L1000CDS2: LINCS L1000 characteristic direction signatures search engine

By Qiaonan Duan, S. Reid, N. R. Clark, Zichen Wang, Nicolas F. Fernandez, Andrew D. Rouillard, B. Readhead, S. Tritsch, R. Hodos, M. Hafner and 6 more. 2016

330 citations recorded by the source corpus.

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.

Subfields: Biophysics, Infectious Diseases, Molecular Biology

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, background): “At present, some of the most popular state-of-the-art methods for signature-based drug repurposing [292] do not use predictive modeling.”
A review of connectivity map and computational approaches in pharmacogenomics (2017, background): “L1000CDS(2) [32] L1000CDS(2) queries gene expression signatures against the”
The Library of Integrated Network-based Cellular Signatures (LINCS) NIH Program: System-level Cataloging of Human Cells Response to Perturbations (2017, background): “L1000CDS2: LINCS L1000 Characteristic Direction Signatures Search Engine L1000CDS2 (http://amp.pharm.mssm.edu/L1000CDS2/) is a search engine that returns LINCS L1000 drug perturbation signatures that either mimic or reverse a user-input signature of gene symbols (Duan et al., 2016).”
Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, background): “LINCS L1000 data-driven search engine, known as L1000CDS 2 , is an open-access search engine that contains data of drugs that can revert the expression of differentially expressed genes; hence, they too can be used for drug discovery [61].”
Deep learning in bioinformatics: introduction, application, and perspective in big data era (2019, methodology): “To reduce the cost of gene profiling, realizing that different genes’ expression can be highly correlated, researchers have developed an affordable method of only profiling around 1000 carefully selected landmark genes and predicting the expression of the other target genes based on computational me”