ArticleGenes2021
Improved SNV Discovery in Barcode-Stratified scRNA-seq Alignments.
Article in Genes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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
12 citing papers in PubMed, 13 citations in OpenAlex.
- Fifteen Years of the Genome Analysis Toolkit as the De Facto Standard in Short-Read Variant Calling.International journal of molecular sciences · 2026Review
- scSNViz: visualization and analysis of cell-specific expressed SNVs.Bioinformatics (Oxford, England) · 2026Article
- Theoretical investigation of AKT1 mutations in breast cancer: a computational approach to structural and functional insights.Journal of computer-aided molecular design · 2025Article
- CanCellVar: A database for single-cell variants map in human cancer.American journal of human genetics · 2024Article
- Primary aldosteronism: molecular medicine meets public health.Nature reviews. Nephrology · 2023Review
- Identifying Aging and Alzheimer Disease-Associated Somatic Variations in Excitatory Neurons From the Human Frontal Cortex.Neurology. Genetics · 2023Article
- SCExecute: custom cell barcode-stratified analyses of scRNA-seq data.Bioinformatics (Oxford, England) · 2023Article
- An effective prognostic model for assessing prognosis of non-small cell lung cancer with brain metastases.Frontiers in genetics · 2023Article
- Enhancing genomic mutation data storage optimization based on the compression of asymmetry of sparsity.Frontiers in genetics · 2023Article
- Revealing the transcriptional heterogeneity of organ-specific metastasis in human gastric cancer using single-cell RNA Sequencing.Clinical and translational medicine · 2022Article
- Article
- SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data.BMC genomics · 2021Article
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Currently, the detection of single nucleotide variants (SNVs) from 10 x Genomics single-cell RNA sequencing data (scRNA-seq) is typically performed on the pooled sequencing reads across all cells in a sample. Here, we assess the gaining of information regarding SNV assessments from individual cell scRNA-seq data, wherein the alignments are split by cellular barcode prior to the variant call. We also reanalyze publicly available data on the MCF7 cell line during anticancer treatment. We assessed SNV calls by three variant callers-GATK, Strelka2, and Mutect2, in combination with a method for the cell-level tabulation of the sequencing read counts bearing variant alleles-SCReadCounts (single-cell read counts). Our analysis shows that variant calls on individual cell alignments identify at least a two-fold higher number of SNVs as compared to the pooled scRNA-seq; these SNVs are enriched in novel variants and in stop-codon and missense substitutions. Our study indicates an immense potential of SNV calls from individual cell scRNA-seq data and emphasizes the need for cell-level variant detection approaches and tools, which can contribute to the understanding of the cellular heterogeneity and the relationships to phenotypes, and help elucidate somatic mutation evolution and functionality.
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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.