Rxivist logo

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

By Raúl Wijfjes, Sandra Smit, Dick de Ridder

Posted 31 Jul 2019
bioRxiv DOI: 10.1101/720805 (published DOI: 10.1186/s12864-019-6153-8)

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. 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 A. thaliana, rice, maize, and tomato. Moreover, it correctly detects dispersed duplications, a type of CNV commonly found in plant species, in contrast to several state-of-the-art tools that erroneously represent this type of CNV as overlapping deletions and tandem duplications. Finally, Hecaton scales well in terms of memory usage and running time when applied to short read datasets of domesticated and wild tomato accessions. Hecaton provides a robust method to detect CNV in plants. We expect it to be of immediate interest to both applied and fundamental research on the relationship between genotype and phenotype in plants.

Download data

  • Downloaded 330 times
  • Download rankings, all-time:
    • Site-wide: 58,303 out of 103,808
    • In bioinformatics: 6,416 out of 9,474
  • Year to date:
    • Site-wide: 92,215 out of 103,808
  • Since beginning of last month:
    • Site-wide: 97,153 out of 103,808

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


PanLingua

Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News