Literature DB >> 22051799

Transcription factor binding predictions using TRAP for the analysis of ChIP-seq data and regulatory SNPs.

Morgane Thomas-Chollier1, Andrew Hufton, Matthias Heinig, Sean O'Keeffe, Nassim El Masri, Helge G Roider, Thomas Manke, Martin Vingron.   

Abstract

The transcription factor affinity prediction (TRAP) method calculates the affinity of transcription factors for DNA sequences on the basis of a biophysical model. This method has proven to be useful for several applications, including for determining the putative target genes of a given factor. This protocol covers two other applications: (i) determining which transcription factors have the highest affinity in a set of sequences (illustrated with chromatin immunoprecipitation-sequencing (ChIP-seq) peaks), and (ii) finding which factor is the most affected by a regulatory single-nucleotide polymorphism. The protocol describes how to use the TRAP web tools to address these questions, and it also presents a way to run TRAP on random control sequences to better estimate the significance of the results. All of the tools are fully available online and do not need any additional installation. The complete protocol takes about 45 min, but each individual tool runs in a few minutes.

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Year:  2011        PMID: 22051799     DOI: 10.1038/nprot.2011.409

Source DB:  PubMed          Journal:  Nat Protoc        ISSN: 1750-2799            Impact factor:   13.491


  32 in total

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Journal:  Nucleic Acids Res       Date:  2010-10-18       Impact factor: 16.971

9.  Statistical modeling of transcription factor binding affinities predicts regulatory interactions.

Authors:  Thomas Manke; Helge G Roider; Martin Vingron
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10.  PAP: a comprehensive workbench for mammalian transcriptional regulatory sequence analysis.

Authors:  Li-Wei Chang; Burr R Fontaine; Gary D Stormo; Rakesh Nagarajan
Journal:  Nucleic Acids Res       Date:  2007-05-21       Impact factor: 16.971

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  109 in total

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5.  QBiC-Pred: quantitative predictions of transcription factor binding changes due to sequence variants.

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9.  In silico mapping of quantitative trait loci (QTL) regulating the milk ionome in mice identifies a milk iron locus on chromosome 1.

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Journal:  Mamm Genome       Date:  2018-08-02       Impact factor: 2.957

10.  Metabolic Reprogramming Promotes Neural Crest Migration via Yap/Tead Signaling.

Authors:  Debadrita Bhattacharya; Ana Paula Azambuja; Marcos Simoes-Costa
Journal:  Dev Cell       Date:  2020-04-02       Impact factor: 12.270

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