ArticleGigaScience2022
Comparative analysis of common alignment tools for single-cell RNA sequencing.
Article in GigaScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 37 citations in OpenAlex.
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- Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single-cell RNA sequencing analyses.Quantitative biology (Beijing, China) · 2025Review
- Unlocking hematopoietic stem cell potential: integrative computational approaches for genomic and transcriptomic analysis.Frontiers in cell and developmental biology · 2025Review
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Topological data analysis in single cell biology.Frontiers in immunology · 2025Review
- Bulk and single-cell transcriptome profiling reveals the dynamic immune response in granulomatous amebic encephalitis caused byFrontiers in immunology · 2025Article
- Uncovering functional lncRNAs by scRNA-seq with ELATUS.Nature communications · 2024Article
- Split Pool Ligation-based Single-cell Transcriptome sequencing (SPLiT-seq) data processing pipeline comparison.BMC genomics · 2024Article
- The implications of single-cell RNA-seq analysis in prostate cancer: unraveling tumor heterogeneity, therapeutic implications and pathways towards personalized therapy.Military Medical Research · 2024Review
- Underrepresentation of activating KIR gene expression in single-cell RNA-seq data is due to KIR gene misassignment.European journal of immunology · 2024Article
- Single cell genomics as a transformative approach for aquaculture research and innovation.Reviews in aquaculture · 2023Review
- A single-cell transcriptome atlas of the maturing zebrafish telencephalon.Genome research · 2023Article
- scAN1.0: A reproducible and standardized pipeline for processing 10X single cell RNAseq data.In silico biology · 2023Article
- Article
- From multitude to singularity: An up-to-date overview of scRNA-seq data generation and analysis.Frontiers in genetics · 2022Review
- Reproductive Performance of Zi-Goose Promoted by Red Color Illumination.Frontiers in veterinary science · 2022Article
- Benchmarking UMI-based single-cell RNA-seq preprocessing workflows.Genome biology · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the rise of single-cell RNA sequencing new bioinformatic tools have been developed to handle specific demands, such as quantifying unique molecular identifiers and correcting cell barcodes. Here, we benchmarked several datasets with the most common alignment tools for single-cell RNA sequencing data. We evaluated differences in the whitelisting, gene quantification, overall performance, and potential variations in clustering or detection of differentially expressed genes. We compared the tools Cell Ranger version 6, STARsolo, Kallisto, Alevin, and Alevin-fry on 3 published datasets for human and mouse, sequenced with different versions of the 10X sequencing protocol.
resultsStriking differences were observed in the overall runtime of the mappers. Besides that, Kallisto and Alevin showed variances in the number of valid cells and detected genes per cell. Kallisto reported the highest number of cells; however, we observed an overrepresentation of cells with low gene content and unknown cell type. Conversely, Alevin rarely reported such low-content cells. Further variations were detected in the set of expressed genes. While STARsolo, Cell Ranger 6, Alevin-fry, and Alevin produced similar gene sets, Kallisto detected additional genes from the Vmn and Olfr gene family, which are likely mapping artefacts. We also observed differences in the mitochondrial content of the resulting cells when comparing a prefiltered annotation set to the full annotation set that includes pseudogenes and other biotypes.
conclusionOverall, this study provides a detailed comparison of common single-cell RNA sequencing mappers and shows their specific properties on 10X Genomics data.
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