Literature DB >> 20223835

Localized motif discovery in gene regulatory sequences.

Vipin Narang1, Ankush Mittal, Wing-Kin Sung.   

Abstract

MOTIVATION: Discovery of nucleotide motifs that are localized with respect to a certain biological landmark is important in several appli-cations, such as in regulatory sequences flanking the transcription start site, in the neighborhood of known transcription factor binding sites, and in transcription factor binding regions discovered by massively parallel sequencing (ChIP-Seq).
RESULTS: We report an algorithm called LocalMotif to discover such localized motifs. The algorithm is based on a novel scoring function, called spatial confinement score, which can determine the exact interval of localization of a motif. This score is combined with other existing scoring measures including over-representation and relative entropy to determine the overall prominence of the motif. The approach successfully discovers biologically relevant motifs and their intervals of localization in scenarios where the motifs cannot be discovered by general motif finding tools. It is especially useful for discovering multiple co-localized motifs in a set of regulatory sequences, such as those identified by ChIP-Seq.
AVAILABILITY AND IMPLEMENTATION: The LocalMotif software is available at http://www.comp.nus.edu.sg/~bioinfo/LocalMotif.

Mesh:

Year:  2010        PMID: 20223835     DOI: 10.1093/bioinformatics/btq106

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


  6 in total

1.  Motif discovery and transcription factor binding sites before and after the next-generation sequencing era.

Authors:  Federico Zambelli; Graziano Pesole; Giulio Pavesi
Journal:  Brief Bioinform       Date:  2012-04-19       Impact factor: 11.622

2.  A flexible integrative approach based on random forest improves prediction of transcription factor binding sites.

Authors:  Bart Hooghe; Stefan Broos; Frans van Roy; Pieter De Bleser
Journal:  Nucleic Acids Res       Date:  2012-04-05       Impact factor: 16.971

3.  A highly efficient and effective motif discovery method for ChIP-seq/ChIP-chip data using positional information.

Authors:  Xiaotu Ma; Ashwinikumar Kulkarni; Zhihua Zhang; Zhenyu Xuan; Robert Serfling; Michael Q Zhang
Journal:  Nucleic Acids Res       Date:  2012-01-06       Impact factor: 16.971

Review 4.  Machine learning for epigenetics and future medical applications.

Authors:  Lawrence B Holder; M Muksitul Haque; Michael K Skinner
Journal:  Epigenetics       Date:  2017-05-19       Impact factor: 4.528

5.  ChIP-Seq-Based Approach in Mouse Enteric Precursor Cells Reveals New Potential Genes with a Role in Enteric Nervous System Development and Hirschsprung Disease.

Authors:  Leticia Villalba-Benito; Ana Torroglosa; Berta Luzón-Toro; Raquel María Fernández; María José Moya-Jiménez; Guillermo Antiñolo; Salud Borrego
Journal:  Int J Mol Sci       Date:  2020-11-28       Impact factor: 5.923

6.  POWRS: position-sensitive motif discovery.

Authors:  Ian W Davis; Christopher Benninger; Philip N Benfey; Tedd Elich
Journal:  PLoS One       Date:  2012-07-05       Impact factor: 3.240

  6 in total

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