Literature DB >> 31070745

ProSampler: an ultrafast and accurate motif finder in large ChIP-seq datasets for combinatory motif discovery.

Yang Li1,2, Pengyu Ni2, Shaoqiang Zhang3, Guojun Li1,2, Zhengchang Su2.   

Abstract

MOTIVATION: The availability of numerous ChIP-seq datasets for transcription factors (TF) has provided an unprecedented opportunity to identify all TF binding sites in genomes. However, the progress has been hindered by the lack of a highly efficient and accurate tool to find not only the target motifs, but also cooperative motifs in very big datasets.
RESULTS: We herein present an ultrafast and accurate motif-finding algorithm, ProSampler, based on a novel numeration method and Gibbs sampler. ProSampler runs orders of magnitude faster than the fastest existing tools while often more accurately identifying motifs of both the target TFs and cooperators. Thus, ProSampler can greatly facilitate the efforts to identify the entire cis-regulatory code in genomes.
AVAILABILITY AND IMPLEMENTATION: Source code and binaries are freely available for download at https://github.com/zhengchangsulab/prosampler. It was implemented in C++ and supported on Linux, macOS and MS Windows platforms. SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.
© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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Mesh:

Year:  2019        PMID: 31070745      PMCID: PMC6853706          DOI: 10.1093/bioinformatics/btz290

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  34 in total

1.  BioProspector: discovering conserved DNA motifs in upstream regulatory regions of co-expressed genes.

Authors:  X Liu; D L Brutlag; J S Liu
Journal:  Pac Symp Biocomput       Date:  2001

2.  Tandem repeats finder: a program to analyze DNA sequences.

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Journal:  Nucleic Acids Res       Date:  1999-01-15       Impact factor: 16.971

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Journal:  Bioinformatics       Date:  2011-07-12       Impact factor: 6.937

Review 4.  ChIP-seq: advantages and challenges of a maturing technology.

Authors:  Peter J Park
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5.  Inferring direct DNA binding from ChIP-seq.

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Journal:  Nucleic Acids Res       Date:  2012-05-18       Impact factor: 16.971

6.  STEME: efficient EM to find motifs in large data sets.

Authors:  John E Reid; Lorenz Wernisch
Journal:  Nucleic Acids Res       Date:  2011-07-23       Impact factor: 16.971

7.  DREME: motif discovery in transcription factor ChIP-seq data.

Authors:  Timothy L Bailey
Journal:  Bioinformatics       Date:  2011-05-04       Impact factor: 6.937

8.  RSAT peak-motifs: motif analysis in full-size ChIP-seq datasets.

Authors:  Morgane Thomas-Chollier; Carl Herrmann; Matthieu Defrance; Olivier Sand; Denis Thieffry; Jacques van Helden
Journal:  Nucleic Acids Res       Date:  2011-12-08       Impact factor: 16.971

9.  WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data.

Authors:  Hongbo Zhang; Lin Zhu; De-Shuang Huang
Journal:  Sci Rep       Date:  2017-06-12       Impact factor: 4.379

10.  TcoF-DB v2: update of the database of human and mouse transcription co-factors and transcription factor interactions.

Authors:  Sebastian Schmeier; Tanvir Alam; Magbubah Essack; Vladimir B Bajic
Journal:  Nucleic Acids Res       Date:  2016-10-26       Impact factor: 16.971

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  5 in total

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Journal:  Database (Oxford)       Date:  2022-04-22       Impact factor: 4.462

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Authors:  Pengyu Ni; David Wilson; Zhengchang Su
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3.  Accurate prediction of cis-regulatory modules reveals a prevalent regulatory genome of humans.

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Journal:  NAR Genom Bioinform       Date:  2021-06-17

4.  STREME: Accurate and versatile sequence motif discovery.

Authors:  Timothy L Bailey
Journal:  Bioinformatics       Date:  2021-03-24       Impact factor: 6.937

5.  Accurate prediction of functional states of cis-regulatory modules reveals common epigenetic rules in humans and mice.

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  5 in total

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