Dataset Information

Data Portal for the Library of Integrated Network-based Cellular Signatures (LINCS) program: integrated access to diverse large-scale cellular perturbation response data.

ABSTRACT: The Library of Integrated Network-based Cellular Signatures (LINCS) program is a national consortium funded by the NIH to generate a diverse and extensive reference library of cell-based perturbation-response signatures, along with novel data analytics tools to improve our understanding of human diseases at the systems level. In contrast to other large-scale data generation efforts, LINCS Data and Signature Generation Centers (DSGCs) employ a wide range of assay technologies cataloging diverse cellular responses. Integration of, and unified access to LINCS data has therefore been particularly challenging. The Big Data to Knowledge (BD2K) LINCS Data Coordination and Integration Center (DCIC) has developed data standards specifications, data processing pipelines, and a suite of end-user software tools to integrate and annotate LINCS-generated data, to make LINCS signatures searchable and usable for different types of users. Here, we describe the LINCS Data Portal (LDP) (http://lincsportal.ccs.miami.edu/), a unified web interface to access datasets generated by the LINCS DSGCs, and its underlying database, LINCS Data Registry (LDR). LINCS data served on the LDP contains extensive metadata and curated annotations. We highlight the features of the LDP user interface that is designed to enable search, browsing, exploration, download and analysis of LINCS data and related curated content.

SUBMITTER: Koleti A

PROVIDER: S-EPMC5753343 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Data Portal for the Library of Integrated Network-based Cellular Signatures (LINCS) program: integrated access to diverse large-scale cellular perturbation response data.

Koleti Amar A Terryn Raymond R Stathias Vasileios V Chung Caty C Cooper Daniel J DJ Turner John P JP Vidovic Dušica D Forlin Michele M Kelley Tanya T TT D'Urso Alessandro A Allen Bryce K BK Torre Denis D Jagodnik Kathleen M KM Wang Lily L Jenkins Sherry L SL Mader Christopher C Niu Wen W Fazel Mehdi M Mahi Naim N Pilarczyk Marcin M Clark Nicholas N Shamsaei Behrouz B Meller Jarek J Vasiliauskas Juozas J Reichard John J Medvedovic Mario M Ma'ayan Avi A Pillai Ajay A Schürer Stephan C SC

Nucleic acids research 20180101 D1

The Library of Integrated Network-based Cellular Signatures (LINCS) program is a national consortium funded by the NIH to generate a diverse and extensive reference library of cell-based perturbation-response signatures, along with novel data analytics tools to improve our understanding of human diseases at the systems level. In contrast to other large-scale data generation efforts, LINCS Data and Signature Generation Centers (DSGCs) employ a wide range of assay technologies cataloging diverse c ...[more]

PMID: 29140462

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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

Data Portal for the Library of Integrated Network-based Cellular Signatures (LINCS) program: integrated access to diverse large-scale cellular perturbation response data.

Publications

Data Portal for the Library of Integrated Network-based Cellular Signatures (LINCS) program: integrated access to diverse large-scale cellular perturbation response data.

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