Methods for copy number aberration detection from single-cell DNA-sequencing data.
Copy number aberrations
Intra-tumor heterogeneity
Single-cell DNA sequencing
Tumor evolution
Journal
Genome biology
ISSN: 1474-760X
Titre abrégé: Genome Biol
Pays: England
ID NLM: 100960660
Informations de publication
Date de publication:
17 08 2020
17 08 2020
Historique:
received:
25
03
2020
accepted:
23
07
2020
entrez:
19
8
2020
pubmed:
19
8
2020
medline:
13
7
2021
Statut:
epublish
Résumé
Copy number aberrations (CNAs), which are pathogenic copy number variations (CNVs), play an important role in the initiation and progression of cancer. Single-cell DNA-sequencing (scDNAseq) technologies produce data that is ideal for inferring CNAs. In this review, we review eight methods that have been developed for detecting CNAs in scDNAseq data, and categorize them according to the steps of a seven-step pipeline that they employ. Furthermore, we review models and methods for evolutionary analyses of CNAs from scDNAseq data and highlight advances and future research directions for computational methods for CNA detection from scDNAseq data.
Identifiants
pubmed: 32807205
doi: 10.1186/s13059-020-02119-8
pii: 10.1186/s13059-020-02119-8
pmc: PMC7433197
doi:
Substances chimiques
DNA
9007-49-2
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
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