Cargando…

A Single Cell Level Based Method for Copy Number Variation Analysis by Low Coverage Massively Parallel Sequencing

Copy number variations (CNVs), a common genomic mutation associated with various diseases, are important in research and clinical applications. Whole genome amplification (WGA) and massively parallel sequencing have been applied to single cell CNVs analysis, which provides new insight for the fields...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Chunlei, Zhang, Chunsheng, Chen, Shengpei, Yin, Xuyang, Pan, Xiaoyu, Lin, Ge, Tan, Yueqiu, Tan, Ke, Xu, Zhengfeng, Hu, Ping, Li, Xuchao, Chen, Fang, Xu, Xun, Li, Yingrui, Zhang, Xiuqing, Jiang, Hui, Wang, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3553135/
https://www.ncbi.nlm.nih.gov/pubmed/23372689
http://dx.doi.org/10.1371/journal.pone.0054236
_version_ 1782256791067820032
author Zhang, Chunlei
Zhang, Chunsheng
Chen, Shengpei
Yin, Xuyang
Pan, Xiaoyu
Lin, Ge
Tan, Yueqiu
Tan, Ke
Xu, Zhengfeng
Hu, Ping
Li, Xuchao
Chen, Fang
Xu, Xun
Li, Yingrui
Zhang, Xiuqing
Jiang, Hui
Wang, Wei
author_facet Zhang, Chunlei
Zhang, Chunsheng
Chen, Shengpei
Yin, Xuyang
Pan, Xiaoyu
Lin, Ge
Tan, Yueqiu
Tan, Ke
Xu, Zhengfeng
Hu, Ping
Li, Xuchao
Chen, Fang
Xu, Xun
Li, Yingrui
Zhang, Xiuqing
Jiang, Hui
Wang, Wei
author_sort Zhang, Chunlei
collection PubMed
description Copy number variations (CNVs), a common genomic mutation associated with various diseases, are important in research and clinical applications. Whole genome amplification (WGA) and massively parallel sequencing have been applied to single cell CNVs analysis, which provides new insight for the fields of biology and medicine. However, the WGA-induced bias significantly limits sensitivity and specificity for CNVs detection. Addressing these limitations, we developed a practical bioinformatic methodology for CNVs detection at the single cell level using low coverage massively parallel sequencing. This method consists of GC correction for WGA-induced bias removal, binary segmentation algorithm for locating CNVs breakpoints, and dynamic threshold determination for final signals filtering. Afterwards, we evaluated our method with seven test samples using low coverage sequencing (4∼9.5%). Four single-cell samples from peripheral blood, whose karyotypes were confirmed by whole genome sequencing analysis, were acquired. Three other test samples derived from blastocysts whose karyotypes were confirmed by SNP-array analysis were also recruited. The detection results for CNVs of larger than 1 Mb were highly consistent with confirmed results reaching 99.63% sensitivity and 97.71% specificity at base-pair level. Our study demonstrates the potential to overcome WGA-bias and to detect CNVs (>1 Mb) at the single cell level through low coverage massively parallel sequencing. It highlights the potential for CNVs research on single cells or limited DNA samples and may prove as a promising tool for research and clinical applications, such as pre-implantation genetic diagnosis/screening, fetal nucleated red blood cells research and cancer heterogeneity analysis.
format Online
Article
Text
id pubmed-3553135
institution National Center for Biotechnology Information
language English
publishDate 2013
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-35531352013-01-31 A Single Cell Level Based Method for Copy Number Variation Analysis by Low Coverage Massively Parallel Sequencing Zhang, Chunlei Zhang, Chunsheng Chen, Shengpei Yin, Xuyang Pan, Xiaoyu Lin, Ge Tan, Yueqiu Tan, Ke Xu, Zhengfeng Hu, Ping Li, Xuchao Chen, Fang Xu, Xun Li, Yingrui Zhang, Xiuqing Jiang, Hui Wang, Wei PLoS One Research Article Copy number variations (CNVs), a common genomic mutation associated with various diseases, are important in research and clinical applications. Whole genome amplification (WGA) and massively parallel sequencing have been applied to single cell CNVs analysis, which provides new insight for the fields of biology and medicine. However, the WGA-induced bias significantly limits sensitivity and specificity for CNVs detection. Addressing these limitations, we developed a practical bioinformatic methodology for CNVs detection at the single cell level using low coverage massively parallel sequencing. This method consists of GC correction for WGA-induced bias removal, binary segmentation algorithm for locating CNVs breakpoints, and dynamic threshold determination for final signals filtering. Afterwards, we evaluated our method with seven test samples using low coverage sequencing (4∼9.5%). Four single-cell samples from peripheral blood, whose karyotypes were confirmed by whole genome sequencing analysis, were acquired. Three other test samples derived from blastocysts whose karyotypes were confirmed by SNP-array analysis were also recruited. The detection results for CNVs of larger than 1 Mb were highly consistent with confirmed results reaching 99.63% sensitivity and 97.71% specificity at base-pair level. Our study demonstrates the potential to overcome WGA-bias and to detect CNVs (>1 Mb) at the single cell level through low coverage massively parallel sequencing. It highlights the potential for CNVs research on single cells or limited DNA samples and may prove as a promising tool for research and clinical applications, such as pre-implantation genetic diagnosis/screening, fetal nucleated red blood cells research and cancer heterogeneity analysis. Public Library of Science 2013-01-23 /pmc/articles/PMC3553135/ /pubmed/23372689 http://dx.doi.org/10.1371/journal.pone.0054236 Text en © 2013 Zhang et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Zhang, Chunlei
Zhang, Chunsheng
Chen, Shengpei
Yin, Xuyang
Pan, Xiaoyu
Lin, Ge
Tan, Yueqiu
Tan, Ke
Xu, Zhengfeng
Hu, Ping
Li, Xuchao
Chen, Fang
Xu, Xun
Li, Yingrui
Zhang, Xiuqing
Jiang, Hui
Wang, Wei
A Single Cell Level Based Method for Copy Number Variation Analysis by Low Coverage Massively Parallel Sequencing
title A Single Cell Level Based Method for Copy Number Variation Analysis by Low Coverage Massively Parallel Sequencing
title_full A Single Cell Level Based Method for Copy Number Variation Analysis by Low Coverage Massively Parallel Sequencing
title_fullStr A Single Cell Level Based Method for Copy Number Variation Analysis by Low Coverage Massively Parallel Sequencing
title_full_unstemmed A Single Cell Level Based Method for Copy Number Variation Analysis by Low Coverage Massively Parallel Sequencing
title_short A Single Cell Level Based Method for Copy Number Variation Analysis by Low Coverage Massively Parallel Sequencing
title_sort single cell level based method for copy number variation analysis by low coverage massively parallel sequencing
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3553135/
https://www.ncbi.nlm.nih.gov/pubmed/23372689
http://dx.doi.org/10.1371/journal.pone.0054236
work_keys_str_mv AT zhangchunlei asinglecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT zhangchunsheng asinglecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT chenshengpei asinglecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT yinxuyang asinglecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT panxiaoyu asinglecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT linge asinglecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT tanyueqiu asinglecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT tanke asinglecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT xuzhengfeng asinglecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT huping asinglecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT lixuchao asinglecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT chenfang asinglecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT xuxun asinglecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT liyingrui asinglecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT zhangxiuqing asinglecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT jianghui asinglecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT wangwei asinglecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT zhangchunlei singlecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT zhangchunsheng singlecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT chenshengpei singlecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT yinxuyang singlecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT panxiaoyu singlecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT linge singlecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT tanyueqiu singlecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT tanke singlecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT xuzhengfeng singlecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT huping singlecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT lixuchao singlecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT chenfang singlecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT xuxun singlecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT liyingrui singlecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT zhangxiuqing singlecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT jianghui singlecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing
AT wangwei singlecelllevelbasedmethodforcopynumbervariationanalysisbylowcoveragemassivelyparallelsequencing