Using ERDS to infer copy-number variants in high-coverage genomes.

Published

Journal Article

Although there are many methods available for inferring copy-number variants (CNVs) from next-generation sequence data, there remains a need for a system that is computationally efficient but that retains good sensitivity and specificity across all types of CNVs. Here, we introduce a new method, estimation by read depth with single-nucleotide variants (ERDS), and use various approaches to compare its performance to other methods. We found that for common CNVs and high-coverage genomes, ERDS performs as well as the best method currently available (Genome STRiP), whereas for rare CNVs and high-coverage genomes, ERDS performs better than any available method. Importantly, ERDS accommodates both unique and highly amplified regions of the genome and does so without requiring separate alignments for calling CNVs and other variants. These comparisons show that for genomes sequenced at high coverage, ERDS provides a computationally convenient method that calls CNVs as well as or better than any currently available method.

Full Text

Duke Authors

Cited Authors

  • Zhu, M; Need, AC; Han, Y; Ge, D; Maia, JM; Zhu, Q; Heinzen, EL; Cirulli, ET; Pelak, K; He, M; Ruzzo, EK; Gumbs, C; Singh, A; Feng, S; Shianna, KV; Goldstein, DB

Published Date

  • September 7, 2012

Published In

Volume / Issue

  • 91 / 3

Start / End Page

  • 408 - 421

PubMed ID

  • 22939633

Pubmed Central ID

  • 22939633

Electronic International Standard Serial Number (EISSN)

  • 1537-6605

Digital Object Identifier (DOI)

  • 10.1016/j.ajhg.2012.07.004

Language

  • eng

Conference Location

  • United States