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DOSE-L1000: unveiling the intricate landscape of compound-induced transcriptional changes

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 transcript...

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Detalles Bibliográficos
Autores principales: Wang, Junmin, Novick, Steven
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10663987/
https://www.ncbi.nlm.nih.gov/pubmed/37952162
http://dx.doi.org/10.1093/bioinformatics/btad683
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author Wang, Junmin
Novick, Steven
author_facet Wang, Junmin
Novick, Steven
author_sort Wang, Junmin
collection PubMed
description 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 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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spelling pubmed-106639872023-11-11 DOSE-L1000: unveiling the intricate landscape of compound-induced transcriptional changes Wang, Junmin Novick, Steven Bioinformatics Original Paper 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 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. Oxford University Press 2023-11-11 /pmc/articles/PMC10663987/ /pubmed/37952162 http://dx.doi.org/10.1093/bioinformatics/btad683 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Paper
Wang, Junmin
Novick, Steven
DOSE-L1000: unveiling the intricate landscape of compound-induced transcriptional changes
title DOSE-L1000: unveiling the intricate landscape of compound-induced transcriptional changes
title_full DOSE-L1000: unveiling the intricate landscape of compound-induced transcriptional changes
title_fullStr DOSE-L1000: unveiling the intricate landscape of compound-induced transcriptional changes
title_full_unstemmed DOSE-L1000: unveiling the intricate landscape of compound-induced transcriptional changes
title_short DOSE-L1000: unveiling the intricate landscape of compound-induced transcriptional changes
title_sort dose-l1000: unveiling the intricate landscape of compound-induced transcriptional changes
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10663987/
https://www.ncbi.nlm.nih.gov/pubmed/37952162
http://dx.doi.org/10.1093/bioinformatics/btad683
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