SIOMICS: a novel approach for systematic identification of motifs in ChIP-seq data

    Authors

    J. Ding; H. Y. Hu;X. M. Li

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    Nucleic Acids Res.

    Keywords

    DNA-BINDING-SITES; CIS-REGULATORY MODULES; HUMAN GENOME; CHROMATIN-IMMUNOPRECIPITATION; GENE-EXPRESSION; PREDICTION; DISCOVERY; PROFILES; ELEMENTS; NETWORK; Biochemistry & Molecular Biology

    Abstract

    The identification of transcription factor binding motifs is important for the study of gene transcriptional regulation. The chromatin immunoprecipitation (ChIP), followed by massive parallel sequencing (ChIP-seq) experiments, provides an unprecedented opportunity to discover binding motifs. Computational methods have been developed to identify motifs from ChIP-seq data, while at the same time encountering several problems. For example, existing methods are often not scalable to the large number of sequences obtained from ChIP-seq peak regions. Some methods heavily rely on well-annotated motifs even though the number of known motifs is limited. To simplify the problem, de novo motif discovery methods often neglect underrepresented motifs in ChIP-seq peak regions. To address these issues, we developed a novel approach called SIOMICS to de novo discover motifs from ChIP-seq data. Tested on 13 ChIP-seq data sets, SIOMICS identified motifs of many known and new cofactors. Tested on 13 simulated random data sets, SIOMICS discovered no motif in any data set. Compared with two recently developed methods for motif discovery, SIOMICS shows advantages in terms of speed, the number of known cofactor motifs predicted in experimental data sets and the number of false motifs predicted in random data sets. The SIOMICS software is freely available at http://eecs.ucf.edu/similar to xiaoman/ SIOMICS/SIOMICS.html.

    Journal Title

    Nucleic Acids Research

    Volume

    42

    Issue/Number

    5

    Publication Date

    1-1-2014

    Document Type

    Article

    Language

    English

    First Page

    9

    WOS Identifier

    WOS:000333093600006

    ISSN

    0305-1048

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