Literature DB >> 25215497

Determination and inference of eukaryotic transcription factor sequence specificity.

Matthew T Weirauch1, Ally Yang2, Mihai Albu2, Atina G Cote2, Alejandro Montenegro-Montero3, Philipp Drewe4, Hamed S Najafabadi2, Samuel A Lambert5, Ishminder Mann2, Kate Cook5, Hong Zheng2, Alejandra Goity3, Harm van Bakel6, Jean-Claude Lozano7, Mary Galli8, Mathew G Lewsey9, Eryong Huang10, Tuhin Mukherjee11, Xiaoting Chen11, John S Reece-Hoyes12, Sridhar Govindarajan13, Gad Shaulsky10, Albertha J M Walhout12, François-Yves Bouget7, Gunnar Ratsch4, Luis F Larrondo3, Joseph R Ecker14, Timothy R Hughes15.   

Abstract

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.
Copyright © 2014 Elsevier Inc. All rights reserved.

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Year:  2014        PMID: 25215497      PMCID: PMC4163041          DOI: 10.1016/j.cell.2014.08.009

Source DB:  PubMed          Journal:  Cell        ISSN: 0092-8674            Impact factor:   41.582


  64 in total

1.  Compact, universal DNA microarrays to comprehensively determine transcription-factor binding site specificities.

Authors:  Michael F Berger; Anthony A Philippakis; Aaron M Qureshi; Fangxue S He; Preston W Estep; Martha L Bulyk
Journal:  Nat Biotechnol       Date:  2006-09-24       Impact factor: 54.908

2.  Transcription factor binding in human cells occurs in dense clusters formed around cohesin anchor sites.

Authors:  Jian Yan; Martin Enge; Thomas Whitington; Kashyap Dave; Jianping Liu; Inderpreet Sur; Bernhard Schmierer; Arttu Jolma; Teemu Kivioja; Minna Taipale; Jussi Taipale
Journal:  Cell       Date:  2013-08-15       Impact factor: 41.582

3.  A new generation of homology search tools based on probabilistic inference.

Authors:  Sean R Eddy
Journal:  Genome Inform       Date:  2009-10

4.  VND-INTERACTING2, a NAC domain transcription factor, negatively regulates xylem vessel formation in Arabidopsis.

Authors:  Masatoshi Yamaguchi; Misato Ohtani; Nobutaka Mitsuda; Minoru Kubo; Masaru Ohme-Takagi; Hiroo Fukuda; Taku Demura
Journal:  Plant Cell       Date:  2010-04-13       Impact factor: 11.277

5.  Evaluation of methods for modeling transcription factor sequence specificity.

Authors:  Matthew T Weirauch; Atina Cote; Raquel Norel; Matti Annala; Yue Zhao; Todd R Riley; Julio Saez-Rodriguez; Thomas Cokelaer; Anastasia Vedenko; Shaheynoor Talukder; Harmen J Bussemaker; Quaid D Morris; Martha L Bulyk; Gustavo Stolovitzky; Timothy R Hughes
Journal:  Nat Biotechnol       Date:  2013-01-27       Impact factor: 54.908

6.  Recognition models to predict DNA-binding specificities of homeodomain proteins.

Authors:  Ryan G Christensen; Metewo Selase Enuameh; Marcus B Noyes; Michael H Brodsky; Scot A Wolfe; Gary D Stormo
Journal:  Bioinformatics       Date:  2012-06-15       Impact factor: 6.937

7.  Quantitative analysis demonstrates most transcription factors require only simple models of specificity.

Authors:  Yue Zhao; Gary D Stormo
Journal:  Nat Biotechnol       Date:  2011-06-07       Impact factor: 54.908

8.  YeTFaSCo: a database of evaluated yeast transcription factor sequence specificities.

Authors:  Carl G de Boer; Timothy R Hughes
Journal:  Nucleic Acids Res       Date:  2011-11-18       Impact factor: 16.971

9.  Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega.

Authors:  Fabian Sievers; Andreas Wilm; David Dineen; Toby J Gibson; Kevin Karplus; Weizhong Li; Rodrigo Lopez; Hamish McWilliam; Michael Remmert; Johannes Söding; Julie D Thompson; Desmond G Higgins
Journal:  Mol Syst Biol       Date:  2011-10-11       Impact factor: 11.429

10.  The role of chromatin accessibility in directing the widespread, overlapping patterns of Drosophila transcription factor binding.

Authors:  Xiao-Yong Li; Sean Thomas; Peter J Sabo; Michael B Eisen; John A Stamatoyannopoulos; Mark D Biggin
Journal:  Genome Biol       Date:  2011-04-07       Impact factor: 13.583

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  617 in total

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Authors:  Mária Šimášková; José Antonio O'Brien; Mamoona Khan; Giel Van Noorden; Krisztina Ötvös; Anne Vieten; Inge De Clercq; Johanna Maria Adriana Van Haperen; Candela Cuesta; Klára Hoyerová; Steffen Vanneste; Peter Marhavý; Krzysztof Wabnik; Frank Van Breusegem; Moritz Nowack; Angus Murphy; Jiří Friml; Dolf Weijers; Tom Beeckman; Eva Benková
Journal:  Nat Commun       Date:  2015-11-06       Impact factor: 14.919

Review 2.  Bioinformatic landscapes for plant transcription factor system research.

Authors:  Yijun Wang; Wenjie Lu; Dexiang Deng
Journal:  Planta       Date:  2015-12-30       Impact factor: 4.116

Review 3.  Small Genetic Circuits and MicroRNAs: Big Players in Polymerase II Transcriptional Control in Plants.

Authors:  Molly Megraw; Jason S Cumbie; Maria G Ivanchenko; Sergei A Filichkin
Journal:  Plant Cell       Date:  2016-02-11       Impact factor: 11.277

4.  Minimum epistasis interpolation for sequence-function relationships.

Authors:  Juannan Zhou; David M McCandlish
Journal:  Nat Commun       Date:  2020-04-14       Impact factor: 14.919

5.  A transcription factor collective defines the HSN serotonergic neuron regulatory landscape.

Authors:  Carla Lloret-Fernández; Miren Maicas; Carlos Mora-Martínez; Alejandro Artacho; Ángela Jimeno-Martín; Laura Chirivella; Peter Weinberg; Nuria Flames
Journal:  Elife       Date:  2018-03-22       Impact factor: 8.140

6.  Gene Regulatory Network Analysis Identifies Sex-Linked Differences in Colon Cancer Drug Metabolism.

Authors:  Camila M Lopes-Ramos; Marieke L Kuijjer; Shuji Ogino; Charles S Fuchs; Dawn L DeMeo; Kimberly Glass; John Quackenbush
Journal:  Cancer Res       Date:  2018-10-01       Impact factor: 12.701

7.  Transcriptome dynamics of developing maize leaves and genomewide prediction of cis elements and their cognate transcription factors.

Authors:  Chun-Ping Yu; Sean Chun-Chang Chen; Yao-Ming Chang; Wen-Yu Liu; Hsin-Hung Lin; Jinn-Jy Lin; Hsiang June Chen; Yu-Ju Lu; Yi-Hsuan Wu; Mei-Yeh Jade Lu; Chen-Hua Lu; Arthur Chun-Chieh Shih; Maurice Sun-Ben Ku; Shin-Han Shiu; Shu-Hsing Wu; Wen-Hsiung Li
Journal:  Proc Natl Acad Sci U S A       Date:  2015-04-27       Impact factor: 11.205

8.  Genome-Wide Association Study Identifies Novel Loci Associated With Diisocyanate-Induced Occupational Asthma.

Authors:  Berran Yucesoy; Kenneth M Kaufman; Zana L Lummus; Matthew T Weirauch; Ge Zhang; André Cartier; Louis-Philippe Boulet; Joaquin Sastre; Santiago Quirce; Susan M Tarlo; Maria-Jesus Cruz; Xavier Munoz; John B Harley; David I Bernstein
Journal:  Toxicol Sci       Date:  2015-04-26       Impact factor: 4.849

9.  Human gene-centered transcription factor networks for enhancers and disease variants.

Authors:  Juan I Fuxman Bass; Nidhi Sahni; Shaleen Shrestha; Aurian Garcia-Gonzalez; Akihiro Mori; Numana Bhat; Song Yi; David E Hill; Marc Vidal; Albertha J M Walhout
Journal:  Cell       Date:  2015-04-23       Impact factor: 41.582

10.  Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework.

Authors:  Jinyu Yang; Anjun Ma; Adam D Hoppe; Cankun Wang; Yang Li; Chi Zhang; Yan Wang; Bingqiang Liu; Qin Ma
Journal:  Nucleic Acids Res       Date:  2019-09-05       Impact factor: 16.971

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