Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing.

ABSTRACT:

Background

Structural variations (SVs) or copy number variations (CNVs) greatly impact the functions of the genes encoded in the genome and are responsible for diverse human diseases. Although a number of existing SV detection algorithms can detect many types of SVs using whole genome sequencing (WGS) data, no single algorithm can call every type of SVs with high precision and high recall.

Results

We comprehensively evaluate the performance of 69 existing SV detection algorithms using multiple simulated and real WGS datasets. The results highlight a subset of algorithms that accurately call SVs depending on specific types and size ranges of the SVs and that accurately determine breakpoints, sizes, and genotypes of the SVs. We enumerate potential good algorithms for each SV c

SUBMITTER: Kosugi S 

PROVIDER: S-EPMC6547561 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature