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[남재용/융합] 제1저자 Briefings in Bioinformatics. 2015 Jul 25. [Epub ahead of print]
등록일 2016/03/17
조회수 3,141

 


 

남재용 / 지도교수: 박웅양

(융합의과학과 석박사통합과정)

 

 

 

 

2015 Jul 25. pii: bbv055. [Epub ahead of print]

Evaluation of somatic copy number estimation tools for whole-exome sequencing data.

 

Abstract

Whole-exome sequencing (WES) has become a standard method for detecting genetic variants in human diseases. Although the primary use of WES data has been the identification of single nucleotide variations and indels, these data also offer a possibility of detecting copy number variations (CNVs) at high resolution. However, WES data have uneven read coverage along the genome owing to the target capture step, and the development of a robust WES-based CNV tool is challenging. Here, we evaluate six WES somatic CNV detection tools: ADTEx, CONTRA, Control-FREEC, EXCAVATOR, ExomeCNV and Varscan2. Using WES data from 50 kidney chromophobe, 50 bladder urothelial carcinoma, and 50 stomach adenocarcinoma patients from The Cancer Genome Atlas, we compared the CNV calls from the six tools with a reference CNV set that was identified by both single nucleotide polymorphism array 6.0 and whole-genome sequencing data. We found that these algorithms gave highly variable results: visual inspection reveals significant differences between the WES-based segmentation profiles and the reference profile, as well as among the WES-based profiles. Using a 50% overlap criterion, 13-77% of WES CNV calls were covered by CNVs from the reference set, up to 21% of the copy gains were called as losses or vice versa, and dramatic differences in CNV sizes and CNV numbers were observed. Overall, ADTEx and EXCAVATOR had the best performance with relatively high precision and sensitivity. We suggest that the current algorithms for somatic CNV detection from WES data are limited in their performance and that more robust algorithms are needed.

© The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

 

KEYWORDS:

CNV algorithms; CNV prediction; somatic alterations; the cancer genome atlas

PMID:26210357[PubMed - as supplied by publisher]
 

(출처_PubMed)

 

 

 

 

박웅양 교수 연구소개     [프로필 보기]  

 

유전체의학실험실은 환자에게 정확한 진단과 가장 적합한 치료를 제공할 수 있도록 각 개인의 유전체를 비롯한 생물정보를 분석하는 것을 목표로 한다. 이를 위하여 microarray와 next generation sequencing을 이용하여 유전체 정보를 분석하고, 여러 생물정보학적 기법을 이용하여 종양환자의 예후예측과 유전질환의 원인 돌연변이를 발굴하고 있다. 동시에 질병발생 기전연구를 위해 유전체 분석을 통해 얻은 질환과 관련된 유전자와 돌연변이에 대해 세포와 모델동물에서 기능분석을 수행하고 있다. 최근에는 종양에 대한 single cell analysis를 수행하여 cancer stem cell에 대한 분석과 tumor heterogeneity에 대한 연구를 수행하고 있다.

 

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