scSNV: accurate dscRNA-seq SNV co-expression analysis using duplicate tag collapsing.
Alignment
Genetic variation
Single-cell RNA-seq
Variant calling
Journal
Genome biology
ISSN: 1474-760X
Titre abrégé: Genome Biol
Pays: England
ID NLM: 100960660
Informations de publication
Date de publication:
07 05 2021
07 05 2021
Historique:
received:
08
07
2020
accepted:
23
04
2021
entrez:
8
5
2021
pubmed:
9
5
2021
medline:
20
1
2022
Statut:
epublish
Résumé
Identifying single nucleotide variants has become common practice for droplet-based single-cell RNA-seq experiments; however, presently, a pipeline does not exist to maximize variant calling accuracy. Furthermore, molecular duplicates generated in these experiments have not been utilized to optimally detect variant co-expression. Herein, we introduce scSNV designed from the ground up to "collapse" molecular duplicates and accurately identify variants and their co-expression. We demonstrate that scSNV is fast, with a reduced false-positive variant call rate, and enables the co-detection of genetic variants and A>G RNA edits across twenty-two samples.
Identifiants
pubmed: 33962667
doi: 10.1186/s13059-021-02364-5
pii: 10.1186/s13059-021-02364-5
pmc: PMC8103760
doi:
Substances chimiques
RNA, Messenger
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
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