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Systematic target function annotation of human transcription factors

BACKGROUND: Transcription factors (TFs), the key players in transcriptional regulation, have attracted great experimental attention, yet the functions of most human TFs remain poorly understood. Recent capabilities in genome-wide protein binding profiling have stimulated systematic studies of the hi...

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Autores principales: Li, Yong Fuga, Altman, Russ B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5795274/
https://www.ncbi.nlm.nih.gov/pubmed/29325558
http://dx.doi.org/10.1186/s12915-017-0469-0
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author Li, Yong Fuga
Altman, Russ B.
author_facet Li, Yong Fuga
Altman, Russ B.
author_sort Li, Yong Fuga
collection PubMed
description BACKGROUND: Transcription factors (TFs), the key players in transcriptional regulation, have attracted great experimental attention, yet the functions of most human TFs remain poorly understood. Recent capabilities in genome-wide protein binding profiling have stimulated systematic studies of the hierarchical organization of human gene regulatory network and DNA-binding specificity of TFs, shedding light on combinatorial gene regulation. We show here that these data also enable a systematic annotation of the biological functions and functional diversity of TFs. RESULT: We compiled a human gene regulatory network for 384 TFs covering the 146,096 TF–target gene (TF–TG) relationships, extracted from over 850 ChIP-seq experiments as well as the literature. By integrating this network of TF–TF and TF–TG relationships with 3715 functional concepts from six sources of gene function annotations, we obtained over 9000 confident functional annotations for 279 TFs. We observe extensive connectivity between TFs and Mendelian diseases, GWAS phenotypes, and pharmacogenetic pathways. Further, we show that TFs link apparently unrelated functions, even when the two functions do not share common genes. Finally, we analyze the pleiotropic functions of TFs and suggest that the increased number of upstream regulators contributes to the functional pleiotropy of TFs. CONCLUSION: Our computational approach is complementary to focused experimental studies on TF functions, and the resulting knowledge can guide experimental design for the discovery of unknown roles of TFs in human disease and drug response. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12915-017-0469-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-57952742018-02-12 Systematic target function annotation of human transcription factors Li, Yong Fuga Altman, Russ B. BMC Biol Research Article BACKGROUND: Transcription factors (TFs), the key players in transcriptional regulation, have attracted great experimental attention, yet the functions of most human TFs remain poorly understood. Recent capabilities in genome-wide protein binding profiling have stimulated systematic studies of the hierarchical organization of human gene regulatory network and DNA-binding specificity of TFs, shedding light on combinatorial gene regulation. We show here that these data also enable a systematic annotation of the biological functions and functional diversity of TFs. RESULT: We compiled a human gene regulatory network for 384 TFs covering the 146,096 TF–target gene (TF–TG) relationships, extracted from over 850 ChIP-seq experiments as well as the literature. By integrating this network of TF–TF and TF–TG relationships with 3715 functional concepts from six sources of gene function annotations, we obtained over 9000 confident functional annotations for 279 TFs. We observe extensive connectivity between TFs and Mendelian diseases, GWAS phenotypes, and pharmacogenetic pathways. Further, we show that TFs link apparently unrelated functions, even when the two functions do not share common genes. Finally, we analyze the pleiotropic functions of TFs and suggest that the increased number of upstream regulators contributes to the functional pleiotropy of TFs. CONCLUSION: Our computational approach is complementary to focused experimental studies on TF functions, and the resulting knowledge can guide experimental design for the discovery of unknown roles of TFs in human disease and drug response. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12915-017-0469-0) contains supplementary material, which is available to authorized users. BioMed Central 2018-01-10 /pmc/articles/PMC5795274/ /pubmed/29325558 http://dx.doi.org/10.1186/s12915-017-0469-0 Text en © Li et al. 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Li, Yong Fuga
Altman, Russ B.
Systematic target function annotation of human transcription factors
title Systematic target function annotation of human transcription factors
title_full Systematic target function annotation of human transcription factors
title_fullStr Systematic target function annotation of human transcription factors
title_full_unstemmed Systematic target function annotation of human transcription factors
title_short Systematic target function annotation of human transcription factors
title_sort systematic target function annotation of human transcription factors
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5795274/
https://www.ncbi.nlm.nih.gov/pubmed/29325558
http://dx.doi.org/10.1186/s12915-017-0469-0
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