4 major steps
- Extracting read depth supporting reference and alternative alleles at each SNP/Indel locus and comparing the total read depth and alternative allele proportion between tumor and matched normal samples
- Performing joint segmentation on the 2 signal dimensions
- Correcting the copy number baseline from which the SCNA state is determined
- Calling SCNA state for each segment based on both signal dimensions.
Table of Contents
SCNA profiling은 복잡하다.
Accurate detection and characterization of genome-wide SCNA profile are further complicated by aneuploidy and heterogeneity of tumor cells and contamination of normal cells.
CNV calling from NGS data은 주로 다음 방법이 있다.
- read depth (RD),
- pair-end mapping (PEM),
- split read (SR) and
- Assembling (AS)
B allele frequency (BAF) 이용할 수 있다.
Control-FREEC가 대표적인 방법이지만, normal을 이용하지 않는다.
Total read depth와 BAF를 이용하는 SAAS-CNV 방법을 제안함 - join segmentation algorithm