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      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit

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          Abstract

          Functional enrichment analysis has played a key role in the biological interpretation of high-throughput omics data. As a long-standing and widely used web application for functional enrichment analysis, WebGestalt has been constantly updated to satisfy the needs of biologists from different research areas. WebGestalt 2017 supports 12 organisms, 324 gene identifiers from various databases and technology platforms, and 150 937 functional categories from public databases and computational analyses. Omics data with gene identifiers not supported by WebGestalt and functional categories not included in the WebGestalt database can also be uploaded for enrichment analysis. In addition to the Over-Representation Analysis in the previous versions, Gene Set Enrichment Analysis and Network Topology-based Analysis have been added to WebGestalt 2017, providing complementary approaches to the interpretation of high-throughput omics data. The new user-friendly output interface and the GOView tool allow interactive and efficient exploration and comparison of enrichment results. Thus, WebGestalt 2017 enables more comprehensive, powerful, flexible and interactive functional enrichment analysis. It is freely available at http://www.webgestalt.org.

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          DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants

          Janet Piñero, Àlex Bravo, Núria Queralt-Rosinach … (2016)
          The information about the genetic basis of human diseases lies at the heart of precision medicine and drug discovery. However, to realize its full potential to support these goals, several problems, such as fragmentation, heterogeneity, availability and different conceptualization of the data must be overcome. To provide the community with a resource free of these hurdles, we have developed DisGeNET (http://www.disgenet.org), one of the largest available collections of genes and variants involved in human diseases. DisGeNET integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. DisGeNET data are homogeneously annotated with controlled vocabularies and community-driven ontologies. Additionally, several original metrics are provided to assist the prioritization of genotype–phenotype relationships. The information is accessible through a web interface, a Cytoscape App, an RDF SPARQL endpoint, scripts in several programming languages and an R package. DisGeNET is a versatile platform that can be used for different research purposes including the investigation of the molecular underpinnings of specific human diseases and their comorbidities, the analysis of the properties of disease genes, the generation of hypothesis on drug therapeutic action and drug adverse effects, the validation of computationally predicted disease genes and the evaluation of text-mining methods performance.
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            The BioGRID interaction database: 2017 update

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

              Andrea Califano, Kai Wang, Jonathan W. C. Lim … (2005)
              We describe a computational protocol for the ARACNE algorithm, an information-theoretic method for identifying transcriptional interactions between gene products using microarray expression profile data. Similar to other algorithms, ARACNE predicts potential functional associations among genes, or novel functions for uncharacterized genes, by identifying statistical dependencies between gene products. However, based on biochemical validation, literature searches and DNA binding site enrichment analysis, ARACNE has also proven effective in identifying bona fide transcriptional targets, even in complex mammalian networks. Thus we envision that predictions made by ARACNE, especially when supplemented with prior knowledge or additional data sources, can provide appropriate hypotheses for the further investigation of cellular networks. While the examples in this protocol use only gene expression profile data, the algorithm's theoretical basis readily extends to a variety of other high-throughput measurements, such as pathway-specific or genome-wide proteomics, microRNA and metabolomics data. As these data become readily available, we expect that ARACNE might prove increasingly useful in elucidating the underlying interaction models. For a microarray data set containing approximately 10,000 probes, reconstructing the network around a single probe completes in several minutes using a desktop computer with a Pentium 4 processor. Reconstructing a genome-wide network generally requires a computational cluster, especially if the recommended bootstrapping procedure is used.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (iso-abbrev): Nucleic Acids Res
                Journal ID (publisher-id): nar
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date (Print): 03 July 2017
                Publication date (Electronic): 03 May 2017
                Publication date PMC-release: 03 May 2017
                Volume: 45
                Issue: Web Server issue
                Pages: W130-W137
                Affiliations
                [1 ]Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
                [2 ]Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
                [3 ]Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +1 713 798 1443; Fax: +1 713 798 1693; Email: bing.zhang@ 123456bcm.edu
                Article
                Publisher ID: gkx356
                DOI: 10.1093/nar/gkx356
                PMC ID: 5570149
                PubMed ID: 28472511
                SO-VID: 510d9139-0f5b-4587-be78-94f148382409
                Copyright © © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                Date accepted : 20 April 2017
                Date revision received : 09 April 2017
                Date received : 31 January 2017
                Page count
                Pages: 8
                Categories
                Subject: Web Server Issue

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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