu.cn/pcernadb/index.jsp) which contains potential ceRNA target-target, and ceRNA target-mimic pairs from 26 plant species. For example, in Arabidopsis lyrata, 311 candidate ceRNAs are identified which could affect 2646 target-miRNA-target interactions. Predicted pairing structure between miRNAs and their target mRNA transcripts, expression levels of ceRNA pairs and associated GO annotations are also stored in the database. A web interface provides convenient browsing and searching for specific genes of interest. Tools are available for the visualization and enrichment analysis of genes in the ceRNA networks. Moreover, users can use PceRBase to predict novel competing mimic-target and target-target interactions from their own data. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Feature genes in metastatic breast cancer identified by MetaDE and SVM classifier methods.

PubMed

Tuo, Youlin; An, Ning; Zhang, Ming

2018-03-01

The aim of the present study was to investigate the feature genes in metastatic breast cancer samples. A total of 5 expression profiles of metastatic breast cancer samples were downloaded from the Gene Expression Omnibus database, which were then analyzed using the MetaQC and MetaDE packages in R language. The feature genes between metastasis and non‑metastasis samples were screened under the threshold of P<0.05. Based on the protein‑protein interactions (PPIs) in the Biological General Repository for Interaction Datasets, Human Protein Reference Database and Biomolecular Interaction Network Database, the PPI network of the feature genes was constructed. The feature genes identified by topological characteristics were then used for support vector machine (SVM) classifier training and verification. The accuracy of the SVM classifier was then evaluated using another independent dataset from The Cancer Genome Atlas database. Finally, function and pathway enrichment analyses for genes in the SVM classifier were performed. A total of 541 feature genes were identified between metastatic and non‑metastatic samples. The top 10 genes with the highest betweenness centrality values in the PPI network of feature genes were Nuclear RNA Export Factor 1, cyclin‑dependent kinase 2 (CDK2), myelocytomatosis proto‑oncogene protein (MYC), Cullin 5, SHC Adaptor Protein 1, Clathrin heavy chain, Nucleolin, WD repeat domain 1, proteasome 26S subunit non‑ATPase 2 and telomeric repeat binding factor 2. The cyclin‑dependent kinase inhibitor 1A (CDKN1A), E2F transcription factor 1 (E2F1), and MYC interacted with CDK2. The SVM classifier constructed by the top 30 feature genes was able to distinguish metastatic samples from non‑metastatic samples [correct rate, specificity, positive predictive value and negative predictive value >0.89; sensitivity >0.84; area under the receiver operating characteristic curve (AUROC) >0.96]. The verification of the SVM classifier in an independent dataset (35 metastatic samples and 143 non‑metastatic samples) revealed an accuracy of 94.38% and AUROC of 0.958. Cell cycle associated functions and pathways were the most significant terms of the 30 feature genes. A SVM classifier was constructed to assess the possibility of breast cancer metastasis, which presented high accuracy in several independent datasets. CDK2, CDKN1A, E2F1 and MYC were indicated as the potential feature genes in metastatic breast cancer.

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.

Analysis of molecular pathways in pancreatic ductal adenocarcinomas with a bioinformatics approach.

PubMed

Wang, Yan; Li, Yan

2015-01-01

Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer death worldwide. Our study aimed to reveal molecular mechanisms. Microarray data of GSE15471 (including 39 matching pairs of pancreatic tumor tissues and patient-matched normal tissues) was downloaded from Gene Expression Omnibus (GEO) database. We identified differentially expressed genes (DEGs) in PDAC tissues compared with normal tissues by limma package in R language. Then GO and KEGG pathway enrichment analyses were conducted with online DAVID. In addition, principal component analysis was performed and a protein-protein interaction network was constructed to study relationships between the DEGs through database STRING. A total of 532 DEGs were identified in the 38 PDAC tissues compared with 33 normal tissues. The results of principal component analysis of the top 20 DEGs could differentiate the PDAC tissues from normal tissues directly. In the PPI network, 8 of the 20 DEGs were all key genes of the collagen family. Additionally, FN1 (fibronectin 1) was also a hub node in the network. The genes of the collagen family as well as FN1 were significantly enriched in complement and coagulation cascades, ECM-receptor interaction and focal adhesion pathways. Our results suggest that genes of collagen family and FN1 may play an important role in PDAC progression. Meanwhile, these DEGs and enriched pathways, such as complement and coagulation cascades, ECM-receptor interaction and focal adhesion may be important molecular mechanisms involved in the development and progression of PDAC.

Network Configuration of Oracle and Database Programming Using SQL

NASA Technical Reports Server (NTRS)

Davis, Melton; Abdurrashid, Jibril; Diaz, Philip; Harris, W. C.

2000-01-01

A database can be defined as a collection of information organized in such a way that it can be retrieved and used. A database management system (DBMS) can further be defined as the tool that enables us to manage and interact with the database. The Oracle 8 Server is a state-of-the-art information management environment. It is a repository for very large amounts of data, and gives users rapid access to that data. The Oracle 8 Server allows for sharing of data between applications; the information is stored in one place and used by many systems. My research will focus primarily on SQL (Structured Query Language) programming. SQL is the way you define and manipulate data in Oracle's relational database. SQL is the industry standard adopted by all database vendors. When programming with SQL, you work on sets of data (i.e., information is not processed one record at a time).

Optical Network Virtualisation Using Multitechnology Monitoring and SDN-Enabled Optical Transceiver

NASA Astrophysics Data System (ADS)

Ou, Yanni; Davis, Matthew; Aguado, Alejandro; Meng, Fanchao; Nejabati, Reza; Simeonidou, Dimitra

2018-05-01

We introduce the real-time multi-technology transport layer monitoring to facilitate the coordinated virtualisation of optical and Ethernet networks supported by optical virtualise-able transceivers (V-BVT). A monitoring and network resource configuration scheme is proposed to include the hardware monitoring in both Ethernet and Optical layers. The scheme depicts the data and control interactions among multiple network layers under the software defined network (SDN) background, as well as the application that analyses the monitored data obtained from the database. We also present a re-configuration algorithm to adaptively modify the composition of virtual optical networks based on two criteria. The proposed monitoring scheme is experimentally demonstrated with OpenFlow (OF) extensions for a holistic (re-)configuration across both layers in Ethernet switches and V-BVTs.

History-Enriched Spaces for Shared Encounters

NASA Astrophysics Data System (ADS)

Konomi, Shin'ichi; Sezaki, Kaoru; Kitsuregawa, Masaru

We discuss "history-enriched spaces" that use historical data to support shared encounters. We first examine our experiences with DeaiExplorer, a social network display that uses RFID and a historical database to support social interactions at academic conferences. This leads to our discussions on three complementary approaches to addressing the issues of supporting social encounters: (1) embedding historical data in embodied interactions, (2) designing for weakly involved interactions such as social navigation, and (3) designing for privacy. Finally, we briefly describe a preliminary prototype of a proxemics-based awareness tool that considers these approaches.

Databases and coordinated research projects at the IAEA on atomic processes in plasmas

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

Braams, Bastiaan J.; Chung, Hyun-Kyung

2012-05-25

The Atomic and Molecular Data Unit at the IAEA works with a network of national data centres to encourage and coordinate production and dissemination of fundamental data for atomic, molecular and plasma-material interaction (A+M/PMI) processes that are relevant to the realization of fusion energy. The Unit maintains numerical and bibliographical databases and has started a Wiki-style knowledge base. The Unit also contributes to A+M database interface standards and provides a search engine that offers a common interface to multiple numerical A+M/PMI databases. Coordinated Research Projects (CRPs) bring together fusion energy researchers and atomic, molecular and surface physicists for joint workmore » towards the development of new data and new methods. The databases and current CRPs on A+M/PMI processes are briefly described here.« less

Distributed policy based access to networked heterogeneous ISR data sources

NASA Astrophysics Data System (ADS)

Bent, G.; Vyvyan, D.; Wood, David; Zerfos, Petros; Calo, Seraphin

2010-04-01

Within a coalition environment, ad hoc Communities of Interest (CoI's) come together, perhaps for only a short time, with different sensors, sensor platforms, data fusion elements, and networks to conduct a task (or set of tasks) with different coalition members taking different roles. In such a coalition, each organization will have its own inherent restrictions on how it will interact with the others. These are usually stated as a set of policies, including security and privacy policies. The capability that we want to enable for a coalition operation is to provide access to information from any coalition partner in conformance with the policies of all. One of the challenges in supporting such ad-hoc coalition operations is that of providing efficient access to distributed sources of data, where the applications requiring the data do not have knowledge of the location of the data within the network. To address this challenge the International Technology Alliance (ITA) program has been developing the concept of a Dynamic Distributed Federated Database (DDFD), also know as a Gaian Database. This type of database provides a means for accessing data across a network of distributed heterogeneous data sources where access to the information is controlled by a mixture of local and global policies. We describe how a network of disparate ISR elements can be expressed as a DDFD and how this approach enables sensor and other information sources to be discovered autonomously or semi-autonomously and/or combined, fused formally defined local and global policies.

PAINT: a promoter analysis and interaction network generation tool for gene regulatory network identification.

PubMed

Vadigepalli, Rajanikanth; Chakravarthula, Praveen; Zak, Daniel E; Schwaber, James S; Gonye, Gregory E

2003-01-01

We have developed a bioinformatics tool named PAINT that automates the promoter analysis of a given set of genes for the presence of transcription factor binding sites. Based on coincidence of regulatory sites, this tool produces an interaction matrix that represents a candidate transcriptional regulatory network. This tool currently consists of (1) a database of promoter sequences of known or predicted genes in the Ensembl annotated mouse genome database, (2) various modules that can retrieve and process the promoter sequences for binding sites of known transcription factors, and (3) modules for visualization and analysis of the resulting set of candidate network connections. This information provides a substantially pruned list of genes and transcription factors that can be examined in detail in further experimental studies on gene regulation. Also, the candidate network can be incorporated into network identification methods in the form of constraints on feasible structures in order to render the algorithms tractable for large-scale systems. The tool can also produce output in various formats suitable for use in external visualization and analysis software. In this manuscript, PAINT is demonstrated in two case studies involving analysis of differentially regulated genes chosen from two microarray data sets. The first set is from a neuroblastoma N1E-115 cell differentiation experiment, and the second set is from neuroblastoma N1E-115 cells at different time intervals following exposure to neuropeptide angiotensin II. PAINT is available for use as an agent in BioSPICE simulation and analysis framework (www.biospice.org), and can also be accessed via a WWW interface at www.dbi.tju.edu/dbi/tools/paint/.

Remote consultation and diagnosis in medical imaging using a global PACS backbone network

NASA Astrophysics Data System (ADS)

Martinez, Ralph; Sutaria, Bijal N.; Kim, Jinman; Nam, Jiseung

1993-10-01

A Global PACS is a national network which interconnects several PACS networks at medical and hospital complexes using a national backbone network. A Global PACS environment enables new and beneficial operations between radiologists and physicians, when they are located in different geographical locations. One operation allows the radiologist to view the same image folder at both Local and Remote sites so that a diagnosis can be performed. The paper describes the user interface, database management, and network communication software which has been developed in the Computer Engineering Research Laboratory and Radiology Research Laboratory. Specifically, a design for a file management system in a distributed environment is presented. In the remote consultation and diagnosis operation, a set of images is requested from the database archive system and sent to the Local and Remote workstation sites on the Global PACS network. Viewing the same images, the radiologists use pointing overlay commands, or frames to point out features on the images. Each workstation transfers these frames, to the other workstation, so that an interactive session for diagnosis takes place. In this phase, we use fixed frames and variable size frames, used to outline an object. The data pockets for these frames traverses the national backbone in real-time. We accomplish this feature by using TCP/IP protocol sockets for communications. The remote consultation and diagnosis operation has been tested in real-time between the University Medical Center and the Bowman Gray School of Medicine at Wake Forest University, over the Internet. In this paper, we show the feasibility of the operation in a Global PACS environment. Future improvements to the system will include real-time voice and interactive compressed video scenarios.

SATRAT: Staphylococcus aureus transcript regulatory network analysis tool.

PubMed

Gopal, Tamilselvi; Nagarajan, Vijayaraj; Elasri, Mohamed O

2015-01-01

Staphylococcus aureus is a commensal organism that primarily colonizes the nose of healthy individuals. S. aureus causes a spectrum of infections that range from skin and soft-tissue infections to fatal invasive diseases. S. aureus uses a large number of virulence factors that are regulated in a coordinated fashion. The complex regulatory mechanisms have been investigated in numerous high-throughput experiments. Access to this data is critical to studying this pathogen. Previously, we developed a compilation of microarray experimental data to enable researchers to search, browse, compare, and contrast transcript profiles. We have substantially updated this database and have built a novel exploratory tool-SATRAT-the S. aureus transcript regulatory network analysis tool, based on the updated database. This tool is capable of performing deep searches using a query and generating an interactive regulatory network based on associations among the regulators of any query gene. We believe this integrated regulatory network analysis tool would help researchers explore the missing links and identify novel pathways that regulate virulence in S. aureus. Also, the data model and the network generation code used to build this resource is open sourced, enabling researchers to build similar resources for other bacterial systems.

Pre-Clinical Drug Prioritization via Prognosis-Guided Genetic Interaction Networks

PubMed Central

Xiong, Jianghui; Liu, Juan; Rayner, Simon; Tian, Ze; Li, Yinghui; Chen, Shanguang

2010-01-01

The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Patient population heterogeneity and unpredictable physiology complicate pre-clinical cancer modeling efforts. We hypothesize that gene networks associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. Here we propose a novel in vivo genetic interaction which we call ‘synergistic outcome determination’ (SOD), a concept similar to ‘Synthetic Lethality’. SOD is defined as the synergy of a gene pair with respect to cancer patients' outcome, whose correlation with outcome is due to cooperative, rather than independent, contributions of genes. The method combines microarray gene expression data with cancer prognostic information to identify synergistic gene-gene interactions that are then used to construct interaction networks based on gene modules (a group of genes which share similar function). In this way, we identified a cluster of important epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a perturbation index for each drug based upon its characteristic perturbation pattern on the inter-module network. Finally, by calculating this index for compounds in the NCI Standard Agent Database, we significantly discriminated successful drugs from a broad set of test compounds, and further revealed the mechanisms of drug combinations. Thus, prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies. PMID:21085674

A Resource Center for the Stimulation of Post Secondary Education Innovation via Computer Network.

ERIC Educational Resources Information Center

Savin, William

The goal of the project described here was to improve the quality of postsecondary education by offering institutions of higher learning information on currently funded educational projects through an interactive database, the Educational Resources Directory (ERD), which contains information on new methods, curricula, and educational technology.…

Competing endogenous RNA and interactome bioinformatic analyses on human telomerase.

PubMed

Arancio, Walter; Pizzolanti, Giuseppe; Genovese, Swonild Ilenia; Baiamonte, Concetta; Giordano, Carla

2014-04-01

We present a classic interactome bioinformatic analysis and a study on competing endogenous (ce) RNAs for hTERT. The hTERT gene codes for the catalytic subunit and limiting component of the human telomerase complex. Human telomerase reverse transcriptase (hTERT) is essential for the integrity of telomeres. Telomere dysfunctions have been widely reported to be involved in aging, cancer, and cellular senescence. The hTERT gene network has been analyzed using the BioGRID interaction database (http://thebiogrid.org/) and related analysis tools such as Osprey (http://biodata.mshri.on.ca/osprey/servlet/Index) and GeneMANIA (http://genemania.org/). The network of interaction of hTERT transcripts has been further analyzed following the competing endogenous (ce) RNA hypotheses (messenger [m] RNAs cross-talk via micro [mi] RNAs) using the miRWalk database and tools (www.ma.uni-heidelberg.de/apps/zmf/mirwalk/). These analyses suggest a role for Akt, nuclear factor-κB (NF-κB), heat shock protein 90 (HSP90), p70/p80 autoantigen, 14-3-3 proteins, and dynein in telomere functions. Roles for histone acetylation/deacetylation and proteoglycan metabolism are also proposed.

Protein-protein interaction analysis of Alzheimer`s disease and NAFLD based on systems biology methods unhide common ancestor pathways.

PubMed

Karbalaei, Reza; Allahyari, Marzieh; Rezaei-Tavirani, Mostafa; Asadzadeh-Aghdaei, Hamid; Zali, Mohammad Reza

2018-01-01

Analysis reconstruction networks from two diseases, NAFLD and Alzheimer`s diseases and their relationship based on systems biology methods. NAFLD and Alzheimer`s diseases are two complex diseases, with progressive prevalence and high cost for countries. There are some reports on relation and same spreading pathways of these two diseases. In addition, they have some similar risk factors, exclusively lifestyle such as feeding, exercises and so on. Therefore, systems biology approach can help to discover their relationship. DisGeNET and STRING databases were sources of disease genes and constructing networks. Three plugins of Cytoscape software, including ClusterONE, ClueGO and CluePedia, were used to analyze and cluster networks and enrichment of pathways. An R package used to define best centrality method. Finally, based on degree and Betweenness, hubs and bottleneck nodes were defined. Common genes between NAFLD and Alzheimer`s disease were 190 genes that used construct a network with STRING database. The resulting network contained 182 nodes and 2591 edges and comprises from four clusters. Enrichment of these clusters separately lead to carbohydrate metabolism, long chain fatty acid and regulation of JAK-STAT and IL-17 signaling pathways, respectively. Also seven genes selected as hub-bottleneck include: IL6, AKT1, TP53, TNF, JUN, VEGFA and PPARG. Enrichment of these proteins and their first neighbors in network by OMIM database lead to diabetes and obesity as ancestors of NAFLD and AD. Systems biology methods, specifically PPI networks, can be useful for analyzing complicated related diseases. Finding Hub and bottleneck proteins should be the goal of drug designing and introducing disease markers.

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

NASA Astrophysics Data System (ADS)

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

2015-11-01

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

BiNA: A Visual Analytics Tool for Biological Network Data

PubMed Central

Gerasch, Andreas; Faber, Daniel; Küntzer, Jan; Niermann, Peter; Kohlbacher, Oliver; Lenhof, Hans-Peter; Kaufmann, Michael

2014-01-01

Interactive visual analysis of biological high-throughput data in the context of the underlying networks is an essential task in modern biomedicine with applications ranging from metabolic engineering to personalized medicine. The complexity and heterogeneity of data sets require flexible software architectures for data analysis. Concise and easily readable graphical representation of data and interactive navigation of large data sets are essential in this context. We present BiNA - the Biological Network Analyzer - a flexible open-source software for analyzing and visualizing biological networks. Highly configurable visualization styles for regulatory and metabolic network data offer sophisticated drawings and intuitive navigation and exploration techniques using hierarchical graph concepts. The generic projection and analysis framework provides powerful functionalities for visual analyses of high-throughput omics data in the context of networks, in particular for the differential analysis and the analysis of time series data. A direct interface to an underlying data warehouse provides fast access to a wide range of semantically integrated biological network databases. A plugin system allows simple customization and integration of new analysis algorithms or visual representations. BiNA is available under the 3-clause BSD license at http://bina.unipax.info/. PMID:24551056

Protein interactions in 3D: from interface evolution to drug discovery.

PubMed

Winter, Christof; Henschel, Andreas; Tuukkanen, Anne; Schroeder, Michael

2012-09-01

Over the past 10years, much research has been dedicated to the understanding of protein interactions. Large-scale experiments to elucidate the global structure of protein interaction networks have been complemented by detailed studies of protein interaction interfaces. Understanding the evolution of interfaces allows one to identify convergently evolved interfaces which are evolutionary unrelated but share a few key residues and hence have common binding partners. Understanding interaction interfaces and their evolution is an important basis for pharmaceutical applications in drug discovery. Here, we review the algorithms and databases on 3D protein interactions and discuss in detail applications in interface evolution, drug discovery, and interface prediction. Copyright © 2012 Elsevier Inc. All rights reserved.

Redrawing the Map of Great Britain from a Network of Human Interactions

PubMed Central

Ratti, Carlo; Sobolevsky, Stanislav; Calabrese, Francesco; Andris, Clio; Reades, Jonathan; Martino, Mauro; Claxton, Rob; Strogatz, Steven H.

2010-01-01

Do regional boundaries defined by governments respect the more natural ways that people interact across space? This paper proposes a novel, fine-grained approach to regional delineation, based on analyzing networks of billions of individual human transactions. Given a geographical area and some measure of the strength of links between its inhabitants, we show how to partition the area into smaller, non-overlapping regions while minimizing the disruption to each person's links. We tested our method on the largest non-Internet human network, inferred from a large telecommunications database in Great Britain. Our partitioning algorithm yields geographically cohesive regions that correspond remarkably well with administrative regions, while unveiling unexpected spatial structures that had previously only been hypothesized in the literature. We also quantify the effects of partitioning, showing for instance that the effects of a possible secession of Wales from Great Britain would be twice as disruptive for the human network than that of Scotland. PMID:21170390

A Quality-Control-Oriented Database for a Mesoscale Meteorological Observation Network

NASA Astrophysics Data System (ADS)

Lussana, C.; Ranci, M.; Uboldi, F.

2012-04-01

In the operational context of a local weather service, data accessibility and quality related issues must be managed by taking into account a wide set of user needs. This work describes the structure and the operational choices made for the operational implementation of a database system storing data from highly automated observing stations, metadata and information on data quality. Lombardy's environmental protection agency, ARPA Lombardia, manages a highly automated mesoscale meteorological network. A Quality Assurance System (QAS) ensures that reliable observational information is collected and disseminated to the users. The weather unit in ARPA Lombardia, at the same time an important QAS component and an intensive data user, has developed a database specifically aimed to: 1) providing quick access to data for operational activities and 2) ensuring data quality for real-time applications, by means of an Automatic Data Quality Control (ADQC) procedure. Quantities stored in the archive include hourly aggregated observations of: precipitation amount, temperature, wind, relative humidity, pressure, global and net solar radiation. The ADQC performs several independent tests on raw data and compares their results in a decision-making procedure. An important ADQC component is the Spatial Consistency Test based on Optimal Interpolation. Interpolated and Cross-Validation analysis values are also stored in the database, providing further information to human operators and useful estimates in case of missing data. The technical solution adopted is based on a LAMP (Linux, Apache, MySQL and Php) system, constituting an open source environment suitable for both development and operational practice. The ADQC procedure itself is performed by R scripts directly interacting with the MySQL database. Users and network managers can access the database by using a set of web-based Php applications.

PROXiMATE: a database of mutant protein-protein complex thermodynamics and kinetics.

PubMed

Jemimah, Sherlyn; Yugandhar, K; Michael Gromiha, M

2017-09-01

We have developed PROXiMATE, a database of thermodynamic data for more than 6000 missense mutations in 174 heterodimeric protein-protein complexes, supplemented with interaction network data from STRING database, solvent accessibility, sequence, structural and functional information, experimental conditions and literature information. Additional features include complex structure visualization, search and display options, download options and a provision for users to upload their data. The database is freely available at http://www.iitm.ac.in/bioinfo/PROXiMATE/ . The website is implemented in Python, and supports recent versions of major browsers such as IE10, Firefox, Chrome and Opera. gromiha@iitm.ac.in. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Topology and weights in a protein domain interaction network--a novel way to predict protein interactions.

PubMed

Wuchty, Stefan

2006-05-23

While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well. We consider a web of interactions between protein domains of the Protein Family database (PFAM), which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation. Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we show a simple way to predict potential protein interactions by utilizing expectation scores of single domain interactions.

NONATObase: a database for Polychaeta (Annelida) from the Southwestern Atlantic Ocean.

PubMed

Pagliosa, Paulo R; Doria, João G; Misturini, Dairana; Otegui, Mariana B P; Oortman, Mariana S; Weis, Wilson A; Faroni-Perez, Larisse; Alves, Alexandre P; Camargo, Maurício G; Amaral, A Cecília Z; Marques, Antonio C; Lana, Paulo C

2014-01-01

Networks can greatly advance data sharing attitudes by providing organized and useful data sets on marine biodiversity in a friendly and shared scientific environment. NONATObase, the interactive database on polychaetes presented herein, will provide new macroecological and taxonomic insights of the Southwestern Atlantic region. The database was developed by the NONATO network, a team of South American researchers, who integrated available information on polychaetes from between 5°N and 80°S in the Atlantic Ocean and near the Antarctic. The guiding principle of the database is to keep free and open access to data based on partnerships. Its architecture consists of a relational database integrated in the MySQL and PHP framework. Its web application allows access to the data from three different directions: species (qualitative data), abundance (quantitative data) and data set (reference data). The database has built-in functionality, such as the filter of data on user-defined taxonomic levels, characteristics of site, sample, sampler, and mesh size used. Considering that there are still many taxonomic issues related to poorly known regional fauna, a scientific committee was created to work out consistent solutions to current misidentifications and equivocal taxonomy status of some species. Expertise from this committee will be incorporated by NONATObase continually. The use of quantitative data was possible by standardization of a sample unit. All data, maps of distribution and references from a data set or a specified query can be visualized and exported to a commonly used data format in statistical analysis or reference manager software. The NONATO network has initialized with NONATObase, a valuable resource for marine ecologists and taxonomists. The database is expected to grow in functionality as it comes in useful, particularly regarding the challenges of dealing with molecular genetic data and tools to assess the effects of global environment change. Database URL: http://nonatobase.ufsc.br/.

NONATObase: a database for Polychaeta (Annelida) from the Southwestern Atlantic Ocean

PubMed Central

Pagliosa, Paulo R.; Doria, João G.; Misturini, Dairana; Otegui, Mariana B. P.; Oortman, Mariana S.; Weis, Wilson A.; Faroni-Perez, Larisse; Alves, Alexandre P.; Camargo, Maurício G.; Amaral, A. Cecília Z.; Marques, Antonio C.; Lana, Paulo C.

2014-01-01

Networks can greatly advance data sharing attitudes by providing organized and useful data sets on marine biodiversity in a friendly and shared scientific environment. NONATObase, the interactive database on polychaetes presented herein, will provide new macroecological and taxonomic insights of the Southwestern Atlantic region. The database was developed by the NONATO network, a team of South American researchers, who integrated available information on polychaetes from between 5°N and 80°S in the Atlantic Ocean and near the Antarctic. The guiding principle of the database is to keep free and open access to data based on partnerships. Its architecture consists of a relational database integrated in the MySQL and PHP framework. Its web application allows access to the data from three different directions: species (qualitative data), abundance (quantitative data) and data set (reference data). The database has built-in functionality, such as the filter of data on user-defined taxonomic levels, characteristics of site, sample, sampler, and mesh size used. Considering that there are still many taxonomic issues related to poorly known regional fauna, a scientific committee was created to work out consistent solutions to current misidentifications and equivocal taxonomy status of some species. Expertise from this committee will be incorporated by NONATObase continually. The use of quantitative data was possible by standardization of a sample unit. All data, maps of distribution and references from a data set or a specified query can be visualized and exported to a commonly used data format in statistical analysis or reference manager software. The NONATO network has initialized with NONATObase, a valuable resource for marine ecologists and taxonomists. The database is expected to grow in functionality as it comes in useful, particularly regarding the challenges of dealing with molecular genetic data and tools to assess the effects of global environment change. Database URL: http://nonatobase.ufsc.br/ PMID:24573879

PACAP Interactions in the Mouse Brain: Implications for Behavioral and Other Disorders

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

Acquaah-Mensah, George; Taylor, Ronald C.; Bhave, Sanjiv V.

2012-01-10

As an activator of adenylate cyclase, the neuropeptide Pituitary Adenylate Cyclase Activating Peptide (PACAP) impacts levels of cyclic AMP, a key second messenger available in brain cells. PACAP is involved in certain adult behaviors. To elucidate PACAP interactions, a compendium of microarrays representing mRNA expression in the adult mouse whole brain was pooled from the Phenogen database for analysis. A regulatory network was computed based on mutual information between gene pairs using gene expression data across the compendium. Clusters among genes directly linked to PACAP, and probable interactions between corresponding proteins were computed. Database 'experts' affirmed some of the inferredmore » relationships. The findings suggest ADCY7 is probably the adenylate cyclase isoform most relevant to PACAP's action. They also support intervening roles for kinases including GSK3B, PI 3-kinase, SGK3 and AMPK. Other high-confidence interactions are hypothesized for future testing. This new information has implications for certain behavioral and other disorders.« less

SPACEWAY: Providing affordable and versatile communication solutions

NASA Astrophysics Data System (ADS)

Fitzpatrick, E. J.

1995-08-01

By the end of this decade, Hughes' SPACEWAY network will provide the first interactive 'bandwidth on demand' communication services for a variety of applications. High quality digital voice, interactive video, global access to multimedia databases, and transborder workgroup computing will make SPACEWAY an essential component of the computer-based workplace of the 21st century. With relatively few satellites to construct, insure, and launch -- plus extensive use of cost-effective, tightly focused spot beams on the world's most populated areas -- the high capacity SPACEWAY system can pass its significant cost savings onto its customers. The SPACEWAY network is different from other proposed global networks in that its geostationary orbit location makes it a truly market driven system: each satellite will make available extensive telecom services to hundreds of millions of people within the continuous view of that satellite, providing immediate capacity within a specific region of the world.

SPACEWAY: Providing affordable and versatile communication solutions

NASA Technical Reports Server (NTRS)

Fitzpatrick, E. J.

1995-01-01

By the end of this decade, Hughes' SPACEWAY network will provide the first interactive 'bandwidth on demand' communication services for a variety of applications. High quality digital voice, interactive video, global access to multimedia databases, and transborder workgroup computing will make SPACEWAY an essential component of the computer-based workplace of the 21st century. With relatively few satellites to construct, insure, and launch -- plus extensive use of cost-effective, tightly focused spot beams on the world's most populated areas -- the high capacity SPACEWAY system can pass its significant cost savings onto its customers. The SPACEWAY network is different from other proposed global networks in that its geostationary orbit location makes it a truly market driven system: each satellite will make available extensive telecom services to hundreds of millions of people within the continuous view of that satellite, providing immediate capacity within a specific region of the world.

PROFESS: a PROtein Function, Evolution, Structure and Sequence database

PubMed Central

Triplet, Thomas; Shortridge, Matthew D.; Griep, Mark A.; Stark, Jaime L.; Powers, Robert; Revesz, Peter

2010-01-01

The proliferation of biological databases and the easy access enabled by the Internet is having a beneficial impact on biological sciences and transforming the way research is conducted. There are ∼1100 molecular biology databases dispersed throughout the Internet. To assist in the functional, structural and evolutionary analysis of the abundant number of novel proteins continually identified from whole-genome sequencing, we introduce the PROFESS (PROtein Function, Evolution, Structure and Sequence) database. Our database is designed to be versatile and expandable and will not confine analysis to a pre-existing set of data relationships. A fundamental component of this approach is the development of an intuitive query system that incorporates a variety of similarity functions capable of generating data relationships not conceived during the creation of the database. The utility of PROFESS is demonstrated by the analysis of the structural drift of homologous proteins and the identification of potential pancreatic cancer therapeutic targets based on the observation of protein–protein interaction networks. Database URL: http://cse.unl.edu/∼profess/ PMID:20624718

Applying pollen DNA metabarcoding to the study of plant–pollinator interactions1

PubMed Central

Bell, Karen L.; Fowler, Julie; Burgess, Kevin S.; Dobbs, Emily K.; Gruenewald, David; Lawley, Brice; Morozumi, Connor; Brosi, Berry J.

2017-01-01

Premise of the study: To study pollination networks in a changing environment, we need accurate, high-throughput methods. Previous studies have shown that more highly resolved networks can be constructed by studying pollen loads taken from bees, relative to field observations. DNA metabarcoding potentially allows for faster and finer-scale taxonomic resolution of pollen compared to traditional approaches (e.g., light microscopy), but has not been applied to pollination networks. Methods: We sampled pollen from 38 bee species collected in Florida from sites differing in forest management. We isolated DNA from pollen mixtures and sequenced rbcL and ITS2 gene regions from all mixtures in a single run on the Illumina MiSeq platform. We identified species from sequence data using comprehensive rbcL and ITS2 databases. Results: We successfully built a proof-of-concept quantitative pollination network using pollen metabarcoding. Discussion: Our work underscores that pollen metabarcoding is not quantitative but that quantitative networks can be constructed based on the number of interacting individuals. Due to the frequency of contamination and false positive reads, isolation and PCR negative controls should be used in every reaction. DNA metabarcoding has advantages in efficiency and resolution over microscopic identification of pollen, and we expect that it will have broad utility for future studies of plant–pollinator interactions. PMID:28690929

RAID: a comprehensive resource for human RNA-associated (RNA–RNA/RNA–protein) interaction

PubMed Central

Zhang, Xiaomeng; Wu, Deng; Chen, Liqun; Li, Xiang; Yang, Jinxurong; Fan, Dandan; Dong, Tingting; Liu, Mingyue; Tan, Puwen; Xu, Jintian; Yi, Ying; Wang, Yuting; Zou, Hua; Hu, Yongfei; Fan, Kaili; Kang, Juanjuan; Huang, Yan; Miao, Zhengqiang; Bi, Miaoman; Jin, Nana; Li, Kongning; Li, Xia; Xu, Jianzhen; Wang, Dong

2014-01-01

Transcriptomic analyses have revealed an unexpected complexity in the eukaryote transcriptome, which includes not only protein-coding transcripts but also an expanding catalog of noncoding RNAs (ncRNAs). Diverse coding and noncoding RNAs (ncRNAs) perform functions through interaction with each other in various cellular processes. In this project, we have developed RAID (http://www.rna-society.org/raid), an RNA-associated (RNA–RNA/RNA–protein) interaction database. RAID intends to provide the scientific community with all-in-one resources for efficient browsing and extraction of the RNA-associated interactions in human. This version of RAID contains more than 6100 RNA-associated interactions obtained by manually reviewing more than 2100 published papers, including 4493 RNA–RNA interactions and 1619 RNA–protein interactions. Each entry contains detailed information on an RNA-associated interaction, including RAID ID, RNA/protein symbol, RNA/protein categories, validated method, expressing tissue, literature references (Pubmed IDs), and detailed functional description. Users can query, browse, analyze, and manipulate RNA-associated (RNA–RNA/RNA–protein) interaction. RAID provides a comprehensive resource of human RNA-associated (RNA–RNA/RNA–protein) interaction network. Furthermore, this resource will help in uncovering the generic organizing principles of cellular function network. PMID:24803509

Monitoring groundwater and river interaction along the Hanford reach of the Columbia River

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

Campbell, M.D.

1994-04-01

As an adjunct to efficient Hanford Site characterization and remediation of groundwater contamination, an automatic monitor network has been used to measure Columbia River and adjacent groundwater levels in several areas of the Hanford Site since 1991. Water levels, temperatures, and electrical conductivity measured by the automatic monitor network provided an initial database with which to calibrate models and from which to infer ground and river water interactions for site characterization and remediation activities. Measurements of the dynamic river/aquifer system have been simultaneous at 1-hr intervals, with a quality suitable for hydrologic modeling and for computer model calibration and testing.more » This report describes the equipment, procedures, and results from measurements done in 1993.« less

Locating overlapping dense subgraphs in gene (protein) association networks and predicting novel protein functional groups among these subgraphs

NASA Astrophysics Data System (ADS)

Palla, Gergely; Derenyi, Imre; Farkas, Illes J.; Vicsek, Tamas

2006-03-01

Most tasks in a cell are performed not by individual proteins, but by functional groups of proteins (either physically interacting with each other or associated in other ways). In gene (protein) association networks these groups show up as sets of densely connected nodes. In the yeast, Saccharomyces cerevisiae, known physically interacting groups of proteins (called protein complexes) strongly overlap: the total number of proteins contained by these complexes by far underestimates the sum of their sizes (2750 vs. 8932). Thus, most functional groups of proteins, both physically interacting and other, are likely to share many of their members with other groups. However, current algorithms searching for dense groups of nodes in networks usually exclude overlaps. With the aim to discover both novel functions of individual proteins and novel protein functional groups we combine in protein association networks (i) a search for overlapping dense subgraphs based on the Clique Percolation Method (CPM) (Palla, G., et.al. Nature 435, 814-818 (2005), http://angel.elte.hu/clustering), which explicitly allows for overlaps among the groups, and (ii) a verification and characterization of the identified groups of nodes (proteins) with the help of standard annotation databases listing known functions.

GeNNet: an integrated platform for unifying scientific workflows and graph databases for transcriptome data analysis

PubMed Central

Gadelha, Luiz; Ribeiro-Alves, Marcelo; Porto, Fábio

2017-01-01

There are many steps in analyzing transcriptome data, from the acquisition of raw data to the selection of a subset of representative genes that explain a scientific hypothesis. The data produced can be represented as networks of interactions among genes and these may additionally be integrated with other biological databases, such as Protein-Protein Interactions, transcription factors and gene annotation. However, the results of these analyses remain fragmented, imposing difficulties, either for posterior inspection of results, or for meta-analysis by the incorporation of new related data. Integrating databases and tools into scientific workflows, orchestrating their execution, and managing the resulting data and its respective metadata are challenging tasks. Additionally, a great amount of effort is equally required to run in-silico experiments to structure and compose the information as needed for analysis. Different programs may need to be applied and different files are produced during the experiment cycle. In this context, the availability of a platform supporting experiment execution is paramount. We present GeNNet, an integrated transcriptome analysis platform that unifies scientific workflows with graph databases for selecting relevant genes according to the evaluated biological systems. It includes GeNNet-Wf, a scientific workflow that pre-loads biological data, pre-processes raw microarray data and conducts a series of analyses including normalization, differential expression inference, clusterization and gene set enrichment analysis. A user-friendly web interface, GeNNet-Web, allows for setting parameters, executing, and visualizing the results of GeNNet-Wf executions. To demonstrate the features of GeNNet, we performed case studies with data retrieved from GEO, particularly using a single-factor experiment in different analysis scenarios. As a result, we obtained differentially expressed genes for which biological functions were analyzed. The results are integrated into GeNNet-DB, a database about genes, clusters, experiments and their properties and relationships. The resulting graph database is explored with queries that demonstrate the expressiveness of this data model for reasoning about gene interaction networks. GeNNet is the first platform to integrate the analytical process of transcriptome data with graph databases. It provides a comprehensive set of tools that would otherwise be challenging for non-expert users to install and use. Developers can add new functionality to components of GeNNet. The derived data allows for testing previous hypotheses about an experiment and exploring new ones through the interactive graph database environment. It enables the analysis of different data on humans, rhesus, mice and rat coming from Affymetrix platforms. GeNNet is available as an open source platform at https://github.com/raquele/GeNNet and can be retrieved as a software container with the command docker pull quelopes/gennet. PMID:28695067

GeNNet: an integrated platform for unifying scientific workflows and graph databases for transcriptome data analysis.

PubMed

Costa, Raquel L; Gadelha, Luiz; Ribeiro-Alves, Marcelo; Porto, Fábio

2017-01-01

There are many steps in analyzing transcriptome data, from the acquisition of raw data to the selection of a subset of representative genes that explain a scientific hypothesis. The data produced can be represented as networks of interactions among genes and these may additionally be integrated with other biological databases, such as Protein-Protein Interactions, transcription factors and gene annotation. However, the results of these analyses remain fragmented, imposing difficulties, either for posterior inspection of results, or for meta-analysis by the incorporation of new related data. Integrating databases and tools into scientific workflows, orchestrating their execution, and managing the resulting data and its respective metadata are challenging tasks. Additionally, a great amount of effort is equally required to run in-silico experiments to structure and compose the information as needed for analysis. Different programs may need to be applied and different files are produced during the experiment cycle. In this context, the availability of a platform supporting experiment execution is paramount. We present GeNNet, an integrated transcriptome analysis platform that unifies scientific workflows with graph databases for selecting relevant genes according to the evaluated biological systems. It includes GeNNet-Wf, a scientific workflow that pre-loads biological data, pre-processes raw microarray data and conducts a series of analyses including normalization, differential expression inference, clusterization and gene set enrichment analysis. A user-friendly web interface, GeNNet-Web, allows for setting parameters, executing, and visualizing the results of GeNNet-Wf executions. To demonstrate the features of GeNNet, we performed case studies with data retrieved from GEO, particularly using a single-factor experiment in different analysis scenarios. As a result, we obtained differentially expressed genes for which biological functions were analyzed. The results are integrated into GeNNet-DB, a database about genes, clusters, experiments and their properties and relationships. The resulting graph database is explored with queries that demonstrate the expressiveness of this data model for reasoning about gene interaction networks. GeNNet is the first platform to integrate the analytical process of transcriptome data with graph databases. It provides a comprehensive set of tools that would otherwise be challenging for non-expert users to install and use. Developers can add new functionality to components of GeNNet. The derived data allows for testing previous hypotheses about an experiment and exploring new ones through the interactive graph database environment. It enables the analysis of different data on humans, rhesus, mice and rat coming from Affymetrix platforms. GeNNet is available as an open source platform at https://github.com/raquele/GeNNet and can be retrieved as a software container with the command docker pull quelopes/gennet.

ReNE: A Cytoscape Plugin for Regulatory Network Enhancement

PubMed Central

Politano, Gianfranco; Benso, Alfredo; Savino, Alessandro; Di Carlo, Stefano

2014-01-01

One of the biggest challenges in the study of biological regulatory mechanisms is the integration, americanmodeling, and analysis of the complex interactions which take place in biological networks. Despite post transcriptional regulatory elements (i.e., miRNAs) are widely investigated in current research, their usage and visualization in biological networks is very limited. Regulatory networks are commonly limited to gene entities. To integrate networks with post transcriptional regulatory data, researchers are therefore forced to manually resort to specific third party databases. In this context, we introduce ReNE, a Cytoscape 3.x plugin designed to automatically enrich a standard gene-based regulatory network with more detailed transcriptional, post transcriptional, and translational data, resulting in an enhanced network that more precisely models the actual biological regulatory mechanisms. ReNE can automatically import a network layout from the Reactome or KEGG repositories, or work with custom pathways described using a standard OWL/XML data format that the Cytoscape import procedure accepts. Moreover, ReNE allows researchers to merge multiple pathways coming from different sources. The merged network structure is normalized to guarantee a consistent and uniform description of the network nodes and edges and to enrich all integrated data with additional annotations retrieved from genome-wide databases like NCBI, thus producing a pathway fully manageable through the Cytoscape environment. The normalized network is then analyzed to include missing transcription factors, miRNAs, and proteins. The resulting enhanced network is still a fully functional Cytoscape network where each regulatory element (transcription factor, miRNA, gene, protein) and regulatory mechanism (up-regulation/down-regulation) is clearly visually identifiable, thus enabling a better visual understanding of its role and the effect in the network behavior. The enhanced network produced by ReNE is exportable in multiple formats for further analysis via third party applications. ReNE can be freely installed from the Cytoscape App Store (http://apps.cytoscape.org/apps/rene) and the full source code is freely available for download through a SVN repository accessible at http://www.sysbio.polito.it/tools_svn/BioInformatics/Rene/releases/. ReNE enhances a network by only integrating data from public repositories, without any inference or prediction. The reliability of the introduced interactions only depends on the reliability of the source data, which is out of control of ReNe developers. PMID:25541727

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

PubMed

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

2015-01-01

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

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.

Evaluating the Paper-to-Screen Translation of Participant-Aided Sociograms with High-Risk Participants

PubMed Central

Hogan, Bernie; Melville, Joshua R.; Philips, Gregory Lee; Janulis, Patrick; Contractor, Noshir; Mustanski, Brian S.; Birkett, Michelle

2016-01-01

While much social network data exists online, key network metrics for high-risk populations must still be captured through self-report. This practice has suffered from numerous limitations in workflow and response burden. However, advances in technology, network drawing libraries and databases are making interactive network drawing increasingly feasible. We describe the translation of an analog-based technique for capturing personal networks into a digital framework termed netCanvas that addresses many existing shortcomings such as: 1) complex data entry; 2) extensive interviewer intervention and field setup; 3) difficulties in data reuse; and 4) a lack of dynamic visualizations. We test this implementation within a health behavior study of a high-risk and difficult-to-reach population. We provide a within–subjects comparison between paper and touchscreens. We assert that touchscreen-based social network capture is now a viable alternative for highly sensitive data and social network data entry tasks. PMID:28018995

Visual analysis and exploration of complex corporate shareholder networks

NASA Astrophysics Data System (ADS)

Tekušová, Tatiana; Kohlhammer, Jörn

2008-01-01

The analysis of large corporate shareholder network structures is an important task in corporate governance, in financing, and in financial investment domains. In a modern economy, large structures of cross-corporation, cross-border shareholder relationships exist, forming complex networks. These networks are often difficult to analyze with traditional approaches. An efficient visualization of the networks helps to reveal the interdependent shareholding formations and the controlling patterns. In this paper, we propose an effective visualization tool that supports the financial analyst in understanding complex shareholding networks. We develop an interactive visual analysis system by combining state-of-the-art visualization technologies with economic analysis methods. Our system is capable to reveal patterns in large corporate shareholder networks, allows the visual identification of the ultimate shareholders, and supports the visual analysis of integrated cash flow and control rights. We apply our system on an extensive real-world database of shareholder relationships, showing its usefulness for effective visual analysis.

Evaluating the Paper-to-Screen Translation of Participant-Aided Sociograms with High-Risk Participants.

PubMed

Hogan, Bernie; Melville, Joshua R; Philips, Gregory Lee; Janulis, Patrick; Contractor, Noshir; Mustanski, Brian S; Birkett, Michelle

2016-05-01

While much social network data exists online, key network metrics for high-risk populations must still be captured through self-report. This practice has suffered from numerous limitations in workflow and response burden. However, advances in technology, network drawing libraries and databases are making interactive network drawing increasingly feasible. We describe the translation of an analog-based technique for capturing personal networks into a digital framework termed netCanvas that addresses many existing shortcomings such as: 1) complex data entry; 2) extensive interviewer intervention and field setup; 3) difficulties in data reuse; and 4) a lack of dynamic visualizations. We test this implementation within a health behavior study of a high-risk and difficult-to-reach population. We provide a within-subjects comparison between paper and touchscreens. We assert that touchscreen-based social network capture is now a viable alternative for highly sensitive data and social network data entry tasks.

Entitymetrics: Measuring the Impact of Entities

PubMed Central

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

2013-01-01

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

SorghumFDB: sorghum functional genomics database with multidimensional network analysis.

PubMed

Tian, Tian; You, Qi; Zhang, Liwei; Yi, Xin; Yan, Hengyu; Xu, Wenying; Su, Zhen

2016-01-01

Sorghum (Sorghum bicolor [L.] Moench) has excellent agronomic traits and biological properties, such as heat and drought-tolerance. It is a C4 grass and potential bioenergy-producing plant, which makes it an important crop worldwide. With the sorghum genome sequence released, it is essential to establish a sorghum functional genomics data mining platform. We collected genomic data and some functional annotations to construct a sorghum functional genomics database (SorghumFDB). SorghumFDB integrated knowledge of sorghum gene family classifications (transcription regulators/factors, carbohydrate-active enzymes, protein kinases, ubiquitins, cytochrome P450, monolignol biosynthesis related enzymes, R-genes and organelle-genes), detailed gene annotations, miRNA and target gene information, orthologous pairs in the model plants Arabidopsis, rice and maize, gene loci conversions and a genome browser. We further constructed a dynamic network of multidimensional biological relationships, comprised of the co-expression data, protein-protein interactions and miRNA-target pairs. We took effective measures to combine the network, gene set enrichment and motif analyses to determine the key regulators that participate in related metabolic pathways, such as the lignin pathway, which is a major biological process in bioenergy-producing plants.Database URL: http://structuralbiology.cau.edu.cn/sorghum/index.html. © The Author(s) 2016. Published by Oxford University Press.

Veterans with recent substance use and aggression: PTSD, substance use, and social network behaviors.

PubMed

Sexton, Minden B; Davis, Alan K; Buchholz, Katherine R; Winters, Jamie J; Rauch, Sheila A M; Yzquibell, Maegan; Bonar, Erin E; Friday, Steven; Chermack, Stephen T

2018-04-23

Violence is a salient concern among veterans, yet relationships between psychiatric comorbidity, social networks, and aggression are poorly understood. We examined associations between biopsychosocial factors (substance use, posttraumatic stress disorder [PTSD], and social network behaviors) with aggression. We recruited veterans endorsing past-year aggression and substance use (N = 180) from Department of Veterans Affairs outpatient treatment clinics. Main and interaction effects between probable PTSD, substance use, social network violence and substance use, and veteran violence were examined with negative binomial regressions-specifically, physical aggression toward a relationship partner (PA-P), physical injury of a partner (PI-P), physical aggression toward nonpartners (PA-NP), and physical injury of nonpartners (PI-NP). Alcohol use yielded consistent main effects. PTSD and social network violence demonstrated main effects for PA-NP and PI-NP. PTSD and social network violence interacted to predict PA-P such that social network violence appeared salient only in the context of PTSD. PTSD was associated with PI-P, PA-NP, and PI-NP in social network substance use models. In the PA-P model including social network substance use, veterans with PTSD reported greater PA-P in the context of greater social network substance use, whereas veterans without PTSD endorsed PA-P concurrent with greater alcohol frequency. For PI-P, PTSD interacted with alcohol to predict a greater likelihood of partner injury in the context of social network substance use. Investigated variables demonstrated unique associations within the context of specific relationships and the severity of behaviors. Overall, the findings underscore the importance of biopsychosocial models for understanding veteran violence. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Disease gene classification with metagraph representations.

PubMed

Kircali Ata, Sezin; Fang, Yuan; Wu, Min; Li, Xiao-Li; Xiao, Xiaokui

2017-12-01

Protein-protein interaction (PPI) networks play an important role in studying the functional roles of proteins, including their association with diseases. However, protein interaction networks are not sufficient without the support of additional biological knowledge for proteins such as their molecular functions and biological processes. To complement and enrich PPI networks, we propose to exploit biological properties of individual proteins. More specifically, we integrate keywords describing protein properties into the PPI network, and construct a novel PPI-Keywords (PPIK) network consisting of both proteins and keywords as two different types of nodes. As disease proteins tend to have a similar topological characteristics on the PPIK network, we further propose to represent proteins with metagraphs. Different from a traditional network motif or subgraph, a metagraph can capture a particular topological arrangement involving the interactions/associations between both proteins and keywords. Based on the novel metagraph representations for proteins, we further build classifiers for disease protein classification through supervised learning. Our experiments on three different PPI databases demonstrate that the proposed method consistently improves disease protein prediction across various classifiers, by 15.3% in AUC on average. It outperforms the baselines including the diffusion-based methods (e.g., RWR) and the module-based methods by 13.8-32.9% for overall disease protein prediction. For predicting breast cancer genes, it outperforms RWR, PRINCE and the module-based baselines by 6.6-14.2%. Finally, our predictions also turn out to have better correlations with literature findings from PubMed. Copyright © 2017 Elsevier Inc. All rights reserved.

Gene expression patterns combined with network analysis identify hub genes associated with bladder cancer.

PubMed

Bi, Dongbin; Ning, Hao; Liu, Shuai; Que, Xinxiang; Ding, Kejia

2015-06-01

To explore molecular mechanisms of bladder cancer (BC), network strategy was used to find biomarkers for early detection and diagnosis. The differentially expressed genes (DEGs) between bladder carcinoma patients and normal subjects were screened using empirical Bayes method of the linear models for microarray data package. Co-expression networks were constructed by differentially co-expressed genes and links. Regulatory impact factors (RIF) metric was used to identify critical transcription factors (TFs). The protein-protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and clusters were obtained through molecular complex detection (MCODE) algorithm. Centralities analyses for complex networks were performed based on degree, stress and betweenness. Enrichment analyses were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Co-expression networks and TFs (based on expression data of global DEGs and DEGs in different stages and grades) were identified. Hub genes of complex networks, such as UBE2C, ACTA2, FABP4, CKS2, FN1 and TOP2A, were also obtained according to analysis of degree. In gene enrichment analyses of global DEGs, cell adhesion, proteinaceous extracellular matrix and extracellular matrix structural constituent were top three GO terms. ECM-receptor interaction, focal adhesion, and cell cycle were significant pathways. Our results provide some potential underlying biomarkers of BC. However, further validation is required and deep studies are needed to elucidate the pathogenesis of BC. Copyright © 2015 Elsevier Ltd. All rights reserved.

Identification of hub subnetwork based on topological features of genes in breast cancer

PubMed Central

ZHUANG, DA-YONG; JIANG, LI; HE, QING-QING; ZHOU, PENG; YUE, TAO

2015-01-01

The aim of this study was to provide functional insight into the identification of hub subnetworks by aggregating the behavior of genes connected in a protein-protein interaction (PPI) network. We applied a protein network-based approach to identify subnetworks which may provide new insight into the functions of pathways involved in breast cancer rather than individual genes. Five groups of breast cancer data were downloaded and analyzed from the Gene Expression Omnibus (GEO) database of high-throughput gene expression data to identify gene signatures using the genome-wide global significance (GWGS) method. A PPI network was constructed using Cytoscape and clusters that focused on highly connected nodes were obtained using the molecular complex detection (MCODE) clustering algorithm. Pathway analysis was performed to assess the functional relevance of selected gene signatures based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Topological centrality was used to characterize the biological importance of gene signatures, pathways and clusters. The results revealed that, cluster1, as well as the cell cycle and oocyte meiosis pathways were significant subnetworks in the analysis of degree and other centralities, in which hub nodes mostly distributed. The most important hub nodes, with top ranked centrality, were also similar with the common genes from the above three subnetwork intersections, which was viewed as a hub subnetwork with more reproducible than individual critical genes selected without network information. This hub subnetwork attributed to the same biological process which was essential in the function of cell growth and death. This increased the accuracy of identifying gene interactions that took place within the same functional process and was potentially useful for the development of biomarkers and networks for breast cancer. PMID:25573623

Identification of key genes related to high-risk gastrointestinal stromal tumors using bioinformatics analysis.

PubMed

Jin, Shuan; Zhu, Wenhua; Li, Jun

2018-01-01

The purpose of this study was to identify predictive biomarkers used for clinical therapy and prognostic evaluation of high-risk gastrointestinal stromal tumors (GISTs). In this study, microarray data GSE31802 were used to identify differentially expressed genes (DEGs) between high-risk GISTs and low-risk GISTs. Then, enrichment analysis of DEGs was conducted based on the gene ontology and kyoto encyclopedia of genes and genomes pathway database. In addition, the transcription factors and cancer-related genes in DEGs were screened according to the TRANSFAC, TSGene, and TAG database. Finally, protein-protein interaction (PPI) network was constructed and analyzed to look for critical genes involved in high-risk GISTs. A total of forty DEGs were obtained and these genes were mainly involved in four pathways, including melanogenesis, neuroactive ligand-receptor interaction, malaria, and hematopoietic cell lineage. The enriched biological processes were related to the regulation of insulin secretion, integrin activation, and neuropeptide signaling pathway. Transcription factor analysis of DEGs indicated that POU domain, class 2, associating factor 1 (POU2AF1) was significantly downregulated in high-risk GISTs. By constructing the PPI network of DEGs, ten genes with high degrees formed local networks, such as PNOC, P2RY14, and SELP. Four genes as POU2AF1, PNOC, P2RY14, and SELP might be used as biomarkers for prognosis of high-risk GISTs.

Classification of ECG beats using deep belief network and active learning.

PubMed

G, Sayantan; T, Kien P; V, Kadambari K

2018-04-12

A new semi-supervised approach based on deep learning and active learning for classification of electrocardiogram signals (ECG) is proposed. The objective of the proposed work is to model a scientific method for classification of cardiac irregularities using electrocardiogram beats. The model follows the Association for the Advancement of medical instrumentation (AAMI) standards and consists of three phases. In phase I, feature representation of ECG is learnt using Gaussian-Bernoulli deep belief network followed by a linear support vector machine (SVM) training in the consecutive phase. It yields three deep models which are based on AAMI-defined classes, namely N, V, S, and F. In the last phase, a query generator is introduced to interact with the expert to label few beats to improve accuracy and sensitivity. The proposed approach depicts significant improvement in accuracy with minimal queries posed to the expert and fast online training as tested on the MIT-BIH Arrhythmia Database and the MIT-BIH Supra-ventricular Arrhythmia Database (SVDB). With 100 queries labeled by the expert in phase III, the method achieves an accuracy of 99.5% in "S" versus all classifications (SVEB) and 99.4% accuracy in "V " versus all classifications (VEB) on MIT-BIH Arrhythmia Database. In a similar manner, it is attributed that an accuracy of 97.5% for SVEB and 98.6% for VEB on SVDB database is achieved respectively. Graphical Abstract Reply- Deep belief network augmented by active learning for efficient prediction of arrhythmia.

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

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.

Construction of phosphorylation interaction networks by text mining of full-length articles using the eFIP system.

PubMed

Tudor, Catalina O; Ross, Karen E; Li, Gang; Vijay-Shanker, K; Wu, Cathy H; Arighi, Cecilia N

2015-01-01

Protein phosphorylation is a reversible post-translational modification where a protein kinase adds a phosphate group to a protein, potentially regulating its function, localization and/or activity. Phosphorylation can affect protein-protein interactions (PPIs), abolishing interaction with previous binding partners or enabling new interactions. Extracting phosphorylation information coupled with PPI information from the scientific literature will facilitate the creation of phosphorylation interaction networks of kinases, substrates and interacting partners, toward knowledge discovery of functional outcomes of protein phosphorylation. Increasingly, PPI databases are interested in capturing the phosphorylation state of interacting partners. We have previously developed the eFIP (Extracting Functional Impact of Phosphorylation) text mining system, which identifies phosphorylated proteins and phosphorylation-dependent PPIs. In this work, we present several enhancements for the eFIP system: (i) text mining for full-length articles from the PubMed Central open-access collection; (ii) the integration of the RLIMS-P 2.0 system for the extraction of phosphorylation events with kinase, substrate and site information; (iii) the extension of the PPI module with new trigger words/phrases describing interactions and (iv) the addition of the iSimp tool for sentence simplification to aid in the matching of syntactic patterns. We enhance the website functionality to: (i) support searches based on protein roles (kinases, substrates, interacting partners) or using keywords; (ii) link protein entities to their corresponding UniProt identifiers if mapped and (iii) support visual exploration of phosphorylation interaction networks using Cytoscape. The evaluation of eFIP on full-length articles achieved 92.4% precision, 76.5% recall and 83.7% F-measure on 100 article sections. To demonstrate eFIP for knowledge extraction and discovery, we constructed phosphorylation-dependent interaction networks involving 14-3-3 proteins identified from cancer-related versus diabetes-related articles. Comparison of the phosphorylation interaction network of kinases, phosphoproteins and interactants obtained from eFIP searches, along with enrichment analysis of the protein set, revealed several shared interactions, highlighting common pathways discussed in the context of both diseases. © The Author(s) 2015. Published by Oxford University Press.

Yucca Mountain licensing support network archive assistant.

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

Dunlavy, Daniel M.; Bauer, Travis L.; Verzi, Stephen J.

2008-03-01

This report describes the Licensing Support Network (LSN) Assistant--a set of tools for categorizing e-mail messages and documents, and investigating and correcting existing archives of categorized e-mail messages and documents. The two main tools in the LSN Assistant are the LSN Archive Assistant (LSNAA) tool for recategorizing manually labeled e-mail messages and documents and the LSN Realtime Assistant (LSNRA) tool for categorizing new e-mail messages and documents. This report focuses on the LSNAA tool. There are two main components of the LSNAA tool. The first is the Sandia Categorization Framework, which is responsible for providing categorizations for documents in anmore » archive and storing them in an appropriate Categorization Database. The second is the actual user interface, which primarily interacts with the Categorization Database, providing a way for finding and correcting categorizations errors in the database. A procedure for applying the LSNAA tool and an example use case of the LSNAA tool applied to a set of e-mail messages are provided. Performance results of the categorization model designed for this example use case are presented.« less

Advice networks in teams: the role of transformational leadership and members' core self-evaluations.

PubMed

Zhang, Zhen; Peterson, Suzanne J

2011-09-01

This article examines the team-level factors promoting advice exchange networks in teams. Drawing upon theory and research on transformational leadership, team diversity, and social networks, we hypothesized that transformational leadership positively influences advice network density in teams and that advice network density serves as a mediating mechanism linking transformational leadership to team performance. We further hypothesized a 3-way interaction in which members' mean core self-evaluation (CSE) and diversity in CSE jointly moderate the transformational leadership-advice network density relationship, such that the relationship is positive and stronger for teams with low diversity in CSE and high mean CSE. In addition, we expected that advice network centralization attenuates the positive influence of network density on team performance. Results based on multisource data from 79 business unit management teams showed support for these hypotheses. The results highlight the pivotal role played by transformational leadership and team members' CSEs in enhancing team social networks and, ultimately, team effectiveness. PsycINFO Database Record (c) 2011 APA, all rights reserved

Correspondence of the brain's functional architecture during activation and rest.

PubMed

Smith, Stephen M; Fox, Peter T; Miller, Karla L; Glahn, David C; Fox, P Mickle; Mackay, Clare E; Filippini, Nicola; Watkins, Kate E; Toro, Roberto; Laird, Angela R; Beckmann, Christian F

2009-08-04

Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is "at rest." In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically "active" even when at "rest."

NaviCom: a web application to create interactive molecular network portraits using multi-level omics data.

PubMed

Dorel, Mathurin; Viara, Eric; Barillot, Emmanuel; Zinovyev, Andrei; Kuperstein, Inna

2017-01-01

Human diseases such as cancer are routinely characterized by high-throughput molecular technologies, and multi-level omics data are accumulated in public databases at increasing rate. Retrieval and visualization of these data in the context of molecular network maps can provide insights into the pattern of regulation of molecular functions reflected by an omics profile. In order to make this task easy, we developed NaviCom, a Python package and web platform for visualization of multi-level omics data on top of biological network maps. NaviCom is bridging the gap between cBioPortal, the most used resource of large-scale cancer omics data and NaviCell, a data visualization web service that contains several molecular network map collections. NaviCom proposes several standardized modes of data display on top of molecular network maps, allowing addressing specific biological questions. We illustrate how users can easily create interactive network-based cancer molecular portraits via NaviCom web interface using the maps of Atlas of Cancer Signalling Network (ACSN) and other maps. Analysis of these molecular portraits can help in formulating a scientific hypothesis on the molecular mechanisms deregulated in the studied disease. NaviCom is available at https://navicom.curie.fr. © The Author(s) 2017. Published by Oxford University Press.

An examination of the perceptions of social network characteristics associated with grandiose and vulnerable narcissism.

PubMed

Lamkin, Joanna; Clifton, Allan; Campbell, W Keith; Miller, Joshua D

2014-04-01

Two dimensions of narcissism exist, grandiose and vulnerable, which are thought to be associated with distinctly different patterns of interpersonal behavior. Social network analysis is a way of quantifying and analyzing interpersonal interactions that may prove useful for characterizing the networks associated with these narcissism dimensions. In the current study, participants (N = 148) completed scales assessing both narcissism dimensions and a measure of the five-factor model of personality. Egocentric network information about participants' 30 closest friends and family members (i.e., "alters") was also obtained. Both narcissism dimensions were characterized by negative perceptions of the individuals who comprise one's social networks, and many of these relations were mediated by individuals' higher levels of antagonism. Grandiose narcissism also interacted with alter centrality (i.e., importance to the network) such that individuals low on grandiose narcissism were less likely to perceive central alters in a negative light and were more attuned to central alters than were individuals high on grandiose narcissism. Overall, both narcissism dimensions were associated with perceiving one's overall social environment negatively because of the high levels of antagonism that characterize both narcissism dimensions. Individuals high on grandiose narcissism, however, appear to be more insensitive to the relative importance of individuals in their social networks. PsycINFO Database Record (c) 2014 APA, all rights reserved

A user-centred methodology for designing an online social network to motivate health behaviour change.

PubMed

Kamal, Noreen; Fels, Sidney

2013-01-01

Positive health behaviour is critical to preventing illness and managing chronic conditions. A user-centred methodology was employed to design an online social network to motivate health behaviour change. The methodology was augmented by utilizing the Appeal, Belonging, Commitment (ABC) Framework, which is based on theoretical models for health behaviour change and use of online social networks. The user-centred methodology included four phases: 1) initial user inquiry on health behaviour and use of online social networks; 2) interview feedback on paper prototypes; 2) laboratory study on medium fidelity prototype; and 4) a field study on the high fidelity prototype. The points of inquiry through these phases were based on the ABC Framework. This yielded an online social network system that linked to external third party databases to deploy to users via an interactive website.

Opinion formation in a social network: The role of human activity

NASA Astrophysics Data System (ADS)

Grabowski, Andrzej

2009-03-01

The model of opinion formation in human population based on social impact theory is investigated numerically. On the basis of a database received from the on-line game server, we examine the structure of social network and human dynamics. We calculate the activity of individuals, i.e. the relative time devoted daily to interactions with others in the artificial society. We study the influence of correlation between the activity of an individual and its connectivity on the process of opinion formation. We find that such correlations have a significant influence on the temperature of the phase transition and the effect of the mass media, modeled as an external stimulation acting on the social network.

The PathoYeastract database: an information system for the analysis of gene and genomic transcription regulation in pathogenic yeasts.

PubMed

Monteiro, Pedro Tiago; Pais, Pedro; Costa, Catarina; Manna, Sauvagya; Sá-Correia, Isabel; Teixeira, Miguel Cacho

2017-01-04

We present the PATHOgenic YEAst Search for Transcriptional Regulators And Consensus Tracking (PathoYeastract - http://pathoyeastract.org) database, a tool for the analysis and prediction of transcription regulatory associations at the gene and genomic levels in the pathogenic yeasts Candida albicans and C. glabrata Upon data retrieval from hundreds of publications, followed by curation, the database currently includes 28 000 unique documented regulatory associations between transcription factors (TF) and target genes and 107 DNA binding sites, considering 134 TFs in both species. Following the structure used for the YEASTRACT database, PathoYeastract makes available bioinformatics tools that enable the user to exploit the existing information to predict the TFs involved in the regulation of a gene or genome-wide transcriptional response, while ranking those TFs in order of their relative importance. Each search can be filtered based on the selection of specific environmental conditions, experimental evidence or positive/negative regulatory effect. Promoter analysis tools and interactive visualization tools for the representation of TF regulatory networks are also provided. The PathoYeastract database further provides simple tools for the prediction of gene and genomic regulation based on orthologous regulatory associations described for other yeast species, a comparative genomics setup for the study of cross-species evolution of regulatory networks. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

COMPUTING THERAPY FOR PRECISION MEDICINE: COLLABORATIVE FILTERING INTEGRATES AND PREDICTS MULTI-ENTITY INTERACTIONS.

PubMed

Regenbogen, Sam; Wilkins, Angela D; Lichtarge, Olivier

2016-01-01

Biomedicine produces copious information it cannot fully exploit. Specifically, there is considerable need to integrate knowledge from disparate studies to discover connections across domains. Here, we used a Collaborative Filtering approach, inspired by online recommendation algorithms, in which non-negative matrix factorization (NMF) predicts interactions among chemicals, genes, and diseases only from pairwise information about their interactions. Our approach, applied to matrices derived from the Comparative Toxicogenomics Database, successfully recovered Chemical-Disease, Chemical-Gene, and Disease-Gene networks in 10-fold cross-validation experiments. Additionally, we could predict each of these interaction matrices from the other two. Integrating all three CTD interaction matrices with NMF led to good predictions of STRING, an independent, external network of protein-protein interactions. Finally, this approach could integrate the CTD and STRING interaction data to improve Chemical-Gene cross-validation performance significantly, and, in a time-stamped study, it predicted information added to CTD after a given date, using only data prior to that date. We conclude that collaborative filtering can integrate information across multiple types of biological entities, and that as a first step towards precision medicine it can compute drug repurposing hypotheses.

COMPUTING THERAPY FOR PRECISION MEDICINE: COLLABORATIVE FILTERING INTEGRATES AND PREDICTS MULTI-ENTITY INTERACTIONS

PubMed Central

REGENBOGEN, SAM; WILKINS, ANGELA D.; LICHTARGE, OLIVIER

2015-01-01

Biomedicine produces copious information it cannot fully exploit. Specifically, there is considerable need to integrate knowledge from disparate studies to discover connections across domains. Here, we used a Collaborative Filtering approach, inspired by online recommendation algorithms, in which non-negative matrix factorization (NMF) predicts interactions among chemicals, genes, and diseases only from pairwise information about their interactions. Our approach, applied to matrices derived from the Comparative Toxicogenomics Database, successfully recovered Chemical-Disease, Chemical-Gene, and Disease-Gene networks in 10-fold cross-validation experiments. Additionally, we could predict each of these interaction matrices from the other two. Integrating all three CTD interaction matrices with NMF led to good predictions of STRING, an independent, external network of protein-protein interactions. Finally, this approach could integrate the CTD and STRING interaction data to improve Chemical-Gene cross-validation performance significantly, and, in a time-stamped study, it predicted information added to CTD after a given date, using only data prior to that date. We conclude that collaborative filtering can integrate information across multiple types of biological entities, and that as a first step towards precision medicine it can compute drug repurposing hypotheses. PMID:26776170

GraphSAW: a web-based system for graphical analysis of drug interactions and side effects using pharmaceutical and molecular data.

PubMed

Shoshi, Alban; Hoppe, Tobias; Kormeier, Benjamin; Ogultarhan, Venus; Hofestädt, Ralf

2015-02-28

Adverse drug reactions are one of the most common causes of death in industrialized Western countries. Nowadays, empirical data from clinical studies for the approval and monitoring of drugs and molecular databases is available. The integration of database information is a promising method for providing well-based knowledge to avoid adverse drug reactions. This paper presents our web-based decision support system GraphSAW which analyzes and evaluates drug interactions and side effects based on data from two commercial and two freely available molecular databases. The system is able to analyze single and combined drug-drug interactions, drug-molecule interactions as well as single and cumulative side effects. In addition, it allows exploring associative networks of drugs, molecules, metabolic pathways, and diseases in an intuitive way. The molecular medication analysis includes the capabilities of the upper features. A statistical evaluation of the integrated data and top 20 drugs concerning drug interactions and side effects is performed. The results of the data analysis give an overview of all theoretically possible drug interactions and side effects. The evaluation shows a mismatch between pharmaceutical and molecular databases. The concordance of drug interactions was about 12% and 9% of drug side effects. An application case with prescribed data of 11 patients is presented in order to demonstrate the functionality of the system under real conditions. For each patient at least two interactions occured in every medication and about 8% of total diseases were possibly induced by drug therapy. GraphSAW (http://tunicata.techfak.uni-bielefeld.de/graphsaw/) is meant to be a web-based system for health professionals and researchers. GraphSAW provides comprehensive drug-related knowledge and an improved medication analysis which may support efforts to reduce the risk of medication errors and numerous drastic side effects.

FunCoup 3.0: database of genome-wide functional coupling networks

PubMed Central

Schmitt, Thomas; Ogris, Christoph; Sonnhammer, Erik L. L.

2014-01-01

We present an update of the FunCoup database (http://FunCoup.sbc.su.se) of functional couplings, or functional associations, between genes and gene products. Identifying these functional couplings is an important step in the understanding of higher level mechanisms performed by complex cellular processes. FunCoup distinguishes between four classes of couplings: participation in the same signaling cascade, participation in the same metabolic process, co-membership in a protein complex and physical interaction. For each of these four classes, several types of experimental and statistical evidence are combined by Bayesian integration to predict genome-wide functional coupling networks. The FunCoup framework has been completely re-implemented to allow for more frequent future updates. It contains many improvements, such as a regularization procedure to automatically downweight redundant evidences and a novel method to incorporate phylogenetic profile similarity. Several datasets have been updated and new data have been added in FunCoup 3.0. Furthermore, we have developed a new Web site, which provides powerful tools to explore the predicted networks and to retrieve detailed information about the data underlying each prediction. PMID:24185702

FunCoup 3.0: database of genome-wide functional coupling networks.

PubMed

Schmitt, Thomas; Ogris, Christoph; Sonnhammer, Erik L L

2014-01-01

We present an update of the FunCoup database (http://FunCoup.sbc.su.se) of functional couplings, or functional associations, between genes and gene products. Identifying these functional couplings is an important step in the understanding of higher level mechanisms performed by complex cellular processes. FunCoup distinguishes between four classes of couplings: participation in the same signaling cascade, participation in the same metabolic process, co-membership in a protein complex and physical interaction. For each of these four classes, several types of experimental and statistical evidence are combined by Bayesian integration to predict genome-wide functional coupling networks. The FunCoup framework has been completely re-implemented to allow for more frequent future updates. It contains many improvements, such as a regularization procedure to automatically downweight redundant evidences and a novel method to incorporate phylogenetic profile similarity. Several datasets have been updated and new data have been added in FunCoup 3.0. Furthermore, we have developed a new Web site, which provides powerful tools to explore the predicted networks and to retrieve detailed information about the data underlying each prediction.

miRNet - dissecting miRNA-target interactions and functional associations through network-based visual analysis

PubMed Central

Fan, Yannan; Siklenka, Keith; Arora, Simran K.; Ribeiro, Paula; Kimmins, Sarah; Xia, Jianguo

2016-01-01

MicroRNAs (miRNAs) can regulate nearly all biological processes and their dysregulation is implicated in various complex diseases and pathological conditions. Recent years have seen a growing number of functional studies of miRNAs using high-throughput experimental technologies, which have produced a large amount of high-quality data regarding miRNA target genes and their interactions with small molecules, long non-coding RNAs, epigenetic modifiers, disease associations, etc. These rich sets of information have enabled the creation of comprehensive networks linking miRNAs with various biologically important entities to shed light on their collective functions and regulatory mechanisms. Here, we introduce miRNet, an easy-to-use web-based tool that offers statistical, visual and network-based approaches to help researchers understand miRNAs functions and regulatory mechanisms. The key features of miRNet include: (i) a comprehensive knowledge base integrating high-quality miRNA-target interaction data from 11 databases; (ii) support for differential expression analysis of data from microarray, RNA-seq and quantitative PCR; (iii) implementation of a flexible interface for data filtering, refinement and customization during network creation; (iv) a powerful fully featured network visualization system coupled with enrichment analysis. miRNet offers a comprehensive tool suite to enable statistical analysis and functional interpretation of various data generated from current miRNA studies. miRNet is freely available at http://www.mirnet.ca. PMID:27105848

RAID: a comprehensive resource for human RNA-associated (RNA-RNA/RNA-protein) interaction.

PubMed

Zhang, Xiaomeng; Wu, Deng; Chen, Liqun; Li, Xiang; Yang, Jinxurong; Fan, Dandan; Dong, Tingting; Liu, Mingyue; Tan, Puwen; Xu, Jintian; Yi, Ying; Wang, Yuting; Zou, Hua; Hu, Yongfei; Fan, Kaili; Kang, Juanjuan; Huang, Yan; Miao, Zhengqiang; Bi, Miaoman; Jin, Nana; Li, Kongning; Li, Xia; Xu, Jianzhen; Wang, Dong

2014-07-01

Transcriptomic analyses have revealed an unexpected complexity in the eukaryote transcriptome, which includes not only protein-coding transcripts but also an expanding catalog of noncoding RNAs (ncRNAs). Diverse coding and noncoding RNAs (ncRNAs) perform functions through interaction with each other in various cellular processes. In this project, we have developed RAID (http://www.rna-society.org/raid), an RNA-associated (RNA-RNA/RNA-protein) interaction database. RAID intends to provide the scientific community with all-in-one resources for efficient browsing and extraction of the RNA-associated interactions in human. This version of RAID contains more than 6100 RNA-associated interactions obtained by manually reviewing more than 2100 published papers, including 4493 RNA-RNA interactions and 1619 RNA-protein interactions. Each entry contains detailed information on an RNA-associated interaction, including RAID ID, RNA/protein symbol, RNA/protein categories, validated method, expressing tissue, literature references (Pubmed IDs), and detailed functional description. Users can query, browse, analyze, and manipulate RNA-associated (RNA-RNA/RNA-protein) interaction. RAID provides a comprehensive resource of human RNA-associated (RNA-RNA/RNA-protein) interaction network. Furthermore, this resource will help in uncovering the generic organizing principles of cellular function network. © 2014 Zhang et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Computational prediction of protein-protein interactions in Leishmania predicted proteomes.

PubMed

Rezende, Antonio M; Folador, Edson L; Resende, Daniela de M; Ruiz, Jeronimo C

2012-01-01

The Trypanosomatids parasites Leishmania braziliensis, Leishmania major and Leishmania infantum are important human pathogens. Despite of years of study and genome availability, effective vaccine has not been developed yet, and the chemotherapy is highly toxic. Therefore, it is clear just interdisciplinary integrated studies will have success in trying to search new targets for developing of vaccines and drugs. An essential part of this rationale is related to protein-protein interaction network (PPI) study which can provide a better understanding of complex protein interactions in biological system. Thus, we modeled PPIs for Trypanosomatids through computational methods using sequence comparison against public database of protein or domain interaction for interaction prediction (Interolog Mapping) and developed a dedicated combined system score to address the predictions robustness. The confidence evaluation of network prediction approach was addressed using gold standard positive and negative datasets and the AUC value obtained was 0.94. As result, 39,420, 43,531 and 45,235 interactions were predicted for L. braziliensis, L. major and L. infantum respectively. For each predicted network the top 20 proteins were ranked by MCC topological index. In addition, information related with immunological potential, degree of protein sequence conservation among orthologs and degree of identity compared to proteins of potential parasite hosts was integrated. This information integration provides a better understanding and usefulness of the predicted networks that can be valuable to select new potential biological targets for drug and vaccine development. Network modularity which is a key when one is interested in destabilizing the PPIs for drug or vaccine purposes along with multiple alignments of the predicted PPIs were performed revealing patterns associated with protein turnover. In addition, around 50% of hypothetical protein present in the networks received some degree of functional annotation which represents an important contribution since approximately 60% of Leishmania predicted proteomes has no predicted function.

Biological network extraction from scientific literature: state of the art and challenges.

PubMed

Li, Chen; Liakata, Maria; Rebholz-Schuhmann, Dietrich

2014-09-01

Networks of molecular interactions explain complex biological processes, and all known information on molecular events is contained in a number of public repositories including the scientific literature. Metabolic and signalling pathways are often viewed separately, even though both types are composed of interactions involving proteins and other chemical entities. It is necessary to be able to combine data from all available resources to judge the functionality, complexity and completeness of any given network overall, but especially the full integration of relevant information from the scientific literature is still an ongoing and complex task. Currently, the text-mining research community is steadily moving towards processing the full body of the scientific literature by making use of rich linguistic features such as full text parsing, to extract biological interactions. The next step will be to combine these with information from scientific databases to support hypothesis generation for the discovery of new knowledge and the extension of biological networks. The generation of comprehensive networks requires technologies such as entity grounding, coordination resolution and co-reference resolution, which are not fully solved and are required to further improve the quality of results. Here, we analyse the state of the art for the extraction of network information from the scientific literature and the evaluation of extraction methods against reference corpora, discuss challenges involved and identify directions for future research. © The Author 2013. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

MIMO: an efficient tool for molecular interaction maps overlap

PubMed Central

2013-01-01

Background Molecular pathways represent an ensemble of interactions occurring among molecules within the cell and between cells. The identification of similarities between molecular pathways across organisms and functions has a critical role in understanding complex biological processes. For the inference of such novel information, the comparison of molecular pathways requires to account for imperfect matches (flexibility) and to efficiently handle complex network topologies. To date, these characteristics are only partially available in tools designed to compare molecular interaction maps. Results Our approach MIMO (Molecular Interaction Maps Overlap) addresses the first problem by allowing the introduction of gaps and mismatches between query and template pathways and permits -when necessary- supervised queries incorporating a priori biological information. It then addresses the second issue by relying directly on the rich graph topology described in the Systems Biology Markup Language (SBML) standard, and uses multidigraphs to efficiently handle multiple queries on biological graph databases. The algorithm has been here successfully used to highlight the contact point between various human pathways in the Reactome database. Conclusions MIMO offers a flexible and efficient graph-matching tool for comparing complex biological pathways. PMID:23672344

Identification of cancer-related miRNA-lncRNA biomarkers using a basic miRNA-lncRNA network.

PubMed

Zhang, Guangle; Pian, Cong; Chen, Zhi; Zhang, Jin; Xu, Mingmin; Zhang, Liangyun; Chen, Yuanyuan

2018-01-01

LncRNAs are regulatory noncoding RNAs that play crucial roles in many biological processes. The dysregulation of lncRNA is thought to be involved in many complex diseases; lncRNAs are often the targets of miRNAs in the indirect regulation of gene expression. Numerous studies have indicated that miRNA-lncRNA interactions are closely related to the occurrence and development of cancers. Thus, it is important to develop an effective method for the identification of cancer-related miRNA-lncRNA interactions. In this study, we compiled 155653 experimentally validated and predicted miRNA-lncRNA associations, which we defined as basic interactions. We next constructed an individual-specific miRNA-lncRNA network (ISMLN) for each cancer sample and a basic miRNA-lncRNA network (BMLN) for each type of cancer by examining the expression profiles of miRNAs and lncRNAs in the TCGA (The Cancer Genome Atlas) database. We then selected potential miRNA-lncRNA biomarkers based on the BLMN. Using this method, we identified cancer-related miRNA-lncRNA biomarkers and modules specific to a certain cancer. This method of profiling will contribute to the diagnosis and treatment of cancers at the level of gene regulatory networks.

Correlated miR-mRNA expression signatures of mouse hematopoietic stem and progenitor cell subsets predict "Stemness" and "Myeloid" interaction networks.

PubMed

Heiser, Diane; Tan, Yee Sun; Kaplan, Ian; Godsey, Brian; Morisot, Sebastien; Cheng, Wen-Chih; Small, Donald; Civin, Curt I

2014-01-01

Several individual miRNAs (miRs) have been implicated as potent regulators of important processes during normal and malignant hematopoiesis. In addition, many miRs have been shown to fine-tune intricate molecular networks, in concert with other regulatory elements. In order to study hematopoietic networks as a whole, we first created a map of global miR expression during early murine hematopoiesis. Next, we determined the copy number per cell for each miR in each of the examined stem and progenitor cell types. As data is emerging indicating that miRs function robustly mainly when they are expressed above a certain threshold (∼100 copies per cell), our database provides a resource for determining which miRs are expressed at a potentially functional level in each cell type. Finally, we combine our miR expression map with matched mRNA expression data and external prediction algorithms, using a Bayesian modeling approach to create a global landscape of predicted miR-mRNA interactions within each of these hematopoietic stem and progenitor cell subsets. This approach implicates several interaction networks comprising a "stemness" signature in the most primitive hematopoietic stem cell (HSC) populations, as well as "myeloid" patterns associated with two branches of myeloid development.

PROGRESS REPORT ON THE DSSTOX DATABASE NETWORK: NEWLY LAUNCHED WEBSITE, APPLICATIONS, FUTURE PLANS

EPA Science Inventory

Progress Report on the DSSTox Database Network: Newly Launched Website, Applications, Future Plans

Progress will be reported on development of the Distributed Structure-Searchable Toxicity (DSSTox) Database Network and the newly launched public website that coordinates and...

Bioinformatics Analysis of Protein Phosphorylation in Plant Systems Biology Using P3DB.

PubMed

Yao, Qiuming; Xu, Dong

2017-01-01

Protein phosphorylation is one of the most pervasive protein post-translational modification events in plant cells. It is involved in many plant biological processes, such as plant growth, organ development, and plant immunology, by regulating or switching signaling and metabolic pathways. High-throughput experimental methods like mass spectrometry can easily characterize hundreds to thousands of phosphorylation events in a single experiment. With the increasing volume of the data sets, Plant Protein Phosphorylation DataBase (P3DB, http://p3db.org ) provides a comprehensive, systematic, and interactive online platform to deposit, query, analyze, and visualize these phosphorylation events in many plant species. It stores the protein phosphorylation sites in the context of identified mass spectra, phosphopeptides, and phosphoproteins contributed from various plant proteome studies. In addition, P3DB associates these plant phosphorylation sites to protein physicochemical information in the protein charts and tertiary structures, while various protein annotations from hierarchical kinase phosphatase families, protein domains, and gene ontology are also added into the database. P3DB not only provides rich information, but also interconnects and provides visualization of the data in networks, in systems biology context. Currently, P3DB includes the KiC (Kinase Client) assay network, the protein-protein interaction network, the kinase-substrate network, the phosphatase-substrate network, and the protein domain co-occurrence network. All of these are available to query for and visualize existing phosphorylation events. Although P3DB only hosts experimentally identified phosphorylation data, it provides a plant phosphorylation prediction model for any unknown queries on the fly. P3DB is an entry point to the plant phosphorylation community to deposit and visualize any customized data sets within this systems biology framework. Nowadays, P3DB has become one of the major bioinformatics platforms of protein phosphorylation in plant biology.

Directed random walks and constraint programming reveal active pathways in hepatocyte growth factor signaling.

PubMed

Kittas, Aristotelis; Delobelle, Aurélien; Schmitt, Sabrina; Breuhahn, Kai; Guziolowski, Carito; Grabe, Niels

2016-01-01

An effective means to analyze mRNA expression data is to take advantage of established knowledge from pathway databases, using methods such as pathway-enrichment analyses. However, pathway databases are not case-specific and expression data could be used to infer gene-regulation patterns in the context of specific pathways. In addition, canonical pathways may not always describe the signaling mechanisms properly, because interactions can frequently occur between genes in different pathways. Relatively few methods have been proposed to date for generating and analyzing such networks, preserving the causality between gene interactions and reasoning over the qualitative logic of regulatory effects. We present an algorithm (MCWalk) integrated with a logic programming approach, to discover subgraphs in large-scale signaling networks by random walks in a fully automated pipeline. As an exemplary application, we uncover the signal transduction mechanisms in a gene interaction network describing hepatocyte growth factor-stimulated cell migration and proliferation from gene-expression measured with microarray and RT-qPCR using in-house perturbation experiments in a keratinocyte-fibroblast co-culture. The resulting subgraphs illustrate possible associations of hepatocyte growth factor receptor c-Met nodes, differentially expressed genes and cellular states. Using perturbation experiments and Answer Set programming, we are able to select those which are more consistent with the experimental data. We discover key regulator nodes by measuring the frequency with which they are traversed when connecting signaling between receptors and significantly regulated genes and predict their expression-shift consistently with the measured data. The Java implementation of MCWalk is publicly available under the MIT license at: https://bitbucket.org/akittas/biosubg. © 2015 FEBS.

Visualizing collaborative electronic health record usage for hospitalized patients with heart failure.

PubMed

Soulakis, Nicholas D; Carson, Matthew B; Lee, Young Ji; Schneider, Daniel H; Skeehan, Connor T; Scholtens, Denise M

2015-03-01

To visualize and describe collaborative electronic health record (EHR) usage for hospitalized patients with heart failure. We identified records of patients with heart failure and all associated healthcare provider record usage through queries of the Northwestern Medicine Enterprise Data Warehouse. We constructed a network by equating access and updates of a patient's EHR to a provider-patient interaction. We then considered shared patient record access as the basis for a second network that we termed the provider collaboration network. We calculated network statistics, the modularity of provider interactions, and provider cliques. We identified 548 patient records accessed by 5113 healthcare providers in 2012. The provider collaboration network had 1504 nodes and 83 998 edges. We identified 7 major provider collaboration modules. Average clique size was 87.9 providers. We used a graph database to demonstrate an ad hoc query of our provider-patient network. Our analysis suggests a large number of healthcare providers across a wide variety of professions access records of patients with heart failure during their hospital stay. This shared record access tends to take place not only in a pairwise manner but also among large groups of providers. EHRs encode valuable interactions, implicitly or explicitly, between patients and providers. Network analysis provided strong evidence of multidisciplinary record access of patients with heart failure across teams of 100+ providers. Further investigation may lead to clearer understanding of how record access information can be used to strategically guide care coordination for patients hospitalized for heart failure. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Linking disease-associated genes to regulatory networks via promoter organization

PubMed Central

Döhr, S.; Klingenhoff, A.; Maier, H.; de Angelis, M. Hrabé; Werner, T.; Schneider, R.

2005-01-01

Pathway- or disease-associated genes may participate in more than one transcriptional co-regulation network. Such gene groups can be readily obtained by literature analysis or by high-throughput techniques such as microarrays or protein-interaction mapping. We developed a strategy that defines regulatory networks by in silico promoter analysis, finding potentially co-regulated subgroups without a priori knowledge. Pairs of transcription factor binding sites conserved in orthologous genes (vertically) as well as in promoter sequences of co-regulated genes (horizontally) were used as seeds for the development of promoter models representing potential co-regulation. This approach was applied to a Maturity Onset Diabetes of the Young (MODY)-associated gene list, which yielded two models connecting functionally interacting genes within MODY-related insulin/glucose signaling pathways. Additional genes functionally connected to our initial gene list were identified by database searches with these promoter models. Thus, data-driven in silico promoter analysis allowed integrating molecular mechanisms with biological functions of the cell. PMID:15701758

The research of network database security technology based on web service

NASA Astrophysics Data System (ADS)

Meng, Fanxing; Wen, Xiumei; Gao, Liting; Pang, Hui; Wang, Qinglin

2013-03-01

Database technology is one of the most widely applied computer technologies, its security is becoming more and more important. This paper introduced the database security, network database security level, studies the security technology of the network database, analyzes emphatically sub-key encryption algorithm, applies this algorithm into the campus-one-card system successfully. The realization process of the encryption algorithm is discussed, this method is widely used as reference in many fields, particularly in management information system security and e-commerce.

Competing endogenous RNA regulatory network in papillary thyroid carcinoma.

PubMed

Chen, Shouhua; Fan, Xiaobin; Gu, He; Zhang, Lili; Zhao, Wenhua

2018-05-11

The present study aimed to screen all types of RNAs involved in the development of papillary thyroid carcinoma (PTC). RNA‑sequencing data of PTC and normal samples were used for screening differentially expressed (DE) microRNAs (DE‑miRNAs), long non‑coding RNAs (DE‑lncRNAs) and genes (DEGs). Subsequently, lncRNA‑miRNA, miRNA‑gene (that is, miRNA‑mRNA) and gene‑gene interaction pairs were extracted and used to construct regulatory networks. Feature genes in the miRNA‑mRNA network were identified by topological analysis and recursive feature elimination analysis. A support vector machine (SVM) classifier was built using 15 feature genes, and its classification effect was validated using two microarray data sets that were downloaded from the Gene Expression Omnibus (GEO) database. In addition, Gene Ontology function and Kyoto Encyclopedia Genes and Genomes pathway enrichment analyses were conducted for genes identified in the ceRNA network. A total of 506 samples, including 447 tumor samples and 59 normal samples, were obtained from The Cancer Genome Atlas (TCGA); 16 DE‑lncRNAs, 917 DEGs and 30 DE‑miRNAs were screened. The miRNA‑mRNA regulatory network comprised 353 nodes and 577 interactions. From these data, 15 feature genes with high predictive precision (>95%) were extracted from the network and were used to form an SVM classifier with an accuracy of 96.05% (486/506) for PTC samples downloaded from TCGA, and accuracies of 96.81 and 98.46% for GEO downloaded data sets. The ceRNA regulatory network comprised 596 lines (or interactions) and 365 nodes. Genes in the ceRNA network were significantly enriched in 'neuron development', 'differentiation', 'neuroactive ligand‑receptor interaction', 'metabolism of xenobiotics by cytochrome P450', 'drug metabolism' and 'cytokine‑cytokine receptor interaction' pathways. Hox transcript antisense RNA, miRNA‑206 and kallikrein‑related peptidase 10 were nodes in the ceRNA regulatory network of the selected feature gene, and they may serve import roles in the development of PTC.

P³DB 3.0: From plant phosphorylation sites to protein networks.

PubMed

Yao, Qiuming; Ge, Huangyi; Wu, Shangquan; Zhang, Ning; Chen, Wei; Xu, Chunhui; Gao, Jianjiong; Thelen, Jay J; Xu, Dong

2014-01-01

In the past few years, the Plant Protein Phosphorylation Database (P(3)DB, http://p3db.org) has become one of the most significant in vivo data resources for studying plant phosphoproteomics. We have substantially updated P(3)DB with respect to format, new datasets and analytic tools. In the P(3)DB 3.0, there are altogether 47 923 phosphosites in 16 477 phosphoproteins curated across nine plant organisms from 32 studies, which have met our multiple quality standards for acquisition of in vivo phosphorylation site data. Centralized by these phosphorylation data, multiple related data and annotations are provided, including protein-protein interaction (PPI), gene ontology, protein tertiary structures, orthologous sequences, kinase/phosphatase classification and Kinase Client Assay (KiC Assay) data--all of which provides context for the phosphorylation event. In addition, P(3)DB 3.0 incorporates multiple network viewers for the above features, such as PPI network, kinase-substrate network, phosphatase-substrate network, and domain co-occurrence network to help study phosphorylation from a systems point of view. Furthermore, the new P(3)DB reflects a community-based design through which users can share datasets and automate data depository processes for publication purposes. Each of these new features supports the goal of making P(3)DB a comprehensive, systematic and interactive platform for phosphoproteomics research.

Atlas - a data warehouse for integrative bioinformatics.

PubMed

Shah, Sohrab P; Huang, Yong; Xu, Tao; Yuen, Macaire M S; Ling, John; Ouellette, B F Francis

2005-02-21

We present a biological data warehouse called Atlas that locally stores and integrates biological sequences, molecular interactions, homology information, functional annotations of genes, and biological ontologies. The goal of the system is to provide data, as well as a software infrastructure for bioinformatics research and development. The Atlas system is based on relational data models that we developed for each of the source data types. Data stored within these relational models are managed through Structured Query Language (SQL) calls that are implemented in a set of Application Programming Interfaces (APIs). The APIs include three languages: C++, Java, and Perl. The methods in these API libraries are used to construct a set of loader applications, which parse and load the source datasets into the Atlas database, and a set of toolbox applications which facilitate data retrieval. Atlas stores and integrates local instances of GenBank, RefSeq, UniProt, Human Protein Reference Database (HPRD), Biomolecular Interaction Network Database (BIND), Database of Interacting Proteins (DIP), Molecular Interactions Database (MINT), IntAct, NCBI Taxonomy, Gene Ontology (GO), Online Mendelian Inheritance in Man (OMIM), LocusLink, Entrez Gene and HomoloGene. The retrieval APIs and toolbox applications are critical components that offer end-users flexible, easy, integrated access to this data. We present use cases that use Atlas to integrate these sources for genome annotation, inference of molecular interactions across species, and gene-disease associations. The Atlas biological data warehouse serves as data infrastructure for bioinformatics research and development. It forms the backbone of the research activities in our laboratory and facilitates the integration of disparate, heterogeneous biological sources of data enabling new scientific inferences. Atlas achieves integration of diverse data sets at two levels. First, Atlas stores data of similar types using common data models, enforcing the relationships between data types. Second, integration is achieved through a combination of APIs, ontology, and tools. The Atlas software is freely available under the GNU General Public License at: http://bioinformatics.ubc.ca/atlas/

Atlas – a data warehouse for integrative bioinformatics

PubMed Central

Shah, Sohrab P; Huang, Yong; Xu, Tao; Yuen, Macaire MS; Ling, John; Ouellette, BF Francis

2005-01-01

Background We present a biological data warehouse called Atlas that locally stores and integrates biological sequences, molecular interactions, homology information, functional annotations of genes, and biological ontologies. The goal of the system is to provide data, as well as a software infrastructure for bioinformatics research and development. Description The Atlas system is based on relational data models that we developed for each of the source data types. Data stored within these relational models are managed through Structured Query Language (SQL) calls that are implemented in a set of Application Programming Interfaces (APIs). The APIs include three languages: C++, Java, and Perl. The methods in these API libraries are used to construct a set of loader applications, which parse and load the source datasets into the Atlas database, and a set of toolbox applications which facilitate data retrieval. Atlas stores and integrates local instances of GenBank, RefSeq, UniProt, Human Protein Reference Database (HPRD), Biomolecular Interaction Network Database (BIND), Database of Interacting Proteins (DIP), Molecular Interactions Database (MINT), IntAct, NCBI Taxonomy, Gene Ontology (GO), Online Mendelian Inheritance in Man (OMIM), LocusLink, Entrez Gene and HomoloGene. The retrieval APIs and toolbox applications are critical components that offer end-users flexible, easy, integrated access to this data. We present use cases that use Atlas to integrate these sources for genome annotation, inference of molecular interactions across species, and gene-disease associations. Conclusion The Atlas biological data warehouse serves as data infrastructure for bioinformatics research and development. It forms the backbone of the research activities in our laboratory and facilitates the integration of disparate, heterogeneous biological sources of data enabling new scientific inferences. Atlas achieves integration of diverse data sets at two levels. First, Atlas stores data of similar types using common data models, enforcing the relationships between data types. Second, integration is achieved through a combination of APIs, ontology, and tools. The Atlas software is freely available under the GNU General Public License at: PMID:15723693

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.

Constraints on signaling network logic reveal functional subgraphs on Multiple Myeloma OMIC data.

PubMed

Miannay, Bertrand; Minvielle, Stéphane; Magrangeas, Florence; Guziolowski, Carito

2018-03-21

The integration of gene expression profiles (GEPs) and large-scale biological networks derived from pathways databases is a subject which is being widely explored. Existing methods are based on network distance measures among significantly measured species. Only a small number of them include the directionality and underlying logic existing in biological networks. In this study we approach the GEP-networks integration problem by considering the network logic, however our approach does not require a prior species selection according to their gene expression level. We start by modeling the biological network representing its underlying logic using Logic Programming. This model points to reachable network discrete states that maximize a notion of harmony between the molecular species active or inactive possible states and the directionality of the pathways reactions according to their activator or inhibitor control role. Only then, we confront these network states with the GEP. From this confrontation independent graph components are derived, each of them related to a fixed and optimal assignment of active or inactive states. These components allow us to decompose a large-scale network into subgraphs and their molecular species state assignments have different degrees of similarity when compared to the same GEP. We apply our method to study the set of possible states derived from a subgraph from the NCI-PID Pathway Interaction Database. This graph links Multiple Myeloma (MM) genes to known receptors for this blood cancer. We discover that the NCI-PID MM graph had 15 independent components, and when confronted to 611 MM GEPs, we find 1 component as being more specific to represent the difference between cancer and healthy profiles.

Global efficiency of local immunization on complex networks

NASA Astrophysics Data System (ADS)

Hébert-Dufresne, Laurent; Allard, Antoine; Young, Jean-Gabriel; Dubé, Louis J.

2013-07-01

Epidemics occur in all shapes and forms: infections propagating in our sparse sexual networks, rumours and diseases spreading through our much denser social interactions, or viruses circulating on the Internet. With the advent of large databases and efficient analysis algorithms, these processes can be better predicted and controlled. In this study, we use different characteristics of network organization to identify the influential spreaders in 17 empirical networks of diverse nature using 2 epidemic models. We find that a judicious choice of local measures, based either on the network's connectivity at a microscopic scale or on its community structure at a mesoscopic scale, compares favorably to global measures, such as betweenness centrality, in terms of efficiency, practicality and robustness. We also develop an analytical framework that highlights a transition in the characteristic scale of different epidemic regimes. This allows to decide which local measure should govern immunization in a given scenario.

Global efficiency of local immunization on complex networks.

PubMed

Hébert-Dufresne, Laurent; Allard, Antoine; Young, Jean-Gabriel; Dubé, Louis J

2013-01-01

Epidemics occur in all shapes and forms: infections propagating in our sparse sexual networks, rumours and diseases spreading through our much denser social interactions, or viruses circulating on the Internet. With the advent of large databases and efficient analysis algorithms, these processes can be better predicted and controlled. In this study, we use different characteristics of network organization to identify the influential spreaders in 17 empirical networks of diverse nature using 2 epidemic models. We find that a judicious choice of local measures, based either on the network's connectivity at a microscopic scale or on its community structure at a mesoscopic scale, compares favorably to global measures, such as betweenness centrality, in terms of efficiency, practicality and robustness. We also develop an analytical framework that highlights a transition in the characteristic scale of different epidemic regimes. This allows to decide which local measure should govern immunization in a given scenario.

Network control processor for a TDMA system

NASA Astrophysics Data System (ADS)

Suryadevara, Omkarmurthy; Debettencourt, Thomas J.; Shulman, R. B.

Two unique aspects of designing a network control processor (NCP) to monitor and control a demand-assigned, time-division multiple-access (TDMA) network are described. The first involves the implementation of redundancy by synchronizing the databases of two geographically remote NCPs. The two sets of databases are kept in synchronization by collecting data on both systems, transferring databases, sending incremental updates, and the parallel updating of databases. A periodic audit compares the checksums of the databases to ensure synchronization. The second aspect involves the use of a tracking algorithm to dynamically reallocate TDMA frame space. This algorithm detects and tracks current and long-term load changes in the network. When some portions of the network are overloaded while others have excess capacity, the algorithm automatically calculates and implements a new burst time plan.

Functional Analysis of OMICs Data and Small Molecule Compounds in an Integrated "Knowledge-Based" Platform.

PubMed

Dubovenko, Alexey; Nikolsky, Yuri; Rakhmatulin, Eugene; Nikolskaya, Tatiana

2017-01-01

Analysis of NGS and other sequencing data, gene variants, gene expression, proteomics, and other high-throughput (OMICs) data is challenging because of its biological complexity and high level of technical and biological noise. One way to deal with both problems is to perform analysis with a high fidelity annotated knowledgebase of protein interactions, pathways, and functional ontologies. This knowledgebase has to be structured in a computer-readable format and must include software tools for managing experimental data, analysis, and reporting. Here, we present MetaCore™ and Key Pathway Advisor (KPA), an integrated platform for functional data analysis. On the content side, MetaCore and KPA encompass a comprehensive database of molecular interactions of different types, pathways, network models, and ten functional ontologies covering human, mouse, and rat genes. The analytical toolkit includes tools for gene/protein list enrichment analysis, statistical "interactome" tool for the identification of over- and under-connected proteins in the dataset, and a biological network analysis module made up of network generation algorithms and filters. The suite also features Advanced Search, an application for combinatorial search of the database content, as well as a Java-based tool called Pathway Map Creator for drawing and editing custom pathway maps. Applications of MetaCore and KPA include molecular mode of action of disease research, identification of potential biomarkers and drug targets, pathway hypothesis generation, analysis of biological effects for novel small molecule compounds and clinical applications (analysis of large cohorts of patients, and translational and personalized medicine).

An analysis of the gene interaction networks identifying the role of PARP1 in metastasis of non-small cell lung cancer.

PubMed

Chen, Kai; Li, Yajie; Xu, Hui; Zhang, Chunfeng; Li, Zhiqiang; Wang, Wei; Wang, Baofeng

2017-10-20

Though there were many researches about the effects of cancer cells on non-small cell lung cancer (NSCLC) currently, it has been rarely reported completed oncogene and its mechanism in tumors by far. Here, we used biological methods with known oncogene of NSCLC to find new oncogene and explore its functionary mechanism in NSCLC. The study firstly built NSCLC genetic interaction network based on bioinformatics methods and then combined shortest path algorithm with significance test to confirmed core genes that were closely involved with given genes; real-time qPCR was conducted to detect expression levels between patients with NSCLC and normal people; additionally, detection of PARP1's role in migration and invasion was performed by trans-well assays and wound-healing. Through gene interaction network, it was found that, core genes like PARP1, EGFR and ALK had a direct interaction. TCGA database showed that PARP1 presented strong expression in NSCLC and the expression level of metastatic NSCLC was significantly higher than that of non-metastatic NSCLC. Cell migration of NSCLC in accordance to the scratch test was suppressed by PARP1 silence but stimulated noticeably by PARP1 overexpression. According to Kaplan-meier survival curve, the higher PARP1 expression, the poorer patient survival rate and prognosis. Thus, PARP1 expression had a negative correction with patient survival rate and prognosis. New oncogene PARP1 was found from known NSCLC oncogene in terms of gene interaction network, demonstrating PARP1's impact on NSCLC cell migration.

Screening the molecular targets of ovarian cancer based on bioinformatics analysis.

PubMed

Du, Lei; Qian, Xiaolei; Dai, Chenyang; Wang, Lihua; Huang, Ding; Wang, Shuying; Shen, Xiaowei

2015-01-01

Ovarian cancer (OC) is the most lethal gynecologic malignancy. This study aims to explore the molecular mechanisms of OC and identify potential molecular targets for OC treatment. Microarray gene expression data (GSE14407) including 12 normal ovarian surface epithelia samples and 12 OC epithelia samples were downloaded from Gene Expression Omnibus database. Differentially expressed genes (DEGs) between 2 kinds of ovarian tissue were identified by using limma package in R language (|log2 fold change| gt;1 and false discovery rate [FDR] lt;0.05). Protein-protein interactions (PPIs) and known OC-related genes were screened from COXPRESdb and GenBank database, respectively. Furthermore, PPI network of top 10 upregulated DEGs and top 10 downregulated DEGs was constructed and visualized through Cytoscape software. Finally, for the genes involved in PPI network, functional enrichment analysis was performed by using DAVID (FDR lt;0.05). In total, 1136 DEGs were identified, including 544 downregulated and 592 upregulated DEGs. Then, PPI network was constructed, and DEGs CDKN2A, MUC1, OGN, ZIC1, SOX17, and TFAP2A interacted with known OC-related genes CDK4, EGFR/JUN, SRC, CLI1, CTNNB1, and TP53, respectively. Moreover, functions about oxygen transport and embryonic development were enriched by the genes involved in the network of downregulated DEGs. We propose that 4 DEGs (OGN, ZIC1, SOX17, and TFAP2A) and 2 functions (oxygen transport and embryonic development) might play a role in the development of OC. These 4 DEGs and known OC-related genes might serve as therapeutic targets for OC. Further studies are required to validate these predictions.

MetNet: Software to Build and Model the Biogenetic Lattice of Arabidopsis

DOE PAGES

Wurtele, Eve Syrkin; Li, Jie; Diao, Lixia; ...

2003-01-01

MetNet (http://www.botany.iastate.edu/∼mash/metnetex/metabolicnetex.html) is publicly available software in development for analysis of genome-wide RNA, protein and metabolite profiling data. The software is designed to enable the biologist to visualize, statistically analyse and model a metabolic and regulatory network map of Arabidopsis , combined with gene expression profiling data. It contains a JAVA interface to an interactions database (MetNetDB) containing information on regulatory and metabolic interactions derived from a combination of web databases (TAIR, KEGG, BRENDA) and input from biologists in their area of expertise. FCModeler captures input from MetNetDB in a graphical form. Sub-networks can be identified and interpreted using simplemore » fuzzy cognitive maps. FCModeler is intended to develop and evaluate hypotheses, and provide a modelling framework for assessing the large amounts of data captured by high-throughput gene expression experiments. FCModeler and MetNetDB are currently being extended to three-dimensional virtual reality display. The MetNet map, together with gene expression data, can be viewed using multivariate graphics tools in GGobi linked with the data analytic tools in R. Users can highlight different parts of the metabolic network and see the relevant expression data highlighted in other data plots. Multi-dimensional expression data can be rotated through different dimensions. Statistical analysis can be computed alongside the visual. MetNet is designed to provide a framework for the formulation of testable hypotheses regarding the function of specific genes, and in the long term provide the basis for identification of metabolic and regulatory networks that control plant composition and development.« less

Brief Report: Databases in the Asia-Pacific Region: The Potential for a Distributed Network Approach.

PubMed

Lai, Edward Chia-Cheng; Man, Kenneth K C; Chaiyakunapruk, Nathorn; Cheng, Ching-Lan; Chien, Hsu-Chih; Chui, Celine S L; Dilokthornsakul, Piyameth; Hardy, N Chantelle; Hsieh, Cheng-Yang; Hsu, Chung Y; Kubota, Kiyoshi; Lin, Tzu-Chieh; Liu, Yanfang; Park, Byung Joo; Pratt, Nicole; Roughead, Elizabeth E; Shin, Ju-Young; Watcharathanakij, Sawaeng; Wen, Jin; Wong, Ian C K; Yang, Yea-Huei Kao; Zhang, Yinghong; Setoguchi, Soko

2015-11-01

This study describes the availability and characteristics of databases in Asian-Pacific countries and assesses the feasibility of a distributed network approach in the region. A web-based survey was conducted among investigators using healthcare databases in the Asia-Pacific countries. Potential survey participants were identified through the Asian Pharmacoepidemiology Network. Investigators from a total of 11 databases participated in the survey. Database sources included four nationwide claims databases from Japan, South Korea, and Taiwan; two nationwide electronic health records from Hong Kong and Singapore; a regional electronic health record from western China; two electronic health records from Thailand; and cancer and stroke registries from Taiwan. We identified 11 databases with capabilities for distributed network approaches. Many country-specific coding systems and terminologies have been already converted to international coding systems. The harmonization of health expenditure data is a major obstacle for future investigations attempting to evaluate issues related to medical costs.

In Silico Enhancing M. tuberculosis Protein Interaction Networks in STRING To Predict Drug-Resistance Pathways and Pharmacological Risks.

PubMed

Mei, Suyu

2018-05-04

Bacterial protein-protein interaction (PPI) networks are significant to reveal the machinery of signal transduction and drug resistance within bacterial cells. The database STRING has collected a large number of bacterial pathogen PPI networks, but most of the data are of low quality without being experimentally or computationally validated, thus restricting its further biomedical applications. We exploit the experimental data via four solutions to enhance the quality of M. tuberculosis H37Rv (MTB) PPI networks in STRING. Computational results show that the experimental data derived jointly by two-hybrid and copurification approaches are the most reliable to train an L 2 -regularized logistic regression model for MTB PPI network validation. On the basis of the validated MTB PPI networks, we further study the three problems via breadth-first graph search algorithm: (1) discovery of MTB drug-resistance pathways through searching for the paths between known drug-target genes and drug-resistance genes, (2) choosing potential cotarget genes via searching for the critical genes located on multiple pathways, and (3) choosing essential drug-target genes via analysis of network degree distribution. In addition, we further combine the validated MTB PPI networks with human PPI networks to analyze the potential pharmacological risks of known and candidate drug-target genes from the point of view of system pharmacology. The evidence from protein structure alignment demonstrates that the drugs that act on MTB target genes could also adversely act on human signaling pathways.

From genomics to chemical genomics: new developments in KEGG

PubMed Central

Kanehisa, Minoru; Goto, Susumu; Hattori, Masahiro; Aoki-Kinoshita, Kiyoko F.; Itoh, Masumi; Kawashima, Shuichi; Katayama, Toshiaki; Araki, Michihiro; Hirakawa, Mika

2006-01-01

The increasing amount of genomic and molecular information is the basis for understanding higher-order biological systems, such as the cell and the organism, and their interactions with the environment, as well as for medical, industrial and other practical applications. The KEGG resource () provides a reference knowledge base for linking genomes to biological systems, categorized as building blocks in the genomic space (KEGG GENES) and the chemical space (KEGG LIGAND), and wiring diagrams of interaction networks and reaction networks (KEGG PATHWAY). A fourth component, KEGG BRITE, has been formally added to the KEGG suite of databases. This reflects our attempt to computerize functional interpretations as part of the pathway reconstruction process based on the hierarchically structured knowledge about the genomic, chemical and network spaces. In accordance with the new chemical genomics initiatives, the scope of KEGG LIGAND has been significantly expanded to cover both endogenous and exogenous molecules. Specifically, RPAIR contains curated chemical structure transformation patterns extracted from known enzymatic reactions, which would enable analysis of genome-environment interactions, such as the prediction of new reactions and new enzyme genes that would degrade new environmental compounds. Additionally, drug information is now stored separately and linked to new KEGG DRUG structure maps. PMID:16381885

Endogenous Versus Exogenous Shocks in Complex Networks: An Empirical Test Using Book Sale Rankings

NASA Astrophysics Data System (ADS)

Sornette, D.; Deschâtres, F.; Gilbert, T.; Ageon, Y.

2004-11-01

We study the precursory and recovery signatures accompanying shocks in complex networks, that we test on a unique database of the Amazon.com ranking of book sales. We find clear distinguishing signatures classifying two types of sales peaks. Exogenous peaks occur abruptly and are followed by a power law relaxation, while endogenous peaks occur after a progressively accelerating power law growth followed by an approximately symmetrical power law relaxation which is slower than for exogenous peaks. These results are rationalized quantitatively by a simple model of epidemic propagation of interactions with long memory within a network of acquaintances. The observed relaxation of sales implies that the sales dynamics is dominated by cascades rather than by the direct effects of news or advertisements, indicating that the social network is close to critical.

Endogenous versus exogenous shocks in complex networks: an empirical test using book sale rankings.

PubMed

Sornette, D; Deschâtres, F; Gilbert, T; Ageon, Y

2004-11-26

We study the precursory and recovery signatures accompanying shocks in complex networks, that we test on a unique database of the Amazon.com ranking of book sales. We find clear distinguishing signatures classifying two types of sales peaks. Exogenous peaks occur abruptly and are followed by a power law relaxation, while endogenous peaks occur after a progressively accelerating power law growth followed by an approximately symmetrical power law relaxation which is slower than for exogenous peaks. These results are rationalized quantitatively by a simple model of epidemic propagation of interactions with long memory within a network of acquaintances. The observed relaxation of sales implies that the sales dynamics is dominated by cascades rather than by the direct effects of news or advertisements, indicating that the social network is close to critical.

The SSABLE system - Automated archive, catalog, browse and distribution of satellite data in near-real time

NASA Technical Reports Server (NTRS)

Simpson, James J.; Harkins, Daniel N.

1993-01-01

Historically, locating and browsing satellite data has been a cumbersome and expensive process. This has impeded the efficient and effective use of satellite data in the geosciences. SSABLE is a new interactive tool for the archive, browse, order, and distribution of satellite date based upon X Window, high bandwidth networks, and digital image rendering techniques. SSABLE provides for automatically constructing relational database queries to archived image datasets based on time, data, geographical location, and other selection criteria. SSABLE also provides a visual representation of the selected archived data for viewing on the user's X terminal. SSABLE is a near real-time system; for example, data are added to SSABLE's database within 10 min after capture. SSABLE is network and machine independent; it will run identically on any machine which satisfies the following three requirements: 1) has a bitmapped display (monochrome or greater); 2) is running the X Window system; and 3) is on a network directly reachable by the SSABLE system. SSABLE has been evaluated at over 100 international sites. Network response time in the United States and Canada varies between 4 and 7 s for browse image updates; reported transmission times to Europe and Australia typically are 20-25 s.

[Establishment of the database of the 3D facial models for the plastic surgery based on network].

PubMed

Liu, Zhe; Zhang, Hai-Lin; Zhang, Zheng-Guo; Qiao, Qun

2008-07-01

To collect the three-dimensional (3D) facial data of 30 facial deformity patients by the 3D scanner and establish a professional database based on Internet. It can be helpful for the clinical intervention. The primitive point data of face topography were collected by the 3D scanner. Then the 3D point cloud was edited by reverse engineering software to reconstruct the 3D model of the face. The database system was divided into three parts, including basic information, disease information and surgery information. The programming language of the web system is Java. The linkages between every table of the database are credibility. The query operation and the data mining are convenient. The users can visit the database via the Internet and use the image analysis system to observe the 3D facial models interactively. In this paper we presented a database and a web system adapt to the plastic surgery of human face. It can be used both in clinic and in basic research.

Visualization and Analysis of MiRNA-Targets Interactions Networks.

PubMed

León, Luis E; Calligaris, Sebastián D

2017-01-01

MicroRNAs are a class of small, noncoding RNA molecules of 21-25 nucleotides in length that regulate the gene expression by base-pairing with the target mRNAs, mainly leading to down-regulation or repression of the target genes. MicroRNAs are involved in diverse regulatory pathways in normal and pathological conditions. In this context, it is highly important to identify the targets of specific microRNA in order to understand the mechanism of its regulation and consequently its involvement in disease. However, the microRNA target identification is experimentally laborious and time-consuming. The in silico prediction of microRNA targets is an extremely useful approach because you can identify potential mRNA targets, reduce the number of possibilities and then, validate a few microRNA-mRNA interactions in an in vitro experimental model. In this chapter, we describe, in a simple way, bioinformatics guidelines to use miRWalk database and Cytoscape software for analyzing microRNA-mRNA interactions through their visualization as a network.

Prediction of host - pathogen protein interactions between Mycobacterium tuberculosis and Homo sapiens using sequence motifs.

PubMed

Huo, Tong; Liu, Wei; Guo, Yu; Yang, Cheng; Lin, Jianping; Rao, Zihe

2015-03-26

Emergence of multiple drug resistant strains of M. tuberculosis (MDR-TB) threatens to derail global efforts aimed at reigning in the pathogen. Co-infections of M. tuberculosis with HIV are difficult to treat. To counter these new challenges, it is essential to study the interactions between M. tuberculosis and the host to learn how these bacteria cause disease. We report a systematic flow to predict the host pathogen interactions (HPIs) between M. tuberculosis and Homo sapiens based on sequence motifs. First, protein sequences were used as initial input for identifying the HPIs by 'interolog' method. HPIs were further filtered by prediction of domain-domain interactions (DDIs). Functional annotations of protein and publicly available experimental results were applied to filter the remaining HPIs. Using such a strategy, 118 pairs of HPIs were identified, which involve 43 proteins from M. tuberculosis and 48 proteins from Homo sapiens. A biological interaction network between M. tuberculosis and Homo sapiens was then constructed using the predicted inter- and intra-species interactions based on the 118 pairs of HPIs. Finally, a web accessible database named PATH (Protein interactions of M. tuberculosis and Human) was constructed to store these predicted interactions and proteins. This interaction network will facilitate the research on host-pathogen protein-protein interactions, and may throw light on how M. tuberculosis interacts with its host.

ChiTaRS-3.1-the enhanced chimeric transcripts and RNA-seq database matched with protein-protein interactions.

PubMed

Gorohovski, Alessandro; Tagore, Somnath; Palande, Vikrant; Malka, Assaf; Raviv-Shay, Dorith; Frenkel-Morgenstern, Milana

2017-01-04

Discovery of chimeric RNAs, which are produced by chromosomal translocations as well as the joining of exons from different genes by trans-splicing, has added a new level of complexity to our study and understanding of the transcriptome. The enhanced ChiTaRS-3.1 database (http://chitars.md.biu.ac.il) is designed to make widely accessible a wealth of mined data on chimeric RNAs, with easy-to-use analytical tools built-in. The database comprises 34 922: chimeric transcripts along with 11 714: cancer breakpoints. In this latest version, we have included multiple cross-references to GeneCards, iHop, PubMed, NCBI, Ensembl, OMIM, RefSeq and the Mitelman collection for every entry in the 'Full Collection'. In addition, for every chimera, we have added a predicted Chimeric Protein-Protein Interaction (ChiPPI) network, which allows for easy visualization of protein partners of both parental and fusion proteins for all human chimeras. The database contains a comprehensive annotation for 34 922: chimeric transcripts from eight organisms, and includes the manual annotation of 200 sense-antiSense (SaS) chimeras. The current improvements in the content and functionality to the ChiTaRS database make it a central resource for the study of chimeric transcripts and fusion proteins. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Signaling Networks among Stem Cell Precursors, Transit-Amplifying Progenitors, and their Niche in Developing Hair Follicles.

PubMed

Rezza, Amélie; Wang, Zichen; Sennett, Rachel; Qiao, Wenlian; Wang, Dongmei; Heitman, Nicholas; Mok, Ka Wai; Clavel, Carlos; Yi, Rui; Zandstra, Peter; Ma'ayan, Avi; Rendl, Michael

2016-03-29

The hair follicle (HF) is a complex miniorgan that serves as an ideal model system to study stem cell (SC) interactions with the niche during growth and regeneration. Dermal papilla (DP) cells are required for SC activation during the adult hair cycle, but signal exchange between niche and SC precursors/transit-amplifying cell (TAC) progenitors that regulates HF morphogenetic growth is largely unknown. Here we use six transgenic reporters to isolate 14 major skin and HF cell populations. With next-generation RNA sequencing, we characterize their transcriptomes and define unique molecular signatures. SC precursors, TACs, and the DP niche express a plethora of ligands and receptors. Signaling interaction network analysis reveals a bird's-eye view of pathways implicated in epithelial-mesenchymal interactions. Using a systematic tissue-wide approach, this work provides a comprehensive platform, linked to an interactive online database, to identify and further explore the SC/TAC/niche crosstalk regulating HF growth. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Diversification of transcription factor-DNA interactions and the evolution of gene regulatory networks.

PubMed

Rogers, Julia M; Bulyk, Martha L

2018-04-25

Sequence-specific transcription factors (TFs) bind short DNA sequences in the genome to regulate the expression of target genes. In the last decade, numerous technical advances have enabled the determination of the DNA-binding specificities of many of these factors. Large-scale screens of many TFs enabled the creation of databases of TF DNA-binding specificities, typically represented as position weight matrices (PWMs). Although great progress has been made in determining and predicting binding specificities systematically, there are still many surprises to be found when studying a particular TF's interactions with DNA in detail. Paralogous TFs' binding specificities can differ in subtle ways, in a manner that is not immediately apparent from looking at their PWMs. These differences affect gene regulatory outputs and enable TFs to rewire transcriptional networks over evolutionary time. This review discusses recent observations made in the study of TF-DNA interactions that highlight the importance of continued in-depth analysis of TF-DNA interactions and their inherent complexity. This article is categorized under: Biological Mechanisms > Regulatory Biology. © 2018 Wiley Periodicals, Inc.

MIR@NT@N: a framework integrating transcription factors, microRNAs and their targets to identify sub-network motifs in a meta-regulation network model

PubMed Central

2011-01-01

Background To understand biological processes and diseases, it is crucial to unravel the concerted interplay of transcription factors (TFs), microRNAs (miRNAs) and their targets within regulatory networks and fundamental sub-networks. An integrative computational resource generating a comprehensive view of these regulatory molecular interactions at a genome-wide scale would be of great interest to biologists, but is not available to date. Results To identify and analyze molecular interaction networks, we developed MIR@NT@N, an integrative approach based on a meta-regulation network model and a large-scale database. MIR@NT@N uses a graph-based approach to predict novel molecular actors across multiple regulatory processes (i.e. TFs acting on protein-coding or miRNA genes, or miRNAs acting on messenger RNAs). Exploiting these predictions, the user can generate networks and further analyze them to identify sub-networks, including motifs such as feedback and feedforward loops (FBL and FFL). In addition, networks can be built from lists of molecular actors with an a priori role in a given biological process to predict novel and unanticipated interactions. Analyses can be contextualized and filtered by integrating additional information such as microarray expression data. All results, including generated graphs, can be visualized, saved and exported into various formats. MIR@NT@N performances have been evaluated using published data and then applied to the regulatory program underlying epithelium to mesenchyme transition (EMT), an evolutionary-conserved process which is implicated in embryonic development and disease. Conclusions MIR@NT@N is an effective computational approach to identify novel molecular regulations and to predict gene regulatory networks and sub-networks including conserved motifs within a given biological context. Taking advantage of the M@IA environment, MIR@NT@N is a user-friendly web resource freely available at http://mironton.uni.lu which will be updated on a regular basis. PMID:21375730

A systems biology model of the regulatory network in Populus leaves reveals interacting regulators and conserved regulation

PubMed Central

2011-01-01

Background Green plant leaves have always fascinated biologists as hosts for photosynthesis and providers of basic energy to many food webs. Today, comprehensive databases of gene expression data enable us to apply increasingly more advanced computational methods for reverse-engineering the regulatory network of leaves, and to begin to understand the gene interactions underlying complex emergent properties related to stress-response and development. These new systems biology methods are now also being applied to organisms such as Populus, a woody perennial tree, in order to understand the specific characteristics of these species. Results We present a systems biology model of the regulatory network of Populus leaves. The network is reverse-engineered from promoter information and expression profiles of leaf-specific genes measured over a large set of conditions related to stress and developmental. The network model incorporates interactions between regulators, such as synergistic and competitive relationships, by evaluating increasingly more complex regulatory mechanisms, and is therefore able to identify new regulators of leaf development not found by traditional genomics methods based on pair-wise expression similarity. The approach is shown to explain available gene function information and to provide robust prediction of expression levels in new data. We also use the predictive capability of the model to identify condition-specific regulation as well as conserved regulation between Populus and Arabidopsis. Conclusions We outline a computationally inferred model of the regulatory network of Populus leaves, and show how treating genes as interacting, rather than individual, entities identifies new regulators compared to traditional genomics analysis. Although systems biology models should be used with care considering the complexity of regulatory programs and the limitations of current genomics data, methods describing interactions can provide hypotheses about the underlying cause of emergent properties and are needed if we are to identify target genes other than those constituting the "low hanging fruit" of genomic analysis. PMID:21232107

Detection of Significant Pneumococcal Meningitis Biomarkers by Ego Network.

PubMed

Wang, Qian; Lou, Zhifeng; Zhai, Liansuo; Zhao, Haibin

2017-06-01

To identify significant biomarkers for detection of pneumococcal meningitis based on ego network. Based on the gene expression data of pneumococcal meningitis and global protein-protein interactions (PPIs) data recruited from open access databases, the authors constructed a differential co-expression network (DCN) to identify pneumococcal meningitis biomarkers in a network view. Here EgoNet algorithm was employed to screen the significant ego networks that could accurately distinguish pneumococcal meningitis from healthy controls, by sequentially seeking ego genes, searching candidate ego networks, refinement of candidate ego networks and significance analysis to identify ego networks. Finally, the functional inference of the ego networks was performed to identify significant pathways for pneumococcal meningitis. By differential co-expression analysis, the authors constructed the DCN that covered 1809 genes and 3689 interactions. From the DCN, a total of 90 ego genes were identified. Starting from these ego genes, three significant ego networks (Module 19, Module 70 and Module 71) that could predict clinical outcomes for pneumococcal meningitis were identified by EgoNet algorithm, and the corresponding ego genes were GMNN, MAD2L1 and TPX2, respectively. Pathway analysis showed that these three ego networks were related to CDT1 association with the CDC6:ORC:origin complex, inactivation of APC/C via direct inhibition of the APC/C complex pathway, and DNA strand elongation, respectively. The authors successfully screened three significant ego modules which could accurately predict the clinical outcomes for pneumococcal meningitis and might play important roles in host response to pathogen infection in pneumococcal meningitis.

A database of human genes and a gene network involved in response to tick-borne encephalitis virus infection.

PubMed

Ignatieva, Elena V; Igoshin, Alexander V; Yudin, Nikolay S

2017-12-28

Tick-borne encephalitis is caused by the neurotropic, positive-sense RNA virus, tick-borne encephalitis virus (TBEV). TBEV infection can lead to a variety of clinical manifestations ranging from slight fever to severe neurological illness. Very little is known about genetic factors predisposing to severe forms of disease caused by TBEV. The aims of the study were to compile a catalog of human genes involved in response to TBEV infection and to rank genes from the catalog based on the number of neighbors in the network of pairwise interactions involving these genes and TBEV RNA or proteins. Based on manual review and curation of scientific publications a catalog comprising 140 human genes involved in response to TBEV infection was developed. To provide access to data on all genes, the TBEVhostDB web resource ( http://icg.nsc.ru/TBEVHostDB/ ) was created. We reconstructed a network formed by pairwise interactions between TBEV virion itself, viral RNA and viral proteins and 140 genes/proteins from TBEVHostDB. Genes were ranked according to the number of interactions in the network. Two genes/proteins (CCR5 and IFNAR1) that had maximal number of interactions were revealed. It was found that the subnetworks formed by CCR5 and IFNAR1 and their neighbors were a fragments of two key pathways functioning during the course of tick-borne encephalitis: (1) the attenuation of interferon-I signaling pathway by the TBEV NS5 protein that targeted peptidase D; (2) proinflammation and tissue damage pathway triggered by chemokine receptor CCR5 interacting with CD4, CCL3, CCL4, CCL2. Among nine genes associated with severe forms of TBEV infection, three genes/proteins (CCR5, IL10, ARID1B) were found to have protein-protein interactions within the network, and two genes/proteins (IFNL3 and the IL10, that was just mentioned) were up- or down-regulated in response to TBEV infection. Based on this finding, potential mechanisms for participation of CCR5, IL10, ARID1B, and IFNL3 in the host response to TBEV infection were suggested. A database comprising 140 human genes involved in response to TBEV infection was compiled and the TBEVHostDB web resource, providing access to all genes was created. This is the first effort of integrating and unifying data on genetic factors that may predispose to severe forms of diseases caused by TBEV. The TBEVHostDB could potentially be used for assessment of risk factors for severe forms of tick-borne encephalitis and for the design of personalized pharmacological strategies for the treatment of TBEV infection.

Visualization of protein interaction networks: problems and solutions

PubMed Central

2013-01-01

Background Visualization concerns the representation of data visually and is an important task in scientific research. Protein-protein interactions (PPI) are discovered using either wet lab techniques, such mass spectrometry, or in silico predictions tools, resulting in large collections of interactions stored in specialized databases. The set of all interactions of an organism forms a protein-protein interaction network (PIN) and is an important tool for studying the behaviour of the cell machinery. Since graphic representation of PINs may highlight important substructures, e.g. protein complexes, visualization is more and more used to study the underlying graph structure of PINs. Although graphs are well known data structures, there are different open problems regarding PINs visualization: the high number of nodes and connections, the heterogeneity of nodes (proteins) and edges (interactions), the possibility to annotate proteins and interactions with biological information extracted by ontologies (e.g. Gene Ontology) that enriches the PINs with semantic information, but complicates their visualization. Methods In these last years many software tools for the visualization of PINs have been developed. Initially thought for visualization only, some of them have been successively enriched with new functions for PPI data management and PIN analysis. The paper analyzes the main software tools for PINs visualization considering four main criteria: (i) technology, i.e. availability/license of the software and supported OS (Operating System) platforms; (ii) interoperability, i.e. ability to import/export networks in various formats, ability to export data in a graphic format, extensibility of the system, e.g. through plug-ins; (iii) visualization, i.e. supported layout and rendering algorithms and availability of parallel implementation; (iv) analysis, i.e. availability of network analysis functions, such as clustering or mining of the graph, and the possibility to interact with external databases. Results Currently, many tools are available and it is not easy for the users choosing one of them. Some tools offer sophisticated 2D and 3D network visualization making available many layout algorithms, others tools are more data-oriented and support integration of interaction data coming from different sources and data annotation. Finally, some specialistic tools are dedicated to the analysis of pathways and cellular processes and are oriented toward systems biology studies, where the dynamic aspects of the processes being studied are central. Conclusion A current trend is the deployment of open, extensible visualization tools (e.g. Cytoscape), that may be incrementally enriched by the interactomics community with novel and more powerful functions for PIN analysis, through the development of plug-ins. On the other hand, another emerging trend regards the efficient and parallel implementation of the visualization engine that may provide high interactivity and near real-time response time, as in NAViGaTOR. From a technological point of view, open-source, free and extensible tools, like Cytoscape, guarantee a long term sustainability due to the largeness of the developers and users communities, and provide a great flexibility since new functions are continuously added by the developer community through new plug-ins, but the emerging parallel, often closed-source tools like NAViGaTOR, can offer near real-time response time also in the analysis of very huge PINs. PMID:23368786

Correspondence of the brain's functional architecture during activation and rest

PubMed Central

Smith, Stephen M.; Fox, Peter T.; Miller, Karla L.; Glahn, David C.; Fox, P. Mickle; Mackay, Clare E.; Filippini, Nicola; Watkins, Kate E.; Toro, Roberto; Laird, Angela R.; Beckmann, Christian F.

2009-01-01

Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is “at rest.” In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically “active” even when at “rest.” PMID:19620724

APID interactomes: providing proteome-based interactomes with controlled quality for multiple species and derived networks

PubMed Central

Alonso-López, Diego; Gutiérrez, Miguel A.; Lopes, Katia P.; Prieto, Carlos; Santamaría, Rodrigo; De Las Rivas, Javier

2016-01-01

APID (Agile Protein Interactomes DataServer) is an interactive web server that provides unified generation and delivery of protein interactomes mapped to their respective proteomes. This resource is a new, fully redesigned server that includes a comprehensive collection of protein interactomes for more than 400 organisms (25 of which include more than 500 interactions) produced by the integration of only experimentally validated protein–protein physical interactions. For each protein–protein interaction (PPI) the server includes currently reported information about its experimental validation to allow selection and filtering at different quality levels. As a whole, it provides easy access to the interactomes from specific species and includes a global uniform compendium of 90,379 distinct proteins and 678,441 singular interactions. APID integrates and unifies PPIs from major primary databases of molecular interactions, from other specific repositories and also from experimentally resolved 3D structures of protein complexes where more than two proteins were identified. For this purpose, a collection of 8,388 structures were analyzed to identify specific PPIs. APID also includes a new graph tool (based on Cytoscape.js) for visualization and interactive analyses of PPI networks. The server does not require registration and it is freely available for use at http://apid.dep.usal.es. PMID:27131791

Evolution and function of CAG/polyglutamine repeats in protein–protein interaction networks

PubMed Central

Schaefer, Martin H.; Wanker, Erich E.; Andrade-Navarro, Miguel A.

2012-01-01

Expanded runs of consecutive trinucleotide CAG repeats encoding polyglutamine (polyQ) stretches are observed in the genes of a large number of patients with different genetic diseases such as Huntington's and several Ataxias. Protein aggregation, which is a key feature of most of these diseases, is thought to be triggered by these expanded polyQ sequences in disease-related proteins. However, polyQ tracts are a normal feature of many human proteins, suggesting that they have an important cellular function. To clarify the potential function of polyQ repeats in biological systems, we systematically analyzed available information stored in sequence and protein interaction databases. By integrating genomic, phylogenetic, protein interaction network and functional information, we obtained evidence that polyQ tracts in proteins stabilize protein interactions. This happens most likely through structural changes whereby the polyQ sequence extends a neighboring coiled-coil region to facilitate its interaction with a coiled-coil region in another protein. Alteration of this important biological function due to polyQ expansion results in gain of abnormal interactions, leading to pathological effects like protein aggregation. Our analyses suggest that research on polyQ proteins should shift focus from expanded polyQ proteins into the characterization of the influence of the wild-type polyQ on protein interactions. PMID:22287626

Estimation of the proteomic cancer co-expression sub networks by using association estimators.

PubMed

Erdoğan, Cihat; Kurt, Zeyneb; Diri, Banu

2017-01-01

In this study, the association estimators, which have significant influences on the gene network inference methods and used for determining the molecular interactions, were examined within the co-expression network inference concept. By using the proteomic data from five different cancer types, the hub genes/proteins within the disease-associated gene-gene/protein-protein interaction sub networks were identified. Proteomic data from various cancer types is collected from The Cancer Proteome Atlas (TCPA). Correlation and mutual information (MI) based nine association estimators that are commonly used in the literature, were compared in this study. As the gold standard to measure the association estimators' performance, a multi-layer data integration platform on gene-disease associations (DisGeNET) and the Molecular Signatures Database (MSigDB) was used. Fisher's exact test was used to evaluate the performance of the association estimators by comparing the created co-expression networks with the disease-associated pathways. It was observed that the MI based estimators provided more successful results than the Pearson and Spearman correlation approaches, which are used in the estimation of biological networks in the weighted correlation network analysis (WGCNA) package. In correlation-based methods, the best average success rate for five cancer types was 60%, while in MI-based methods the average success ratio was 71% for James-Stein Shrinkage (Shrink) and 64% for Schurmann-Grassberger (SG) association estimator, respectively. Moreover, the hub genes and the inferred sub networks are presented for the consideration of researchers and experimentalists.

Estimation of the proteomic cancer co-expression sub networks by using association estimators

PubMed Central

Kurt, Zeyneb; Diri, Banu

2017-01-01

In this study, the association estimators, which have significant influences on the gene network inference methods and used for determining the molecular interactions, were examined within the co-expression network inference concept. By using the proteomic data from five different cancer types, the hub genes/proteins within the disease-associated gene-gene/protein-protein interaction sub networks were identified. Proteomic data from various cancer types is collected from The Cancer Proteome Atlas (TCPA). Correlation and mutual information (MI) based nine association estimators that are commonly used in the literature, were compared in this study. As the gold standard to measure the association estimators’ performance, a multi-layer data integration platform on gene-disease associations (DisGeNET) and the Molecular Signatures Database (MSigDB) was used. Fisher's exact test was used to evaluate the performance of the association estimators by comparing the created co-expression networks with the disease-associated pathways. It was observed that the MI based estimators provided more successful results than the Pearson and Spearman correlation approaches, which are used in the estimation of biological networks in the weighted correlation network analysis (WGCNA) package. In correlation-based methods, the best average success rate for five cancer types was 60%, while in MI-based methods the average success ratio was 71% for James-Stein Shrinkage (Shrink) and 64% for Schurmann-Grassberger (SG) association estimator, respectively. Moreover, the hub genes and the inferred sub networks are presented for the consideration of researchers and experimentalists. PMID:29145449

Inferring Network Controls from Topology Using the Chomp Database

DTIC Science & Technology

2015-12-03

AFRL-AFOSR-VA-TR-2016-0033 INFERRING NETWORK CONTROLS FROM TOPOLOGY USING THE CHOMP DATABASE John Harer DUKE UNIVERSITY Final Report 12/03/2015...INFERRING NETWORK CONTROLS FROM TOPOLOGY USING THE CHOMP DATABASE 5a. CONTRACT NUMBER 5b. GRANT NUMBER FA9550-10-1-0436 5c. PROGRAM ELEMENT NUMBER 6...area of Topological Data Analysis (TDA) and it’s application to dynamical systems. The role of this work in the Complex Networks program is based on

Regulatory interactions between long noncoding RNA LINC00968 and miR-9-3p in non-small cell lung cancer: A bioinformatic analysis based on miRNA microarray, GEO and TCGA.

PubMed

Li, Dong-Yao; Chen, Wen-Jie; Shang, Jun; Chen, Gang; Li, Shi-Kang

2018-06-01

Long non-coding RNAs (lncRNAs) have been demonstrated to mediate carcinogenesis in various types of cancer. However, the regulatory role of lncRNA LINC00968 in lung adenocarcinoma remains unclear. The microRNA (miRNA) expression in LINC00968-overexpressing human lung adenocarcinoma A549 cells was detected using miRNA microarray analysis. miR-9-3p was selected for further analysis, and its expression was verified in the Gene Expression Omnibus (GEO) database. In addition, the regulatory axis of LINC00968 was validated using The Cancer Genome Atlas (TCGA) database. Results of the GEO database indicated miR-9-3p expression in lung adenocarcinoma was significantly higher compared with normal tissues. Functional enrichment analyses of the target genes of miR-9-3p indicated protein binding and the AMP-activated protein kinase pathway were the most enriched Gene Ontology and KEGG terms, respectively. Combining target genes with the correlated genes of LINC00968 and miR-9-3p, 120 objective genes were obtained, which were used to construct a protein-protein interaction (PPI) network. Cyclin A2 (CCNA2) was identified to have a vital role in the PPI network. Significant correlations were detected between LINC00968, miR-9-3p and CCNA2 in lung adenocarcinoma. The LINC00968/miR-9-3p/CCNA2 regulatory axis provides a new foundation for further evaluating the regulatory mechanisms of LINC00968 in lung adenocarcinoma.

PoMaMo--a comprehensive database for potato genome data.

PubMed

Meyer, Svenja; Nagel, Axel; Gebhardt, Christiane

2005-01-01

A database for potato genome data (PoMaMo, Potato Maps and More) was established. The database contains molecular maps of all twelve potato chromosomes with about 1000 mapped elements, sequence data, putative gene functions, results from BLAST analysis, SNP and InDel information from different diploid and tetraploid potato genotypes, publication references, links to other public databases like GenBank (http://www.ncbi.nlm.nih.gov/) or SGN (Solanaceae Genomics Network, http://www.sgn.cornell.edu/), etc. Flexible search and data visualization interfaces enable easy access to the data via internet (https://gabi.rzpd.de/PoMaMo.html). The Java servlet tool YAMB (Yet Another Map Browser) was designed to interactively display chromosomal maps. Maps can be zoomed in and out, and detailed information about mapped elements can be obtained by clicking on an element of interest. The GreenCards interface allows a text-based data search by marker-, sequence- or genotype name, by sequence accession number, gene function, BLAST Hit or publication reference. The PoMaMo database is a comprehensive database for different potato genome data, and to date the only database containing SNP and InDel data from diploid and tetraploid potato genotypes.

PoMaMo—a comprehensive database for potato genome data

PubMed Central

Meyer, Svenja; Nagel, Axel; Gebhardt, Christiane

2005-01-01

A database for potato genome data (PoMaMo, Potato Maps and More) was established. The database contains molecular maps of all twelve potato chromosomes with about 1000 mapped elements, sequence data, putative gene functions, results from BLAST analysis, SNP and InDel information from different diploid and tetraploid potato genotypes, publication references, links to other public databases like GenBank (http://www.ncbi.nlm.nih.gov/) or SGN (Solanaceae Genomics Network, http://www.sgn.cornell.edu/), etc. Flexible search and data visualization interfaces enable easy access to the data via internet (https://gabi.rzpd.de/PoMaMo.html). The Java servlet tool YAMB (Yet Another Map Browser) was designed to interactively display chromosomal maps. Maps can be zoomed in and out, and detailed information about mapped elements can be obtained by clicking on an element of interest. The GreenCards interface allows a text-based data search by marker-, sequence- or genotype name, by sequence accession number, gene function, BLAST Hit or publication reference. The PoMaMo database is a comprehensive database for different potato genome data, and to date the only database containing SNP and InDel data from diploid and tetraploid potato genotypes. PMID:15608284

Coactivation of cognitive control networks during task switching.

PubMed

Yin, Shouhang; Deák, Gedeon; Chen, Antao

2018-01-01

The ability to flexibly switch between tasks is considered an important component of cognitive control that involves frontal and parietal cortical areas. The present study was designed to characterize network dynamics across multiple brain regions during task switching. Functional magnetic resonance images (fMRI) were captured during a standard rule-switching task to identify switching-related brain regions. Multiregional psychophysiological interaction (PPI) analysis was used to examine effective connectivity between these regions. During switching trials, behavioral performance declined and activation of a generic cognitive control network increased. Concurrently, task-related connectivity increased within and between cingulo-opercular and fronto-parietal cognitive control networks. Notably, the left inferior frontal junction (IFJ) was most consistently coactivated with the 2 cognitive control networks. Furthermore, switching-dependent effective connectivity was negatively correlated with behavioral switch costs. The strength of effective connectivity between left IFJ and other regions in the networks predicted individual differences in switch costs. Task switching was supported by coactivated connections within cognitive control networks, with left IFJ potentially acting as a key hub between the fronto-parietal and cingulo-opercular networks. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Reconstituting protein interaction networks using parameter-dependent domain-domain interactions

PubMed Central

2013-01-01

Background We can describe protein-protein interactions (PPIs) as sets of distinct domain-domain interactions (DDIs) that mediate the physical interactions between proteins. Experimental data confirm that DDIs are more consistent than their corresponding PPIs, lending support to the notion that analyses of DDIs may improve our understanding of PPIs and lead to further insights into cellular function, disease, and evolution. However, currently available experimental DDI data cover only a small fraction of all existing PPIs and, in the absence of structural data, determining which particular DDI mediates any given PPI is a challenge. Results We present two contributions to the field of domain interaction analysis. First, we introduce a novel computational strategy to merge domain annotation data from multiple databases. We show that when we merged yeast domain annotations from six annotation databases we increased the average number of domains per protein from 1.05 to 2.44, bringing it closer to the estimated average value of 3. Second, we introduce a novel computational method, parameter-dependent DDI selection (PADDS), which, given a set of PPIs, extracts a small set of domain pairs that can reconstruct the original set of protein interactions, while attempting to minimize false positives. Based on a set of PPIs from multiple organisms, our method extracted 27% more experimentally detected DDIs than existing computational approaches. Conclusions We have provided a method to merge domain annotation data from multiple sources, ensuring large and consistent domain annotation for any given organism. Moreover, we provided a method to extract a small set of DDIs from the underlying set of PPIs and we showed that, in contrast to existing approaches, our method was not biased towards DDIs with low or high occurrence counts. Finally, we used these two methods to highlight the influence of the underlying annotation density on the characteristics of extracted DDIs. Although increased annotations greatly expanded the possible DDIs, the lack of knowledge of the true biological false positive interactions still prevents an unambiguous assignment of domain interactions responsible for all protein network interactions. Executable files and examples are given at: http://www.bhsai.org/downloads/padds/ PMID:23651452

CoryneRegNet 3.0--an interactive systems biology platform for the analysis of gene regulatory networks in corynebacteria and Escherichia coli.

PubMed

Baumbach, Jan; Wittkop, Tobias; Rademacher, Katrin; Rahmann, Sven; Brinkrolf, Karina; Tauch, Andreas

2007-04-30

CoryneRegNet is an ontology-based data warehouse for the reconstruction and visualization of transcriptional regulatory interactions in prokaryotes. To extend the biological content of CoryneRegNet, we added comprehensive data on transcriptional regulations in the model organism Escherichia coli K-12, originally deposited in the international reference database RegulonDB. The enhanced web interface of CoryneRegNet offers several types of search options. The results of a search are displayed in a table-based style and include a visualization of the genetic organization of the respective gene region. Information on DNA binding sites of transcriptional regulators is depicted by sequence logos. The results can also be displayed by several layouters implemented in the graphical user interface GraphVis, allowing, for instance, the visualization of genome-wide network reconstructions and the homology-based inter-species comparison of reconstructed gene regulatory networks. In an application example, we compare the composition of the gene regulatory networks involved in the SOS response of E. coli and Corynebacterium glutamicum. CoryneRegNet is available at the following URL: http://www.cebitec.uni-bielefeld.de/groups/gi/software/coryneregnet/.

NetCoffee: a fast and accurate global alignment approach to identify functionally conserved proteins in multiple networks.

PubMed

Hu, Jialu; Kehr, Birte; Reinert, Knut

2014-02-15

Owing to recent advancements in high-throughput technologies, protein-protein interaction networks of more and more species become available in public databases. The question of how to identify functionally conserved proteins across species attracts a lot of attention in computational biology. Network alignments provide a systematic way to solve this problem. However, most existing alignment tools encounter limitations in tackling this problem. Therefore, the demand for faster and more efficient alignment tools is growing. We present a fast and accurate algorithm, NetCoffee, which allows to find a global alignment of multiple protein-protein interaction networks. NetCoffee searches for a global alignment by maximizing a target function using simulated annealing on a set of weighted bipartite graphs that are constructed using a triplet approach similar to T-Coffee. To assess its performance, NetCoffee was applied to four real datasets. Our results suggest that NetCoffee remedies several limitations of previous algorithms, outperforms all existing alignment tools in terms of speed and nevertheless identifies biologically meaningful alignments. The source code and data are freely available for download under the GNU GPL v3 license at https://code.google.com/p/netcoffee/.

Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content

PubMed Central

Kling, Teresia; Johansson, Patrik; Sanchez, José; Marinescu, Voichita D.; Jörnsten, Rebecka; Nelander, Sven

2015-01-01

Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools and resources are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as The Cancer Genome Atlas (TCGA). To address this, we describe a new data driven modeling method, based on generalized Sparse Inverse Covariance Selection (SICS). The method integrates genetic, epigenetic and transcriptional data from multiple cancers, to define links that are present in multiple cancers, a subset of cancers, or a single cancer. It is shown to be statistically robust and effective at detecting direct pathway links in data from TCGA. To facilitate interpretation of the results, we introduce a publicly accessible tool (cancerlandscapes.org), in which the derived networks are explored as interactive web content, linked to several pathway and pharmacological databases. To evaluate the performance of the method, we constructed a model for eight TCGA cancers, using data from 3900 patients. The model rediscovered known mechanisms and contained interesting predictions. Possible applications include prediction of regulatory relationships, comparison of network modules across multiple forms of cancer and identification of drug targets. PMID:25953855

An open source web interface for linking models to infrastructure system databases

NASA Astrophysics Data System (ADS)

Knox, S.; Mohamed, K.; Harou, J. J.; Rheinheimer, D. E.; Medellin-Azuara, J.; Meier, P.; Tilmant, A.; Rosenberg, D. E.

2016-12-01

Models of networked engineered resource systems such as water or energy systems are often built collaboratively with developers from different domains working at different locations. These models can be linked to large scale real world databases, and they are constantly being improved and extended. As the development and application of these models becomes more sophisticated, and the computing power required for simulations and/or optimisations increases, so has the need for online services and tools which enable the efficient development and deployment of these models. Hydra Platform is an open source, web-based data management system, which allows modellers of network-based models to remotely store network topology and associated data in a generalised manner, allowing it to serve multiple disciplines. Hydra Platform uses a web API using JSON to allow external programs (referred to as `Apps') to interact with its stored networks and perform actions such as importing data, running models, or exporting the networks to different formats. Hydra Platform supports multiple users accessing the same network and has a suite of functions for managing users and data. We present ongoing development in Hydra Platform, the Hydra Web User Interface, through which users can collaboratively manage network data and models in a web browser. The web interface allows multiple users to graphically access, edit and share their networks, run apps and view results. Through apps, which are located on the server, the web interface can give users access to external data sources and models without the need to install or configure any software. This also ensures model results can be reproduced by removing platform or version dependence. Managing data and deploying models via the web interface provides a way for multiple modellers to collaboratively manage data, deploy and monitor model runs and analyse results.

Rigid-Docking Approaches to Explore Protein-Protein Interaction Space.

PubMed

Matsuzaki, Yuri; Uchikoga, Nobuyuki; Ohue, Masahito; Akiyama, Yutaka

Protein-protein interactions play core roles in living cells, especially in the regulatory systems. As information on proteins has rapidly accumulated on publicly available databases, much effort has been made to obtain a better picture of protein-protein interaction networks using protein tertiary structure data. Predicting relevant interacting partners from their tertiary structure is a challenging task and computer science methods have the potential to assist with this. Protein-protein rigid docking has been utilized by several projects, docking-based approaches having the advantages that they can suggest binding poses of predicted binding partners which would help in understanding the interaction mechanisms and that comparing docking results of both non-binders and binders can lead to understanding the specificity of protein-protein interactions from structural viewpoints. In this review we focus on explaining current computational prediction methods to predict pairwise direct protein-protein interactions that form protein complexes.

External access to ALICE controls conditions data

NASA Astrophysics Data System (ADS)

Jadlovský, J.; Jadlovská, A.; Sarnovský, J.; Jajčišin, Š.; Čopík, M.; Jadlovská, S.; Papcun, P.; Bielek, R.; Čerkala, J.; Kopčík, M.; Chochula, P.; Augustinus, A.

2014-06-01

ALICE Controls data produced by commercial SCADA system WINCCOA is stored in ORACLE database on the private experiment network. The SCADA system allows for basic access and processing of the historical data. More advanced analysis requires tools like ROOT and needs therefore a separate access method to the archives. The present scenario expects that detector experts create simple WINCCOA scripts, which retrieves and stores data in a form usable for further studies. This relatively simple procedure generates a lot of administrative overhead - users have to request the data, experts needed to run the script, the results have to be exported outside of the experiment network. The new mechanism profits from database replica, which is running on the CERN campus network. Access to this database is not restricted and there is no risk of generating a heavy load affecting the operation of the experiment. The developed tools presented in this paper allow for access to this data. The users can use web-based tools to generate the requests, consisting of the data identifiers and period of time of interest. The administrators maintain full control over the data - an authorization and authentication mechanism helps to assign privileges to selected users and restrict access to certain groups of data. Advanced caching mechanism allows the user to profit from the presence of already processed data sets. This feature significantly reduces the time required for debugging as the retrieval of raw data can last tens of minutes. A highly configurable client allows for information retrieval bypassing the interactive interface. This method is for example used by ALICE Offline to extract operational conditions after a run is completed. Last but not least, the software can be easily adopted to any underlying database structure and is therefore not limited to WINCCOA.

Identification of potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma.

PubMed

Pan, Yue; Lu, Lingyun; Chen, Junquan; Zhong, Yong; Dai, Zhehao

2018-01-01

This study aimed to identify potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma by comprehensive bioinformatics analysis. Data of gene expression profiles (GSE28424) and miRNA expression profiles (GSE28423) were downloaded from GEO database. The differentially expressed genes (DEGs) and miRNAs (DEMIs) were obtained by R Bioconductor packages. Functional and enrichment analyses of selected genes were performed using DAVID database. Protein-protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. The relationships among the DEGs and module in PPI network were analyzed by plug-in NetworkAnalyzer and MCODE seperately. Through the TargetScan and comparing target genes with DEGs, the miRNA-mRNA regulation network was established. Totally 346 DEGs and 90 DEMIs were found to be differentially expressed. These DEGs were enriched in biological processes and KEGG pathway of inflammatory immune response. 25 genes in the PPI network were selected as hub genes. Top 10 hub genes were TYROBP, HLA-DRA, VWF, PPBP, SERPING1, HLA-DPA1, SERPINA1, KIF20A, FERMT3, HLA-E. PPI network of DEGs followed a pattern of power law network and met the characteristics of small-world network. MCODE analysis identified 4 clusters and the most significant cluster consisted of 11 nodes and 55 edges. SEPP1, CKS2, TCAP, BPI were identified as the seed genes in their own clusters, respectively. The miRNA-mRNA regulation network which was composed of 89 pairs was established. MiR-210 had the highest connectivity with 12 target genes. Among the predicted target of MiR-96, HLA-DPA1 and TYROBP were the hub genes. Our study indicated possible differentially expressed genes and miRNA, and microRNA-mRNA negative regulatory networks in osteosarcoma by bioinformatics analysis, which may provide novel insights for unraveling pathogenesis of osteosarcoma.

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

Hamdani, Hazrina Yusof, E-mail: hazrina@mfrlab.org; Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam, Kepala Batas; Artymiuk, Peter J., E-mail: p.artymiuk@sheffield.ac.uk

A fundamental understanding of the atomic level interactions in ribonucleic acid (RNA) and how they contribute towards RNA architecture is an important knowledge platform to develop through the discovery of motifs from simple arrangements base pairs, to more complex arrangements such as triples and larger patterns involving non-standard interactions. The network of hydrogen bond interactions is important in connecting bases to form potential tertiary motifs. Therefore, there is an urgent need for the development of automated methods for annotating RNA 3D structures based on hydrogen bond interactions. COnnection tables Graphs for Nucleic ACids (COGNAC) is automated annotation system using graphmore » theoretical approaches that has been developed for the identification of RNA 3D motifs. This program searches for patterns in the unbroken networks of hydrogen bonds for RNA structures and capable of annotating base pairs and higher-order base interactions, which ranges from triples to sextuples. COGNAC was able to discover 22 out of 32 quadruples occurrences of the Haloarcula marismortui large ribosomal subunit (PDB ID: 1FFK) and two out of three occurrences of quintuple interaction reported by the non-canonical interactions in RNA (NCIR) database. These and several other interactions of interest will be discussed in this paper. These examples demonstrate that the COGNAC program can serve as an automated annotation system that can be used to annotate conserved base-base interactions and could be added as additional information to established RNA secondary structure prediction methods.« less

Dynamic phenomena and human activity in an artificial society

NASA Astrophysics Data System (ADS)

Grabowski, A.; Kruszewska, N.; Kosiński, R. A.

2008-12-01

We study dynamic phenomena in a large social network of nearly 3×104 individuals who interact in the large virtual world of a massive multiplayer online role playing game. On the basis of a database received from the online game server, we examine the structure of the friendship network and human dynamics. To investigate the relation between networks of acquaintances in virtual and real worlds, we carried out a survey among the players. We show that, even though the virtual network did not develop as a growing graph of an underlying network of social acquaintances in the real world, it influences it. Furthermore we find very interesting scaling laws concerning human dynamics. Our research shows how long people are interested in a single task and how much time they devote to it. Surprisingly, exponent values in both cases are close to -1 . We calculate the activity of individuals, i.e., the relative time daily devoted to interactions with others in the artificial society. Our research shows that the distribution of activity is not uniform and is highly correlated with the degree of the node, and that such human activity has a significant influence on dynamic phenomena, e.g., epidemic spreading and rumor propagation, in complex networks. We find that spreading is accelerated (an epidemic) or decelerated (a rumor) as a result of superspreaders’ various behavior.

Inborn errors of metabolism and the human interactome: a systems medicine approach.

PubMed

Woidy, Mathias; Muntau, Ania C; Gersting, Søren W

2018-02-05

The group of inborn errors of metabolism (IEM) displays a marked heterogeneity and IEM can affect virtually all functions and organs of the human organism; however, IEM share that their associated proteins function in metabolism. Most proteins carry out cellular functions by interacting with other proteins, and thus are organized in biological networks. Therefore, diseases are rarely the consequence of single gene mutations but of the perturbations caused in the related cellular network. Systematic approaches that integrate multi-omics and database information into biological networks have successfully expanded our knowledge of complex disorders but network-based strategies have been rarely applied to study IEM. We analyzed IEM on a proteome scale and found that IEM-associated proteins are organized as a network of linked modules within the human interactome of protein interactions, the IEM interactome. Certain IEM disease groups formed self-contained disease modules, which were highly interlinked. On the other hand, we observed disease modules consisting of proteins from many different disease groups in the IEM interactome. Moreover, we explored the overlap between IEM and non-IEM disease genes and applied network medicine approaches to investigate shared biological pathways, clinical signs and symptoms, and links to drug targets. The provided resources may help to elucidate the molecular mechanisms underlying new IEM, to uncover the significance of disease-associated mutations, to identify new biomarkers, and to develop novel therapeutic strategies.

SolCyc: a database hub at the Sol Genomics Network (SGN) for the manual curation of metabolic networks in Solanum and Nicotiana specific databases

PubMed Central

Foerster, Hartmut; Bombarely, Aureliano; Battey, James N D; Sierro, Nicolas; Ivanov, Nikolai V; Mueller, Lukas A

2018-01-01

Abstract SolCyc is the entry portal to pathway/genome databases (PGDBs) for major species of the Solanaceae family hosted at the Sol Genomics Network. Currently, SolCyc comprises six organism-specific PGDBs for tomato, potato, pepper, petunia, tobacco and one Rubiaceae, coffee. The metabolic networks of those PGDBs have been computationally predicted by the pathologic component of the pathway tools software using the manually curated multi-domain database MetaCyc (http://www.metacyc.org/) as reference. SolCyc has been recently extended by taxon-specific databases, i.e. the family-specific SolanaCyc database, containing only curated data pertinent to species of the nightshade family, and NicotianaCyc, a genus-specific database that stores all relevant metabolic data of the Nicotiana genus. Through manual curation of the published literature, new metabolic pathways have been created in those databases, which are complemented by the continuously updated, relevant species-specific pathways from MetaCyc. At present, SolanaCyc comprises 199 pathways and 29 superpathways and NicotianaCyc accounts for 72 pathways and 13 superpathways. Curator-maintained, taxon-specific databases such as SolanaCyc and NicotianaCyc are characterized by an enrichment of data specific to these taxa and free of falsely predicted pathways. Both databases have been used to update recently created Nicotiana-specific databases for Nicotiana tabacum, Nicotiana benthamiana, Nicotiana sylvestris and Nicotiana tomentosiformis by propagating verifiable data into those PGDBs. In addition, in-depth curation of the pathways in N.tabacum has been carried out which resulted in the elimination of 156 pathways from the 569 pathways predicted by pathway tools. Together, in-depth curation of the predicted pathway network and the supplementation with curated data from taxon-specific databases has substantially improved the curation status of the species–specific N.tabacum PGDB. The implementation of this strategy will significantly advance the curation status of all organism-specific databases in SolCyc resulting in the improvement on database accuracy, data analysis and visualization of biochemical networks in those species. Database URL https://solgenomics.net/tools/solcyc/ PMID:29762652

Overarching framework for data-based modelling

NASA Astrophysics Data System (ADS)

Schelter, Björn; Mader, Malenka; Mader, Wolfgang; Sommerlade, Linda; Platt, Bettina; Lai, Ying-Cheng; Grebogi, Celso; Thiel, Marco

2014-02-01

One of the main modelling paradigms for complex physical systems are networks. When estimating the network structure from measured signals, typically several assumptions such as stationarity are made in the estimation process. Violating these assumptions renders standard analysis techniques fruitless. We here propose a framework to estimate the network structure from measurements of arbitrary non-linear, non-stationary, stochastic processes. To this end, we propose a rigorous mathematical theory that underlies this framework. Based on this theory, we present a highly efficient algorithm and the corresponding statistics that are immediately sensibly applicable to measured signals. We demonstrate its performance in a simulation study. In experiments of transitions between vigilance stages in rodents, we infer small network structures with complex, time-dependent interactions; this suggests biomarkers for such transitions, the key to understand and diagnose numerous diseases such as dementia. We argue that the suggested framework combines features that other approaches followed so far lack.

The Comparative Toxicogenomics Database: update 2017.

PubMed

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

2017-01-04

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

Visualizing and Understanding Socio-Environmental Dynamics in Baltimore

NASA Astrophysics Data System (ADS)

Zaitchik, B. F.; Omeara, K.; Guikema, S.; Scott, A.; Bessho, A.; Logan, T. M.

2015-12-01

The City of Baltimore, like any city, is the sum of its component neighborhoods, institutions, businesses, cultures, and, ultimately, its people. It is also an organism in its own right, with distinct geography, history, infrastructure, and environments that shape its residents even as it is shaped by them. Sometimes these interactions are obvious but often they are not; while basic economic patterns are widely documented, the distribution of socio-spatial and environmental connections often hides below the surface, as does the potential that those connections hold. Here we present results of a collaborative initiative on the geography, design, and policy of socio-environmental dynamics of Baltimore. Geospatial data derived from satellite imagery, demographic databases, social media feeds, infrastructure plans, and in situ environmental networks, among other sources, are applied to generate an interactive portrait of Baltimore City's social, health, and well-being dynamics. The layering of data serves as a platform for visualizing the interconnectedness of the City and as a database for modeling risk interactions, vulnerabilities, and strengths within and between communities. This presentation will provide an overview of project findings and highlight linkages to education and policy.

dictyExpress: a Dictyostelium discoideum gene expression database with an explorative data analysis web-based interface.

PubMed

Rot, Gregor; Parikh, Anup; Curk, Tomaz; Kuspa, Adam; Shaulsky, Gad; Zupan, Blaz

2009-08-25

Bioinformatics often leverages on recent advancements in computer science to support biologists in their scientific discovery process. Such efforts include the development of easy-to-use web interfaces to biomedical databases. Recent advancements in interactive web technologies require us to rethink the standard submit-and-wait paradigm, and craft bioinformatics web applications that share analytical and interactive power with their desktop relatives, while retaining simplicity and availability. We have developed dictyExpress, a web application that features a graphical, highly interactive explorative interface to our database that consists of more than 1000 Dictyostelium discoideum gene expression experiments. In dictyExpress, the user can select experiments and genes, perform gene clustering, view gene expression profiles across time, view gene co-expression networks, perform analyses of Gene Ontology term enrichment, and simultaneously display expression profiles for a selected gene in various experiments. Most importantly, these tasks are achieved through web applications whose components are seamlessly interlinked and immediately respond to events triggered by the user, thus providing a powerful explorative data analysis environment. dictyExpress is a precursor for a new generation of web-based bioinformatics applications with simple but powerful interactive interfaces that resemble that of the modern desktop. While dictyExpress serves mainly the Dictyostelium research community, it is relatively easy to adapt it to other datasets. We propose that the design ideas behind dictyExpress will influence the development of similar applications for other model organisms.

dictyExpress: a Dictyostelium discoideum gene expression database with an explorative data analysis web-based interface

PubMed Central

Rot, Gregor; Parikh, Anup; Curk, Tomaz; Kuspa, Adam; Shaulsky, Gad; Zupan, Blaz

2009-01-01

Background Bioinformatics often leverages on recent advancements in computer science to support biologists in their scientific discovery process. Such efforts include the development of easy-to-use web interfaces to biomedical databases. Recent advancements in interactive web technologies require us to rethink the standard submit-and-wait paradigm, and craft bioinformatics web applications that share analytical and interactive power with their desktop relatives, while retaining simplicity and availability. Results We have developed dictyExpress, a web application that features a graphical, highly interactive explorative interface to our database that consists of more than 1000 Dictyostelium discoideum gene expression experiments. In dictyExpress, the user can select experiments and genes, perform gene clustering, view gene expression profiles across time, view gene co-expression networks, perform analyses of Gene Ontology term enrichment, and simultaneously display expression profiles for a selected gene in various experiments. Most importantly, these tasks are achieved through web applications whose components are seamlessly interlinked and immediately respond to events triggered by the user, thus providing a powerful explorative data analysis environment. Conclusion dictyExpress is a precursor for a new generation of web-based bioinformatics applications with simple but powerful interactive interfaces that resemble that of the modern desktop. While dictyExpress serves mainly the Dictyostelium research community, it is relatively easy to adapt it to other datasets. We propose that the design ideas behind dictyExpress will influence the development of similar applications for other model organisms. PMID:19706156

Freight Transportation Energy Use : Volume 3. Freight Network and Operations Database.

DOT National Transportation Integrated Search

1979-07-01

The data sources, procedures, and assumptions used to generate the TSC national freight network and operations database are documented. National rail, highway, waterway, and pipeline networks are presented, and estimates of facility capacity, travel ...

A Systems Biology Approach to Reveal Putative Host-Derived Biomarkers of Periodontitis by Network Topology Characterization of MMP-REDOX/NO and Apoptosis Integrated Pathways.

PubMed

Zeidán-Chuliá, Fares; Gürsoy, Mervi; Neves de Oliveira, Ben-Hur; Özdemir, Vural; Könönen, Eija; Gürsoy, Ulvi K

2015-01-01

Periodontitis, a formidable global health burden, is a common chronic disease that destroys tooth-supporting tissues. Biomarkers of the early phase of this progressive disease are of utmost importance for global health. In this context, saliva represents a non-invasive biosample. By using systems biology tools, we aimed to (1) identify an integrated interactome between matrix metalloproteinase (MMP)-REDOX/nitric oxide (NO) and apoptosis upstream pathways of periodontal inflammation, and (2) characterize the attendant topological network properties to uncover putative biomarkers to be tested in saliva from patients with periodontitis. Hence, we first generated a protein-protein network model of interactions ("BIOMARK" interactome) by using the STRING 10 database, a search tool for the retrieval of interacting genes/proteins, with "Experiments" and "Databases" as input options and a confidence score of 0.400. Second, we determined the centrality values (closeness, stress, degree or connectivity, and betweenness) for the "BIOMARK" members by using the Cytoscape software. We found Ubiquitin C (UBC), Jun proto-oncogene (JUN), and matrix metalloproteinase-14 (MMP14) as the most central hub- and non-hub-bottlenecks among the 211 genes/proteins of the whole interactome. We conclude that UBC, JUN, and MMP14 are likely an optimal candidate group of host-derived biomarkers, in combination with oral pathogenic bacteria-derived proteins, for detecting periodontitis at its early phase by using salivary samples from patients. These findings therefore have broader relevance for systems medicine in global health as well.

Identification of Biological Targets of Therapeutic Intervention for Hepatocellular Carcinoma by Integrated Bioinformatical Analysis.

PubMed

Hu, Wei Qi; Wang, Wei; Fang, Di Long; Yin, Xue Feng

2018-05-24

BACKGROUND We screened the potential molecular targets and investigated the molecular mechanisms of hepatocellular carcinoma (HCC). MATERIAL AND METHODS Microarray data of GSE47786, including the 40 μM berberine-treated HepG2 human hepatoma cell line and 0.08% DMSO-treated as control cells samples, was downloaded from the GEO database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed; the protein-protein interaction (PPI) networks were constructed using STRING database and Cytoscape; the genetic alteration, neighboring genes networks, and survival analysis of hub genes were explored by cBio portal; and the expression of mRNA level of hub genes was obtained from the Oncomine databases. RESULTS A total of 56 upregulated and 8 downregulated DEGs were identified. The GO analysis results were significantly enriched in cell-cycle arrest, regulation of transcription, DNA-dependent, protein amino acid phosphorylation, cell cycle, and apoptosis. The KEGG pathway analysis showed that DEGs were enriched in MAPK signaling pathway, ErbB signaling pathway, and p53 signaling pathway. JUN, EGR1, MYC, and CDKN1A were identified as hub genes in PPI networks. The genetic alteration of hub genes was mainly concentrated in amplification. TP53, NDRG1, and MAPK15 were found in neighboring genes networks. Altered genes had worse overall survival and disease-free survival than unaltered genes. The expressions of EGR1, MYC, and CDKN1A were significantly increased, but expression of JUN was not, in the Roessler Liver datasets. CONCLUSIONS We found that JUN, EGR1, MYC, and CDKN1A might be used as diagnostic and therapeutic molecular biomarkers and broaden our understanding of the molecular mechanisms of HCC.

Prediction of Ras-effector interactions using position energy matrices.

PubMed

Kiel, Christina; Serrano, Luis

2007-09-01

One of the more challenging problems in biology is to determine the cellular protein interaction network. Progress has been made to predict protein-protein interactions based on structural information, assuming that structural similar proteins interact in a similar way. In a previous publication, we have determined a genome-wide Ras-effector interaction network based on homology models, with a high accuracy of predicting binding and non-binding domains. However, for a prediction on a genome-wide scale, homology modelling is a time-consuming process. Therefore, we here successfully developed a faster method using position energy matrices, where based on different Ras-effector X-ray template structures, all amino acids in the effector binding domain are sequentially mutated to all other amino acid residues and the effect on binding energy is calculated. Those pre-calculated matrices can then be used to score for binding any Ras or effector sequences. Based on position energy matrices, the sequences of putative Ras-binding domains can be scanned quickly to calculate an energy sum value. By calibrating energy sum values using quantitative experimental binding data, thresholds can be defined and thus non-binding domains can be excluded quickly. Sequences which have energy sum values above this threshold are considered to be potential binding domains, and could be further analysed using homology modelling. This prediction method could be applied to other protein families sharing conserved interaction types, in order to determine in a fast way large scale cellular protein interaction networks. Thus, it could have an important impact on future in silico structural genomics approaches, in particular with regard to increasing structural proteomics efforts, aiming to determine all possible domain folds and interaction types. All matrices are deposited in the ADAN database (http://adan-embl.ibmc.umh.es/). Supplementary data are available at Bioinformatics online.

The database of the Nikolaev Astronomical Observatory as a unit of an international virtual observatory

NASA Astrophysics Data System (ADS)

Protsyuk, Yu.; Pinigin, G.; Shulga, A.

2005-06-01

Results of the development and organization of the digital database of the Nikolaev Astronomical Observatory (NAO) are presented. At present, three telescopes are connected to the local area network of NAO. All the data obtained, and results of data processing are entered into the common database of NAO. The daily average volume of new astronomical information obtained from the CCD instruments ranges from 300 MB up to 2 GB, depending on the purposes and conditions of observations. The overwhelming majority of the data are stored in the FITS format. Development and further improvement of storage standards, procedures of data handling and data processing are being carried out. It is planned to create an astronomical web portal with the possibility to have interactive access to databases and telescopes. In the future, this resource may become a part of an international virtual observatory. There are the prototypes of search tools with the use of PHP and MySQL. Efforts for getting more links to the Internet are being made.

Multiple Representations-Based Face Sketch-Photo Synthesis.

PubMed

Peng, Chunlei; Gao, Xinbo; Wang, Nannan; Tao, Dacheng; Li, Xuelong; Li, Jie

2016-11-01

Face sketch-photo synthesis plays an important role in law enforcement and digital entertainment. Most of the existing methods only use pixel intensities as the feature. Since face images can be described using features from multiple aspects, this paper presents a novel multiple representations-based face sketch-photo-synthesis method that adaptively combines multiple representations to represent an image patch. In particular, it combines multiple features from face images processed using multiple filters and deploys Markov networks to exploit the interacting relationships between the neighboring image patches. The proposed framework could be solved using an alternating optimization strategy and it normally converges in only five outer iterations in the experiments. Our experimental results on the Chinese University of Hong Kong (CUHK) face sketch database, celebrity photos, CUHK Face Sketch FERET Database, IIIT-D Viewed Sketch Database, and forensic sketches demonstrate the effectiveness of our method for face sketch-photo synthesis. In addition, cross-database and database-dependent style-synthesis evaluations demonstrate the generalizability of this novel method and suggest promising solutions for face identification in forensic science.

MOSAIC: a chemical-genetic interaction data repository and web resource for exploring chemical modes of action.

PubMed

Nelson, Justin; Simpkins, Scott W; Safizadeh, Hamid; Li, Sheena C; Piotrowski, Jeff S; Hirano, Hiroyuki; Yashiroda, Yoko; Osada, Hiroyuki; Yoshida, Minoru; Boone, Charles; Myers, Chad L

2018-04-01

Chemical-genomic approaches that map interactions between small molecules and genetic perturbations offer a promising strategy for functional annotation of uncharacterized bioactive compounds. We recently developed a new high-throughput platform for mapping chemical-genetic (CG) interactions in yeast that can be scaled to screen large compound collections, and we applied this system to generate CG interaction profiles for more than 13 000 compounds. When integrated with the existing global yeast genetic interaction network, CG interaction profiles can enable mode-of-action prediction for previously uncharacterized compounds as well as discover unexpected secondary effects for known drugs. To facilitate future analysis of these valuable data, we developed a public database and web interface named MOSAIC. The website provides a convenient interface for querying compounds, bioprocesses (Gene Ontology terms) and genes for CG information including direct CG interactions, bioprocesses and gene-level target predictions. MOSAIC also provides access to chemical structure information of screened molecules, chemical-genomic profiles and the ability to search for compounds sharing structural and functional similarity. This resource will be of interest to chemical biologists for discovering new small molecule probes with specific modes-of-action as well as computational biologists interested in analysing CG interaction networks. MOSAIC is available at http://mosaic.cs.umn.edu. hisyo@riken.jp, yoshidam@riken.jp, charlie.boone@utoronto.ca or chadm@umn.edu. Supplementary data are available at Bioinformatics online.

The Interactomic Analysis Reveals Pathogenic Protein Networks in Phomopsis longicolla Underlying Seed Decay of Soybean.

PubMed

Li, Shuxian; Musungu, Bryan; Lightfoot, David; Ji, Pingsheng

2018-01-01

Phomopsis longicolla T. W. Hobbs (syn. Diaporthe longicolla ) is the primary cause of Phomopsis seed decay (PSD) in soybean, Glycine max (L.) Merrill. This disease results in poor seed quality and is one of the most economically important seed diseases in soybean. The objectives of this study were to infer protein-protein interactions (PPI) and to identify conserved global networks and pathogenicity subnetworks in P. longicolla including orthologous pathways for cell signaling and pathogenesis. The interlog method used in the study identified 215,255 unique PPIs among 3,868 proteins. There were 1,414 pathogenicity related genes in P. longicolla identified using the pathogen host interaction (PHI) database. Additionally, 149 plant cell wall degrading enzymes (PCWDE) were detected. The network captured five different classes of carbohydrate degrading enzymes, including the auxiliary activities, carbohydrate esterases, glycoside hydrolases, glycosyl transferases, and carbohydrate binding molecules. From the PPI analysis, novel interacting partners were determined for each of the PCWDE classes. The most predominant class of PCWDE was a group of 60 glycoside hydrolases proteins. The glycoside hydrolase subnetwork was found to be interacting with 1,442 proteins within the network and was among the largest clusters. The orthologous proteins FUS3, HOG, CYP1, SGE1, and the g5566t.1 gene identified in this study could play an important role in pathogenicity. Therefore, the P. longicolla protein interactome (PiPhom) generated in this study can lead to a better understanding of PPIs in soybean pathogens. Furthermore, the PPI may aid in targeting of genes and proteins for further studies of the pathogenicity mechanisms.

The Interactomic Analysis Reveals Pathogenic Protein Networks in Phomopsis longicolla Underlying Seed Decay of Soybean

PubMed Central

Li, Shuxian; Musungu, Bryan; Lightfoot, David; Ji, Pingsheng

2018-01-01

Phomopsis longicolla T. W. Hobbs (syn. Diaporthe longicolla) is the primary cause of Phomopsis seed decay (PSD) in soybean, Glycine max (L.) Merrill. This disease results in poor seed quality and is one of the most economically important seed diseases in soybean. The objectives of this study were to infer protein–protein interactions (PPI) and to identify conserved global networks and pathogenicity subnetworks in P. longicolla including orthologous pathways for cell signaling and pathogenesis. The interlog method used in the study identified 215,255 unique PPIs among 3,868 proteins. There were 1,414 pathogenicity related genes in P. longicolla identified using the pathogen host interaction (PHI) database. Additionally, 149 plant cell wall degrading enzymes (PCWDE) were detected. The network captured five different classes of carbohydrate degrading enzymes, including the auxiliary activities, carbohydrate esterases, glycoside hydrolases, glycosyl transferases, and carbohydrate binding molecules. From the PPI analysis, novel interacting partners were determined for each of the PCWDE classes. The most predominant class of PCWDE was a group of 60 glycoside hydrolases proteins. The glycoside hydrolase subnetwork was found to be interacting with 1,442 proteins within the network and was among the largest clusters. The orthologous proteins FUS3, HOG, CYP1, SGE1, and the g5566t.1 gene identified in this study could play an important role in pathogenicity. Therefore, the P. longicolla protein interactome (PiPhom) generated in this study can lead to a better understanding of PPIs in soybean pathogens. Furthermore, the PPI may aid in targeting of genes and proteins for further studies of the pathogenicity mechanisms. PMID:29666630

A design for the geoinformatics system

NASA Astrophysics Data System (ADS)

Allison, M. L.

2002-12-01

Informatics integrates and applies information technologies with scientific and technical disciplines. A geoinformatics system targets the spatially based sciences. The system is not a master database, but will collect pertinent information from disparate databases distributed around the world. Seamless interoperability of databases promises quantum leaps in productivity not only for scientific researchers but also for many areas of society including business and government. The system will incorporate: acquisition of analog and digital legacy data; efficient information and data retrieval mechanisms (via data mining and web services); accessibility to and application of visualization, analysis, and modeling capabilities; online workspace, software, and tutorials; GIS; integration with online scientific journal aggregates and digital libraries; access to real time data collection and dissemination; user-defined automatic notification and quality control filtering for selection of new resources; and application to field techniques such as mapping. In practical terms, such a system will provide the ability to gather data over the Web from a variety of distributed sources, regardless of computer operating systems, database formats, and servers. Search engines will gather data about any geographic location, above, on, or below ground, covering any geologic time, and at any scale or detail. A distributed network of digital geolibraries can archive permanent copies of databases at risk of being discontinued and those that continue to be maintained by the data authors. The geoinformatics system will generate results from widely distributed sources to function as a dynamic data network. Instead of posting a variety of pre-made tables, charts, or maps based on static databases, the interactive dynamic system creates these products on the fly, each time an inquiry is made, using the latest information in the appropriate databases. Thus, in the dynamic system, a map generated today may differ from one created yesterday and one to be created tomorrow, because the databases used to make it are constantly (and sometimes automatically) being updated.

Screening of differentially expressed genes between multiple trauma patients with and without sepsis.

PubMed

Ji, S C; Pan, Y T; Lu, Q Y; Sun, Z Y; Liu, Y Z

2014-03-17

The purpose of this study was to identify critical genes associated with septic multiple trauma by comparing peripheral whole blood samples from multiple trauma patients with and without sepsis. A microarray data set was downloaded from the Gene Expression Omnibus (GEO) database. This data set included 70 samples, 36 from multiple trauma patients with sepsis and 34 from multiple trauma patients without sepsis (as a control set). The data were preprocessed, and differentially expressed genes (DEGs) were then screened for using packages of the R language. Functional analysis of DEGs was performed with DAVID. Interaction networks were then established for the most up- and down-regulated genes using HitPredict. Pathway-enrichment analysis was conducted for genes in the networks using WebGestalt. Fifty-eight DEGs were identified. The expression levels of PLAU (down-regulated) and MMP8 (up-regulated) presented the largest fold-changes, and interaction networks were established for these genes. Further analysis revealed that PLAT (plasminogen activator, tissue) and SERPINF2 (serpin peptidase inhibitor, clade F, member 2), which interact with PLAU, play important roles in the pathway of the component and coagulation cascade. We hypothesize that PLAU is a major regulator of the component and coagulation cascade, and down-regulation of PLAU results in dysfunction of the pathway, causing sepsis.

PlaMoM: a comprehensive database compiles plant mobile macromolecules

PubMed Central

Guan, Daogang; Yan, Bin; Thieme, Christoph; Hua, Jingmin; Zhu, Hailong; Boheler, Kenneth R.; Zhao, Zhongying; Kragler, Friedrich; Xia, Yiji; Zhang, Shoudong

2017-01-01

In plants, various phloem-mobile macromolecules including noncoding RNAs, mRNAs and proteins are suggested to act as important long-distance signals in regulating crucial physiological and morphological transition processes such as flowering, plant growth and stress responses. Given recent advances in high-throughput sequencing technologies, numerous mobile macromolecules have been identified in diverse plant species from different plant families. However, most of the identified mobile macromolecules are not annotated in current versions of species-specific databases and are only available as non-searchable datasheets. To facilitate study of the mobile signaling macromolecules, we compiled the PlaMoM (Plant Mobile Macromolecules) database, a resource that provides convenient and interactive search tools allowing users to retrieve, to analyze and also to predict mobile RNAs/proteins. Each entry in the PlaMoM contains detailed information such as nucleotide/amino acid sequences, ortholog partners, related experiments, gene functions and literature. For the model plant Arabidopsis thaliana, protein–protein interactions of mobile transcripts are presented as interactive molecular networks. Furthermore, PlaMoM provides a built-in tool to identify potential RNA mobility signals such as tRNA-like structures. The current version of PlaMoM compiles a total of 17 991 mobile macromolecules from 14 plant species/ecotypes from published data and literature. PlaMoM is available at http://www.systembioinfo.org/plamom/. PMID:27924044

General and craniofacial development are complex adaptive processes influenced by diversity.

PubMed

Brook, A H; O'Donnell, M Brook; Hone, A; Hart, E; Hughes, T E; Smith, R N; Townsend, G C

2014-06-01

Complex systems are present in such diverse areas as social systems, economies, ecosystems and biology and, therefore, are highly relevant to dental research, education and practice. A Complex Adaptive System in biological development is a dynamic process in which, from interacting components at a lower level, higher level phenomena and structures emerge. Diversity makes substantial contributions to the performance of complex adaptive systems. It enhances the robustness of the process, allowing multiple responses to external stimuli as well as internal changes. From diversity comes variation in outcome and the possibility of major change; outliers in the distribution enhance the tipping points. The development of the dentition is a valuable, accessible model with extensive and reliable databases for investigating the role of complex adaptive systems in craniofacial and general development. The general characteristics of such systems are seen during tooth development: self-organization; bottom-up emergence; multitasking; self-adaptation; variation; tipping points; critical phases; and robustness. Dental findings are compatible with the Random Network Model, the Threshold Model and also with the Scale Free Network Model which has a Power Law distribution. In addition, dental development shows the characteristics of Modularity and Clustering to form Hierarchical Networks. The interactions between the genes (nodes) demonstrate Small World phenomena, Subgraph Motifs and Gene Regulatory Networks. Genetic mechanisms are involved in the creation and evolution of variation during development. The genetic factors interact with epigenetic and environmental factors at the molecular level and form complex networks within the cells. From these interactions emerge the higher level tissues, tooth germs and mineralized teeth. Approaching development in this way allows investigation of why there can be variations in phenotypes from identical genotypes; the phenotype is the outcome of perturbations in the cellular systems and networks, as well as of the genotype. Understanding and applying complexity theory will bring about substantial advances not only in dental research and education but also in the organization and delivery of oral health care. © 2014 Australian Dental Association.

Discovery of multiple interacting partners of gankyrin, a proteasomal chaperone and an oncoprotein--evidence for a common hot spot site at the interface and its functional relevance.

PubMed

Nanaware, Padma P; Ramteke, Manoj P; Somavarapu, Arun K; Venkatraman, Prasanna

2014-07-01

Gankyrin, a non-ATPase component of the proteasome and a chaperone of proteasome assembly, is also an oncoprotein. Gankyrin regulates a variety of oncogenic signaling pathways in cancer cells and accelerates degradation of tumor suppressor proteins p53 and Rb. Therefore gankyrin may be a unique hub integrating signaling networks with the degradation pathway. To identify new interactions that may be crucial in consolidating its role as an oncogenic hub, crystal structure of gankyrin-proteasome ATPase complex was used to predict novel interacting partners. EEVD, a four amino acid linear sequence seems a hot spot site at this interface. By searching for EEVD in exposed regions of human proteins in PDB database, we predicted 34 novel interactions. Eight proteins were tested and seven of them were found to interact with gankyrin. Affinity of four interactions is high enough for endogenous detection. Others require gankyrin overexpression in HEK 293 cells or occur endogenously in breast cancer cell line- MDA-MB-435, reflecting lower affinity or presence of a deregulated network. Mutagenesis and peptide inhibition confirm that EEVD is the common hot spot site at these interfaces and therefore a potential polypharmacological drug target. In MDA-MB-231 cells in which the endogenous CLIC1 is silenced, trans-expression of Wt protein (CLIC1_EEVD) and not the hot spot site mutant (CLIC1_AAVA) resulted in significant rescue of the migratory potential. Our approach can be extended to identify novel functionally relevant protein-protein interactions, in expansion of oncogenic networks and in identifying potential therapeutic targets. © 2013 Wiley Periodicals, Inc.

Tcof1-Related Molecular Networks in Treacher Collins Syndrome.

PubMed

Dai, Jiewen; Si, Jiawen; Wang, Minjiao; Huang, Li; Fang, Bing; Shi, Jun; Wang, Xudong; Shen, Guofang

2016-09-01

Treacher Collins syndrome (TCS) is a rare, autosomal-dominant disorder characterized by craniofacial deformities, and is primarily caused by mutations in the Tcof1 gene. This article was aimed to perform a comprehensive literature review and systematic bioinformatic analysis of Tcof1-related molecular networks in TCS. First, the up- and down-regulated genes in Tcof1 heterozygous haploinsufficient mutant mice embryos and Tcof1 knockdown and Tcof1 over-expressed neuroblastoma N1E-115 cells were obtained from the Gene Expression Omnibus database. The GeneDecks database was used to calculate the 500 genes most closely related to Tcof1. Then, the relationships between 4 gene sets (a predicted set and sets comparing the wildtype with the 3 Gene Expression Omnibus datasets) were analyzed using the DAVID, GeneMANIA and STRING databases. The analysis results showed that the Tcof1-related genes were enriched in various biological processes, including cell proliferation, apoptosis, cell cycle, differentiation, and migration. They were also enriched in several signaling pathways, such as the ribosome, p53, cell cycle, and WNT signaling pathways. Additionally, these genes clearly had direct or indirect interactions with Tcof1 and between each other. Literature review and bioinformatic analysis finds imply that special attention should be given to these pathways, as they may offer target points for TCS therapies.

A review on computational systems biology of pathogen–host interactions

PubMed Central

Durmuş, Saliha; Çakır, Tunahan; Özgür, Arzucan; Guthke, Reinhard

2015-01-01

Pathogens manipulate the cellular mechanisms of host organisms via pathogen–host interactions (PHIs) in order to take advantage of the capabilities of host cells, leading to infections. The crucial role of these interspecies molecular interactions in initiating and sustaining infections necessitates a thorough understanding of the corresponding mechanisms. Unlike the traditional approach of considering the host or pathogen separately, a systems-level approach, considering the PHI system as a whole is indispensable to elucidate the mechanisms of infection. Following the technological advances in the post-genomic era, PHI data have been produced in large-scale within the last decade. Systems biology-based methods for the inference and analysis of PHI regulatory, metabolic, and protein–protein networks to shed light on infection mechanisms are gaining increasing demand thanks to the availability of omics data. The knowledge derived from the PHIs may largely contribute to the identification of new and more efficient therapeutics to prevent or cure infections. There are recent efforts for the detailed documentation of these experimentally verified PHI data through Web-based databases. Despite these advances in data archiving, there are still large amounts of PHI data in the biomedical literature yet to be discovered, and novel text mining methods are in development to unearth such hidden data. Here, we review a collection of recent studies on computational systems biology of PHIs with a special focus on the methods for the inference and analysis of PHI networks, covering also the Web-based databases and text-mining efforts to unravel the data hidden in the literature. PMID:25914674

Network-based drug discovery by integrating systems biology and computational technologies

PubMed Central

Leung, Elaine L.; Cao, Zhi-Wei; Jiang, Zhi-Hong; Zhou, Hua

2013-01-01

Network-based intervention has been a trend of curing systemic diseases, but it relies on regimen optimization and valid multi-target actions of the drugs. The complex multi-component nature of medicinal herbs may serve as valuable resources for network-based multi-target drug discovery due to its potential treatment effects by synergy. Recently, robustness of multiple systems biology platforms shows powerful to uncover molecular mechanisms and connections between the drugs and their targeting dynamic network. However, optimization methods of drug combination are insufficient, owning to lacking of tighter integration across multiple ‘-omics’ databases. The newly developed algorithm- or network-based computational models can tightly integrate ‘-omics’ databases and optimize combinational regimens of drug development, which encourage using medicinal herbs to develop into new wave of network-based multi-target drugs. However, challenges on further integration across the databases of medicinal herbs with multiple system biology platforms for multi-target drug optimization remain to the uncertain reliability of individual data sets, width and depth and degree of standardization of herbal medicine. Standardization of the methodology and terminology of multiple system biology and herbal database would facilitate the integration. Enhance public accessible databases and the number of research using system biology platform on herbal medicine would be helpful. Further integration across various ‘-omics’ platforms and computational tools would accelerate development of network-based drug discovery and network medicine. PMID:22877768

Toward the automated generation of genome-scale metabolic networks in the SEED.

PubMed

DeJongh, Matthew; Formsma, Kevin; Boillot, Paul; Gould, John; Rycenga, Matthew; Best, Aaron

2007-04-26

Current methods for the automated generation of genome-scale metabolic networks focus on genome annotation and preliminary biochemical reaction network assembly, but do not adequately address the process of identifying and filling gaps in the reaction network, and verifying that the network is suitable for systems level analysis. Thus, current methods are only sufficient for generating draft-quality networks, and refinement of the reaction network is still largely a manual, labor-intensive process. We have developed a method for generating genome-scale metabolic networks that produces substantially complete reaction networks, suitable for systems level analysis. Our method partitions the reaction space of central and intermediary metabolism into discrete, interconnected components that can be assembled and verified in isolation from each other, and then integrated and verified at the level of their interconnectivity. We have developed a database of components that are common across organisms, and have created tools for automatically assembling appropriate components for a particular organism based on the metabolic pathways encoded in the organism's genome. This focuses manual efforts on that portion of an organism's metabolism that is not yet represented in the database. We have demonstrated the efficacy of our method by reverse-engineering and automatically regenerating the reaction network from a published genome-scale metabolic model for Staphylococcus aureus. Additionally, we have verified that our method capitalizes on the database of common reaction network components created for S. aureus, by using these components to generate substantially complete reconstructions of the reaction networks from three other published metabolic models (Escherichia coli, Helicobacter pylori, and Lactococcus lactis). We have implemented our tools and database within the SEED, an open-source software environment for comparative genome annotation and analysis. Our method sets the stage for the automated generation of substantially complete metabolic networks for over 400 complete genome sequences currently in the SEED. With each genome that is processed using our tools, the database of common components grows to cover more of the diversity of metabolic pathways. This increases the likelihood that components of reaction networks for subsequently processed genomes can be retrieved from the database, rather than assembled and verified manually.

Insertion algorithms for network model database management systems

NASA Astrophysics Data System (ADS)

Mamadolimov, Abdurashid; Khikmat, Saburov

2017-12-01

The network model is a database model conceived as a flexible way of representing objects and their relationships. Its distinguishing feature is that the schema, viewed as a graph in which object types are nodes and relationship types are arcs, forms partial order. When a database is large and a query comparison is expensive then the efficiency requirement of managing algorithms is minimizing the number of query comparisons. We consider updating operation for network model database management systems. We develop a new sequantial algorithm for updating operation. Also we suggest a distributed version of the algorithm.

Analysis of gene expression profile microarray data in complex regional pain syndrome.

PubMed

Tan, Wulin; Song, Yiyan; Mo, Chengqiang; Jiang, Shuangjian; Wang, Zhongxing

2017-09-01

The aim of the present study was to predict key genes and proteins associated with complex regional pain syndrome (CRPS) using bioinformatics analysis. The gene expression profiling microarray data, GSE47603, which included peripheral blood samples from 4 patients with CRPS and 5 healthy controls, was obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in CRPS patients compared with healthy controls were identified using the GEO2R online tool. Functional enrichment analysis was then performed using The Database for Annotation Visualization and Integrated Discovery online tool. Protein‑protein interaction (PPI) network analysis was subsequently performed using Search Tool for the Retrieval of Interaction Genes database and analyzed with Cytoscape software. A total of 257 DEGs were identified, including 243 upregulated genes and 14 downregulated ones. Genes in the human leukocyte antigen (HLA) family were most significantly differentially expressed. Enrichment analysis demonstrated that signaling pathways, including immune response, cell motion, adhesion and angiogenesis were associated with CRPS. PPI network analysis revealed that key genes, including early region 1A binding protein p300 (EP300), CREB‑binding protein (CREBBP), signal transducer and activator of transcription (STAT)3, STAT5A and integrin α M were associated with CRPS. The results suggest that the immune response may therefore serve an important role in CRPS development. In addition, genes in the HLA family, such as HLA‑DQB1 and HLA‑DRB1, may present potential biomarkers for the diagnosis of CRPS. Furthermore, EP300, its paralog CREBBP, and the STAT family genes, STAT3 and STAT5 may be important in the development of CRPS.

A similarity learning approach to content-based image retrieval: application to digital mammography.

PubMed

El-Naqa, Issam; Yang, Yongyi; Galatsanos, Nikolas P; Nishikawa, Robert M; Wernick, Miles N

2004-10-01

In this paper, we describe an approach to content-based retrieval of medical images from a database, and provide a preliminary demonstration of our approach as applied to retrieval of digital mammograms. Content-based image retrieval (CBIR) refers to the retrieval of images from a database using information derived from the images themselves, rather than solely from accompanying text indices. In the medical-imaging context, the ultimate aim of CBIR is to provide radiologists with a diagnostic aid in the form of a display of relevant past cases, along with proven pathology and other suitable information. CBIR may also be useful as a training tool for medical students and residents. The goal of information retrieval is to recall from a database information that is relevant to the user's query. The most challenging aspect of CBIR is the definition of relevance (similarity), which is used to guide the retrieval machine. In this paper, we pursue a new approach, in which similarity is learned from training examples provided by human observers. Specifically, we explore the use of neural networks and support vector machines to predict the user's notion of similarity. Within this framework we propose using a hierarchal learning approach, which consists of a cascade of a binary classifier and a regression module to optimize retrieval effectiveness and efficiency. We also explore how to incorporate online human interaction to achieve relevance feedback in this learning framework. Our experiments are based on a database consisting of 76 mammograms, all of which contain clustered microcalcifications (MCs). Our goal is to retrieve mammogram images containing similar MC clusters to that in a query. The performance of the retrieval system is evaluated using precision-recall curves computed using a cross-validation procedure. Our experimental results demonstrate that: 1) the learning framework can accurately predict the perceptual similarity reported by human observers, thereby serving as a basis for CBIR; 2) the learning-based framework can significantly outperform a simple distance-based similarity metric; 3) the use of the hierarchical two-stage network can improve retrieval performance; and 4) relevance feedback can be effectively incorporated into this learning framework to achieve improvement in retrieval precision based on online interaction with users; and 5) the retrieved images by the network can have predicting value for the disease condition of the query.

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

BioPAX – A community standard for pathway data sharing

PubMed Central

Demir, Emek; Cary, Michael P.; Paley, Suzanne; Fukuda, Ken; Lemer, Christian; Vastrik, Imre; Wu, Guanming; D’Eustachio, Peter; Schaefer, Carl; Luciano, Joanne; Schacherer, Frank; Martinez-Flores, Irma; Hu, Zhenjun; Jimenez-Jacinto, Veronica; Joshi-Tope, Geeta; Kandasamy, Kumaran; Lopez-Fuentes, Alejandra C.; Mi, Huaiyu; Pichler, Elgar; Rodchenkov, Igor; Splendiani, Andrea; Tkachev, Sasha; Zucker, Jeremy; Gopinath, Gopal; Rajasimha, Harsha; Ramakrishnan, Ranjani; Shah, Imran; Syed, Mustafa; Anwar, Nadia; Babur, Ozgun; Blinov, Michael; Brauner, Erik; Corwin, Dan; Donaldson, Sylva; Gibbons, Frank; Goldberg, Robert; Hornbeck, Peter; Luna, Augustin; Murray-Rust, Peter; Neumann, Eric; Reubenacker, Oliver; Samwald, Matthias; van Iersel, Martijn; Wimalaratne, Sarala; Allen, Keith; Braun, Burk; Whirl-Carrillo, Michelle; Dahlquist, Kam; Finney, Andrew; Gillespie, Marc; Glass, Elizabeth; Gong, Li; Haw, Robin; Honig, Michael; Hubaut, Olivier; Kane, David; Krupa, Shiva; Kutmon, Martina; Leonard, Julie; Marks, Debbie; Merberg, David; Petri, Victoria; Pico, Alex; Ravenscroft, Dean; Ren, Liya; Shah, Nigam; Sunshine, Margot; Tang, Rebecca; Whaley, Ryan; Letovksy, Stan; Buetow, Kenneth H.; Rzhetsky, Andrey; Schachter, Vincent; Sobral, Bruno S.; Dogrusoz, Ugur; McWeeney, Shannon; Aladjem, Mirit; Birney, Ewan; Collado-Vides, Julio; Goto, Susumu; Hucka, Michael; Le Novère, Nicolas; Maltsev, Natalia; Pandey, Akhilesh; Thomas, Paul; Wingender, Edgar; Karp, Peter D.; Sander, Chris; Bader, Gary D.

2010-01-01

BioPAX (Biological Pathway Exchange) is a standard language to represent biological pathways at the molecular and cellular level. Its major use is to facilitate the exchange of pathway data (http://www.biopax.org). Pathway data captures our understanding of biological processes, but its rapid growth necessitates development of databases and computational tools to aid interpretation. However, the current fragmentation of pathway information across many databases with incompatible formats presents barriers to its effective use. BioPAX solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. BioPAX was created through a community process. Through BioPAX, millions of interactions organized into thousands of pathways across many organisms, from a growing number of sources, are available. Thus, large amounts of pathway data are available in a computable form to support visualization, analysis and biological discovery. PMID:20829833

MDB: the Metalloprotein Database and Browser at The Scripps Research Institute

PubMed Central

Castagnetto, Jesus M.; Hennessy, Sean W.; Roberts, Victoria A.; Getzoff, Elizabeth D.; Tainer, John A.; Pique, Michael E.

2002-01-01

The Metalloprotein Database and Browser (MDB; http://metallo.scripps.edu) at The Scripps Research Institute is a web-accessible resource for metalloprotein research. It offers the scientific community quantitative information on geometrical parameters of metal-binding sites in protein structures available from the Protein Data Bank (PDB). The MDB also offers analytical tools for the examination of trends or patterns in the indexed metal-binding sites. A user can perform interactive searches, metal-site structure visualization (via a Java applet), and analysis of the quantitative data by accessing the MDB through a web browser without requiring an external application or platform-dependent plugin. The MDB also has a non-interactive interface with which other web sites and network-aware applications can seamlessly incorporate data or statistical analysis results from metal-binding sites. The information contained in the MDB is periodically updated with automated algorithms that find and index metal sites from new protein structures released by the PDB. PMID:11752342

Insights into the molecular mechanisms of Polygonum multiflorum Thunb-induced liver injury: a computational systems toxicology approach.

PubMed

Wang, Yin-Yin; Li, Jie; Wu, Zeng-Rui; Zhang, Bo; Yang, Hong-Bin; Wang, Qin; Cai, Ying-Chun; Liu, Gui-Xia; Li, Wei-Hua; Tang, Yun

2017-05-01

An increasing number of cases of herb-induced liver injury (HILI) have been reported, presenting new clinical challenges. In this study, taking Polygonum multiflorum Thunb (PmT) as an example, we proposed a computational systems toxicology approach to explore the molecular mechanisms of HILI. First, the chemical components of PmT were extracted from 3 main TCM databases as well as the literature related to natural products. Then, the known targets were collected through data integration, and the potential compound-target interactions (CTIs) were predicted using our substructure-drug-target network-based inference (SDTNBI) method. After screening for hepatotoxicity-related genes by assessing the symptoms of HILI, a compound-target interaction network was constructed. A scoring function, namely, Ascore, was developed to estimate the toxicity of chemicals in the liver. We conducted network analysis to determine the possible mechanisms of the biphasic effects using the analysis tools, including BiNGO, pathway enrichment, organ distribution analysis and predictions of interactions with CYP450 enzymes. Among the chemical components of PmT, 54 components with good intestinal absorption were used for analysis, and 2939 CTIs were obtained. After analyzing the mRNA expression data in the BioGPS database, 1599 CTIs and 125 targets related to liver diseases were identified. In the top 15 compounds, seven with Ascore values >3000 (emodin, quercetin, apigenin, resveratrol, gallic acid, kaempferol and luteolin) were obviously associated with hepatotoxicity. The results from the pathway enrichment analysis suggest that multiple interactions between apoptosis and metabolism may underlie PmT-induced liver injury. Many of the pathways have been verified in specific compounds, such as glutathione metabolism, cytochrome P450 metabolism, and the p53 pathway, among others. Hepatitis symptoms, the perturbation of nine bile acids and yellow or tawny urine also had corresponding pathways, justifying our method. In conclusion, this computational systems toxicology method reveals possible toxic components and could be very helpful for understanding the mechanisms of HILI. In this way, the method might also facilitate the identification of novel hepatotoxic herbs.

Differential gene expression analysis in glioblastoma cells and normal human brain cells based on GEO database.

PubMed

Wang, Anping; Zhang, Guibin

2017-11-01

The differentially expressed genes between glioblastoma (GBM) cells and normal human brain cells were investigated to performed pathway analysis and protein interaction network analysis for the differentially expressed genes. GSE12657 and GSE42656 gene chips, which contain gene expression profile of GBM were obtained from Gene Expression Omniub (GEO) database of National Center for Biotechnology Information (NCBI). The 'limma' data packet in 'R' software was used to analyze the differentially expressed genes in the two gene chips, and gene integration was performed using 'RobustRankAggreg' package. Finally, pheatmap software was used for heatmap analysis and Cytoscape, DAVID, STRING and KOBAS were used for protein-protein interaction, Gene Ontology (GO) and KEGG analyses. As results: i) 702 differentially expressed genes were identified in GSE12657, among those genes, 548 were significantly upregulated and 154 were significantly downregulated (p<0.01, fold-change >1), and 1,854 differentially expressed genes were identified in GSE42656, among the genes, 1,068 were significantly upregulated and 786 were significantly downregulated (p<0.01, fold-change >1). A total of 167 differentially expressed genes including 100 upregulated genes and 67 downregulated genes were identified after gene integration, and the genes showed significantly different expression levels in GBM compared with normal human brain cells (p<0.05). ii) Interactions between the protein products of 101 differentially expressed genes were identified using STRING and expression network was established. A key gene, called CALM3, was identified by Cytoscape software. iii) GO enrichment analysis showed that differentially expressed genes were mainly enriched in 'neurotransmitter:sodium symporter activity' and 'neurotransmitter transporter activity', which can affect the activity of neurotransmitter transportation. KEGG pathway analysis showed that the differentially expressed genes were mainly enriched in 'protein processing in endoplasmic reticulum', which can affect protein processing in endoplasmic reticulum. The results showed that: i) 167 differentially expressed genes were identified from two gene chips after integration; and ii) protein interaction network was established, and GO and KEGG pathway analyses were successfully performed to identify and annotate the key gene, which provide new insights for the studies on GBN at gene level.

Friending, IMing, and hanging out face-to-face: overlap in adolescents' online and offline social networks.

PubMed

Reich, Stephanie M; Subrahmanyam, Kaveri; Espinoza, Guadalupe

2012-03-01

Many new and important developmental issues are encountered during adolescence, which is also a time when Internet use becomes increasingly popular. Studies have shown that adolescents are using these online spaces to address developmental issues, especially needs for intimacy and connection to others. Online communication with its potential for interacting with unknown others, may put teens at increased risk. Two hundred and fifty-one high school students completed an in-person survey, and 126 of these completed an additional online questionnaire about how and why they use the Internet, their activities on social networking sites (e.g., Facebook, MySpace) and their reasons for participation, and how they perceive these online spaces to impact their friendships. To examine the extent of overlap between online and offline friends, participants were asked to list the names of their top interaction partners offline and online (Facebook and instant messaging). Results reveal that adolescents mainly use social networking sites to connect with others, in particular with people known from offline contexts. While adolescents report little monitoring by their parents, there was no evidence that teens are putting themselves at risk by interacting with unknown others. Instead, adolescents seem to use the Internet, especially social networking sites, to connect with known others. While the study found moderate overlap between teens' closest online and offline friends, the patterns suggest that adolescents use online contexts to strengthen offline relationships. PsycINFO Database Record (c) 2012 APA, all rights reserved.

Structure, function, and control of the human musculoskeletal network

PubMed Central

Murphy, Andrew C.; Muldoon, Sarah F.; Baker, David; Lastowka, Adam; Bennett, Brittany; Yang, Muzhi

2018-01-01

The human body is a complex organism, the gross mechanical properties of which are enabled by an interconnected musculoskeletal network controlled by the nervous system. The nature of musculoskeletal interconnection facilitates stability, voluntary movement, and robustness to injury. However, a fundamental understanding of this network and its control by neural systems has remained elusive. Here we address this gap in knowledge by utilizing medical databases and mathematical modeling to reveal the organizational structure, predicted function, and neural control of the musculoskeletal system. We constructed a highly simplified whole-body musculoskeletal network in which single muscles connect to multiple bones via both origin and insertion points. We demonstrated that, using this simplified model, a muscle’s role in this network could offer a theoretical prediction of the susceptibility of surrounding components to secondary injury. Finally, we illustrated that sets of muscles cluster into network communities that mimic the organization of control modules in primary motor cortex. This novel formalism for describing interactions between the muscular and skeletal systems serves as a foundation to develop and test therapeutic responses to injury, inspiring future advances in clinical treatments. PMID:29346370

Net2Align: An Algorithm For Pairwise Global Alignment of Biological Networks

PubMed Central

Wadhwab, Gulshan; Upadhyayaa, K. C.

2016-01-01

The amount of data on molecular interactions is growing at an enormous pace, whereas the progress of methods for analysing this data is still lacking behind. Particularly, in the area of comparative analysis of biological networks, where one wishes to explore the similarity between two biological networks, this holds a potential problem. In consideration that the functionality primarily runs at the network level, it advocates the need for robust comparison methods. In this paper, we describe Net2Align, an algorithm for pairwise global alignment that can perform node-to-node correspondences as well as edge-to-edge correspondences into consideration. The uniqueness of our algorithm is in the fact that it is also able to detect the type of interaction, which is essential in case of directed graphs. The existing algorithm is only able to identify the common nodes but not the common edges. Another striking feature of the algorithm is that it is able to remove duplicate entries in case of variable datasets being aligned. This is achieved through creation of a local database which helps exclude duplicate links. In a pervasive computational study on gene regulatory network, we establish that our algorithm surpasses its counterparts in its results. Net2Align has been implemented in Java 7 and the source code is available as supplementary files. PMID:28356678

The salience of social referents: a field experiment on collective norms and harassment behavior in a school social network.

PubMed

Paluck, Elizabeth Levy; Shepherd, Hana

2012-12-01

Persistent, widespread harassment in schools can be understood as a product of collective school norms that deem harassment, and behavior allowing harassment to escalate, as typical and even desirable. Thus, one approach to reducing harassment is to change students' perceptions of these collective norms. Theory suggests that the public behavior of highly connected and chronically salient actors in a group, called social referents, may provide influential cues for individuals' perception of collective norms. Using repeated, complete social network surveys of a public high school, we demonstrate that changing the public behavior of a randomly assigned subset of student social referents changes their peers' perceptions of school collective norms and their harassment behavior. Social referents exert their influence over peers' perceptions of collective norms through the mechanism of everyday social interaction, particularly interaction that is frequent and personally motivated, in contrast to interaction shaped by institutional channels like shared classes. These findings clarify the development of collective social norms: They depend on certain patterns of and motivations for social interactions within groups across time, and are not static but constantly reshaped and reproduced through these interactions. Understanding this process creates opportunities for changing collective norms and behavior. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

Electronic Reference Library: Silverplatter's Database Networking Solution.

ERIC Educational Resources Information Center

Millea, Megan

Silverplatter's Electronic Reference Library (ERL) provides wide area network access to its databases using TCP/IP communications and client-server architecture. ERL has two main components: The ERL clients (retrieval interface) and the ERL server (search engines). ERL clients provide patrons with seamless access to multiple databases on multiple…

Database Software Selection for the Egyptian National STI Network.

ERIC Educational Resources Information Center

Slamecka, Vladimir

The evaluation and selection of information/data management system software for the Egyptian National Scientific and Technical (STI) Network are described. An overview of the state-of-the-art of database technology elaborates on the differences between information retrieval and database management systems (DBMS). The desirable characteristics of…

The Relationship Between Online Social Networking and Depression: A Systematic Review of Quantitative Studies.

PubMed

Baker, David A; Algorta, Guillermo Perez

2016-11-01

Online social networking sites (SNSs) such as Facebook, Twitter, and MySpace are used by billions of people every day to communicate and interact with others. There has been increasing interest in the potential impact of online social networking on wellbeing, with a broadening body of new research into factors associated with both positive and negative mental health outcomes such as depression. This systematic review of empirical studies (n = 30) adds to existing research in this field by examining current quantitative studies focused on the relationship between online social networking and symptoms of depression. The academic databases PsycINFO, Web of Science, CINAHL, MEDLINE, and EMBASE were searched systematically using terms related to online social networking and depression. Reporting quality was critically appraised and the findings discussed with reference to their wider implications. The findings suggest that the relationship between online social networking and symptoms of depression may be complex and associated with multiple psychological, social, behavioral, and individual factors. Furthermore, the impact of online social networking on wellbeing may be both positive and negative, highlighting the need for future research to determine the impact of candidate mediators and moderators underlying these heterogeneous outcomes across evolving networks.

Differential reconstructed gene interaction networks for deriving toxicity threshold in chemical risk assessment.

PubMed

Yang, Yi; Maxwell, Andrew; Zhang, Xiaowei; Wang, Nan; Perkins, Edward J; Zhang, Chaoyang; Gong, Ping

2013-01-01

Pathway alterations reflected as changes in gene expression regulation and gene interaction can result from cellular exposure to toxicants. Such information is often used to elucidate toxicological modes of action. From a risk assessment perspective, alterations in biological pathways are a rich resource for setting toxicant thresholds, which may be more sensitive and mechanism-informed than traditional toxicity endpoints. Here we developed a novel differential networks (DNs) approach to connect pathway perturbation with toxicity threshold setting. Our DNs approach consists of 6 steps: time-series gene expression data collection, identification of altered genes, gene interaction network reconstruction, differential edge inference, mapping of genes with differential edges to pathways, and establishment of causal relationships between chemical concentration and perturbed pathways. A one-sample Gaussian process model and a linear regression model were used to identify genes that exhibited significant profile changes across an entire time course and between treatments, respectively. Interaction networks of differentially expressed (DE) genes were reconstructed for different treatments using a state space model and then compared to infer differential edges/interactions. DE genes possessing differential edges were mapped to biological pathways in databases such as KEGG pathways. Using the DNs approach, we analyzed a time-series Escherichia coli live cell gene expression dataset consisting of 4 treatments (control, 10, 100, 1000 mg/L naphthenic acids, NAs) and 18 time points. Through comparison of reconstructed networks and construction of differential networks, 80 genes were identified as DE genes with a significant number of differential edges, and 22 KEGG pathways were altered in a concentration-dependent manner. Some of these pathways were perturbed to a degree as high as 70% even at the lowest exposure concentration, implying a high sensitivity of our DNs approach. Findings from this proof-of-concept study suggest that our approach has a great potential in providing a novel and sensitive tool for threshold setting in chemical risk assessment. In future work, we plan to analyze more time-series datasets with a full spectrum of concentrations and sufficient replications per treatment. The pathway alteration-derived thresholds will also be compared with those derived from apical endpoints such as cell growth rate.

A Database of Tornado Events as Perceived by the USArray Transportable Array Network

NASA Astrophysics Data System (ADS)

Tytell, J. E.; Vernon, F.; Reyes, J. C.

2015-12-01

Over the course of the deployment of Earthscope's USArray Transportable Array (TA) network there have numerous tornado events that have occurred within the changing footprint of its network. The Array Network Facility based in San Diego, California, has compiled a database of these tornado events based on data provided by the NOAA Storm Prediction Center (SPC). The SPC data itself consists of parameters such as start-end point track data for each event, maximum EF intensities, and maximum track widths. Our database is Antelope driven and combines these data from the SPC with detailed station information from the TA network. We are now able to list all available TA stations during any specific tornado event date and also provide a single calculated "nearest" TA station per individual tornado event. We aim to provide this database as a starting resource for those with an interest in investigating tornado signatures within surface pressure and seismic response data. On a larger scale, the database may be of particular interest to the infrasound research community

The Network Configuration of an Object Relational Database Management System

NASA Technical Reports Server (NTRS)

Diaz, Philip; Harris, W. C.

2000-01-01

The networking and implementation of the Oracle Database Management System (ODBMS) requires developers to have knowledge of the UNIX operating system as well as all the features of the Oracle Server. The server is an object relational database management system (DBMS). By using distributed processing, processes are split up between the database server and client application programs. The DBMS handles all the responsibilities of the server. The workstations running the database application concentrate on the interpretation and display of data.

The new geographic information system in ETVA VI.PE.

NASA Astrophysics Data System (ADS)

Xagoraris, Zafiris; Soulis, George

2016-08-01

ETVA VI.PE. S.A. is a member of the Piraeus Bank Group of Companies and its activities include designing, developing, exploiting and managing Industrial Areas throughout Greece. Inside ETVA VI.PE.'s thirty-one Industrial Parks there are currently 2,500 manufacturing companies established, with 40,000 employees and € 2.5 billion of invested funds. In each one of the industrial areas ETVA VI.PE guarantees the companies industrial lots of land (sites) with propitious building codes and complete infrastructure networks of water supply, sewerage, paved roads, power supply, communications, cleansing services, etc. The development of Geographical Information System for ETVA VI.PE.'s Industrial Parks started at the beginning of 1992 and consists of three subsystems: Cadastre, that manages the information for the land acquisition of Industrial Areas; Street Layout - Sites, that manages the sites sold to manufacturing companies; Networks, that manages the infrastructure networks (roads, water supply, sewerage etc). The mapping of each Industrial Park is made incorporating state-of-the-art photogrammetric, cartographic and surveying methods and techniques. Passing through the phases of initial design (hybrid GIS) and system upgrade (integrated Gis solution with spatial database), the system is currently operating on a new upgrade (integrated gIS solution with spatial database) that includes redesigning and merging the system's database schemas, along with the creation of central security policies, and the development of a new web GIS application for advanced data entry, highly customisable and standard reports, and dynamic interactive maps. The new GIS bring the company to advanced levels of productivity and introduce the new era for decision making and business management.

New model for distributed multimedia databases and its application to networking of museums

NASA Astrophysics Data System (ADS)

Kuroda, Kazuhide; Komatsu, Naohisa; Komiya, Kazumi; Ikeda, Hiroaki

1998-02-01

This paper proposes a new distributed multimedia data base system where the databases storing MPEG-2 videos and/or super high definition images are connected together through the B-ISDN's, and also refers to an example of the networking of museums on the basis of the proposed database system. The proposed database system introduces a new concept of the 'retrieval manager' which functions an intelligent controller so that the user can recognize a set of image databases as one logical database. A user terminal issues a request to retrieve contents to the retrieval manager which is located in the nearest place to the user terminal on the network. Then, the retrieved contents are directly sent through the B-ISDN's to the user terminal from the server which stores the designated contents. In this case, the designated logical data base dynamically generates the best combination of such a retrieving parameter as a data transfer path referring to directly or data on the basis of the environment of the system. The generated retrieving parameter is then executed to select the most suitable data transfer path on the network. Therefore, the best combination of these parameters fits to the distributed multimedia database system.

Balancing Your Database Network Licenses against Your Budget.

ERIC Educational Resources Information Center

Bauer, Benjamin F.

1995-01-01

Discussion of choosing database access to satisfy users and budgetary constraints highlights a method to make educated estimates of simultaneous usage levels. Topics include pricing; advances in networks and CD-ROM technology; and two networking scenarios, one in an academic library and one in a corporate research facility. (LRW)

77 FR 72335 - Proposed Collection; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-05

... computer networks, systems, or databases. The records contain the individual's name; social security number... control and track access to DLA-controlled networks, computer systems, and databases. The records may also...

BiologicalNetworks 2.0 - an integrative view of genome biology data

PubMed Central

2010-01-01

Background A significant problem in the study of mechanisms of an organism's development is the elucidation of interrelated factors which are making an impact on the different levels of the organism, such as genes, biological molecules, cells, and cell systems. Numerous sources of heterogeneous data which exist for these subsystems are still not integrated sufficiently enough to give researchers a straightforward opportunity to analyze them together in the same frame of study. Systematic application of data integration methods is also hampered by a multitude of such factors as the orthogonal nature of the integrated data and naming problems. Results Here we report on a new version of BiologicalNetworks, a research environment for the integral visualization and analysis of heterogeneous biological data. BiologicalNetworks can be queried for properties of thousands of different types of biological entities (genes/proteins, promoters, COGs, pathways, binding sites, and other) and their relations (interactions, co-expression, co-citations, and other). The system includes the build-pathways infrastructure for molecular interactions/relations and module discovery in high-throughput experiments. Also implemented in BiologicalNetworks are the Integrated Genome Viewer and Comparative Genomics Browser applications, which allow for the search and analysis of gene regulatory regions and their conservation in multiple species in conjunction with molecular pathways/networks, experimental data and functional annotations. Conclusions The new release of BiologicalNetworks together with its back-end database introduces extensive functionality for a more efficient integrated multi-level analysis of microarray, sequence, regulatory, and other data. BiologicalNetworks is freely available at http://www.biologicalnetworks.org. PMID:21190573

A novel interacting multiple model based network intrusion detection scheme

NASA Astrophysics Data System (ADS)

Xin, Ruichi; Venkatasubramanian, Vijay; Leung, Henry

2006-04-01

In today's information age, information and network security are of primary importance to any organization. Network intrusion is a serious threat to security of computers and data networks. In internet protocol (IP) based network, intrusions originate in different kinds of packets/messages contained in the open system interconnection (OSI) layer 3 or higher layers. Network intrusion detection and prevention systems observe the layer 3 packets (or layer 4 to 7 messages) to screen for intrusions and security threats. Signature based methods use a pre-existing database that document intrusion patterns as perceived in the layer 3 to 7 protocol traffics and match the incoming traffic for potential intrusion attacks. Alternately, network traffic data can be modeled and any huge anomaly from the established traffic pattern can be detected as network intrusion. The latter method, also known as anomaly based detection is gaining popularity for its versatility in learning new patterns and discovering new attacks. It is apparent that for a reliable performance, an accurate model of the network data needs to be established. In this paper, we illustrate using collected data that network traffic is seldom stationary. We propose the use of multiple models to accurately represent the traffic data. The improvement in reliability of the proposed model is verified by measuring the detection and false alarm rates on several datasets.

The Design and Implementation of a Relational to Network Query Translator for a Distributed Database Management System.

DTIC Science & Technology

1985-12-01

RELATIONAL TO NETWORK QUERY TRANSLATOR FOR A DISTRIBUTED DATABASE MANAGEMENT SYSTEM TH ESI S .L Kevin H. Mahoney -- Captain, USAF AFIT/GCS/ENG/85D-7...NETWORK QUERY TRANSLATOR FOR A DISTRIBUTED DATABASE MANAGEMENT SYSTEM - THESIS Presented to the Faculty of the School of Engineering of the Air Force...Institute of Technology Air University In Partial Fulfillment of the Requirements for the Degree of Master of Science in Computer Systems - Kevin H. Mahoney

Using the underlying biological organization of the Mycobacterium tuberculosis functional network for protein function prediction.

PubMed

Mazandu, Gaston K; Mulder, Nicola J

2012-07-01

Despite ever-increasing amounts of sequence and functional genomics data, there is still a deficiency of functional annotation for many newly sequenced proteins. For Mycobacterium tuberculosis (MTB), more than half of its genome is still uncharacterized, which hampers the search for new drug targets within the bacterial pathogen and limits our understanding of its pathogenicity. As for many other genomes, the annotations of proteins in the MTB proteome were generally inferred from sequence homology, which is effective but its applicability has limitations. We have carried out large-scale biological data integration to produce an MTB protein functional interaction network. Protein functional relationships were extracted from the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and additional functional interactions from microarray, sequence and protein signature data. The confidence level of protein relationships in the additional functional interaction data was evaluated using a dynamic data-driven scoring system. This functional network has been used to predict functions of uncharacterized proteins using Gene Ontology (GO) terms, and the semantic similarity between these terms measured using a state-of-the-art GO similarity metric. To achieve better trade-off between improvement of quality, genomic coverage and scalability, this prediction is done by observing the key principles driving the biological organization of the functional network. This study yields a new functionally characterized MTB strain CDC1551 proteome, consisting of 3804 and 3698 proteins out of 4195 with annotations in terms of the biological process and molecular function ontologies, respectively. These data can contribute to research into the Development of effective anti-tubercular drugs with novel biological mechanisms of action. Copyright © 2011 Elsevier B.V. All rights reserved.

In silico design of porous polymer networks: high-throughput screening for methane storage materials.

PubMed

Martin, Richard L; Simon, Cory M; Smit, Berend; Haranczyk, Maciej

2014-04-02

Porous polymer networks (PPNs) are a class of advanced porous materials that combine the advantages of cheap and stable polymers with the high surface areas and tunable chemistry of metal-organic frameworks. They are of particular interest for gas separation or storage applications, for instance, as methane adsorbents for a vehicular natural gas tank or other portable applications. PPNs are self-assembled from distinct building units; here, we utilize commercially available chemical fragments and two experimentally known synthetic routes to design in silico a large database of synthetically realistic PPN materials. All structures from our database of 18,000 materials have been relaxed with semiempirical electronic structure methods and characterized with Grand-canonical Monte Carlo simulations for methane uptake and deliverable (working) capacity. A number of novel structure-property relationships that govern methane storage performance were identified. The relationships are translated into experimental guidelines to realize the ideal PPN structure. We found that cooperative methane-methane attractions were present in all of the best-performing materials, highlighting the importance of guest interaction in the design of optimal materials for methane storage.

BioNetSim: a Petri net-based modeling tool for simulations of biochemical processes.

PubMed

Gao, Junhui; Li, Li; Wu, Xiaolin; Wei, Dong-Qing

2012-03-01

BioNetSim, a Petri net-based software for modeling and simulating biochemistry processes, is developed, whose design and implement are presented in this paper, including logic construction, real-time access to KEGG (Kyoto Encyclopedia of Genes and Genomes), and BioModel database. Furthermore, glycolysis is simulated as an example of its application. BioNetSim is a helpful tool for researchers to download data, model biological network, and simulate complicated biochemistry processes. Gene regulatory networks, metabolic pathways, signaling pathways, and kinetics of cell interaction are all available in BioNetSim, which makes modeling more efficient and effective. Similar to other Petri net-based softwares, BioNetSim does well in graphic application and mathematic construction. Moreover, it shows several powerful predominances. (1) It creates models in database. (2) It realizes the real-time access to KEGG and BioModel and transfers data to Petri net. (3) It provides qualitative analysis, such as computation of constants. (4) It generates graphs for tracing the concentration of every molecule during the simulation processes.

Modeling of cell signaling pathways in macrophages by semantic networks

PubMed Central

Hsing, Michael; Bellenson, Joel L; Shankey, Conor; Cherkasov, Artem

2004-01-01

Background Substantial amounts of data on cell signaling, metabolic, gene regulatory and other biological pathways have been accumulated in literature and electronic databases. Conventionally, this information is stored in the form of pathway diagrams and can be characterized as highly "compartmental" (i.e. individual pathways are not connected into more general networks). Current approaches for representing pathways are limited in their capacity to model molecular interactions in their spatial and temporal context. Moreover, the critical knowledge of cause-effect relationships among signaling events is not reflected by most conventional approaches for manipulating pathways. Results We have applied a semantic network (SN) approach to develop and implement a model for cell signaling pathways. The semantic model has mapped biological concepts to a set of semantic agents and relationships, and characterized cell signaling events and their participants in the hierarchical and spatial context. In particular, the available information on the behaviors and interactions of the PI3K enzyme family has been integrated into the SN environment and a cell signaling network in human macrophages has been constructed. A SN-application has been developed to manipulate the locations and the states of molecules and to observe their actions under different biological scenarios. The approach allowed qualitative simulation of cell signaling events involving PI3Ks and identified pathways of molecular interactions that led to known cellular responses as well as other potential responses during bacterial invasions in macrophages. Conclusions We concluded from our results that the semantic network is an effective method to model cell signaling pathways. The semantic model allows proper representation and integration of information on biological structures and their interactions at different levels. The reconstruction of the cell signaling network in the macrophage allowed detailed investigation of connections among various essential molecules and reflected the cause-effect relationships among signaling events. The simulation demonstrated the dynamics of the semantic network, where a change of states on a molecule can alter its function and potentially cause a chain-reaction effect in the system. PMID:15494071

BioSYNTHESIS: access to a knowledge network of health sciences databases.

PubMed

Broering, N C; Hylton, J S; Guttmann, R; Eskridge, D

1991-04-01

Users of the IAIMS Knowledge Network at the Georgetown University Medical Center have access to multiple in-house and external databases from a single point of entry through BioSYNTHESIS. The IAIMS project has developed a rich environment of biomedical information resources that represent a medical decision support system for campus physicians and students. The BioSYNTHESIS system is an information navigator that provides transparent access to a Knowledge Network of over a dozen databases. These multiple health sciences databases consist of bibliographic, informational, diagnostic, and research systems which reside on diverse computers such as DEC VAXs, SUN 490, AT&T 3B2s, Macintoshes, IBM PC/PS2s and the AT&T ISN and SYTEK network systems. Ethernet and TCP/IP protocols are used in the network architecture. BioSYNTHESIS also provides network links to the other campus libraries and to external institutions. As additional knowledge resources and technological advances have become available. BioSYNTHESIS has evolved from a two phase to a three phase program. Major components of the system including recent achievements and future plans are described.

A systematic study of chemogenomics of carbohydrates.

PubMed

Gu, Jiangyong; Luo, Fang; Chen, Lirong; Yuan, Gu; Xu, Xiaojie

2014-03-04

Chemogenomics focuses on the interactions between biologically active molecules and protein targets for drug discovery. Carbohydrates are the most abundant compounds in natural products. Compared with other drugs, the carbohydrate drugs show weaker side effects. Searching for multi-target carbohydrate drugs can be regarded as a solution to improve therapeutic efficacy and safety. In this work, we collected 60 344 carbohydrates from the Universal Natural Products Database (UNPD) and explored the chemical space of carbohydrates by principal component analysis. We found that there is a large quantity of potential lead compounds among carbohydrates. Then we explored the potential of carbohydrates in drug discovery by using a network-based multi-target computational approach. All carbohydrates were docked to 2389 target proteins. The most potential carbohydrates for drug discovery and their indications were predicted based on a docking score-weighted prediction model. We also explored the interactions between carbohydrates and target proteins to find the pathological networks, potential drug candidates and new indications.

MGDB: a comprehensive database of genes involved in melanoma.

PubMed

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

2015-01-01

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

An Examination of Job Skills Posted on Internet Databases: Implications for Information Systems Degree Programs.

ERIC Educational Resources Information Center

Liu, Xia; Liu, Lai C.; Koong, Kai S.; Lu, June

2003-01-01

Analysis of 300 information technology job postings in two Internet databases identified the following skill categories: programming languages (Java, C/C++, and Visual Basic were most frequent); website development (57% sought SQL and HTML skills); databases (nearly 50% required Oracle); networks (only Windows NT or wide-area/local-area networks);…

Experience in running relational databases on clustered storage

NASA Astrophysics Data System (ADS)

Gaspar Aparicio, Ruben; Potocky, Miroslav

2015-12-01

For past eight years, CERN IT Database group has based its backend storage on NAS (Network-Attached Storage) architecture, providing database access via NFS (Network File System) protocol. In last two and half years, our storage has evolved from a scale-up architecture to a scale-out one. This paper describes our setup and a set of functionalities providing key features to other services like Database on Demand [1] or CERN Oracle backup and recovery service. It also outlines possible trend of evolution that, storage for databases could follow.

PlaMoM: a comprehensive database compiles plant mobile macromolecules.

PubMed

Guan, Daogang; Yan, Bin; Thieme, Christoph; Hua, Jingmin; Zhu, Hailong; Boheler, Kenneth R; Zhao, Zhongying; Kragler, Friedrich; Xia, Yiji; Zhang, Shoudong

2017-01-04

In plants, various phloem-mobile macromolecules including noncoding RNAs, mRNAs and proteins are suggested to act as important long-distance signals in regulating crucial physiological and morphological transition processes such as flowering, plant growth and stress responses. Given recent advances in high-throughput sequencing technologies, numerous mobile macromolecules have been identified in diverse plant species from different plant families. However, most of the identified mobile macromolecules are not annotated in current versions of species-specific databases and are only available as non-searchable datasheets. To facilitate study of the mobile signaling macromolecules, we compiled the PlaMoM (Plant Mobile Macromolecules) database, a resource that provides convenient and interactive search tools allowing users to retrieve, to analyze and also to predict mobile RNAs/proteins. Each entry in the PlaMoM contains detailed information such as nucleotide/amino acid sequences, ortholog partners, related experiments, gene functions and literature. For the model plant Arabidopsis thaliana, protein-protein interactions of mobile transcripts are presented as interactive molecular networks. Furthermore, PlaMoM provides a built-in tool to identify potential RNA mobility signals such as tRNA-like structures. The current version of PlaMoM compiles a total of 17 991 mobile macromolecules from 14 plant species/ecotypes from published data and literature. PlaMoM is available at http://www.systembioinfo.org/plamom/. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Predicting language diversity with complex networks.

PubMed

Raducha, Tomasz; Gubiec, Tomasz

2018-01-01

We analyze the model of social interactions with coevolution of the topology and states of the nodes. This model can be interpreted as a model of language change. We propose different rewiring mechanisms and perform numerical simulations for each. Obtained results are compared with the empirical data gathered from two online databases and anthropological study of Solomon Islands. We study the behavior of the number of languages for different system sizes and we find that only local rewiring, i.e. triadic closure, is capable of reproducing results for the empirical data in a qualitative manner. Furthermore, we cancel the contradiction between previous models and the Solomon Islands case. Our results demonstrate the importance of the topology of the network, and the rewiring mechanism in the process of language change.

Recognition of degraded handwritten digits using dynamic Bayesian networks

NASA Astrophysics Data System (ADS)

Likforman-Sulem, Laurence; Sigelle, Marc

2007-01-01

We investigate in this paper the application of dynamic Bayesian networks (DBNs) to the recognition of handwritten digits. The main idea is to couple two separate HMMs into various architectures. First, a vertical HMM and a horizontal HMM are built observing the evolving streams of image columns and image rows respectively. Then, two coupled architectures are proposed to model interactions between these two streams and to capture the 2D nature of character images. Experiments performed on the MNIST handwritten digit database show that coupled architectures yield better recognition performances than non-coupled ones. Additional experiments conducted on artificially degraded (broken) characters demonstrate that coupled architectures better cope with such degradation than non coupled ones and than discriminative methods such as SVMs.

Functional network analysis of genes differentially expressed during xylogenesis in soc1ful woody Arabidopsis plants.

PubMed

Davin, Nicolas; Edger, Patrick P; Hefer, Charles A; Mizrachi, Eshchar; Schuetz, Mathias; Smets, Erik; Myburg, Alexander A; Douglas, Carl J; Schranz, Michael E; Lens, Frederic

2016-06-01

Many plant genes are known to be involved in the development of cambium and wood, but how the expression and functional interaction of these genes determine the unique biology of wood remains largely unknown. We used the soc1ful loss of function mutant - the woodiest genotype known in the otherwise herbaceous model plant Arabidopsis - to investigate the expression and interactions of genes involved in secondary growth (wood formation). Detailed anatomical observations of the stem in combination with mRNA sequencing were used to assess transcriptome remodeling during xylogenesis in wild-type and woody soc1ful plants. To interpret the transcriptome changes, we constructed functional gene association networks of differentially expressed genes using the STRING database. This analysis revealed functionally enriched gene association hubs that are differentially expressed in herbaceous and woody tissues. In particular, we observed the differential expression of genes related to mechanical stress and jasmonate biosynthesis/signaling during wood formation in soc1ful plants that may be an effect of greater tension within woody tissues. Our results suggest that habit shifts from herbaceous to woody life forms observed in many angiosperm lineages could have evolved convergently by genetic changes that modulate the gene expression and interaction network, and thereby redeploy the conserved wood developmental program. © 2016 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.

A Physics-driven Neural Networks-based Simulation System (PhyNNeSS) for multimodal interactive virtual environments involving nonlinear deformable objects

PubMed Central

De, Suvranu; Deo, Dhannanjay; Sankaranarayanan, Ganesh; Arikatla, Venkata S.

2012-01-01

Background While an update rate of 30 Hz is considered adequate for real time graphics, a much higher update rate of about 1 kHz is necessary for haptics. Physics-based modeling of deformable objects, especially when large nonlinear deformations and complex nonlinear material properties are involved, at these very high rates is one of the most challenging tasks in the development of real time simulation systems. While some specialized solutions exist, there is no general solution for arbitrary nonlinearities. Methods In this work we present PhyNNeSS - a Physics-driven Neural Networks-based Simulation System - to address this long-standing technical challenge. The first step is an off-line pre-computation step in which a database is generated by applying carefully prescribed displacements to each node of the finite element models of the deformable objects. In the next step, the data is condensed into a set of coefficients describing neurons of a Radial Basis Function network (RBFN). During real-time computation, these neural networks are used to reconstruct the deformation fields as well as the interaction forces. Results We present realistic simulation examples from interactive surgical simulation with real time force feedback. As an example, we have developed a deformable human stomach model and a Penrose-drain model used in the Fundamentals of Laparoscopic Surgery (FLS) training tool box. Conclusions A unique computational modeling system has been developed that is capable of simulating the response of nonlinear deformable objects in real time. The method distinguishes itself from previous efforts in that a systematic physics-based pre-computational step allows training of neural networks which may be used in real time simulations. We show, through careful error analysis, that the scheme is scalable, with the accuracy being controlled by the number of neurons used in the simulation. PhyNNeSS has been integrated into SoFMIS (Software Framework for Multimodal Interactive Simulation) for general use. PMID:22629108

Neural Network Modeling of UH-60A Pilot Vibration

NASA Technical Reports Server (NTRS)

Kottapalli, Sesi

2003-01-01

Full-scale flight-test pilot floor vibration is modeled using neural networks and full-scale wind tunnel test data for low speed level flight conditions. Neural network connections between the wind tunnel test data and the tlxee flight test pilot vibration components (vertical, lateral, and longitudinal) are studied. Two full-scale UH-60A Black Hawk databases are used. The first database is the NASMArmy UH-60A Airloads Program flight test database. The second database is the UH-60A rotor-only wind tunnel database that was acquired in the NASA Ames SO- by 120- Foot Wind Tunnel with the Large Rotor Test Apparatus (LRTA). Using neural networks, the flight-test pilot vibration is modeled using the wind tunnel rotating system hub accelerations, and separately, using the hub loads. The results show that the wind tunnel rotating system hub accelerations and the operating parameters can represent the flight test pilot vibration. The six components of the wind tunnel N/rev balance-system hub loads and the operating parameters can also represent the flight test pilot vibration. The present neural network connections can significandy increase the value of wind tunnel testing.

A dedicated database system for handling multi-level data in systems biology.

PubMed

Pornputtapong, Natapol; Wanichthanarak, Kwanjeera; Nilsson, Avlant; Nookaew, Intawat; Nielsen, Jens

2014-01-01

Advances in high-throughput technologies have enabled extensive generation of multi-level omics data. These data are crucial for systems biology research, though they are complex, heterogeneous, highly dynamic, incomplete and distributed among public databases. This leads to difficulties in data accessibility and often results in errors when data are merged and integrated from varied resources. Therefore, integration and management of systems biological data remain very challenging. To overcome this, we designed and developed a dedicated database system that can serve and solve the vital issues in data management and hereby facilitate data integration, modeling and analysis in systems biology within a sole database. In addition, a yeast data repository was implemented as an integrated database environment which is operated by the database system. Two applications were implemented to demonstrate extensibility and utilization of the system. Both illustrate how the user can access the database via the web query function and implemented scripts. These scripts are specific for two sample cases: 1) Detecting the pheromone pathway in protein interaction networks; and 2) Finding metabolic reactions regulated by Snf1 kinase. In this study we present the design of database system which offers an extensible environment to efficiently capture the majority of biological entities and relations encountered in systems biology. Critical functions and control processes were designed and implemented to ensure consistent, efficient, secure and reliable transactions. The two sample cases on the yeast integrated data clearly demonstrate the value of a sole database environment for systems biology research.

Web-based access to near real-time and archived high-density time-series data: cyber infrastructure challenges & developments in the open-source Waveform Server

NASA Astrophysics Data System (ADS)

Reyes, J. C.; Vernon, F. L.; Newman, R. L.; Steidl, J. H.

2010-12-01

The Waveform Server is an interactive web-based interface to multi-station, multi-sensor and multi-channel high-density time-series data stored in Center for Seismic Studies (CSS) 3.0 schema relational databases (Newman et al., 2009). In the last twelve months, based on expanded specifications and current user feedback, both the server-side infrastructure and client-side interface have been extensively rewritten. The Python Twisted server-side code-base has been fundamentally modified to now present waveform data stored in cluster-based databases using a multi-threaded architecture, in addition to supporting the pre-existing single database model. This allows interactive web-based access to high-density (broadband @ 40Hz to strong motion @ 200Hz) waveform data that can span multiple years; the common lifetime of broadband seismic networks. The client-side interface expands on it's use of simple JSON-based AJAX queries to now incorporate a variety of User Interface (UI) improvements including standardized calendars for defining time ranges, applying on-the-fly data calibration to display SI-unit data, and increased rendering speed. This presentation will outline the various cyber infrastructure challenges we have faced while developing this application, the use-cases currently in existence, and the limitations of web-based application development.

A comprehensive map of the mTOR signaling network

PubMed Central

Caron, Etienne; Ghosh, Samik; Matsuoka, Yukiko; Ashton-Beaucage, Dariel; Therrien, Marc; Lemieux, Sébastien; Perreault, Claude; Roux, Philippe P; Kitano, Hiroaki

2010-01-01

The mammalian target of rapamycin (mTOR) is a central regulator of cell growth and proliferation. mTOR signaling is frequently dysregulated in oncogenic cells, and thus an attractive target for anticancer therapy. Using CellDesigner, a modeling support software for graphical notation, we present herein a comprehensive map of the mTOR signaling network, which includes 964 species connected by 777 reactions. The map complies with both the systems biology markup language (SBML) and graphical notation (SBGN) for computational analysis and graphical representation, respectively. As captured in the mTOR map, we review and discuss our current understanding of the mTOR signaling network and highlight the impact of mTOR feedback and crosstalk regulations on drug-based cancer therapy. This map is available on the Payao platform, a Web 2.0 based community-wide interactive process for creating more accurate and information-rich databases. Thus, this comprehensive map of the mTOR network will serve as a tool to facilitate systems-level study of up-to-date mTOR network components and signaling events toward the discovery of novel regulatory processes and therapeutic strategies for cancer. PMID:21179025

Insight: An ontology-based integrated database and analysis platform for epilepsy self-management research.

PubMed

Sahoo, Satya S; Ramesh, Priya; Welter, Elisabeth; Bukach, Ashley; Valdez, Joshua; Tatsuoka, Curtis; Bamps, Yvan; Stoll, Shelley; Jobst, Barbara C; Sajatovic, Martha

2016-10-01

We present Insight as an integrated database and analysis platform for epilepsy self-management research as part of the national Managing Epilepsy Well Network. Insight is the only available informatics platform for accessing and analyzing integrated data from multiple epilepsy self-management research studies with several new data management features and user-friendly functionalities. The features of Insight include, (1) use of Common Data Elements defined by members of the research community and an epilepsy domain ontology for data integration and querying, (2) visualization tools to support real time exploration of data distribution across research studies, and (3) an interactive visual query interface for provenance-enabled research cohort identification. The Insight platform contains data from five completed epilepsy self-management research studies covering various categories of data, including depression, quality of life, seizure frequency, and socioeconomic information. The data represents over 400 participants with 7552 data points. The Insight data exploration and cohort identification query interface has been developed using Ruby on Rails Web technology and open source Web Ontology Language Application Programming Interface to support ontology-based reasoning. We have developed an efficient ontology management module that automatically updates the ontology mappings each time a new version of the Epilepsy and Seizure Ontology is released. The Insight platform features a Role-based Access Control module to authenticate and effectively manage user access to different research studies. User access to Insight is managed by the Managing Epilepsy Well Network database steering committee consisting of representatives of all current collaborating centers of the Managing Epilepsy Well Network. New research studies are being continuously added to the Insight database and the size as well as the unique coverage of the dataset allows investigators to conduct aggregate data analysis that will inform the next generation of epilepsy self-management studies. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Abnormal DNA methylation may contribute to the progression of osteosarcoma.

PubMed

Chen, Xiao-Gang; Ma, Liang; Xu, Jia-Xin

2018-01-01

The identification of optimal methylation biomarkers to achieve maximum diagnostic ability remains a challenge. The present study aimed to elucidate the potential molecular mechanisms underlying osteosarcoma (OS) using DNA methylation analysis. Based on the GSE36002 dataset obtained from the Gene Expression Omnibus database, differentially methylated genes were extracted between patients with OS and controls using t‑tests. Subsequently, hierarchical clustering was performed to segregate the samples into two distinct clusters, OS and normal. Gene Ontology (GO) and pathway enrichment analyses for differentially methylated genes were performed using the Database for Annotation, Visualization and Integrated Discovery tool. A protein‑protein interaction (PPI) network was established, followed by hub gene identification. Using the cut‑off threshold of ≥0.2 average β‑value difference, 3,725 unique CpGs (2,862 genes) were identified to be differentially methylated between the OS and normal groups. Among these 2,862 genes, 510 genes were differentially hypermethylated and 2,352 were differentially hypomethylated. The differentially hypermethylated genes were primarily involved in 20 GO terms, and the top 3 terms were associated with potassium ion transport. For differentially hypomethylated genes, GO functions principally included passive transmembrane transporter activity, channel activity and metal ion transmembrane transporter activity. In addition, a total of 10 significant pathways were enriched by differentially hypomethylated genes; notably, neuroactive ligand‑receptor interaction was the most significant pathway. Based on a connectivity degree >90, 7 hub genes were selected from the PPI network, including neuromedin U (NMU; degree=103) and NMU receptor 1 (NMUR1; degree=103). Functional terms (potassium ion transport, transmembrane transporter activity, and neuroactive ligand‑receptor interaction) and hub genes (NMU and NMUR1) may serve as potential targets for the treatment and diagnosis of OS.

Knowledge representation in metabolic pathway databases.

PubMed

Stobbe, Miranda D; Jansen, Gerbert A; Moerland, Perry D; van Kampen, Antoine H C

2014-05-01

The accurate representation of all aspects of a metabolic network in a structured format, such that it can be used for a wide variety of computational analyses, is a challenge faced by a growing number of researchers. Analysis of five major metabolic pathway databases reveals that each database has made widely different choices to address this challenge, including how to deal with knowledge that is uncertain or missing. In concise overviews, we show how concepts such as compartments, enzymatic complexes and the direction of reactions are represented in each database. Importantly, also concepts which a database does not represent are described. Which aspects of the metabolic network need to be available in a structured format and to what detail differs per application. For example, for in silico phenotype prediction, a detailed representation of gene-protein-reaction relations and the compartmentalization of the network is essential. Our analysis also shows that current databases are still limited in capturing all details of the biology of the metabolic network, further illustrated with a detailed analysis of three metabolic processes. Finally, we conclude that the conceptual differences between the databases, which make knowledge exchange and integration a challenge, have not been resolved, so far, by the exchange formats in which knowledge representation is standardized.

Database of Industrial Technological Information in Kanagawa : Networks for Technology Activities

NASA Astrophysics Data System (ADS)

Saito, Akira; Shindo, Tadashi

This system is one of the databases which require participation by its members and of which premise is to open all the data in it. Aiming at free technological cooperation and exchange among industries it was constructed by Kanagawa Prefecture in collaboration with enterprises located in it. The input data is 36 items such as major product, special and advantageous technology, technolagy to be wanted for cooperation, facility and equipment, which technologically characterize each enterprise. They are expressed in 2,000 characters and written by natural language including Kanji except for some coded items. 24 search items are accessed by natural language so that in addition to interactive searching procedures including menu-type it enables extensive searching. The information service started in Oct., 1986 covering data from 2,000 enterprisen.

A study of the Immune Epitope Database for some fungi species using network topological indices.

PubMed

Vázquez-Prieto, Severo; Paniagua, Esperanza; Solana, Hugo; Ubeira, Florencio M; González-Díaz, Humberto

2017-08-01

In the last years, the encryption of system structure information with different network topological indices has been a very active field of research. In the present study, we assembled for the first time a complex network using data obtained from the Immune Epitope Database for fungi species, and we then considered the general topology, the node degree distribution, and the local structure of this network. We also calculated eight node centrality measures for the observed network and compared it with three theoretical models. In view of the results obtained, we may expect that the present approach can become a valuable tool to explore the complexity of this database, as well as for the storage, manipulation, comparison, and retrieval of information contained therein.

Minimum reaction network necessary to describe Ar/CF4 plasma etch

NASA Astrophysics Data System (ADS)

Helpert, Sofia; Chopra, Meghali; Bonnecaze, Roger T.

2018-03-01

Predicting the etch and deposition profiles created using plasma processes is challenging due to the complexity of plasma discharges and plasma-surface interactions. Volume-averaged global models allow for efficient prediction of important processing parameters and provide a means to quickly determine the effect of a variety of process inputs on the plasma discharge. However, global models are limited based on simplifying assumptions to describe the chemical reaction network. Here a database of 128 reactions is compiled and their corresponding rate constants collected from 24 sources for an Ar/CF4 plasma using the platform RODEo (Recipe Optimization for Deposition and Etching). Six different reaction sets were tested which employed anywhere from 12 to all 128 reactions to evaluate the impact of the reaction database on particle species densities and electron temperature. Because many the reactions used in our database had conflicting rate constants as reported in literature, we also present a method to deal with those uncertainties when constructing the model which includes weighting each reaction rate and filtering outliers. By analyzing the link between a reaction's rate constant and its impact on the predicted plasma densities and electron temperatures, we determine the conditions at which a reaction is deemed necessary to the plasma model. The results of this study provide a foundation for determining which minimal set of reactions must be included in the reaction set of the plasma model.

PAGER 2.0: an update to the pathway, annotated-list and gene-signature electronic repository for Human Network Biology

PubMed Central

Yue, Zongliang; Zheng, Qi; Neylon, Michael T; Yoo, Minjae; Shin, Jimin; Zhao, Zhiying; Tan, Aik Choon

2018-01-01

Abstract Integrative Gene-set, Network and Pathway Analysis (GNPA) is a powerful data analysis approach developed to help interpret high-throughput omics data. In PAGER 1.0, we demonstrated that researchers can gain unbiased and reproducible biological insights with the introduction of PAGs (Pathways, Annotated-lists and Gene-signatures) as the basic data representation elements. In PAGER 2.0, we improve the utility of integrative GNPA by significantly expanding the coverage of PAGs and PAG-to-PAG relationships in the database, defining a new metric to quantify PAG data qualities, and developing new software features to simplify online integrative GNPA. Specifically, we included 84 282 PAGs spanning 24 different data sources that cover human diseases, published gene-expression signatures, drug–gene, miRNA–gene interactions, pathways and tissue-specific gene expressions. We introduced a new normalized Cohesion Coefficient (nCoCo) score to assess the biological relevance of genes inside a PAG, and RP-score to rank genes and assign gene-specific weights inside a PAG. The companion web interface contains numerous features to help users query and navigate the database content. The database content can be freely downloaded and is compatible with third-party Gene Set Enrichment Analysis tools. We expect PAGER 2.0 to become a major resource in integrative GNPA. PAGER 2.0 is available at http://discovery.informatics.uab.edu/PAGER/. PMID:29126216

Working with Specify in a Paleo-Geological Context

NASA Astrophysics Data System (ADS)

Molineux, A.; Thompson, A. C.; Appleton, L.

2014-12-01

For geological collections with limited funding an open source relational database provides an opportunity to digitize specimens and related data. At the Non-vertebrate Paleontology Lab, a large mixed paleo and geological repository on a restricted budget, we opted for one such database, Specify. Initially created at Kansas University for neontological collections and based on a single computer, Specify has moved into the networked scene and will soon be web-based as Specify 7. We currently use the server version of Specify 6, networked to all computers in the lab each running a desktop client, often with six users at any one time. Along with improved access there have been great efforts to broaden the applicability of this database to other disciplines. Current developments are of great importance to us because they focus on the geological aspects of lithostratigraphy and chronostratigaphy and their relationship to other variables. Adoption of this software has required constant change as we move to take advantage of the great improvements. We enjoy the interaction with the developers and their willingness to listen and consider our issues. Here we discuss some of the ways in which we have fashioned Specify into a database that provides us with the flexibility that we need without removing the ability to share our data with other aggregators through accepted protocols. We discuss the customization of forms, the attachment of media and tracking of original media files, our efforts to incorporate geological specimens, and our plans to link the individual specimen record GUIDs to an IGSN numbers and thence to future connections to data derived from our specimens.

An approach to efficient mobility management in intelligent networks

NASA Technical Reports Server (NTRS)

Murthy, K. M. S.

1995-01-01

Providing personal communication systems supporting full mobility require intelligent networks for tracking mobile users and facilitating outgoing and incoming calls over different physical and network environments. In realizing the intelligent network functionalities, databases play a major role. Currently proposed network architectures envision using the SS7-based signaling network for linking these DB's and also for interconnecting DB's with switches. If the network has to support ubiquitous, seamless mobile services, then it has to support additionally mobile application parts, viz., mobile origination calls, mobile destination calls, mobile location updates and inter-switch handovers. These functions will generate significant amount of data and require them to be transferred between databases (HLR, VLR) and switches (MSC's) very efficiently. In the future, the users (fixed or mobile) may use and communicate with sophisticated CPE's (e.g. multimedia, multipoint and multisession calls) which may require complex signaling functions. This will generate volumness service handling data and require efficient transfer of these message between databases and switches. Consequently, the network providers would be able to add new services and capabilities to their networks incrementally, quickly and cost-effectively.

iCAVE: an open source tool for visualizing biomolecular networks in 3D, stereoscopic 3D and immersive 3D

PubMed Central

Liluashvili, Vaja; Kalayci, Selim; Fluder, Eugene; Wilson, Manda; Gabow, Aaron

2017-01-01

Abstract Visualizations of biomolecular networks assist in systems-level data exploration in many cellular processes. Data generated from high-throughput experiments increasingly inform these networks, yet current tools do not adequately scale with concomitant increase in their size and complexity. We present an open source software platform, interactome-CAVE (iCAVE), for visualizing large and complex biomolecular interaction networks in 3D. Users can explore networks (i) in 3D using a desktop, (ii) in stereoscopic 3D using 3D-vision glasses and a desktop, or (iii) in immersive 3D within a CAVE environment. iCAVE introduces 3D extensions of known 2D network layout, clustering, and edge-bundling algorithms, as well as new 3D network layout algorithms. Furthermore, users can simultaneously query several built-in databases within iCAVE for network generation or visualize their own networks (e.g., disease, drug, protein, metabolite). iCAVE has modular structure that allows rapid development by addition of algorithms, datasets, or features without affecting other parts of the code. Overall, iCAVE is the first freely available open source tool that enables 3D (optionally stereoscopic or immersive) visualizations of complex, dense, or multi-layered biomolecular networks. While primarily designed for researchers utilizing biomolecular networks, iCAVE can assist researchers in any field. PMID:28814063

iCAVE: an open source tool for visualizing biomolecular networks in 3D, stereoscopic 3D and immersive 3D.

PubMed

Liluashvili, Vaja; Kalayci, Selim; Fluder, Eugene; Wilson, Manda; Gabow, Aaron; Gümüs, Zeynep H

2017-08-01

Visualizations of biomolecular networks assist in systems-level data exploration in many cellular processes. Data generated from high-throughput experiments increasingly inform these networks, yet current tools do not adequately scale with concomitant increase in their size and complexity. We present an open source software platform, interactome-CAVE (iCAVE), for visualizing large and complex biomolecular interaction networks in 3D. Users can explore networks (i) in 3D using a desktop, (ii) in stereoscopic 3D using 3D-vision glasses and a desktop, or (iii) in immersive 3D within a CAVE environment. iCAVE introduces 3D extensions of known 2D network layout, clustering, and edge-bundling algorithms, as well as new 3D network layout algorithms. Furthermore, users can simultaneously query several built-in databases within iCAVE for network generation or visualize their own networks (e.g., disease, drug, protein, metabolite). iCAVE has modular structure that allows rapid development by addition of algorithms, datasets, or features without affecting other parts of the code. Overall, iCAVE is the first freely available open source tool that enables 3D (optionally stereoscopic or immersive) visualizations of complex, dense, or multi-layered biomolecular networks. While primarily designed for researchers utilizing biomolecular networks, iCAVE can assist researchers in any field. © The Authors 2017. Published by Oxford University Press.

Evolutionary Dynamics of Collective Action in Structured Populations

NASA Astrophysics Data System (ADS)

Santos, Marta Daniela de Almeida

The pervasiveness of cooperation in Nature is not easily explained. If evolution is characterized by competition and survival of the fittest, why should selfish individuals cooperate with each other? Evolutionary Game Theory (EGT) provides a suitable mathematical framework to study this problem, central to many areas of science. Conventionally, interactions between individuals are modeled in terms of one-shot, symmetric 2-Person Dilemmas of Cooperation, but many real-life situations involve decisions within groups with more than 2 individuals, which are best-dealt in the framework of N-Person games. In this Thesis, we investigate the evolutionary dynamics of two paradigmatic collective social dilemmas - the N-Person Prisoner's Dilemma (NPD) and the N-Person Snowdrift Game (NSG) on structured populations, modeled by networks with diverse topological properties. Cooperative strategies are just one example of the many traits that can be transmitted on social networks. Several recent studies based on empirical evidence from a medical database have suggested the existence of a 3 degrees of influence rule, according to which not only our "friends", but also our friends' friends, and our friends' friends' friends, have a non-trivial influence on our decisions. We investigate the degree of peer influence that emerges from the spread of cooperative strategies, opinions and diseases on populations with distinct underlying networks of contacts. Our results show that networks naturally entangle individuals into interactions of many-body nature and that for each network class considered different processes lead to identical degrees of influence. None

SignaLink 2 – a signaling pathway resource with multi-layered regulatory networks

PubMed Central

2013-01-01

Background Signaling networks in eukaryotes are made up of upstream and downstream subnetworks. The upstream subnetwork contains the intertwined network of signaling pathways, while the downstream regulatory part contains transcription factors and their binding sites on the DNA as well as microRNAs and their mRNA targets. Currently, most signaling and regulatory databases contain only a subsection of this network, making comprehensive analyses highly time-consuming and dependent on specific data handling expertise. The need for detailed mapping of signaling systems is also supported by the fact that several drug development failures were caused by undiscovered cross-talk or regulatory effects of drug targets. We previously created a uniformly curated signaling pathway resource, SignaLink, to facilitate the analysis of pathway cross-talks. Here, we present SignaLink 2, which significantly extends the coverage and applications of its predecessor. Description We developed a novel concept to integrate and utilize different subsections (i.e., layers) of the signaling network. The multi-layered (onion-like) database structure is made up of signaling pathways, their pathway regulators (e.g., scaffold and endocytotic proteins) and modifier enzymes (e.g., phosphatases, ubiquitin ligases), as well as transcriptional and post-transcriptional regulators of all of these components. The user-friendly website allows the interactive exploration of how each signaling protein is regulated. The customizable download page enables the analysis of any user-specified part of the signaling network. Compared to other signaling resources, distinctive features of SignaLink 2 are the following: 1) it involves experimental data not only from humans but from two invertebrate model organisms, C. elegans and D. melanogaster; 2) combines manual curation with large-scale datasets; 3) provides confidence scores for each interaction; 4) operates a customizable download page with multiple file formats (e.g., BioPAX, Cytoscape, SBML). Non-profit users can access SignaLink 2 free of charge at http://SignaLink.org. Conclusions With SignaLink 2 as a single resource, users can effectively analyze signaling pathways, scaffold proteins, modifier enzymes, transcription factors and miRNAs that are important in the regulation of signaling processes. This integrated resource allows the systems-level examination of how cross-talks and signaling flow are regulated, as well as provide data for cross-species comparisons and drug discovery analyses. PMID:23331499

SignaLink 2 - a signaling pathway resource with multi-layered regulatory networks.

PubMed

Fazekas, Dávid; Koltai, Mihály; Türei, Dénes; Módos, Dezső; Pálfy, Máté; Dúl, Zoltán; Zsákai, Lilian; Szalay-Bekő, Máté; Lenti, Katalin; Farkas, Illés J; Vellai, Tibor; Csermely, Péter; Korcsmáros, Tamás

2013-01-18

Signaling networks in eukaryotes are made up of upstream and downstream subnetworks. The upstream subnetwork contains the intertwined network of signaling pathways, while the downstream regulatory part contains transcription factors and their binding sites on the DNA as well as microRNAs and their mRNA targets. Currently, most signaling and regulatory databases contain only a subsection of this network, making comprehensive analyses highly time-consuming and dependent on specific data handling expertise. The need for detailed mapping of signaling systems is also supported by the fact that several drug development failures were caused by undiscovered cross-talk or regulatory effects of drug targets. We previously created a uniformly curated signaling pathway resource, SignaLink, to facilitate the analysis of pathway cross-talks. Here, we present SignaLink 2, which significantly extends the coverage and applications of its predecessor. We developed a novel concept to integrate and utilize different subsections (i.e., layers) of the signaling network. The multi-layered (onion-like) database structure is made up of signaling pathways, their pathway regulators (e.g., scaffold and endocytotic proteins) and modifier enzymes (e.g., phosphatases, ubiquitin ligases), as well as transcriptional and post-transcriptional regulators of all of these components. The user-friendly website allows the interactive exploration of how each signaling protein is regulated. The customizable download page enables the analysis of any user-specified part of the signaling network. Compared to other signaling resources, distinctive features of SignaLink 2 are the following: 1) it involves experimental data not only from humans but from two invertebrate model organisms, C. elegans and D. melanogaster; 2) combines manual curation with large-scale datasets; 3) provides confidence scores for each interaction; 4) operates a customizable download page with multiple file formats (e.g., BioPAX, Cytoscape, SBML). Non-profit users can access SignaLink 2 free of charge at http://SignaLink.org. With SignaLink 2 as a single resource, users can effectively analyze signaling pathways, scaffold proteins, modifier enzymes, transcription factors and miRNAs that are important in the regulation of signaling processes. This integrated resource allows the systems-level examination of how cross-talks and signaling flow are regulated, as well as provide data for cross-species comparisons and drug discovery analyses.

Phylogenetically informed logic relationships improve detection of biological network organization

PubMed Central

2011-01-01

Background A "phylogenetic profile" refers to the presence or absence of a gene across a set of organisms, and it has been proven valuable for understanding gene functional relationships and network organization. Despite this success, few studies have attempted to search beyond just pairwise relationships among genes. Here we search for logic relationships involving three genes, and explore its potential application in gene network analyses. Results Taking advantage of a phylogenetic matrix constructed from the large orthologs database Roundup, we invented a method to create balanced profiles for individual triplets of genes that guarantee equal weight on the different phylogenetic scenarios of coevolution between genes. When we applied this idea to LAPP, the method to search for logic triplets of genes, the balanced profiles resulted in significant performance improvement and the discovery of hundreds of thousands more putative triplets than unadjusted profiles. We found that logic triplets detected biological network organization and identified key proteins and their functions, ranging from neighbouring proteins in local pathways, to well separated proteins in the whole pathway, and to the interactions among different pathways at the system level. Finally, our case study suggested that the directionality in a logic relationship and the profile of a triplet could disclose the connectivity between the triplet and surrounding networks. Conclusion Balanced profiles are superior to the raw profiles employed by traditional methods of phylogenetic profiling in searching for high order gene sets. Gene triplets can provide valuable information in detection of biological network organization and identification of key genes at different levels of cellular interaction. PMID:22172058

Sub-Network Kernels for Measuring Similarity of Brain Connectivity Networks in Disease Diagnosis.

PubMed

Jie, Biao; Liu, Mingxia; Zhang, Daoqiang; Shen, Dinggang

2018-05-01

As a simple representation of interactions among distributed brain regions, brain networks have been widely applied to automated diagnosis of brain diseases, such as Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment (MCI). In brain network analysis, a challenging task is how to measure the similarity between a pair of networks. Although many graph kernels (i.e., kernels defined on graphs) have been proposed for measuring the topological similarity of a pair of brain networks, most of them are defined using general graphs, thus ignoring the uniqueness of each node in brain networks. That is, each node in a brain network denotes a particular brain region, which is a specific characteristics of brain networks. Accordingly, in this paper, we construct a novel sub-network kernel for measuring the similarity between a pair of brain networks and then apply it to brain disease classification. Different from current graph kernels, our proposed sub-network kernel not only takes into account the inherent characteristic of brain networks, but also captures multi-level (from local to global) topological properties of nodes in brain networks, which are essential for defining the similarity measure of brain networks. To validate the efficacy of our method, we perform extensive experiments on subjects with baseline functional magnetic resonance imaging data obtained from the Alzheimer's disease neuroimaging initiative database. Experimental results demonstrate that the proposed method outperforms several state-of-the-art graph-based methods in MCI classification.

Target gene screening and evaluation of prognostic values in non-small cell lung cancers by bioinformatics analysis.

PubMed

Piao, Junjie; Sun, Jie; Yang, Yang; Jin, Tiefeng; Chen, Liyan; Lin, Zhenhua

2018-03-20

Non-small cell lung cancer (NSCLC) is the major leading cause of cancer-related deaths worldwide. This study aims to explore molecular mechanism of NSCLC. Microarray dataset was obtained from the Gene Expression Omnibus (GEO) database, and analyzed by using GEO2R. Functional and pathway enrichment analysis were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Then, STRING, Cytoscape and MCODE were applied to construct the Protein-protein interaction (PPI) network and screen hub genes. Following, overall survival (OS) analysis of hub genes was performed by using the Kaplan-Meier plotter online tool. Moreover, miRecords was also applied to predict the targets of the differentially expressed microRNAs (DEMs). A total of 228 DEGs were identified, and they were mainly enriched in the terms of cell adhesion molecules, leukocyte transendothelial migration and ECM-receptor interaction. A PPI network was constructed, and 16 hub genes were identified, including TEK, ANGPT1, MMP9, VWF, CDH5, EDN1, ESAM, CCNE1, CDC45, PRC1, CCNB2, AURKA, MELK, CDC20, TOP2A and PTTG1. Among the genes, expressions of 14 hub genes were associated with prognosis of NSCLC patients. Additionally, a total of 11 DEMs were also identified. Our results provide some potential underlying biomarkers for NSCLC. Further studies are required to elucidate the pathogenesis of NSCLC. Copyright © 2018 Elsevier B.V. All rights reserved.

Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes.

PubMed

Hassani-Pak, Keywan; Rawlings, Christopher

2017-06-13

Genetics and "omics" studies designed to uncover genotype to phenotype relationships often identify large numbers of potential candidate genes, among which the causal genes are hidden. Scientists generally lack the time and technical expertise to review all relevant information available from the literature, from key model species and from a potentially wide range of related biological databases in a variety of data formats with variable quality and coverage. Computational tools are needed for the integration and evaluation of heterogeneous information in order to prioritise candidate genes and components of interaction networks that, if perturbed through potential interventions, have a positive impact on the biological outcome in the whole organism without producing negative side effects. Here we review several bioinformatics tools and databases that play an important role in biological knowledge discovery and candidate gene prioritization. We conclude with several key challenges that need to be addressed in order to facilitate biological knowledge discovery in the future.

Intelligent Interfaces for Mining Large-Scale RNAi-HCS Image Databases

PubMed Central

Lin, Chen; Mak, Wayne; Hong, Pengyu; Sepp, Katharine; Perrimon, Norbert

2010-01-01

Recently, High-content screening (HCS) has been combined with RNA interference (RNAi) to become an essential image-based high-throughput method for studying genes and biological networks through RNAi-induced cellular phenotype analyses. However, a genome-wide RNAi-HCS screen typically generates tens of thousands of images, most of which remain uncategorized due to the inadequacies of existing HCS image analysis tools. Until now, it still requires highly trained scientists to browse a prohibitively large RNAi-HCS image database and produce only a handful of qualitative results regarding cellular morphological phenotypes. For this reason we have developed intelligent interfaces to facilitate the application of the HCS technology in biomedical research. Our new interfaces empower biologists with computational power not only to effectively and efficiently explore large-scale RNAi-HCS image databases, but also to apply their knowledge and experience to interactive mining of cellular phenotypes using Content-Based Image Retrieval (CBIR) with Relevance Feedback (RF) techniques. PMID:21278820

Astronomical data analysis software and systems I; Proceedings of the 1st Annual Conference, Tucson, AZ, Nov. 6-8, 1991

NASA Technical Reports Server (NTRS)

Worrall, Diana M. (Editor); Biemesderfer, Chris (Editor); Barnes, Jeannette (Editor)

1992-01-01

Consideration is given to a definition of a distribution format for X-ray data, the Einstein on-line system, the NASA/IPAC extragalactic database, COBE astronomical databases, Cosmic Background Explorer astronomical databases, the ADAM software environment, the Groningen Image Processing System, search for a common data model for astronomical data analysis systems, deconvolution for real and synthetic apertures, pitfalls in image reconstruction, a direct method for spectral and image restoration, and a discription of a Poisson imagery super resolution algorithm. Also discussed are multivariate statistics on HI and IRAS images, a faint object classification using neural networks, a matched filter for improving SNR of radio maps, automated aperture photometry of CCD images, interactive graphics interpreter, the ROSAT extreme ultra-violet sky survey, a quantitative study of optimal extraction, an automated analysis of spectra, applications of synthetic photometry, an algorithm for extra-solar planet system detection and data reduction facilities for the William Herschel telescope.

omiRas: a Web server for differential expression analysis of miRNAs derived from small RNA-Seq data.

PubMed

Müller, Sören; Rycak, Lukas; Winter, Peter; Kahl, Günter; Koch, Ina; Rotter, Björn

2013-10-15

Small RNA deep sequencing is widely used to characterize non-coding RNAs (ncRNAs) differentially expressed between two conditions, e.g. healthy and diseased individuals and to reveal insights into molecular mechanisms underlying condition-specific phenotypic traits. The ncRNAome is composed of a multitude of RNAs, such as transfer RNA, small nucleolar RNA and microRNA (miRNA), to name few. Here we present omiRas, a Web server for the annotation, comparison and visualization of interaction networks of ncRNAs derived from next-generation sequencing experiments of two different conditions. The Web tool allows the user to submit raw sequencing data and results are presented as: (i) static annotation results including length distribution, mapping statistics, alignments and quantification tables for each library as well as lists of differentially expressed ncRNAs between conditions and (ii) an interactive network visualization of user-selected miRNAs and their target genes based on the combination of several miRNA-mRNA interaction databases. The omiRas Web server is implemented in Python, PostgreSQL, R and can be accessed at: http://tools.genxpro.net/omiras/.

Microarray analysis to identify the similarities and differences of pathogenesis between aortic occlusive disease and abdominal aortic aneurysm.

PubMed

Wang, Guofu; Bi, Lechang; Wang, Gaofeng; Huang, Feilai; Lu, Mingjing; Zhu, Kai

2018-06-01

Objectives Expression profile of GSE57691 was analyzed to identify the similarities and differences between aortic occlusive disease and abdominal aortic aneurysm. Methods The expression profile of GSE57691 was downloaded from Gene Expression Omnibus database, including 20 small abdominal aortic aneurysm samples, 29 large abdominal aortic aneurysm samples, 9 aortic occlusive disease samples, and 10 control samples. Using the limma package in R, the differentially expressed genes were screened. Followed by enrichment analysis was performed for the differentially expressed genes using database for annotation, visualization, and integrated discovery online tool. Based on string online tool and Cytoscape software, protein-protein interaction network and module analyses were carried out. Moreover, integrated TF platform database and Cytoscape software were used for constructing transcriptional regulatory networks. Results As a result, 1757, 354, and 396 differentially expressed genes separately were identified in aortic occlusive disease, large abdominal aortic aneurysm, and small abdominal aortic aneurysm samples. UBB was significantly enriched in proteolysis related pathways with a high degree in three groups. SPARCL1 was another gene shared by these groups and regulated by NFIA, which had a high degree in transcriptional regulatory network. ACTB, a significant upregulated gene in abdominal aortic aneurysm samples, could be regulated by CLIC4, which was significantly enriched in cell motions. ACLY and NFIB were separately identified in aortic occlusive disease and small abdominal aortic aneurysm samples, and separately enriched in lipid metabolism and negative regulation of cell proliferation. Conclusions The downregulated UBB, NFIA, and SPARCL1 might play key roles in both aortic occlusive disease and abdominal aortic aneurysm, while the upregulated ACTB might only involve in abdominal aortic aneurysm. ACLY and NFIB were specifically involved in aortic occlusive disease and small abdominal aortic aneurysm separately.

[Validation of interaction databases in psychopharmacotherapy].

PubMed

Hahn, M; Roll, S C

2018-03-01

Drug-drug interaction databases are an important tool to increase drug safety in polypharmacy. There are several drug interaction databases available but it is unclear which one shows the best results and therefore increases safety for the user of the databases and the patients. So far, there has been no validation of German drug interaction databases. Validation of German drug interaction databases regarding the number of hits, mechanisms of drug interaction, references, clinical advice, and severity of the interaction. A total of 36 drug interactions which were published in the last 3-5 years were checked in 5 different databases. Besides the number of hits, it was also documented if the mechanism was correct, clinical advice was given, primary literature was cited, and the severity level of the drug-drug interaction was given. All databases showed weaknesses regarding the hit rate of the tested drug interactions, with a maximum of 67.7% hits. The highest score in this validation was achieved by MediQ with 104 out of 180 points. PsiacOnline achieved 83 points, arznei-telegramm® 58, ifap index® 54 and the ABDA-database 49 points. Based on this validation MediQ seems to be the most suitable databank for the field of psychopharmacotherapy. The best results in this comparison were achieved by MediQ but this database also needs improvement with respect to the hit rate so that the users can rely on the results and therefore increase drug therapy safety.

Network pharmacology-based identification of key pharmacological pathways of Yin-Huang-Qing-Fei capsule acting on chronic bronchitis.

PubMed

Yu, Guohua; Zhang, Yanqiong; Ren, Weiqiong; Dong, Ling; Li, Junfang; Geng, Ya; Zhang, Yi; Li, Defeng; Xu, Haiyu; Yang, Hongjun

2017-01-01

For decades in China, the Yin-Huang-Qing-Fei capsule (YHQFC) has been widely used in the treatment of chronic bronchitis, with good curative effects. Owing to the complexity of traditional Chinese herbal formulas, the pharmacological mechanism of YHQFC remains unclear. To address this problem, a network pharmacology-based strategy was proposed in this study. At first, the putative target profile of YHQFC was predicted using MedChem Studio, based on structural and functional similarities of all available YHQFC components to the known drugs obtained from the DrugBank database. Then, an interaction network was constructed using links between putative YHQFC targets and known therapeutic targets of chronic bronchitis. Following the calculation of four topological features (degree, betweenness, closeness, and coreness) of each node in the network, 475 major putative targets of YHQFC and their topological importance were identified. In addition, a pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes pathway database indicated that the major putative targets of YHQFC are significantly associated with various pathways involved in anti-inflammation processes, immune responses, and pathological changes caused by asthma. More interestingly, eight major putative targets of YHQFC (interleukin [IL]-3, IL-4, IL-5, IL-10, IL-13, FCER1G, CCL11, and EPX) were demonstrated to be associated with the inflammatory process that occurs during the progression of asthma. Finally, a molecular docking simulation was performed and the results exhibited that 17 pairs of chemical components and candidate YHQFC targets involved in asthma pathway had strong binding efficiencies. In conclusion, this network pharmacology-based investigation revealed that YHQFC may attenuate the inflammatory reaction of chronic bronchitis by regulating its candidate targets, which may be implicated in the major pathological processes of the asthma pathway.

MSD-MAP: A Network-Based Systems Biology Platform for Predicting Disease-Metabolite Links.

PubMed

Wathieu, Henri; Issa, Naiem T; Mohandoss, Manisha; Byers, Stephen W; Dakshanamurthy, Sivanesan

2017-01-01

Cancer-associated metabolites result from cell-wide mechanisms of dysregulation. The field of metabolomics has sought to identify these aberrant metabolites as disease biomarkers, clues to understanding disease mechanisms, or even as therapeutic agents. This study was undertaken to reliably predict metabolites associated with colorectal, esophageal, and prostate cancers. Metabolite and disease biological action networks were compared in a computational platform called MSD-MAP (Multi Scale Disease-Metabolite Association Platform). Using differential gene expression analysis with patient-based RNAseq data from The Cancer Genome Atlas, genes up- or down-regulated in cancer compared to normal tissue were identified. Relational databases were used to map biological entities including pathways, functions, and interacting proteins, to those differential disease genes. Similar relational maps were built for metabolites, stemming from known and in silico predicted metabolite-protein associations. The hypergeometric test was used to find statistically significant relationships between disease and metabolite biological signatures at each tier, and metabolites were assessed for multi-scale association with each cancer. Metabolite networks were also directly associated with various other diseases using a disease functional perturbation database. Our platform recapitulated metabolite-disease links that have been empirically verified in the scientific literature, with network-based mapping of jointly-associated biological activity also matching known disease mechanisms. This was true for colorectal, esophageal, and prostate cancers, using metabolite action networks stemming from both predicted and known functional protein associations. By employing systems biology concepts, MSD-MAP reliably predicted known cancermetabolite links, and may serve as a predictive tool to streamline conventional metabolomic profiling methodologies. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Network pharmacology-based identification of key pharmacological pathways of Yin–Huang–Qing–Fei capsule acting on chronic bronchitis

PubMed Central

Yu, Guohua; Zhang, Yanqiong; Ren, Weiqiong; Dong, Ling; Li, Junfang; Geng, Ya; Zhang, Yi; Li, Defeng; Xu, Haiyu; Yang, Hongjun

2017-01-01

For decades in China, the Yin–Huang–Qing–Fei capsule (YHQFC) has been widely used in the treatment of chronic bronchitis, with good curative effects. Owing to the complexity of traditional Chinese herbal formulas, the pharmacological mechanism of YHQFC remains unclear. To address this problem, a network pharmacology-based strategy was proposed in this study. At first, the putative target profile of YHQFC was predicted using MedChem Studio, based on structural and functional similarities of all available YHQFC components to the known drugs obtained from the DrugBank database. Then, an interaction network was constructed using links between putative YHQFC targets and known therapeutic targets of chronic bronchitis. Following the calculation of four topological features (degree, betweenness, closeness, and coreness) of each node in the network, 475 major putative targets of YHQFC and their topological importance were identified. In addition, a pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes pathway database indicated that the major putative targets of YHQFC are significantly associated with various pathways involved in anti-inflammation processes, immune responses, and pathological changes caused by asthma. More interestingly, eight major putative targets of YHQFC (interleukin [IL]-3, IL-4, IL-5, IL-10, IL-13, FCER1G, CCL11, and EPX) were demonstrated to be associated with the inflammatory process that occurs during the progression of asthma. Finally, a molecular docking simulation was performed and the results exhibited that 17 pairs of chemical components and candidate YHQFC targets involved in asthma pathway had strong binding efficiencies. In conclusion, this network pharmacology-based investigation revealed that YHQFC may attenuate the inflammatory reaction of chronic bronchitis by regulating its candidate targets, which may be implicated in the major pathological processes of the asthma pathway. PMID:28053519

Filmless PACS in a multiple facility environment

NASA Astrophysics Data System (ADS)

Wilson, Dennis L.; Glicksman, Robert A.; Prior, Fred W.; Siu, Kai-Yeung; Goldburgh, Mitchell M.

1996-05-01

A Picture Archiving and Communication System centered on a shared image file server can support a filmless hospital. Systems based on this architecture have proven themselves in over four years of clinical operation. Changes in healthcare delivery are causing radiology groups to support multiple facilities for remote clinic support and consolidation of services. There will be a corresponding need for communicating over a standardized wide area network (WAN). Interactive workflow, a natural extension to the single facility case, requires a means to work effectively and seamlessly across moderate to low speed communication networks. Several schemes for supporting a consortium of medical treatment facilities over a WAN are explored. Both centralized and distributed database approaches are evaluated against several WAN scenarios. Likewise, several architectures for distributing image file servers or buffers over a WAN are explored, along with the caching and distribution strategies that support them. An open system implementation is critical to the success of a wide area system. The role of the Digital Imaging and Communications in Medicine (DICOM) standard in supporting multi- facility and multi-vendor open systems is also addressed. An open system can be achieved by using a DICOM server to provide a view of the system-wide distributed database. The DICOM server interface to a local version of the global database lets a local workstation treat the multiple, distributed data servers as though they were one local server for purposes of examination queries. The query will recover information about the examination that will permit retrieval over the network from the server on which the examination resides. For efficiency reasons, the ability to build cross-facility radiologist worklists and clinician-oriented patient folders is essential. The technologies of the World-Wide-Web can be used to generate worklists and patient folders across facilities. A reliable broadcast protocol may be a convenient way to notify many different users and many image servers about new activities in the network of image servers. In addition to ensuring reliability of message delivery and global serialization of each broadcast message in the network, the broadcast protocol should not introduce significant communication overhead.

National Transportation Atlas Databases : 1999

DOT National Transportation Integrated Search

1999-01-01

The National Transportation Atlas Databases -- 1999 (NTAD99) is a set of national : geographic databases of transportation facilities. These databases include geospatial : information for transportation modal networks and intermodal terminals, and re...

National Transportation Atlas Databases : 2001

DOT National Transportation Integrated Search

2001-01-01

The National Transportation Atlas Databases-2001 (NTAD-2001) is a set of national geographic databases of transportation facilities. These databases include geospatial information for transportation modal networks and intermodal terminals and related...

National Transportation Atlas Databases : 1996

DOT National Transportation Integrated Search

1996-01-01

The National Transportation Atlas Databases -- 1996 (NTAD96) is a set of national : geographic databases of transportation facilities. These databases include geospatial : information for transportation modal networks and intermodal terminals, and re...

National Transportation Atlas Databases : 2000

DOT National Transportation Integrated Search

2000-01-01

The National Transportation Atlas Databases-2000 (NTAD-2000) is a set of national geographic databases of transportation facilities. These databases include geospatial information for transportation modal networks and intermodal terminals and related...

National Transportation Atlas Databases : 1997

DOT National Transportation Integrated Search

1997-01-01

The National Transportation Atlas Databases -- 1997 (NTAD97) is a set of national : geographic databases of transportation facilities. These databases include geospatial : information for transportation modal networks and intermodal terminals, and re...

NCG 4.0: the network of cancer genes in the era of massive mutational screenings of cancer genomes

PubMed Central

An, Omer; Pendino, Vera; D’Antonio, Matteo; Ratti, Emanuele; Gentilini, Marco; Ciccarelli, Francesca D.

2014-01-01

NCG 4.0 is the latest update of the Network of Cancer Genes, a web-based repository of systems-level properties of cancer genes. In its current version, the database collects information on 537 known (i.e. experimentally supported) and 1463 candidate (i.e. inferred using statistical methods) cancer genes. Candidate cancer genes derive from the manual revision of 67 original publications describing the mutational screening of 3460 human exomes and genomes in 23 different cancer types. For all 2000 cancer genes, duplicability, evolutionary origin, expression, functional annotation, interaction network with other human proteins and with microRNAs are reported. In addition to providing a substantial update of cancer-related information, NCG 4.0 also introduces two new features. The first is the annotation of possible false-positive cancer drivers, defined as candidate cancer genes inferred from large-scale screenings whose association with cancer is likely to be spurious. The second is the description of the systems-level properties of 64 human microRNAs that are causally involved in cancer progression (oncomiRs). Owing to the manual revision of all information, NCG 4.0 constitutes a complete and reliable resource on human coding and non-coding genes whose deregulation drives cancer onset and/or progression. NCG 4.0 can also be downloaded as a free application for Android smart phones. Database URL: http://bio.ieo.eu/ncg/ PMID:24608173

The New Face of FLUXNET: Redesigning the Web Site and Data Organization to Enhance the User Experience

NASA Astrophysics Data System (ADS)

Shanafield, Harold; Shamblin, Stephanie; Devarakonda, Ranjeet; McMurry, Ben; Walker Beaty, Tammy; Wilson, Bruce; Cook, Robert B.

2011-02-01

The FLUXNET global network of regional flux tower networks serves to coordinate the regional and global analysis of eddy covariance based CO2, water vapor and energy flux measurements taken at more than 500 sites in continuous long-term operation. The FLUXNET database presently contains information about the location, characteristics, and data availability of each of these sites. To facilitate the coordination and distribution of this information, we redesigned the underlying database and associated web site. We chose the PostgreSQL database as a platform based on its performance, stability and GIS extensions. PostreSQL allows us to enhance our search and presentation capabilities, which will in turn provide increased functionality for users seeking to understand the FLUXNET data. The redesigned database will also significantly decrease the burden of managing such highly varied data. The website is being developed using the Drupal content management system, which provides many community-developed modules and a robust framework for custom feature development. In parallel, we are working with the regional networks to ensure that the information in the FLUXNET database is identical to that in the regional networks. Going forward, we also plan to develop an automated way to synchronize information with the regional networks.

A prototype system based on visual interactive SDM called VGC

NASA Astrophysics Data System (ADS)

Jia, Zelu; Liu, Yaolin; Liu, Yanfang

2009-10-01

In many application domains, data is collected and referenced by its geo-spatial location. Spatial data mining, or the discovery of interesting patterns in such databases, is an important capability in the development of database systems. Spatial data mining recently emerges from a number of real applications, such as real-estate marketing, urban planning, weather forecasting, medical image analysis, road traffic accident analysis, etc. It demands for efficient solutions for many new, expensive, and complicated problems. For spatial data mining of large data sets to be effective, it is also important to include humans in the data exploration process and combine their flexibility, creativity, and general knowledge with the enormous storage capacity and computational power of today's computers. Visual spatial data mining applies human visual perception to the exploration of large data sets. Presenting data in an interactive, graphical form often fosters new insights, encouraging the information and validation of new hypotheses to the end of better problem-solving and gaining deeper domain knowledge. In this paper a visual interactive spatial data mining prototype system (visual geo-classify) based on VC++6.0 and MapObject2.0 are designed and developed, the basic algorithms of the spatial data mining is used decision tree and Bayesian networks, and data classify are used training and learning and the integration of the two to realize. The result indicates it's a practical and extensible visual interactive spatial data mining tool.

Investigating multiple dysregulated pathways in rheumatoid arthritis based on pathway interaction network.

PubMed

Song, Xian-Dong; Song, Xian-Xu; Liu, Gui-Bo; Ren, Chun-Hui; Sun, Yuan-Bo; Liu, Ke-Xin; Liu, Bo; Liang, Shuang; Zhu, Zhu

2018-03-01

The traditional methods of identifying biomarkers in rheumatoid arthritis (RA) have focussed on the differentially expressed pathways or individual pathways, which however, neglect the interactions between pathways. To better understand the pathogenesis of RA, we aimed to identify dysregulated pathway sets using a pathway interaction network (PIN), which considered interactions among pathways. Firstly, RA-related gene expression profile data, protein-protein interactions (PPI) data and pathway data were taken up from the corresponding databases. Secondly, principal component analysis method was used to calculate the pathway activity of each of the pathway, and then a seed pathway was identified using data gleaned from the pathway activity. A PIN was then constructed based on the gene expression profile, pathway data, and PPI information. Finally, the dysregulated pathways were extracted from the PIN based on the seed pathway using the method of support vector machines and an area under the curve (AUC) index. The PIN comprised of a total of 854 pathways and 1064 pathway interactions. The greatest change in the activity score between RA and control samples was observed in the pathway of epigenetic regulation of gene expression, which was extracted and regarded as the seed pathway. Starting with this seed pathway, one maximum pathway set containing 10 dysregulated pathways was extracted from the PIN, having an AUC of 0.8249, and the result indicated that this pathway set could distinguish RA from the controls. These 10 dysregulated pathways might be potential biomarkers for RA diagnosis and treatment in the future.

Detection of time delays and directional interactions based on time series from complex dynamical systems

NASA Astrophysics Data System (ADS)

Ma, Huanfei; Leng, Siyang; Tao, Chenyang; Ying, Xiong; Kurths, Jürgen; Lai, Ying-Cheng; Lin, Wei

2017-07-01

Data-based and model-free accurate identification of intrinsic time delays and directional interactions is an extremely challenging problem in complex dynamical systems and their networks reconstruction. A model-free method with new scores is proposed to be generally capable of detecting single, multiple, and distributed time delays. The method is applicable not only to mutually interacting dynamical variables but also to self-interacting variables in a time-delayed feedback loop. Validation of the method is carried out using physical, biological, and ecological models and real data sets. Especially, applying the method to air pollution data and hospital admission records of cardiovascular diseases in Hong Kong reveals the major air pollutants as a cause of the diseases and, more importantly, it uncovers a hidden time delay (about 30-40 days) in the causal influence that previous studies failed to detect. The proposed method is expected to be universally applicable to ascertaining and quantifying subtle interactions (e.g., causation) in complex systems arising from a broad range of disciplines.

Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency.

PubMed

Yeh, Hsiang-Yuan; Cheng, Shih-Wu; Lin, Yu-Chun; Yeh, Cheng-Yu; Lin, Shih-Fang; Soo, Von-Wun

2009-12-21

Prostate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer. To deal with missing values in microarray data, we used a K-nearest-neighbors (KNN) algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD) as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2) regulated by RUNX1 and STAT3 is correlated to the pathological stage. We provide a computational framework to reconstruct the genetic regulatory network from the microarray data using biological knowledge and constraint-based inferences. Our method is helpful in verifying possible interaction relations in gene regulatory networks and filtering out incorrect relations inferred by imperfect methods. We predicted not only individual gene related to cancer but also discovered significant gene regulation networks. Our method is also validated in several enriched published papers and databases and the significant gene regulatory networks perform critical biological functions and processes including cell adhesion molecules, androgen and estrogen metabolism, smooth muscle contraction, and GO-annotated processes. Those significant gene regulations and the critical concept of tumor progression are useful to understand cancer biology and disease treatment.

Critical assessment of human metabolic pathway databases: a stepping stone for future integration

PubMed Central

2011-01-01

Background Multiple pathway databases are available that describe the human metabolic network and have proven their usefulness in many applications, ranging from the analysis and interpretation of high-throughput data to their use as a reference repository. However, so far the various human metabolic networks described by these databases have not been systematically compared and contrasted, nor has the extent to which they differ been quantified. For a researcher using these databases for particular analyses of human metabolism, it is crucial to know the extent of the differences in content and their underlying causes. Moreover, the outcomes of such a comparison are important for ongoing integration efforts. Results We compared the genes, EC numbers and reactions of five frequently used human metabolic pathway databases. The overlap is surprisingly low, especially on reaction level, where the databases agree on 3% of the 6968 reactions they have combined. Even for the well-established tricarboxylic acid cycle the databases agree on only 5 out of the 30 reactions in total. We identified the main causes for the lack of overlap. Importantly, the databases are partly complementary. Other explanations include the number of steps a conversion is described in and the number of possible alternative substrates listed. Missing metabolite identifiers and ambiguous names for metabolites also affect the comparison. Conclusions Our results show that each of the five networks compared provides us with a valuable piece of the puzzle of the complete reconstruction of the human metabolic network. To enable integration of the networks, next to a need for standardizing the metabolite names and identifiers, the conceptual differences between the databases should be resolved. Considerable manual intervention is required to reach the ultimate goal of a unified and biologically accurate model for studying the systems biology of human metabolism. Our comparison provides a stepping stone for such an endeavor. PMID:21999653

Atomic interaction networks in the core of protein domains and their native folds.

PubMed

Soundararajan, Venkataramanan; Raman, Rahul; Raguram, S; Sasisekharan, V; Sasisekharan, Ram

2010-02-23

Vastly divergent sequences populate a majority of protein folds. In the quest to identify features that are conserved within protein domains belonging to the same fold, we set out to examine the entire protein universe on a fold-by-fold basis. We report that the atomic interaction network in the solvent-unexposed core of protein domains are fold-conserved, extraordinary sequence divergence notwithstanding. Further, we find that this feature, termed protein core atomic interaction network (or PCAIN) is significantly distinguishable across different folds, thus appearing to be "signature" of a domain's native fold. As part of this study, we computed the PCAINs for 8698 representative protein domains from families across the 1018 known protein folds to construct our seed database and an automated framework was developed for PCAIN-based characterization of the protein fold universe. A test set of randomly selected domains that are not in the seed database was classified with over 97% accuracy, independent of sequence divergence. As an application of this novel fold signature, a PCAIN-based scoring scheme was developed for comparative (homology-based) structure prediction, with 1-2 angstroms (mean 1.61A) C(alpha) RMSD generally observed between computed structures and reference crystal structures. Our results are consistent across the full spectrum of test domains including those from recent CASP experiments and most notably in the 'twilight' and 'midnight' zones wherein <30% and <10% target-template sequence identity prevails (mean twilight RMSD of 1.69A). We further demonstrate the utility of the PCAIN protocol to derive biological insight into protein structure-function relationships, by modeling the structure of the YopM effector novel E3 ligase (NEL) domain from plague-causative bacterium Yersinia Pestis and discussing its implications for host adaptive and innate immune modulation by the pathogen. Considering the several high-throughput, sequence-identity-independent applications demonstrated in this work, we suggest that the PCAIN is a fundamental fold feature that could be a valuable addition to the arsenal of protein modeling and analysis tools.

Atomic Interaction Networks in the Core of Protein Domains and Their Native Folds

PubMed Central

Soundararajan, Venkataramanan; Raman, Rahul; Raguram, S.; Sasisekharan, V.; Sasisekharan, Ram

2010-01-01

Vastly divergent sequences populate a majority of protein folds. In the quest to identify features that are conserved within protein domains belonging to the same fold, we set out to examine the entire protein universe on a fold-by-fold basis. We report that the atomic interaction network in the solvent-unexposed core of protein domains are fold-conserved, extraordinary sequence divergence notwithstanding. Further, we find that this feature, termed protein core atomic interaction network (or PCAIN) is significantly distinguishable across different folds, thus appearing to be “signature” of a domain's native fold. As part of this study, we computed the PCAINs for 8698 representative protein domains from families across the 1018 known protein folds to construct our seed database and an automated framework was developed for PCAIN-based characterization of the protein fold universe. A test set of randomly selected domains that are not in the seed database was classified with over 97% accuracy, independent of sequence divergence. As an application of this novel fold signature, a PCAIN-based scoring scheme was developed for comparative (homology-based) structure prediction, with 1–2 angstroms (mean 1.61A) Cα RMSD generally observed between computed structures and reference crystal structures. Our results are consistent across the full spectrum of test domains including those from recent CASP experiments and most notably in the ‘twilight’ and ‘midnight’ zones wherein <30% and <10% target-template sequence identity prevails (mean twilight RMSD of 1.69A). We further demonstrate the utility of the PCAIN protocol to derive biological insight into protein structure-function relationships, by modeling the structure of the YopM effector novel E3 ligase (NEL) domain from plague-causative bacterium Yersinia Pestis and discussing its implications for host adaptive and innate immune modulation by the pathogen. Considering the several high-throughput, sequence-identity-independent applications demonstrated in this work, we suggest that the PCAIN is a fundamental fold feature that could be a valuable addition to the arsenal of protein modeling and analysis tools. PMID:20186337

Predicting language diversity with complex networks

PubMed Central

Gubiec, Tomasz

2018-01-01

We analyze the model of social interactions with coevolution of the topology and states of the nodes. This model can be interpreted as a model of language change. We propose different rewiring mechanisms and perform numerical simulations for each. Obtained results are compared with the empirical data gathered from two online databases and anthropological study of Solomon Islands. We study the behavior of the number of languages for different system sizes and we find that only local rewiring, i.e. triadic closure, is capable of reproducing results for the empirical data in a qualitative manner. Furthermore, we cancel the contradiction between previous models and the Solomon Islands case. Our results demonstrate the importance of the topology of the network, and the rewiring mechanism in the process of language change. PMID:29702699

Designs on a National Research Network.

ERIC Educational Resources Information Center

Walsh, John

1988-01-01

Discusses the addition of the National Aeronautics and Space Administration database to the National Science Foundation's NSFnet data communication network. Outlines the history of databases in the United States and enumerates proposed upgrades from a new Office of Science and Technology policy report. (TW)

The Private Lives of Minerals: Social Network Analysis Applied to Mineralogy and Petrology

NASA Astrophysics Data System (ADS)

Hazen, R. M.; Morrison, S. M.; Fox, P. A.; Golden, J. J.; Downs, R. T.; Eleish, A.; Prabhu, A.; Li, C.; Liu, C.

2016-12-01

Comprehensive databases of mineral species (rruff.info/ima) and their geographic localities and co-existing mineral assemblages (mindat.org) reveal patterns of mineral association and distribution that mimic social networks, as commonly applied to such varied topics as social media interactions, the spread of disease, terrorism networks, and research collaborations. Applying social network analysis (SNA) to common assemblages of rock-forming igneous and regional metamorphic mineral species, we find patterns of cohesion, segregation, density, and cliques that are similar to those of human social networks. These patterns highlight classic trends in lithologic evolution and are illustrated with sociograms, in which mineral species are the "nodes" and co-existing species form "links." Filters based on chemistry, age, structural group, and other parameters highlight visually both familiar and new aspects of mineralogy and petrology. We quantify sociograms with SNA metrics, including connectivity (based on the frequency of co-occurrence of mineral pairs), homophily (the extent to which co-existing mineral species share compositional and other characteristics), network closure (based on the degree of network interconnectivity), and segmentation (as revealed by isolated "cliques" of mineral species). Exploitation of large and growing mineral data resources with SNA offers promising avenues for discovering previously hidden trends in mineral diversity-distribution systematics, as well as providing new pedagogical approaches to teaching mineralogy and petrology.

NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways.

PubMed

Brohée, Sylvain; Faust, Karoline; Lima-Mendez, Gipsi; Sand, Olivier; Janky, Rekin's; Vanderstocken, Gilles; Deville, Yves; van Helden, Jacques

2008-07-01

The network analysis tools (NeAT) (http://rsat.ulb.ac.be/neat/) provide a user-friendly web access to a collection of modular tools for the analysis of networks (graphs) and clusters (e.g. microarray clusters, functional classes, etc.). A first set of tools supports basic operations on graphs (comparison between two graphs, neighborhood of a set of input nodes, path finding and graph randomization). Another set of programs makes the connection between networks and clusters (graph-based clustering, cliques discovery and mapping of clusters onto a network). The toolbox also includes programs for detecting significant intersections between clusters/classes (e.g. clusters of co-expression versus functional classes of genes). NeAT are designed to cope with large datasets and provide a flexible toolbox for analyzing biological networks stored in various databases (protein interactions, regulation and metabolism) or obtained from high-throughput experiments (two-hybrid, mass-spectrometry and microarrays). The web interface interconnects the programs in predefined analysis flows, enabling to address a series of questions about networks of interest. Each tool can also be used separately by entering custom data for a specific analysis. NeAT can also be used as web services (SOAP/WSDL interface), in order to design programmatic workflows and integrate them with other available resources.

Group-oriented coordination models for distributed client-server computing

NASA Technical Reports Server (NTRS)

Adler, Richard M.; Hughes, Craig S.

1994-01-01

This paper describes group-oriented control models for distributed client-server interactions. These models transparently coordinate requests for services that involve multiple servers, such as queries across distributed databases. Specific capabilities include: decomposing and replicating client requests; dispatching request subtasks or copies to independent, networked servers; and combining server results into a single response for the client. The control models were implemented by combining request broker and process group technologies with an object-oriented communication middleware tool. The models are illustrated in the context of a distributed operations support application for space-based systems.

Educational websites--Bioinformatics Tools II.

PubMed

Lomberk, Gwen

2009-01-01

In this issue, the highlighted websites are a continuation of a series of educational websites; this one in particular from a couple of years ago, Bioinformatics Tools [Pancreatology 2005;5:314-315]. These include sites that are valuable resources for many research needs in genomics and proteomics. Bioinformatics has become a laboratory tool to map sequences to databases, develop models of molecular interactions, evaluate structural compatibilities, describe differences between normal and disease-associated DNA, identify conserved motifs within proteins, and chart extensive signaling networks, all in silico. Copyright 2008 S. Karger AG, Basel and IAP.

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

PubMed Central

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

2015-01-01

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

A broadband multimedia TeleLearning system

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

Wang, Ruiping; Karmouch, A.

1996-12-31

In this paper we discuss a broadband multimedia TeleLearning system under development in the Multimedia Information Research Laboratory at the University of Ottawa. The system aims at providing a seamless environment for TeleLearning using the latest telecommunication and multimedia information processing technology. It basically consists of a media production center, a courseware author site, a courseware database, a courseware user site, and an on-line facilitator site. All these components are distributed over an ATM network and work together to offer a multimedia interactive courseware service. An MHEG-based model is exploited in designing the system architecture to achieve the real-time, interactive,more » and reusable information interchange through heterogeneous platforms. The system architecture, courseware processing strategies, courseware document models are presented.« less

Development and Implementation of Kumamoto Technopolis Regional Database T-KIND

NASA Astrophysics Data System (ADS)

Onoue, Noriaki

T-KIND (Techno-Kumamoto Information Network for Data-Base) is a system for effectively searching information of technology, human resources and industries which are necessary to realize Kumamoto Technopolis. It is composed of coded database, image database and LAN inside technoresearch park which is the center of R & D in the Technopolis. It constructs on-line system by networking general-purposed computers, minicomputers, optical disk file systems and so on, and provides the service through public telephone line. Two databases are now available on enterprise information and human resource information. The former covers about 4,000 enterprises, and the latter does about 2,000 persons.

Internet Portal For A Distributed Management of Groundwater

NASA Astrophysics Data System (ADS)

Meissner, U. F.; Rueppel, U.; Gutzke, T.; Seewald, G.; Petersen, M.

The management of groundwater resources for the supply of German cities and sub- urban areas has become a matter of public interest during the last years. Negative headlines in the Rhein-Main-Area dealt with cracks in buildings as well as damaged woodlands and inundated agriculture areas as an effect of varying groundwater levels. Usually a holistic management of groundwater resources is not existent because of the complexity of the geological system, the large number of involved groups and their divergent interests and a lack of essential information. The development of a network- based information system for an efficient groundwater management was the target of the project: ?Grundwasser-Online?[1]. The management of groundwater resources has to take into account various hydro- geological, climatic, water-economical, chemical and biological interrelations [2]. Thus, the traditional approaches in information retrieval, which are characterised by a high personnel and time expenditure, are not sufficient. Furthermore, the efficient control of the groundwater cultivation requires a direct communication between the different water supply companies, the consultant engineers, the scientists, the govern- mental agencies and the public, by using computer networks. The presented groundwater information system consists of different components, especially for the collection, storage, evaluation and visualisation of groundwater- relevant information. Network-based technologies are used [3]. For the collection of time-dependant groundwater-relevant information, modern technologies of Mobile Computing have been analysed in order to provide an integrated approach in the man- agement of large groundwater systems. The aggregated information is stored within a distributed geo-scientific database system which enables a direct integration of simu- lation programs for the evaluation of interactions in groundwater systems. Thus, even a prognosis for the evolution of groundwater states can be given. In order to gener- ate reports automatically, technologies are utilised. The visualisation of geo-scientific databases in the internet considering their geographic reference is performed with internet map servers. According to the communication of the map server with the un- derlying geo-scientific database, it is necessary that the demanded data can be filtered interactively in the internet browser using chronological and logical criteria. With re- gard to public use the security aspects within the described distributed system are of 1 major importance. Therefore, security methods for the modelling of access rights in combination with digital signatures have been analysed and implemented in order to provide a secure data exchange and communication between the different partners in the network 2

TrypsNetDB: An integrated framework for the functional characterization of trypanosomatid proteins

PubMed Central

Gazestani, Vahid H.; Yip, Chun Wai; Nikpour, Najmeh; Berghuis, Natasha

2017-01-01

Trypanosomatid parasites cause serious infections in humans and production losses in livestock. Due to the high divergence from other eukaryotes, such as humans and model organisms, the functional roles of many trypanosomatid proteins cannot be predicted by homology-based methods, rendering a significant portion of their proteins as uncharacterized. Recent technological advances have led to the availability of multiple systematic and genome-wide datasets on trypanosomatid parasites that are informative regarding the biological role(s) of their proteins. Here, we report TrypsNetDB (http://trypsNetDB.org), a web-based resource for the functional annotation of 16 different species/strains of trypanosomatid parasites. The database not only visualizes the network context of the queried protein(s) in an intuitive way but also examines the response of the represented network in more than 50 different biological contexts and its enrichment for various biological terms and pathways, protein sequence signatures, and potential RNA regulatory elements. The interactome core of the database, as of Jan 23, 2017, contains 101,187 interactions among 13,395 trypanosomatid proteins inferred from 97 genome-wide and focused studies on the interactome of these organisms. PMID:28158179

Programming Wireless Handheld Devices for Applications in Teaching Astronomy

NASA Astrophysics Data System (ADS)

Budiardja, R.; Saranathan, V.; Guidry, M.

2002-12-01

Wireless technology implemented with handheld devices has attractive features because of the potential to access large amounts of data and the prospect of on-the-fly computational analysis from a device that can be carried in a shirt pocket. We shall describe applications of such technology to the general paradigm of making digital wireless connections from the field to upload information and queries to network servers, executing (potentially complex) data analysis and/or database operations on fast network computers, and returning real-time information from this analysis to the handheld device in the field. As illustration, we shall describe several client/server programs that we have written for applications in teaching introductory astronomy. For example, one program allows static and dynamic properties of astronomical objects to be accessed in a remote observation laboratory setting using a digital cell phone or PDA. Another implements interactive quizzing over a cell phone or PDA using a 700-question introductory astronomy quiz database, thus permitting students to study for astronomy quizzes in any environment in which they have a few free minutes and a digital cell phone or wireless PDA. The presentation will include hands-on demonstrations with real devices.

A first evaluation of a pedagogical network for medical students at the University Hospital of Rennes.

PubMed

Fresnel, A; Jarno, P; Burgun, A; Delamarre, D; Denier, P; Cleret, M; Courtin, C; Seka, L P; Pouliquen, B; Cléran, L; Riou, C; Leduff, F; Lesaux, H; Duvauferrier, R; Le Beux, P

1998-01-01

A pedagogical network has been developed at University Hospital of Rennes from 1996. The challenge is to give medical information and informatics tools to all medical students in the clinical wards of the University Hospital. At first, nine wards were connected to the medical school server which is linked to the Internet. Client software electronic mail and WWW Netscape on Macintosh computers. Sever software is set up on Unix SUN providing a local homepage with selected pedagogical resources. These documents are stored in a DBMS database ORACLE and queries can be provided by specialty, authors or disease. The students can access a set of interactive teaching programs or electronic textbooks and can explore the Internet through the library information system and search engines. The teachers can send URL and indexation of pedagogical documents and can produce clinical cases: the database updating will be done by the users. This experience of using Web tools generated enthusiasm when we first introduced it to students. The evaluation shows that if the students can use this training early on, they will adapt the resources of the Internet to their own needs.

fusionDB: assessing microbial diversity and environmental preferences via functional similarity networks

PubMed Central

Zhu, Chengsheng; Miller, Maximilian

2018-01-01

Abstract Microbial functional diversification is driven by environmental factors, i.e. microorganisms inhabiting the same environmental niche tend to be more functionally similar than those from different environments. In some cases, even closely phylogenetically related microbes differ more across environments than across taxa. While microbial similarities are often reported in terms of taxonomic relationships, no existing databases directly link microbial functions to the environment. We previously developed a method for comparing microbial functional similarities on the basis of proteins translated from their sequenced genomes. Here, we describe fusionDB, a novel database that uses our functional data to represent 1374 taxonomically distinct bacteria annotated with available metadata: habitat/niche, preferred temperature, and oxygen use. Each microbe is encoded as a set of functions represented by its proteome and individual microbes are connected via common functions. Users can search fusionDB via combinations of organism names and metadata. Moreover, the web interface allows mapping new microbial genomes to the functional spectrum of reference bacteria, rendering interactive similarity networks that highlight shared functionality. fusionDB provides a fast means of comparing microbes, identifying potential horizontal gene transfer events, and highlighting key environment-specific functionality. PMID:29112720

Bioinformatical and in vitro approaches to essential oil-induced matrix metalloproteinase inhibition.

PubMed

Zeidán-Chuliá, Fares; Rybarczyk-Filho, José L; Gursoy, Mervi; Könönen, Eija; Uitto, Veli-Jukka; Gursoy, Orhan V; Cakmakci, Lutfu; Moreira, José C F; Gursoy, Ulvi K

2012-06-01

Essential oils carry diverse antimicrobial and anti-enzymatic properties. Matrix metalloproteinase (MMP) inhibition characteristics of Salvia fruticosa Miller (Labiatae), Myrtus communis Linnaeus (Myrtaceae), Juniperus communis Linnaeus (Cupressaceae), and Lavandula stoechas Linnaeus (Labiatae) essential oils were evaluated. Chemical compositions of the essential oils were analyzed by gas chromatography-mass spectrometry (GC-MS). Bioinformatical database analysis was performed by STRING 9.0 and STITCH 2.0 databases, and ViaComplex software. Antibacterial activity of essential oils against periodontopathogens was tested by the disc diffusion assay and the agar dilution method. Cellular proliferation and cytotoxicity were determined by commercial kits. MMP-2 and MMP-9 activities were measured by zymography. Bioinformatical database analyses, under a score of 0.4 (medium) and a prior correction of 0.0, gave rise to a model of protein (MMPs and tissue inhibitors of metalloproteinases) vs. chemical (essential oil components) interaction network; where MMPs and essential oil components interconnected through interaction with hydroxyl radicals, molecular oxygen, and hydrogen peroxide. Components from L. stoechas potentially displayed a higher grade of interaction with MMP-2 and -9. Although antibacterial and growth inhibitory effects of essential oils on the tested periodontopathogens were limited, all of them inhibited MMP-2 in vitro at concentrations of 1 and 5 µL/mL. Moreover, same concentrations of M. communis and L. stoechas also inhibited MMP-9. MMP-inhibiting concentrations of essential oils were not cytotoxic against keratinocytes. We propose essential oils of being useful therapeutic agents as MMP inhibitors through a mechanism possibly based on their antioxidant potential.

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

PubMed

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

2010-03-20

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

WATERSHED INFORMATION NETWORK

EPA Science Inventory

Resource Purpose:The Watershed Information Network is a set of about 30 web pages that are organized by topic. These pages access existing databases like the American Heritage Rivers Services database and Surf Your Watershed. WIN in itself has no data or data sets.
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