Hecaton: reliably detecting copy number variation in plant genomes using short read sequencing data.

ABSTRACT: BACKGROUND:Copy number variation (CNV) is thought to actively contribute to adaptive evolution of plant species. While many computational algorithms are available to detect copy number variation from whole genome sequencing datasets, the typical complexity of plant data likely introduces false positive calls. RESULTS:To enable reliable and comprehensive detection of CNV in plant genomes, we developed Hecaton, a novel computational workflow tailored to plants, that integrates calls from multiple state-of-the-art algorithms through a machine-learning approach. In this paper, we demonstrate that Hecaton outperforms current methods when applied to short read sequencing data of Arabidopsis thaliana, rice, maize, and tomato. Moreover, it correctly detects dispersed duplications, a type of CNV co

SUBMITTER: Wijfjes RY 

PROVIDER: S-EPMC6836508 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature