Dataset Information

PatternCNV: a versatile tool for detecting copy number changes from exome sequencing data.

ABSTRACT:

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/.

SUBMITTER: Wang C

PROVIDER: S-EPMC4155258 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

PatternCNV: a versatile tool for detecting copy number changes from exome sequencing data.

Wang Chen C Evans Jared M JM Bhagwate Aditya V AV Prodduturi Naresh N Sarangi Vivekananda V Middha Mridu M Sicotte Hugues H Vedell Peter T PT Hart Steven N SN Oliver Gavin R GR Kocher Jean-Pierre A JP Maurer Matthew J MJ Novak Anne J AJ Slager Susan L SL Cerhan James R JR Asmann Yan W YW

Bioinformatics (Oxford, England) 20140529 18

<h4>Motivation</h4>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.<h4>Results</h4>We developed a novel method named PatternCNV, which (i) accounts for the read coverage ...[more]

PMID: 24876377

Similar Datasets

Project description:BackgroundExome sequencing (ES) is a first-tier diagnostic test for many suspected Mendelian disorders. While it is routine to detect small sequence variants, it is not a standard practice in clinical settings to detect germline copy-number variants (CNVs) from ES data due to several reasons relating to performance. In this work, we comprehensively characterized one of the most sensitive ES-based CNV tools, ExomeDepth, against SNP array, a standard of care test in clinical settings to detect genome-wide CNVs.MethodsWe propose a modified ExomeDepth workflow by excluding exons with low mappability prior to variant calling to drastically reduce the false positives originating from the repetitive regions of the genome, and an iterative variant calling framework to assess the reproducibility. We used a cohort of 307 individuals with clinical ES data and clinical SNP array to estimate the sensitivity and false discovery rate of the CNV detection using exome sequencing. Further, we performed targeted testing of the STRC gene in 1972 individuals. To reduce the number of variants for downstream analysis, we performed a large-scale iterative variant calling process with random control cohorts to assess the reproducibility of the CNVs.ResultsThe modified workflow presented in this paper reduced the number of total variants identified by one third while retaining a higher sensitivity of 97% and resulted in an improved false discovery rate of 11.4% compared to the default ExomeDepth pipeline. The exclusion of exons with low mappability removes 4.5% of the exons, including a subset of exons (0.6%) in disease-associated genes which are intractable by short-read next-generation sequencing (NGS). Results from the reproducibility analysis showed that the clinically reported variants were reproducible 100% of the time and that the modified workflow can be used to rank variants from high to low confidence. Targeted testing of 30 CNVs identified in STRC, a challenging gene to ascertain by NGS, showed a 100% validation rate.ConclusionsIn summary, we introduced a modification to the default ExomeDepth workflow to reduce the false positives originating from the repetitive regions of the genome, created a large-scale iterative variant calling framework for reproducibility, and provided recommendations for implementation in clinical settings.

Dataset Information

PatternCNV: a versatile tool for detecting copy number changes from exome sequencing data.

Motivation

Results

Availability and implementation

Publications

PatternCNV: a versatile tool for detecting copy number changes from exome sequencing data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets