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scSNV: accurate dscRNA-seq SNV co-expression analysis using duplicate tag collapsing

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 optimall...

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Detalles Bibliográficos
Autores principales: Wilson, Gavin W., Derouet, Mathieu, Darling, Gail E., Yeung, Jonathan C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8103760/
https://www.ncbi.nlm.nih.gov/pubmed/33962667
http://dx.doi.org/10.1186/s13059-021-02364-5
Descripción
Sumario: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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02364-5.