Ximmer: A system for improving accuracy and consistency of CNV calling from exome data
Journal Title
GigaScience
Publication Type
Journal Article in press
Abstract
Background: While exome and targeted next generation DNA sequencing are primarily used for detecting single nucleotide changes and small indels, detection of copy number variants (CNVs) can provide highly valuable additional information from the data. Although there are dozens of exome CNV detection methods available, these are often difficult to use and accuracy varies unpredictably between and within data sets. Findings: We present Ximmer, a tool which supports an end to end process for evaluating, tuning and running analysis methods for detection of CNVs in germline samples. Ximmer includes a simulation framework, implementations of several commonly used CNV detection methods, and a visualisation and curation tool which together enable interactive exploration and quality control of CNV results. Using Ximmer, we comprehensively evaluate CNV detection on four data sets using five different detection methods. We show that application of Ximmer can improve accuracy and aid in quality control of CNV detection results. In addition, Ximmer can be used to run analyses and explore CNV results in exome data. Conclusions: Ximmer offers a comprehensive tool and method for applying and improving accuracy of CNV detection methods for exome data.
Publisher
Oxford Academic
WEHI Research Division(s)
Inflammation
PubMed ID
30192941
Open Access at Publisher's Site
https://doi.org/10.1093/gigascience/giy112
Rights Notice
Refer to copyright notice on published article.


Creation Date: 2018-09-14 02:33:44
Last Modified: 2018-09-14 02:43:56
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