ArticleBioinformatics (Oxford, England)2023
DOSE-L1000: unveiling the intricate landscape of compound-induced transcriptional changes.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Advances in high-throughput drug screening based on pharmacotranscriptomics.Journal of advanced research · 2026Review
- ExPO: an exposure-conditioned neural operator for L1000 signature prediction.Journal of cheminformatics · 2026Article
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- SIMD: Synergistic integration mutualistic platform based on single-cell and proteotranscriptomics for drug repositioning.NPJ breast cancer · 2025Article
- Benchmarking of dimensionality reduction methods to capture drug response in transcriptome data.Scientific reports · 2025Article
- L2S2: chemical perturbation and CRISPR KO LINCS L1000 signature search engine.Nucleic acids research · 2025Article
- DOSE-L1000-Viz: an interactive Shiny application for dose-response transcriptomic analysis, target-centric exploration, and signature search.Bioinformatics (Oxford, England) · 2025Article
- ComBat-met: adjusting batch effects in DNA methylation data.NAR genomics and bioinformatics · 2025Article
- Protein subinteractomes of human microsomal cytochromes P450.Molecular biology reports · 2025Review
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Authors and funding
2 authors.
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Abstract
motivationThe 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.
resultsWe 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 uses 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 AND IMPLEMENTATION: DOSE-L1000 is publicly available at https://doi.org/10.5281/zenodo.8286375.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.