PennCNV2

PennCNV2 is a software tool for detecting copy number alterations (CNAs) in human genomes using data from SNP genotyping arrays. It addresses challenges in CNA detection in tumor samples, such as aneuploidy, stromal contamination, genomic waves, and intra-tumor heterogeneity, which need optimally handled by other leading methods.

Key features of PennCNV2 include:

1. Estimating stromal contamination using a maximum likelihood approach over multiple genomic intervals.

2. Accounting for aneuploidy and genomic waves by conditioning on signal intensity across the genome.

3. A hidden Markov model is used to infer CNAs, integrating multiple information sources, such as total and allele-specific signal intensity at each SNP and physical maps.

4. Improved accuracy and computational efficiency compared to existing methods, as demonstrated using real data from cancer cell lines and patient tumors.