Accurate identification of structural variations from cancer samples-Reference-Cited by-同舟云学术

Accurate identification of structural variations from cancer samples

Published:2023-11-22 Issue:1 Volume:25 Page:
ISSN:1467-5463
Container-title:Briefings in Bioinformatics
language:en
Short-container-title:

Author:

Li Le¹,Hong Chenyang¹,Xu Jie²,Chung Claire Yik-Lok³,Leung Alden King-Yung³,Boncan Delbert Almerick T³,Cheng Lixin¹,Lo Kwok-Wai⁴,Lai Paul B S⁵,Wong John⁵,Zhou Jingying⁶,Cheng Alfred Sze-Lok⁶,Chan Ting-Fung³⁷⁸,Yue Feng²,Yip Kevin Y¹⁷⁸⁹

Affiliation:

1. Department of Computer Science and Engineering, The Chinese University of Hong Kong , Shatin, New Territories , Hong Kong

2. Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Northwestern University , Chicago, Illinois 60208 , USA

3. School of Life Sciences and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong , Shatin, New Territories , Hong Kong

4. Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong , Shatin, New Territories , Hong Kong

5. Department of Surgery, The Chinese University of Hong Kong , Shatin, New Territories , Hong Kong

6. School of Biomedical Sciences, The Chinese University of Hong Kong , Shatin, New Territories , Hong Kong

7. Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong , Shatin, New Territories , Hong Kong

8. Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong , Shatin, New Territories , Hong Kong

9. Sanford Burnham Prebys Medical Discovery Institute , La Jolla, California 92037 , USA

Abstract

Abstract Structural variations (SVs) are commonly found in cancer genomes. They can cause gene amplification, deletion and fusion, among other functional consequences. With an average read length of hundreds of kilobases, nano-channel-based optical DNA mapping is powerful in detecting large SVs. However, existing SV calling methods are not tailored for cancer samples, which have special properties such as mixed cell types and sub-clones. Here we propose the Cancer Optical Mapping for detecting Structural Variations (COMSV) method that is specifically designed for cancer samples. It shows high sensitivity and specificity in benchmark comparisons. Applying to cancer cell lines and patient samples, COMSV identifies hundreds of novel SVs per sample.

Funder

Hong Kong Research Grants Council Collaborative Research Fund

Innovation and Technology Commission, Hong Kong Special Administrative Region Government to the State Key Laboratory of Agrobiotechnology

Government of the Hong Kong Special Administrative Region or the Innovation and Technology Commission

Hong Kong Research Grants Council Collaborative Research Funds

General Research Funds

Hong Kong Epigenomics Project

Chinese University of Hong Kong Young Researcher Award

Outstanding Fellowship and Project Impact Enhancement Fund

National Institute of Health

Publisher

Oxford University Press (OUP)

Link

https://academic.oup.com/bib/article-pdf/25/1/bbad520/56149026/bbad520.pdf

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