L1000FWD: fireworks visualization of drug-induced transcriptomic signatures
Open accessAbstract
Motivation: As part of the NIH Library of Integrated Network-based Cellular Signatures program, hundreds of thousands of transcriptomic signatures were generated with the L1000 technology, profiling the response of human cell lines to over 20 000 small molecule compounds. This effort is a promising approach toward revealing the mechanisms-of-action (MOA) for marketed drugs and other less studied potential therapeutic compounds. Results: L1000 fireworks display (L1000FWD) is a web application that provides interactive visualization of over 16 000 drug and small-molecule induced gene expression signatures. L1000FWD enables coloring of signatures by different attributes such as cell type, time point, concentration, as well as drug attributes such as MOA and clinical phase. Signature similarity search is implemented to enable the search for mimicking or opposing signatures given as input of up and down gene sets. Each point on the L1000FWD interactive map is linked to a signature landing page, which provides multifaceted knowledge from various sources about the signature and the drug. Notably such information includes most frequent diagnoses, co-prescribed drugs and age distribution of prescriptions as extracted from the Mount Sinai Health System electronic medical records. Overall, L1000FWD serves as a platform for identifying functions for novel small molecules using unsupervised clustering, as well as for exploring drug MOA. Availability and implementation: L1000FWD is freely accessible at: http://amp.pharm.mssm.edu/L1000FWD. Supplementary information: Supplementary data are available at Bioinformatics online.
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The same confidence principle applies to transcriptomics profiles of cell lines.
“The same applies to e.g. transcriptomics profiles of cell lines.279”
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- Authors
- Wang, Z., Lachmann, A., Keenan, A. B., & Ma’ayan, A.
- Affiliations
- Icahn School of Medicine at Mount Sinai (US)
- Corresponding author
- Ma’ayan, Avi
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-02-06
- Volume
- 34
- Issue
- 12
- Pages
- 2150-2152
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC6454499
- OpenAlex
- W2791340464
- Cited by
- 195
- References
- 14
- Citations by year
- 2026: 30 · 2025: 29 · 2024: 25 · 2023: 23 · 2022: 27 · 2021: 25 · 2020: 16 · 2019: 10 · 2018: 8
- FWCI
- 10.90
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Bioinformatics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- NIH (U54HL127624, U24CA224260, U54CA189201)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- L1000, gene expression signatures, drug-induced gene expression, mechanism of action, small molecules, human cell lines, interactive visualization, web application, drug repurposing, electronic health records, Mount Sinai Health System
- MeSH terms
- Unsupervised Machine Learning, Data Visualization, Cell Line, Gene Expression Regulation, Humans, Pharmacogenetics, Software, Cluster Analysis, Transcriptome
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