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Systematic target function annotation of human transcription factors

Overview of attention for article published in BMC Biology, January 2018
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Title
Systematic target function annotation of human transcription factors
Published in
BMC Biology, January 2018
DOI 10.1186/s12915-017-0469-0
Pubmed ID
Authors

Yong Fuga Li, Russ B. Altman

Abstract

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. 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. 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.

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Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 19%
Researcher 5 14%
Student > Master 5 14%
Professor 4 11%
Student > Doctoral Student 3 8%
Other 6 16%
Unknown 7 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 27%
Biochemistry, Genetics and Molecular Biology 8 22%
Computer Science 5 14%
Engineering 3 8%
Medicine and Dentistry 1 3%
Other 1 3%
Unknown 9 24%