SCCNV: A Software Tool for Identifying Copy Number Variation From Single-Cell Whole-Genome Sequencing

Xiao Dong, Lei Zhang, Xiaoxiao Hao, Tao Wang, Jan Vijg

Research output: Contribution to journalArticlepeer-review

Abstract

Identification of de novo copy number variations (CNVs) across the genome in single cells requires single-cell whole-genome amplification (WGA) and sequencing. Although many experimental protocols of amplification methods have been developed, all suffer from uneven distribution of read depth across the genome after sequencing of DNA amplicons, which constrains the usage of conventional CNV calling methodologies. Here, we present SCCNV, a software tool for detecting CNVs from whole genome-amplified single cells. SCCNV is a read-depth based approach with adjustment for the WGA bias. We demonstrate its performance by analyzing data obtained with most of the single-cell amplification methods that have been employed for CNV analysis, including DOP-PCR, MDA, MALBAC, and LIANTI. SCCNV is freely available at https://github.com/biosinodx/SCCNV.

Original languageEnglish (US)
Article number505441
JournalFrontiers in Genetics
Volume11
DOIs
StatePublished - Nov 16 2020
Externally publishedYes

Bibliographical note

Funding Information:
This manuscript has been released as a Pre-Print at https://www.biorxiv.org/content/10.1101/535807v1 (Dong et al., 2019). Funding. This work has been supported by the NIH grants P01 AG017242, P01 AG047200, P30 AG038072, K99 AG056656, U01 HL145560, and U01 ES029519, and the Paul F. Glenn Center for the Biology of Human Aging.

Publisher Copyright:
© Copyright © 2020 Dong, Zhang, Hao, Wang and Vijg.

Keywords

  • amplification bias
  • copy number variation
  • single-cell whole-genome amplification
  • single-cell whole-genome sequencing
  • software development

PubMed: MeSH publication types

  • Journal Article

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