Detection of copy number variations (CNVs) from various types of SNP arrays.
Comprehensive identification and cataloging of copy number variations (CNVs) is required to provide a complete view of human genetic variation. We developed PennCNV, a hidden Markov model (HMM) based approach, for kilobase-resolution detection of CNVs from Illumina high-density SNP genotyping data.
PennCNV was written by a mixture of Perl and C. It was published in 2007, and has since become one of the most widely used software tools for CNV detection from SNP arrays.
PennCNV implements a hidden Markov model (HMM) that integrates multiple sources of information to infer CNV calls for individual genotyped samples. It differs form segmentation-based algorithm in that it considered SNP allelic ratio distribution as well as other factors, in addition to signal intensity alone. In addition, PennCNV can optionally utilize family information to generate family-based CNV calls by several different algorithms. Furthermore, PennCNV can generate CNV calls given a specific set of candidate CNV regions, through a validation-calling algorithm.
Currently it can handle signal intensity data from Illumina and Affymetrix arrays. With appropriate preparation of file format, it can also handle other types of SNP arrays and oligonucleotide arrays. Additionally, users can run PennCNV within Illumina BeadStudio/GenomeStudio software to facilitate automatic processing and visualization of CNV calls.
- Wang K, Li M, Hadley D, Liu R, Glessner J, Grant S, Hakonarson H, Bucan M. PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data Genome Research 17:1665-1674, 2007
- Diskin SJ, Li M, Hou C, Yang S, Glessner J, Hakonarson H, Bucan M, Maris JM, Wang K. Adjustment of genomic waves in signal intensities from whole-genome SNP genotyping platforms Nucleic Acids Research 36:e126, 2008
- Wang K, Chen Z, Tadesse MG, Glessner J, Grant SFA, Hakonarson H, Bucan M, Li M. Modeling genetic inheritance of copy number variations Nucleic Acids Research 36:e138, 2008