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Licensed Unlicensed Requires Authentication Published by De Gruyter April 28, 2018

Noise-robust assessment of SNP array based CNV calls through local noise estimation of log R ratios

  • Nele Cosemans ORCID logo , Peter Claes , Nathalie Brison , Joris Robert Vermeesch and Hilde Peeters EMAIL logo

Abstract

Arrays based on single nucleotide polymorphisms (SNPs) have been successful for the large scale discovery of copy number variants (CNVs). However, current CNV calling algorithms still have limitations in detecting CNVs with high specificity and sensitivity, especially in case of small (<100 kb) CNVs. Therefore, this study presents a simple statistical analysis to evaluate CNV calls from SNP arrays in order to improve the noise-robustness of existing CNV calling algorithms. The proposed approach estimates local noise of log R ratios and returns the probability that a certain observation is different from this log R ratio noise level. This probability can be triggered at different thresholds to tailor specificity and/or sensitivity in a flexible way. Moreover, a comparison based on qPCR experiments showed that the proposed noise-robust CNV calls outperformed original ones for multiple threshold values.

Acknowledgement

Hilde Peeters is a Senior Clinical Investigator of The Research Foundation – Flanders (FWO). This study was supported by the “Opening the Future” Leuven University Fund. The script used for the noise-robust CNV assessment is provided with this manuscript.

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Published Online: 2018-04-28

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