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Hum Mol Genet. 2019 Aug 27. pii: ddz207. doi: 10.1093/hmg/ddz207. [Epub ahead of print]

SNV identification from single-cell RNA sequencing data.

Author information

1
Department of Urology, University of Michigan Medical School, Ann Arbor, Michigan, USA.
2
Department of Medicine, University of Chicago, Chicago, Illinois, USA.
3
Biointerfaces Institute, University of Michigan Medical School, Ann Arbor, Michigan, USA.
4
Department of Biostatistics, University of Michigan Medical School, Ann Arbor, Michigan, USA.
5
Center for Statistical Genetics, University of Michigan Medical School, Ann Arbor, Michigan, USA.

Abstract

Integrating single-cell RNA sequencing (scRNA-seq) data with genotypes obtained from DNA sequencing studies facilitates the detection of functional genetic variants underlying cell type specific gene expression variation. Unfortunately, most existing scRNA-seq studies do not come with DNA sequencing data; thus, being able to call single nucleotide variants (SNVs) from scRNA-seq data alone can provide crucial and complementary information, detection of functional SNVs, maximizing the potential of existing scRNA-seq studies. Here, we perform extensive analyses to evaluate the utility of two SNV calling pipelines (GATK and Monovar), originally designed for SNV calling in either bulk or single cell DNA sequencing data. In both pipelines, we examined various parameter settings to determine the accuracy of the final SNV call set and provide practical recommendations for applied analysts. We found that combining all reads from the single cells and following GATK Best Practices resulted in the highest number of SNVs identified with a high concordance. In individual single cells, Monovar resulted in better quality SNVs even though none of the pipelines analysed is capable of calling a reasonable number of SNVs with high accuracy. In addition, we found that SNV calling quality varies across different functional genomic regions. Our results open doors for novel ways to leverage the use of scRNA-seq for the future investigation of SNV function.

PMID:
31504520
DOI:
10.1093/hmg/ddz207

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