Supporting data for "Ximmer: A System for Improving Accuracy and Consistency of CNV Calling from Exome Data"
Dataset type: Workflow, Software, Virtual-Machine
Data released on August 23, 2018
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.
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.
Ximmer offers a comprehensive tool and method for applying and improving accuracy of CNV detection methods for exome data.
Read the peer-reviewed publication(s):
Sadedin, S. P., Ellis, J. A., Masters, S. L., & Oshlack, A. (2018). Ximmer: a system for improving accuracy and consistency of CNV calling from exome data. GigaScience, 7(10). doi:10.1093/gigascience/giy112