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Compound signature detection on LINCS L1000 big data.

ABSTRACT: The Library of Integrated Network-based Cellular Signatures (LINCS) L1000 big data provide gene expression profiles induced by over 10?000 compounds, shRNAs, and kinase inhibitors using the L1000 platform. We developed csNMF, a systematic compound signature discovery pipeline covering from raw L1000 data processing to drug screening and mechanism generation. The csNMF pipeline demonstrated better performance than the original L1000 pipeline. The discovered compound signatures of breast cancer were consistent with the LINCS KINOMEscan data and were clinically relevant. The csNMF pipeline provided a novel and complete tool to expedite signature-based drug discovery leveraging the LINCS L1000 resources.

SUBMITTER: Liu C

PROVIDER: S-EPMC4333019 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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Compound signature detection on LINCS L1000 big data.

Liu Chenglin C Su Jing J Yang Fei F Wei Kun K Ma Jinwen J Zhou Xiaobo X

Molecular bioSystems 20150122 3

The Library of Integrated Network-based Cellular Signatures (LINCS) L1000 big data provide gene expression profiles induced by over 10 000 compounds, shRNAs, and kinase inhibitors using the L1000 platform. We developed csNMF, a systematic compound signature discovery pipeline covering from raw L1000 data processing to drug screening and mechanism generation. The csNMF pipeline demonstrated better performance than the original L1000 pipeline. The discovered compound signatures of breast cancer we ...[more]

PMID: 25609570

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Project description:The library of integrated network-based cellular signatures (LINCS) L1000 data set currently comprises of over a million gene expression profiles of chemically perturbed human cell lines. Through unique several intrinsic and extrinsic benchmarking schemes, we demonstrate that processing the L1000 data with the characteristic direction (CD) method significantly improves signal to noise compared with the MODZ method currently used to compute L1000 signatures. The CD processed L1000 signatures are served through a state-of-the-art web-based search engine application called L1000CDS2. The L1000CDS2 search engine provides prioritization of thousands of small-molecule signatures, and their pairwise combinations, predicted to either mimic or reverse an input gene expression signature using two methods. The L1000CDS2 search engine also predicts drug targets for all the small molecules profiled by the L1000 assay that we processed. Targets are predicted by computing the cosine similarity between the L1000 small-molecule signatures and a large collection of signatures extracted from the gene expression omnibus (GEO) for single-gene perturbations in mammalian cells. We applied L1000CDS2 to prioritize small molecules that are predicted to reverse expression in 670 disease signatures also extracted from GEO, and prioritized small molecules that can mimic expression of 22 endogenous ligand signatures profiled by the L1000 assay. As a case study, to further demonstrate the utility of L1000CDS2, we collected expression signatures from human cells infected with Ebola virus at 30, 60 and 120 min. Querying these signatures with L1000CDS2 we identified kenpaullone, a GSK3B/CDK2 inhibitor that we show, in subsequent experiments, has a dose-dependent efficacy in inhibiting Ebola infection in vitro without causing cellular toxicity in human cell lines. In summary, the L1000CDS2 tool can be applied in many biological and biomedical settings, while improving the extraction of knowledge from the LINCS L1000 resource.

Dataset Information

Compound signature detection on LINCS L1000 big data.

Publications

Compound signature detection on LINCS L1000 big data.

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