SegCNV

SegCNV detects germline copy number variations (CNVs) from single nucleotide polymorphism (SNP) array data by integrating log R ratio (LRR) and B allele frequency (BAF) to improve identification of deletions and duplications in Illumina 550K and 610K genotyping datasets.

Key Features:

• Integrative Segmentation Method: SegCNV employs a segmentation approach that integrates log R ratio (LRR) and B allele frequency (BAF) data to call CNVs.
• Enhanced Detection Power: Simulation studies report modestly improved power for detecting deletions and substantially better performance for identifying duplications compared with circular binary segmentation (CBS), and superior detection of deletions and comparable duplication performance relative to PennCNV and QuantiSNP.
• Efficiency: SegCNV is faster than hidden Markov model (HMM)-based methods, processing genome-wide data in a few seconds per subject.

Scientific Applications:

• Validation Against Gold Standards: In HapMap subjects with deep sequence data as a gold standard, SegCNV detected more true short deletions than PennCNV and QuantiSNP.
• Experimental Validation: In the AGRE dataset, SegCNV, QuantiSNV, and PennCNV identified all 21 experimentally validated short duplications, whereas CBS detected three.

Methodology:

SegCNV performs integrative segmentation by combining LRR and BAF signals from SNP arrays to identify deletions and duplications.