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SigCom LINCS: data and metadata search engine for a million gene expression signatures
John Erol Evangelista1, Daniel J B Clarke1, Zhuorui Xie1
1Department of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA.
Nucleic Acids Research
|May 7, 2022
Summary
SigCom LINCS integrates millions of gene expression signatures from LINCS, GTEx, and GEO. This webserver accelerates drug discovery by enabling rapid searches for gene expression connections and pathway analysis.
Area of Science:
- Bioinformatics
- Computational Biology
- Systems Pharmacology
Background:
- The Library of Integrated Network-based Cellular Signatures (LINCS) program generated millions of transcriptome samples.
- Integrating these with Genotype-Tissue Expression (GTEx) and Gene Expression Omnibus (GEO) data enables discovery of drug, gene, pathway, and disease connections.
Purpose of the Study:
- To present SigCom LINCS, a webserver providing access to over a million processed gene expression signatures.
- To facilitate drug and target discovery through advanced signature analysis and search capabilities.
Main Methods:
- Developed SigCom LINCS using Signature Commons, a cloud-agnostic Data Commons.
- Implemented rapid signature similarity search (mimickers and reversers) based on gene sets, single genes, or search terms.
- Enabled metadata searches for signature analysis and information retrieval on genes and drugs.
Main Results:
- SigCom LINCS offers access to over a million analyzed gene expression signatures from LINCS, GTEx, and GEO.
- The platform provides rapid similarity searches and metadata analysis functionalities.
- Data and signatures are FAIR (findable, accessible, interoperable, reusable) and available via API.
Conclusions:
- SigCom LINCS is a valuable resource for systems pharmacology, accelerating drug and target discovery.
- The webserver's comprehensive signature data and search tools support research in gene expression analysis and disease-related pathways.
