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Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity
Author(s) -
Matthew T. Weirauch,
Ally Yang,
Mihai Albu,
Atina G. Coté,
Alejandro MontenegroMontero,
Philipp Drewe,
Hamed S. Najafabadi,
Samuel A. Lambert,
Ishminder K. Mann,
Kate B. Cook,
Hong Zheng,
Alejandra Goity,
Harm van Bakel,
Jean-Claude Lozano,
Mary Galli,
Mathew G. Lewsey,
Eryong Huang,
Tuhin Mukherjee,
Xiaoting Chen,
John Reece-Hoyes,
Sridhar Govindarajan,
Gad Shaulsky,
Albertha J.M. Walhout,
François-Yves Bouget,
Gunnar Rätsch,
Luis Larrondo,
Joseph R. Ecker,
Timothy R. Hughes
Publication year - 2014
Publication title -
cell
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 26.304
H-Index - 776
eISSN - 1097-4172
pISSN - 0092-8674
DOI - 10.1016/j.cell.2014.08.009
Subject(s) - biology , inference , computational biology , genetics , transcription factor , sequence (biology) , transcription (linguistics) , evolutionary biology , gene , artificial intelligence , linguistics , philosophy , computer science
Transcription factor (TF) DNA sequence preferences direct their regulatory activity, but are currently known for only ∼1% of eukaryotic TFs. Broadly sampling DNA-binding domain (DBD) types from multiple eukaryotic clades, we determined DNA sequence preferences for >1,000 TFs encompassing 54 different DBD classes from 131 diverse eukaryotes. We find that closely related DBDs almost always have very similar DNA sequence preferences, enabling inference of motifs for ∼34% of the ∼170,000 known or predicted eukaryotic TFs. Sequences matching both measured and inferred motifs are enriched in chromatin immunoprecipitation sequencing (ChIP-seq) peaks and upstream of transcription start sites in diverse eukaryotic lineages. SNPs defining expression quantitative trait loci in Arabidopsis promoters are also enriched for predicted TF binding sites. Importantly, our motif "library" can be used to identify specific TFs whose binding may be altered by human disease risk alleles. These data present a powerful resource for mapping transcriptional networks across eukaryotes.

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