Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data
Abstract Background Systematic interrogation of single-nucleotide variants (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...
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| Format: | Article |
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BMC
2019-11-01
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| Series: | Genome Biology |
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| Online Access: | http://link.springer.com/article/10.1186/s13059-019-1863-4 |
| _version_ | 1819019437253066752 |
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| author | Fenglin Liu Yuanyuan Zhang Lei Zhang Ziyi Li Qiao Fang Ranran Gao Zemin Zhang |
| author_facet | Fenglin Liu Yuanyuan Zhang Lei Zhang Ziyi Li Qiao Fang Ranran Gao Zemin Zhang |
| author_sort | Fenglin Liu |
| collection | DOAJ |
| description | Abstract Background Systematic interrogation of single-nucleotide variants (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. Results Here, we perform 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 identify multiple elements influencing their performance. While the specificities are generally high, with sensitivities exceeding 90% for most tools when calling homozygous SNVs in high-confident coding regions with sufficient read depths, such sensitivities dramatically decrease when calling SNVs with low read depths, low variant allele frequencies, or in specific genomic contexts. SAMtools shows the highest sensitivity in most cases especially with low supporting reads, despite the relatively low specificity in introns or high-identity regions. Strelka2 shows consistently good performance when sufficient supporting reads are provided, while FreeBayes shows good performance in the cases of high variant allele frequencies. Conclusions We recommend SAMtools, Strelka2, FreeBayes, or CTAT, depending on the specific conditions of usage. Our study provides the first benchmarking to evaluate the performances of different SNV detection tools for scRNA-seq data. |
| first_indexed | 2024-12-21T03:35:18Z |
| format | Article |
| id | doaj.art-cf38a7a3a3e142e6b16d93113f058132 |
| institution | Directory Open Access Journal |
| issn | 1474-760X |
| language | English |
| last_indexed | 2024-12-21T03:35:18Z |
| publishDate | 2019-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj.art-cf38a7a3a3e142e6b16d93113f0581322022-12-21T19:17:21ZengBMCGenome Biology1474-760X2019-11-0120111510.1186/s13059-019-1863-4Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing dataFenglin Liu0Yuanyuan Zhang1Lei Zhang2Ziyi Li3Qiao Fang4Ranran Gao5Zemin Zhang6School of Life Sciences and BIOPIC, Peking UniversitySchool of Life Sciences and BIOPIC, Peking UniversityBeijing Advanced Innovation Centre for Genomics, Peking-Tsinghua Centre for Life Sciences, Peking UniversitySchool of Life Sciences and BIOPIC, Peking UniversityBeijing Advanced Innovation Centre for Genomics, Peking-Tsinghua Centre for Life Sciences, Peking UniversitySchool of Life Sciences and BIOPIC, Peking UniversitySchool of Life Sciences and BIOPIC, Peking UniversityAbstract Background Systematic interrogation of single-nucleotide variants (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. Results Here, we perform 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 identify multiple elements influencing their performance. While the specificities are generally high, with sensitivities exceeding 90% for most tools when calling homozygous SNVs in high-confident coding regions with sufficient read depths, such sensitivities dramatically decrease when calling SNVs with low read depths, low variant allele frequencies, or in specific genomic contexts. SAMtools shows the highest sensitivity in most cases especially with low supporting reads, despite the relatively low specificity in introns or high-identity regions. Strelka2 shows consistently good performance when sufficient supporting reads are provided, while FreeBayes shows good performance in the cases of high variant allele frequencies. Conclusions We recommend SAMtools, Strelka2, FreeBayes, or CTAT, depending on the specific conditions of usage. Our study provides the first benchmarking to evaluate the performances of different SNV detection tools for scRNA-seq data.http://link.springer.com/article/10.1186/s13059-019-1863-4Single-nucleotide variant detectionSomatic mutationsSingle-cell RNA sequencingBenchmarking |
| spellingShingle | Fenglin Liu Yuanyuan Zhang Lei Zhang Ziyi Li Qiao Fang Ranran Gao Zemin Zhang Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data Genome Biology Single-nucleotide variant detection Somatic mutations Single-cell RNA sequencing Benchmarking |
| title | Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data |
| title_full | Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data |
| title_fullStr | Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data |
| title_full_unstemmed | Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data |
| title_short | Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data |
| title_sort | systematic comparative analysis of single nucleotide variant detection methods from single cell rna sequencing data |
| topic | Single-nucleotide variant detection Somatic mutations Single-cell RNA sequencing Benchmarking |
| url | http://link.springer.com/article/10.1186/s13059-019-1863-4 |
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