ArticleGenome biology2019
Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data.
Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
67 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Clonal Hematopoiesis Instructs Maladaptive Tissue Repair to Promote Fibrosis.bioRxiv : the preprint server for biology · 2026Article
- Single cell transcriptomic landscape of adult and pediatric non-ischemic cardiomyopathy.Journal of molecular and cellular cardiology · 2026Review
- Mutations in sugar metabolism-related genes driving global drug-resistant Mycobacterium tuberculosis transmission revealed by whole-genome sequencing.BMC infectious diseases · 2026Article
- scSNViz: visualization and analysis of cell-specific expressed SNVs.Bioinformatics (Oxford, England) · 2026Article
- AI/ML-Driven Gene Analysis: New Perspectives on Variant Calling in Normal Human Tissue Using scRNA-seq Data.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Respiratory chain gene mutations associated with global phylogenetic clustering of drug-resistantFrontiers in immunology · 2026Article
- Personalizing DNA Cancer Vaccines.Journal of personalized medicine · 2025Review
- Identification of malignant cells in single-cell transcriptomics data.Communications biology · 2025Review
- Reidentification of hybridization events with transcriptomic data and phylogenomic study in Seabuckthorn.Scientific reports · 2025Article
- Spatial Transcriptomics in Lung Cancer and Pulmonary Diseases: A Comprehensive Review.Cancers · 2025Review
- Application of RNA-seq for single nucleotide variation identification in a cohort of patients with hypertrophic cardiomyopathy.Scientific reports · 2025Article
- SpatialSNV: A novel method for identifying and analyzing spatially resolved SNVs in tumor microenvironments.GigaScience · 2025Article
- Single-cell splicing QTL analysis in pancreatic islets.Frontiers in bioinformatics · 2025Article
- Association between toxin-antitoxin system mutations and global transmission of MDR-TB.BMC infectious diseases · 2024Article
- Molecular targets and strategies in the development of nucleic acid cancer vaccines: from shared to personalized antigens.Journal of biomedical science · 2024Review
- Characterization and trans-generation dynamics of mitogene pool in the silver carp (Hypophthalmichthys molitrix).G3 (Bethesda, Md.) · 2024Article
- Association of mutations in Mycobacterium tuberculosis complex (MTBC) respiration chain genes with hyper-transmission.BMC genomics · 2024Article
- CanCellVar: A database for single-cell variants map in human cancer.American journal of human genetics · 2024Article
- The Advancement and Application of the Single-Cell Transcriptome in Biological and Medical Research.Biology · 2024Review
7 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSystematic 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.
resultsHere, 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.
conclusionsWe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.