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    From reads to regions: a Bioconductor workflow to detect differential binding in ChIP-seq data.

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    Author
    Lun, ATL; Smyth, GK
    Date
    2015
    Source Title
    F1000Research
    Publisher
    F1000 Research Ltd
    University of Melbourne Author/s
    Smyth, Gordon
    Affiliation
    School of Mathematics and Statistics
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Lun, A. T. L. & Smyth, G. K. (2015). From reads to regions: a Bioconductor workflow to detect differential binding in ChIP-seq data.. F1000Res, 4, pp.1080-. https://doi.org/10.12688/f1000research.7016.2.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/259257
    DOI
    10.12688/f1000research.7016.2
    Open Access at PMC
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4706055
    Abstract
    Chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) is widely used to identify the genomic binding sites for protein of interest. Most conventional approaches to ChIP-seq data analysis involve the detection of the absolute presence (or absence) of a binding site. However, an alternative strategy is to identify changes in the binding intensity between two biological conditions, i.e., differential binding (DB). This may yield more relevant results than conventional analyses, as changes in binding can be associated with the biological difference being investigated. The aim of this article is to facilitate the implementation of DB analyses, by comprehensively describing a computational workflow for the detection of DB regions from ChIP-seq data. The workflow is based primarily on R software packages from the open-source Bioconductor project and covers all steps of the analysis pipeline, from alignment of read sequences to interpretation and visualization of putative DB regions. In particular, detection of DB regions will be conducted using the counts for sliding windows from the csaw package, with statistical modelling performed using methods in the edgeR package. Analyses will be demonstrated on real histone mark and transcription factor data sets. This will provide readers with practical usage examples that can be applied in their own studies.

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