PT  - JOURNAL ARTICLE
AU  - Daniel K. Putnam
AU  - Ma Xiaotu
AU  - Stephen V. Rice
AU  - Yu Liu
AU  - Jinghui Zhang
AU  - Xiang Chen
TI  - VCF2CNA: A tool for efficiently detecting copy-number alterations in VCF genotype data
AID  - 10.1101/131235
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 131235
4099  - http://biorxiv.org/content/early/2017/04/26/131235.short
4100  - http://biorxiv.org/content/early/2017/04/26/131235.full
AB  - VCF2CNA is a web interface tool for copy-number alteration (CNA) analysis of VCF and other variant file formats. We applied it to 46 adult glioblastoma and 146 pediatric neuroblastoma samples sequenced by Illumina and Complete Genomics (CGI) platforms respectively. VCF2CNA was highly consistent with a state-of-the-art algorithm using raw sequencing data (mean F1-score=0.994) in high-quality glioblastoma samples and was robust to uneven coverage introduced by library artifacts. In the neuroblastoma set, VCF2CNA identified MYCN high-level amplifications in 31 of 32 clinically validated samples compared to 15 found by CGI’s HMM-based CNA model. The findings suggest that VCF2CNA is an accurate, efficient and platform-independent tool for CNA analyses without accessing raw sequence data.