NGS로 SCNA를 Data analysis하는 것은 새로운 도전이 되고 있다. tumor Aneuploidy와 heterogeneity뿐 아니라, normal cell contamination 때문에.

R package, saasCNV를 개발함.

4 major steps

1. 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
2. Performing joint segmentation on the 2 signal dimensions
3. Correcting the copy number baseline from which the SCNA state is determined
4. Calling SCNA state for each segment based on both signal dimensions.

이 방법은 WGS, WES, SNP array 보두 적용 가능

Summary #

Introduction #

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.

1. read depth (RD),
2. pair-end mapping (PEM),
3. split read (SR) and
4. Assembling (AS)

RD-based methods - CNV-seq, SegSeq, ExomeCNV and PatternCNV - "bottom-up" procedures - germline CNV에는 좋으나, SCNA에는 아니다. copy neutral loss of heterozygosity (CN-LOH)를 찾을 수 없다.

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

Methods and Materials #

Data #

Analysis pipeline #

Data analysis #

Results #

An illustration of the data and method #

Analysis of Dataset I (where Ho is true) #

Analysis of Dataset II (where Ha is true) #

Discussion #

Suggested Pages #

• 0.025 April 8
• 0.025 matplotlib
• 0.025 Pentasomy
• 0.025 OS X
• 0.025 SciPy
• 0.025 Whole genome sequencing
• 0.025 Comparative genomic hybridization
• 0.025 Oncotarget
• 0.025 falcon
• 0.025 Nucleic Acids Research
• More suggestions...