Literature DB >> 20736340

Deep and wide digging for binding motifs in ChIP-Seq data.

I V Kulakovskiy1, V A Boeva, A V Favorov, V J Makeev.   

Abstract

SUMMARY: ChIP-Seq data are a new challenge for motif discovery. Such a data typically consists of thousands of DNA segments with base-specific coverage values. We present a new version of our DNA motif discovery software ChIPMunk adapted for ChIP-Seq data. ChIPMunk is an iterative algorithm that combines greedy optimization with bootstrapping and uses coverage profiles as motif positional preferences. ChIPMunk does not require truncation of long DNA segments and it is practical for processing up to tens of thousands of data sequences. Comparison with traditional (MEME) or ChIP-Seq-oriented (HMS) motif discovery tools shows that ChIPMunk identifies the correct motifs with the same or better quality but works dramatically faster.
AVAILABILITY AND IMPLEMENTATION: ChIPMunk is freely available within the ru_genetika Java package: http://line.imb.ac.ru/ChIPMunk. Web-based version is also available. CONTACT: ivan.kulakovskiy@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Year:  2010        PMID: 20736340     DOI: 10.1093/bioinformatics/btq488

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


  73 in total

1.  CompleteMOTIFs: DNA motif discovery platform for transcription factor binding experiments.

Authors:  Lakshmi Kuttippurathu; Michael Hsing; Yongchao Liu; Bertil Schmidt; Douglas L Maskell; Kyungjoon Lee; Aibin He; William T Pu; Sek Won Kong
Journal:  Bioinformatics       Date:  2010-12-23       Impact factor: 6.937

2.  The NF-κB genomic landscape in lymphoblastoid B cells.

Authors:  Bo Zhao; Luis A Barrera; Ina Ersing; Bradford Willox; Stefanie C S Schmidt; Hannah Greenfeld; Hufeng Zhou; Sarah B Mollo; Tommy T Shi; Kaoru Takasaki; Sizun Jiang; Ellen Cahir-McFarland; Manolis Kellis; Martha L Bulyk; Elliott Kieff; Benjamin E Gewurz
Journal:  Cell Rep       Date:  2014-08-21       Impact factor: 9.423

3.  Discriminative motif optimization based on perceptron training.

Authors:  Ronak Y Patel; Gary D Stormo
Journal:  Bioinformatics       Date:  2013-12-24       Impact factor: 6.937

4.  Motif-based analysis of large nucleotide data sets using MEME-ChIP.

Authors:  Wenxiu Ma; William S Noble; Timothy L Bailey
Journal:  Nat Protoc       Date:  2014-05-22       Impact factor: 13.491

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

Authors:  Yang Li; Pengyu Ni; Shaoqiang Zhang; Guojun Li; Zhengchang Su
Journal:  Bioinformatics       Date:  2019-11-01       Impact factor: 6.937

6.  Sequence-based model of gap gene regulatory network.

Authors:  Konstantin Kozlov; Vitaly Gursky; Ivan Kulakovskiy; Maria Samsonova
Journal:  BMC Genomics       Date:  2014-12-19       Impact factor: 3.969

7.  Computational analysis of auxin responsive elements in the Arabidopsis thaliana L. genome.

Authors:  Victoria V Mironova; Nadezda A Omelyanchuk; Daniil S Wiebe; Victor G Levitsky
Journal:  BMC Genomics       Date:  2014-12-19       Impact factor: 3.969

8.  A complete workflow for the analysis of full-size ChIP-seq (and similar) data sets using peak-motifs.

Authors:  Morgane Thomas-Chollier; Elodie Darbo; Carl Herrmann; Matthieu Defrance; Denis Thieffry; Jacques van Helden
Journal:  Nat Protoc       Date:  2012-07-26       Impact factor: 13.491

Review 9.  ChIP-seq and beyond: new and improved methodologies to detect and characterize protein-DNA interactions.

Authors:  Terrence S Furey
Journal:  Nat Rev Genet       Date:  2012-10-23       Impact factor: 53.242

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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