Ximmer: a system for improving accuracy and consistency of CNV calling from exome data.

Publisher: Oxford University Press Country of Publication: United States NLM ID: 101596872 Publication Model: Electronic Cited Medium: Internet ISSN: 2047-217X (Electronic) Linking ISSN: 2047217X NLM ISO Abbreviation: Gigascience Subsets: MEDLINE

Publication: 2017- : New York : Oxford University Press
Original Publication: London : BioMed Central

DNA Copy Number Variations* ; Exome* ; High-Throughput Nucleotide Sequencing* ; Software*; Computational Biology/*methods; Algorithms ; Computer Simulation ; Humans ; ROC Curve ; Reproducibility of Results ; Sensitivity and Specificity

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 datasets.
Findings: We present Ximmer, a tool that 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 visualization and curation tool that together enable interactive exploration and quality control of CNV results. Using Ximmer, we comprehensively evaluate CNV detection on four datasets 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.

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UM1 HG008900 United States HG NHGRI NIH HHS

Date Created: 20180908 Date Completed: 20190221 Latest Revision: 20210109

20250114

PMC6177737

10.1093/gigascience/giy112

30192941