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GRNdb: decoding the gene regulatory networks in diverse human and mouse conditions
Gene regulatory networks (GRNs) formed by transcription factors (TFs) and their downstream target genes play essential roles in gene expression regulation. Moreover, GRNs can be dynamic changing across different conditions, which are crucial for understanding the underlying mechanisms of disease pat...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7779055/ https://www.ncbi.nlm.nih.gov/pubmed/33151298 http://dx.doi.org/10.1093/nar/gkaa995 |
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author | Fang, Li Li, Yunjin Ma, Lu Xu, Qiyue Tan, Fei Chen, Geng |
author_facet | Fang, Li Li, Yunjin Ma, Lu Xu, Qiyue Tan, Fei Chen, Geng |
author_sort | Fang, Li |
collection | PubMed |
description | Gene regulatory networks (GRNs) formed by transcription factors (TFs) and their downstream target genes play essential roles in gene expression regulation. Moreover, GRNs can be dynamic changing across different conditions, which are crucial for understanding the underlying mechanisms of disease pathogenesis. However, no existing database provides comprehensive GRN information for various human and mouse normal tissues and diseases at the single-cell level. Based on the known TF-target relationships and the large-scale single-cell RNA-seq data collected from public databases as well as the bulk data of The Cancer Genome Atlas and the Genotype-Tissue Expression project, we systematically predicted the GRNs of 184 different physiological and pathological conditions of human and mouse involving >633 000 cells and >27 700 bulk samples. We further developed GRNdb, a freely accessible and user-friendly database (http://www.grndb.com/) for searching, comparing, browsing, visualizing, and downloading the predicted information of 77 746 GRNs, 19 687 841 TF-target pairs, and related binding motifs at single-cell/bulk resolution. GRNdb also allows users to explore the gene expression profile, correlations, and the associations between expression levels and the patient survival of diverse cancers. Overall, GRNdb provides a valuable and timely resource to the scientific community to elucidate the functions and mechanisms of gene expression regulation in various conditions. |
format | Online Article Text |
id | pubmed-7779055 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-77790552021-01-07 GRNdb: decoding the gene regulatory networks in diverse human and mouse conditions Fang, Li Li, Yunjin Ma, Lu Xu, Qiyue Tan, Fei Chen, Geng Nucleic Acids Res Database Issue Gene regulatory networks (GRNs) formed by transcription factors (TFs) and their downstream target genes play essential roles in gene expression regulation. Moreover, GRNs can be dynamic changing across different conditions, which are crucial for understanding the underlying mechanisms of disease pathogenesis. However, no existing database provides comprehensive GRN information for various human and mouse normal tissues and diseases at the single-cell level. Based on the known TF-target relationships and the large-scale single-cell RNA-seq data collected from public databases as well as the bulk data of The Cancer Genome Atlas and the Genotype-Tissue Expression project, we systematically predicted the GRNs of 184 different physiological and pathological conditions of human and mouse involving >633 000 cells and >27 700 bulk samples. We further developed GRNdb, a freely accessible and user-friendly database (http://www.grndb.com/) for searching, comparing, browsing, visualizing, and downloading the predicted information of 77 746 GRNs, 19 687 841 TF-target pairs, and related binding motifs at single-cell/bulk resolution. GRNdb also allows users to explore the gene expression profile, correlations, and the associations between expression levels and the patient survival of diverse cancers. Overall, GRNdb provides a valuable and timely resource to the scientific community to elucidate the functions and mechanisms of gene expression regulation in various conditions. Oxford University Press 2020-11-05 /pmc/articles/PMC7779055/ /pubmed/33151298 http://dx.doi.org/10.1093/nar/gkaa995 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Database Issue Fang, Li Li, Yunjin Ma, Lu Xu, Qiyue Tan, Fei Chen, Geng GRNdb: decoding the gene regulatory networks in diverse human and mouse conditions |
title | GRNdb: decoding the gene regulatory networks in diverse human and mouse conditions |
title_full | GRNdb: decoding the gene regulatory networks in diverse human and mouse conditions |
title_fullStr | GRNdb: decoding the gene regulatory networks in diverse human and mouse conditions |
title_full_unstemmed | GRNdb: decoding the gene regulatory networks in diverse human and mouse conditions |
title_short | GRNdb: decoding the gene regulatory networks in diverse human and mouse conditions |
title_sort | grndb: decoding the gene regulatory networks in diverse human and mouse conditions |
topic | Database Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7779055/ https://www.ncbi.nlm.nih.gov/pubmed/33151298 http://dx.doi.org/10.1093/nar/gkaa995 |
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