Discovering and linking public omics data sets using the Omics Discovery Index

Perez‐Riverol, Yasset; Bai, Mingze; Leprevost, Felipe da Veiga; Squizzato, Silvano; Park, Young Mi; Haug, Kenneth; Carroll, Adam J.; Spalding, J. Dylan; Paschall, Justin; Wang, Mingxun; del-Toro, Noemi; Ternent, Tobias; Zhang, Peng; Buso, Nicola; Bandeira, Nuno; Deutsch, Eric W.; Campbell, D. S.; Beavis, Ronald C.; Salek, Reza M.; Sarkans, Ugis; Petryszak, Robert; Keays, Maria; Fahy, Eoin; Sud, Manish; Subramaniam, Shankar; Barberá, Ariana; Jiménez, Rafael C.; Nesvizhskii, Alexey I.; Sansone, Susanna Assunta; Steinbeck, Christoph; López, Rodrigo; Vizcaíno, Juan Antonio; Ping, Peipei; Hermjakob, Henning

doi:10.1038/nbt.3790

Cited by 176 publications

(114 citation statements)

References 10 publications

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“…Many excellent tools and resources have been developed in the field of cell signaling 'omics [30][31][32][33][34][35][36][37][38][39][40][41] . Here, we set out to complement these resources to allow researches to routinely answer questions such as: What cell cycle-related factors are regulated by FGF receptors in the liver?…”

Section: Discussionmentioning

confidence: 99%

The Signaling Pathways Project: an integrated ‘omics knowledgebase for mammalian cellular signaling pathways

Ochsner

Abraham

Martin

et al. 2018

Preprint

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Integrated mining of public transcriptomic and ChIP-Seq datasets has the potential to illuminate facets of mammalian cellular signaling pathways not yet explored in the research literature.Here, we designed a web knowledgebase, the Signaling Pathways Project (SPP), which incorporates stable community classifications of the four major categories of signaling pathway node (receptors, enzymes, transcription factors and co-nodes) and their cognate bioactive small molecules (BSMs). We then mapped over 10,000 public transcriptomic or cistromic experiments to their relevant signaling pathway node, BSM or biosample of study. To provide for prediction of pathway node-target transcriptional regulatory relationships, we generated consensus 'omics signatures, or consensomes, based on measures of significant differential expression of genomic targets across all underlying transcriptomic experiments. To expose the SPP knowledgebase to researchers, a web browser interface accommodates a variety of routine data mining strategies. Consensomes were validated using alignment with literature-based knowledge, gene target-level integration of transcriptomic and ChIP-Seq data points, and in bench experiments that confirmed previously uncharacterized node-gene target regulatory relationships. SPP is freely accessible at https://beta.signalingpathways.org.Individual dataset pages enable integration of SPP with the research literature via digital object identifier (DOI)-driven links from external sites, as well as for citation of datasets to enhance their FAIR status 3,4 .

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Section: Discussionmentioning

confidence: 99%

The Signaling Pathways Project: an integrated ‘omics knowledgebase for mammalian cellular signaling pathways

Ochsner

Abraham

Martin

et al. 2018

Preprint

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“…In conclusion, it is important to note that, at the moment of writing, several popular resources and tools are already using OLS Client as annotation source, including resources such as IntAct, [10] OmicsDI, [11] BioModels, [12] and the Reactome Pathway Annotation Tool, [13] or stand-alone tools (which use OLS Dialog on top) such as PeptideShaker, [14] the Laboratory Information Management System (LIMS) colims (https://github.com/compomics/colims) or the already mentioned PX Submission Tool (for a complete list see Table 1). The widespread use of the library ensures its stability, continued development, and community support.…”

Section: Ols Dialog 20 a New Version Of The Ols Dialogmentioning

confidence: 99%

“…PeptideShaker [14] Search engine independent platform for the interpretation of proteomics identification results http://compomics.github.io/projects/peptide-shaker OLS Client Omics Discovery Index [11] A multi-omics dataset discovery resource BioModels Database [12] BioModels Database is a repository of computational models of biological processes.…”

Section: Abbreviationsmentioning

confidence: 99%

OLS Client and OLS Dialog: Open Source Tools to Annotate Public Omics Datasets

et al. 2017

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The availability of user‐friendly software to annotate biological datasets and experimental details is becoming essential in data management practices, both in local storage systems and in public databases. The Ontology Lookup Service (OLS, http://www.ebi.ac.uk/ols) is a popular centralized service to query, browse and navigate biomedical ontologies and controlled vocabularies. Recently, the OLS framework has been completely redeveloped (version 3.0), including enhancements in the data model, like the added support for Web Ontology Language based ontologies, among many other improvements. However, the new OLS is not backwards compatible and new software tools are needed to enable access to this widely used framework now that the previous version is no longer available. We here present the OLS Client as a free, open‐source Java library to retrieve information from the new version of the OLS. It enables rapid tool creation by providing a robust, pluggable programming interface and common data model to programmatically access the OLS. The library has already been integrated and is routinely used by several bioinformatics resources and related data annotation tools. Secondly, we also introduce an updated version of the OLS Dialog (version 2.0), a Java graphical user interface that can be easily plugged into Java desktop applications to access the OLS. The software and related documentation are freely available at https://github.com/PRIDE-Utilities/ols-client and https://github.com/PRIDE-Toolsuite/ols-dialog.

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“…Liver-related pathways are mainly imported from SMPDB [35]. The metabolomics data were retrieved from MetaboLights and Metabolomics Workbench whose metadata have been indexed by OmicsDI [36–38]. Liver diseases are imported from DO [1], and UMLS [39].…”

Section: Construction and Contentmentioning

confidence: 99%

LiverWiki: a wiki-based database for human liver

Chen¹,

et al. 2017

BMC Bioinformatics

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BackgroundRecent advances in omics technology have produced a large amount of liver-related data. A comprehensive and up-to-date source of liver-related data is needed to allow biologists to access the latest data. However, current liver-related data sources each cover only a specific part of the liver. It is difficult for them to keep pace with the rapid increase of liver-related data available at those data resources. Integrating diverse liver-related data is a critical yet formidable challenge, as it requires sustained human effort.ResultsWe present LiverWiki, a first wiki-based database that integrates liver-related genes, homolog genes, gene expressions in microarray datasets and RNA-Seq datasets, proteins, protein interactions, post-translational modifications, associated pathways, diseases, metabolites identified in the metabolomics datasets, and literatures into an easily accessible and searchable resource for community-driven sharing. LiverWiki houses information in a total of 141,897 content pages, including 19,787 liver-related gene pages, 17,077 homolog gene pages, 50,251 liver-related protein pages, 36,122 gene expression pages, 2067 metabolites identified in the metabolomics datasets, 16,366 disease-related molecules, and 227 liver disease pages. Other than assisting users in searching, browsing, reviewing, refining the contents on LiverWiki, the most important contribution of LiverWiki is to allow the community to create and update biological data of liver in visible and editable tables. This integrates newly produced data with existing knowledge. Implemented in mediawiki, LiverWiki provides powerful extensions to support community contributions.ConclusionsThe main goal of LiverWiki is to provide the research community with comprehensive liver-related data, as well as to allow the research community to share their liver-related data flexibly and efficiently. It also enables rapid sharing new discoveries by allowing the discoveries to be integrated and shared immediately, rather than relying on expert curators. The database is available online at http://liverwiki.hupo.org.cn/.

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Discovering and linking public omics data sets using the Omics Discovery Index

Cited by 176 publications

References 10 publications

The Signaling Pathways Project: an integrated ‘omics knowledgebase for mammalian cellular signaling pathways

The Signaling Pathways Project: an integrated ‘omics knowledgebase for mammalian cellular signaling pathways

OLS Client and OLS Dialog: Open Source Tools to Annotate Public Omics Datasets

LiverWiki: a wiki-based database for human liver

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