Literature DB >> 25432957

Estimating binding properties of transcription factors from genome-wide binding profiles.

Nicolae Radu Zabet1, Boris Adryan2.   

Abstract

The binding of transcription factors (TFs) is essential for gene expression. One important characteristic is the actual occupancy of a putative binding site in the genome. In this study, we propose an analytical model to predict genomic occupancy that incorporates the preferred target sequence of a TF in the form of a position weight matrix (PWM), DNA accessibility data (in the case of eukaryotes), the number of TF molecules expected to be bound specifically to the DNA and a parameter that modulates the specificity of the TF. Given actual occupancy data in the form of ChIP-seq profiles, we backwards inferred copy number and specificity for five Drosophila TFs during early embryonic development: Bicoid, Caudal, Giant, Hunchback and Kruppel. Our results suggest that these TFs display thousands of molecules that are specifically bound to the DNA and that whilst Bicoid and Caudal display a higher specificity, the other three TFs (Giant, Hunchback and Kruppel) display lower specificity in their binding (despite having PWMs with higher information content). This study gives further weight to earlier investigations into TF copy numbers that suggest a significant proportion of molecules are not bound specifically to the DNA.
© The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

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Year:  2014        PMID: 25432957      PMCID: PMC4288167          DOI: 10.1093/nar/gku1269

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


  75 in total

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Review 3.  Determining the specificity of protein-DNA interactions.

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5.  An effective model for natural selection in promoters.

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6.  Comprehensive genome-wide protein-DNA interactions detected at single-nucleotide resolution.

Authors:  Ho Sung Rhee; B Franklin Pugh
Journal:  Cell       Date:  2011-12-09       Impact factor: 41.582

7.  The specificity of protein-DNA crosslinking by formaldehyde: in vitro and in drosophila embryos.

Authors:  J Toth; M D Biggin
Journal:  Nucleic Acids Res       Date:  2000-01-15       Impact factor: 16.971

8.  The formation of the Bicoid morphogen gradient requires protein movement from anteriorly localized mRNA.

Authors:  Shawn C Little; Gašper Tkačik; Thomas B Kneeland; Eric F Wieschaus; Thomas Gregor
Journal:  PLoS Biol       Date:  2011-03-01       Impact factor: 8.029

9.  Single-molecule imaging of transcription factor binding to DNA in live mammalian cells.

Authors:  J Christof M Gebhardt; David M Suter; Rahul Roy; Ziqing W Zhao; Alec R Chapman; Srinjan Basu; Tom Maniatis; X Sunney Xie
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  14 in total

1.  Expanding the repertoire of DNA shape features for genome-scale studies of transcription factor binding.

Authors:  Jinsen Li; Jared M Sagendorf; Tsu-Pei Chiu; Marco Pasi; Alberto Perez; Remo Rohs
Journal:  Nucleic Acids Res       Date:  2017-12-15       Impact factor: 16.971

2.  A novel method for improved accuracy of transcription factor binding site prediction.

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3.  Transcription factor organic cation transporter 1 (OCT-1) affects the expression of porcine Klotho (KL) gene.

Authors:  Yan Li; Lei Wang; Jiawei Zhou; Fenge Li
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5.  NucTools: analysis of chromatin feature occupancy profiles from high-throughput sequencing data.

Authors:  Yevhen Vainshtein; Karsten Rippe; Vladimir B Teif
Journal:  BMC Genomics       Date:  2017-02-14       Impact factor: 3.969

6.  Transcription factor target site search and gene regulation in a background of unspecific binding sites.

Authors:  J Hettich; J C M Gebhardt
Journal:  J Theor Biol       Date:  2018-06-02       Impact factor: 2.691

7.  Chromatin architecture reorganization during neuronal cell differentiation in Drosophila genome.

Authors:  Keerthi T Chathoth; Nicolae Radu Zabet
Journal:  Genome Res       Date:  2019-02-01       Impact factor: 9.043

8.  Transcription factor binding site clusters identify target genes with similar tissue-wide expression and buffer against mutations.

Authors:  Ruipeng Lu; Peter K Rogan
Journal:  F1000Res       Date:  2018-12-14

9.  Reliable scaling of position weight matrices for binding strength comparisons between transcription factors.

Authors:  Xiaoyan Ma; Daphne Ezer; Carmen Navarro; Boris Adryan
Journal:  BMC Bioinformatics       Date:  2015-08-20       Impact factor: 3.169

10.  Canonical and single-cell Hi-C reveal distinct chromatin interaction sub-networks of mammalian transcription factors.

Authors:  Xiaoyan Ma; Daphne Ezer; Boris Adryan; Tim J Stevens
Journal:  Genome Biol       Date:  2018-10-25       Impact factor: 13.583

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