PatternCNV
Computational Tool
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General Information
DescriptionA versatile tool for detecting copy number changes from exome sequencing data
Homepagehttp://bioinformaticstools.mayo.edu/research/patterncnv/
Publications
PatternCNV: a versatile tool for detecting copy number changes from exome sequencing data
Chen Wang; Jared M. Evans; Aditya V. Bhagwate; Naresh Prodduturi; Vivekananda Sarangi; Mridu Middha; Hugues Sicotte; Peter T. Vedell; Steven N. Hart; Gavin R. Oliver; Jean-Pierre A. Kocher; Matthew J. Maurer; Anne J. Novak; Susan L. Slager; James R. Cerhan; Yan W. Asmann
Motivation: Exome sequencing (exome-seq) data, which are typically used for calling exonic mutations, have also been utilized in detecting DNA copy number variations (CNVs). Despite the existence of several CNV detection tools, there is still a great need for a sensitive and an accurate CNV-calling algorithm with built-in QC steps, and does not require a paired reference for each sample.
Results: We developed a novel method named PatternCNV, which (i) accounts for the read coverage variations between exons while leveraging the consistencies of this variability across different samples; (ii) reduces alignment BAM files to WIG format and therefore greatly accelerates computation; (iii) incorporates multiple QC measures designed to identify outlier samples and batch effects; and (iv) provides a variety of visualization options including chromosome, gene and exon-level views of CNVs, along with a tabular summarization of the exon-level CNVs. Compared with other CNV-calling algorithms using data from a lymphoma exome-seq study, PatternCNV has higher sensitivity and specificity.
Availability and implementation: The software for PatternCNV is implemented using Perl and R, and can be used in Mac or Linux environments. Software and user manual are available at http://bioinformaticstools.mayo.edu/research/patterncnv/ , and R package at https://github.com/topsoil/patternCNV/ .
Contact: Asmann.Yan@mayo.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Metrics:
12
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