GRAND: a database of gene regulatory network models across human conditions.

Autor:	Ben Guebila M; Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA., Lopes-Ramos CM; Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA., Weighill D; Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA., Sonawane AR; Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA02115, USA., Burkholz R; Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA., Shamsaei B; Division of Biostatistics and Bioinformatics, Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, Cincinnati, OH, USA., Platig J; Channing Division of Network Medicine, Department of Medicine, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA., Glass K; Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.; Channing Division of Network Medicine, Department of Medicine, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA., Kuijjer ML; Center for Molecular Medicine Norway, Faculty of Medicine, University of Oslo, Oslo, Norway.; Leiden University Medical Center, Leiden, The Netherlands., Quackenbush J; Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.; Channing Division of Network Medicine, Department of Medicine, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA.
Jazyk:	angličtina
Zdroj:	Nucleic acids research [Nucleic Acids Res] 2022 Jan 07; Vol. 50 (D1), pp. D610-D621.
DOI:	10.1093/nar/gkab778
Abstrakt:	Gene regulation plays a fundamental role in shaping tissue identity, function, and response to perturbation. Regulatory processes are controlled by complex networks of interacting elements, including transcription factors, miRNAs and their target genes. The structure of these networks helps to determine phenotypes and can ultimately influence the development of disease or response to therapy. We developed GRAND (https://grand.networkmedicine.org) as a database for computationally-inferred, context-specific gene regulatory network models that can be compared between biological states, or used to predict which drugs produce changes in regulatory network structure. The database includes 12 468 genome-scale networks covering 36 human tissues, 28 cancers, 1378 unperturbed cell lines, as well as 173 013 TF and gene targeting scores for 2858 small molecule-induced cell line perturbation paired with phenotypic information. GRAND allows the networks to be queried using phenotypic information and visualized using a variety of interactive tools. In addition, it includes a web application that matches disease states to potentially therapeutic small molecule drugs using regulatory network properties. (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)
Databáze:	MEDLINE
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