Literature DB >> 30428075

BiFET: sequencing Bias-free transcription factor Footprint Enrichment Test.

Ahrim Youn1, Eladio J Marquez1, Nathan Lawlor1, Michael L Stitzel1,2,3, Duygu Ucar1,2,3.   

Abstract

Transcription factor (TF) footprinting uncovers putative protein-DNA binding via combined analyses of chromatin accessibility patterns and their underlying TF sequence motifs. TF footprints are frequently used to identify TFs that regulate activities of cell/condition-specific genomic regions (target loci) in comparison to control regions (background loci) using standard enrichment tests. However, there is a strong association between the chromatin accessibility level and the GC content of a locus and the number and types of TF footprints that can be detected at this site. Traditional enrichment tests (e.g. hypergeometric) do not account for this bias and inflate false positive associations. Therefore, we developed a novel post-processing method, Bias-free Footprint Enrichment Test (BiFET), that corrects for the biases arising from the differences in chromatin accessibility levels and GC contents between target and background loci in footprint enrichment analyses. We applied BiFET on TF footprint calls obtained from EndoC-βH1 ATAC-seq samples using three different algorithms (CENTIPEDE, HINT-BC and PIQ) and showed BiFET's ability to increase power and reduce false positive rate when compared to hypergeometric test. Furthermore, we used BiFET to study TF footprints from human PBMC and pancreatic islet ATAC-seq samples to show its utility to identify putative TFs associated with cell-type-specific loci.

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Year:  2019        PMID: 30428075      PMCID: PMC6344870          DOI: 10.1093/nar/gky1117

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


  39 in total

1.  Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data.

Authors:  Roger Pique-Regi; Jacob F Degner; Athma A Pai; Daniel J Gaffney; Yoav Gilad; Jonathan K Pritchard
Journal:  Genome Res       Date:  2010-11-24       Impact factor: 9.043

2.  Genome-wide footprinting: ready for prime time?

Authors:  Myong-Hee Sung; Songjoon Baek; Gordon L Hager
Journal:  Nat Methods       Date:  2016-03       Impact factor: 28.547

3.  BinDNase: a discriminatory approach for transcription factor binding prediction using DNase I hypersensitivity data.

Authors:  Juhani Kähärä; Harri Lähdesmäki
Journal:  Bioinformatics       Date:  2015-05-07       Impact factor: 6.937

4.  DNA-binding specificities of human transcription factors.

Authors:  Arttu Jolma; Jian Yan; Thomas Whitington; Jarkko Toivonen; Kazuhiro R Nitta; Pasi Rastas; Ekaterina Morgunova; Martin Enge; Mikko Taipale; Gonghong Wei; Kimmo Palin; Juan M Vaquerizas; Renaud Vincentelli; Nicholas M Luscombe; Timothy R Hughes; Patrick Lemaire; Esko Ukkonen; Teemu Kivioja; Jussi Taipale
Journal:  Cell       Date:  2013-01-17       Impact factor: 41.582

Review 5.  Genomic footprinting.

Authors:  Jeff Vierstra; John A Stamatoyannopoulos
Journal:  Nat Methods       Date:  2016-03       Impact factor: 28.547

6.  A genetically engineered human pancreatic β cell line exhibiting glucose-inducible insulin secretion.

Authors:  Philippe Ravassard; Yasmine Hazhouz; Séverine Pechberty; Emilie Bricout-Neveu; Mathieu Armanet; Paul Czernichow; Raphael Scharfmann
Journal:  J Clin Invest       Date:  2011-08-25       Impact factor: 14.808

7.  Transcriptional activation by the thyroid hormone receptor through ligand-dependent receptor recruitment and chromatin remodelling.

Authors:  Lars Grøntved; Joshua J Waterfall; Dong Wook Kim; Songjoon Baek; Myong-Hee Sung; Li Zhao; Jeong Won Park; Ronni Nielsen; Robert L Walker; Yuelin J Zhu; Paul S Meltzer; Gordon L Hager; Sheue-Yann Cheng
Journal:  Nat Commun       Date:  2015-04-28       Impact factor: 14.919

8.  DNase footprint signatures are dictated by factor dynamics and DNA sequence.

Authors:  Myong-Hee Sung; Michael J Guertin; Songjoon Baek; Gordon L Hager
Journal:  Mol Cell       Date:  2014-09-18       Impact factor: 17.970

9.  Wellington: a novel method for the accurate identification of digital genomic footprints from DNase-seq data.

Authors:  Jason Piper; Markus C Elze; Pierre Cauchy; Peter N Cockerill; Constanze Bonifer; Sascha Ott
Journal:  Nucleic Acids Res       Date:  2013-09-25       Impact factor: 16.971

10.  On Accounting for Sequence-Specific Bias in Genome-Wide Chromatin Accessibility Experiments: Recent Advances and Contradictions.

Authors:  Pedro Madrigal
Journal:  Front Bioeng Biotechnol       Date:  2015-09-22
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  4 in total

Review 1.  Sequence and chromatin determinants of transcription factor binding and the establishment of cell type-specific binding patterns.

Authors:  Divyanshi Srivastava; Shaun Mahony
Journal:  Biochim Biophys Acta Gene Regul Mech       Date:  2019-10-19       Impact factor: 4.490

2.  Analytical Approaches for ATAC-seq Data Analysis.

Authors:  Jason P Smith; Nathan C Sheffield
Journal:  Curr Protoc Hum Genet       Date:  2020-06

3.  3D promoter architecture re-organization during iPSC-derived neuronal cell differentiation implicates target genes for neurodevelopmental disorders.

Authors:  Chun Su; Mariana Argenziano; Sumei Lu; James A Pippin; Matthew C Pahl; Michelle E Leonard; Diana L Cousminer; Matthew E Johnson; Chiara Lasconi; Andrew D Wells; Alessandra Chesi; Struan F A Grant
Journal:  Prog Neurobiol       Date:  2021-02-02       Impact factor: 10.885

4.  Efficient hemogenic endothelial cell specification by RUNX1 is dependent on baseline chromatin accessibility of RUNX1-regulated TGFβ target genes.

Authors:  Elizabeth D Howell; Amanda D Yzaguirre; Peng Gao; Raphael Lis; Bing He; Melike Lakadamyali; Shahin Rafii; Kai Tan; Nancy A Speck
Journal:  Genes Dev       Date:  2021-10-21       Impact factor: 11.361

  4 in total

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