A machine-learning approach for accurate detection of copy number variants from exome sequencing.

ABSTRACT: Copy number variants (CNVs) are a major cause of several genetic disorders, making their detection an essential component of genetic analysis pipelines. Current methods for detecting CNVs from exome-sequencing data are limited by high false-positive rates and low concordance because of inherent biases of individual algorithms. To overcome these issues, calls generated by two or more algorithms are often intersected using Venn diagram approaches to identify "high-confidence" CNVs. However, this approach is inadequate, because it misses potentially true calls that do not have consensus from multiple callers. Here, we present CN-Learn, a machine-learning framework that integrates calls from multiple CNV detection algorithms and learns to accurately identify true CNVs using caller-specific and

SUBMITTER: Pounraja VK 

PROVIDER: S-EPMC6633262 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature