Nevin Manimala Statistics

DOSE-L1000: Unveiling the Intricate Landscape of Compound-Induced Transcriptional Changes

Bioinformatics. 2023 Nov 11:btad683. doi: 10.1093/bioinformatics/btad683. Online ahead of print.


MOTIVATION: The LINCS L1000 project has collected gene expression profiles for thousands of compounds across a wide array of concentrations, cell lines, and time points. However, conventional analysis methods often fall short in capturing the rich information encapsulated within the L1000 transcriptional dose-response data.

RESULTS: We present DOSE-L1000, a database that unravels the potency and efficacy of compound-gene pairs and the intricate landscape of compound-induced transcriptional changes. Our study employs the fitting of over 140 million generalized additive models and robust linear models, spanning the complete spectrum of compounds and landmark genes within the LINCS L1000 database. This systematic approach provides quantitative insights into differential gene expression and the potency and efficacy of compound-gene pairs across diverse cellular contexts. Through examples, we showcase the application of DOSE-L1000 in tasks such as cell line and compound comparisons, along with clustering analyses and predictions of drug-target interactions. DOSE-L1000 fosters applications in drug discovery, accelerating the transition to omics-driven drug development.

AVAILABILITY: DOSE-L1000 is publicly available at

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:37952162 | DOI:10.1093/bioinformatics/btad683

By Nevin Manimala

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