Systematic comparative analysis of single-nucleotide variants detection methods from single-cell RNA sequencing data
Systematic interrogation of single nucleotide variations (SNVs) is one of the most promising approaches to delineate the cellular heterogeneity and phylogenetic relationships at the single cell level. While SNV detection from abundant single cell RNA sequencing (scRNA-seq) data is applicable and cost-effective in identifying expressed variants, inferring sub-clones, and deciphering genotype-phenotype linkages, there is a lack of computational methods specifically developed for SNV calling in scRNA-seq. Although variant callers for bulk RNA-seq have been sporadically used in scRNA-seq, the performances of different tools have not been assessed. Here, we performed a systematic comparison of seven tools including SAMtools, the GATK pipeline, CTAT, FreeBayes, MuTect2, Strelka2 and VarScan2, using both simulation and scRNA-seq datasets, and identified multiple elements influencing their performance. Our study provided the first benchmarking to evaluate the performances of different SNV detection tools for scRNA-seq data.
- Type: Other
- Archiver: EGA European Genome-Phenome Archive
Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data
|EGAD00001005373||Illumina HiSeq 4000||75|
Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data.
Genome Biol 20: 2019 242