Linked-read analysis identifies mutations in single-cell DNA-sequencing data.
Journal
Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904
Informations de publication
Date de publication:
04 2019
04 2019
Historique:
received:
22
01
2018
accepted:
01
02
2019
pubmed:
20
3
2019
medline:
20
4
2019
entrez:
20
3
2019
Statut:
ppublish
Résumé
Whole-genome sequencing of DNA from single cells has the potential to reshape our understanding of mutational heterogeneity in normal and diseased tissues. However, a major difficulty is distinguishing amplification artifacts from biologically derived somatic mutations. Here, we describe linked-read analysis (LiRA), a method that accurately identifies somatic single-nucleotide variants (sSNVs) by using read-level phasing with nearby germline heterozygous polymorphisms, thereby enabling the characterization of mutational signatures and estimation of somatic mutation rates in single cells.
Identifiants
pubmed: 30886424
doi: 10.1038/s41588-019-0366-2
pii: 10.1038/s41588-019-0366-2
pmc: PMC6900933
mid: NIHMS1061349
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
749-754Subventions
Organisme : NINDS NIH HHS
ID : R01 NS032457
Pays : United States
Organisme : NIGMS NIH HHS
ID : T32 GM007753
Pays : United States
Organisme : NHGRI NIH HHS
ID : T32 HG002295
Pays : United States
Organisme : NIMH NIH HHS
ID : U01 MH106883
Pays : United States
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