CLAMMS: a scalable algorithm for calling common and rare copy number variants from exome sequencing data.

摘要: Motivation: Several algorithms exist for detecting copy number variants (CNVs) from human exome sequencing read depth, but previous tools have not been well suited large population studies on the order of tens or hundreds thousands exomes. Their limitations include being difficult to integrate into automated variant-calling pipelines and ill-suited common variants. To address these issues, we developed a new algorithm—Copy estimation using Lattice-Aligned Mixture Models (CLAMMS)—which is highly scalable suitable CNVs across whole allele frequency spectrum. Results: In this note, summarize methods intended use-case CLAMMS, compare it briefly describe results validation experiments. We evaluate adherence CNV calls CLAMMS four other Mendelian inheritance patterns pedigree; SNP genotyping arrays set 3164 samples; use TaqMan quantitative polymerase chain reaction validate predicted by at 39 loci (95% rare validate; 19 variant loci, mean precision recall are 99% 94%, respectively). Supplementary Materials (available Github repository), present our in greater detail. Availability implementation: https://github.com/rgcgithub/clamms (implemented C). Contact: moc.noreneger@dier.yerffej Supplementary information: data available Bioinformatics online.