STARsolo: accurate, fast and versatile mapping/quantification of single-cell and single-nucleus RNA-seq data

Kaminow, Benjamin; Yunusov, Dinar; Dobin, Alexander

doi:10.1101/2021.05.05.442755

Cited by 258 publications

(260 citation statements)

References 49 publications

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“…In a third preprint the group from STARsolo performed a benchmark of STARsolo, Alevin, and Kallisto and claimed that STARsolo is more precise and outperforms the pseudo-alignment tools Alevin and Kallisto with simulated data. With a real dataset STARsolo replicated the results from Cell Ranger significantly faster while consuming much less memory [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of common alignment tools for single-cell RNA sequencing

et al. 2022

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Background With 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. Results Striking 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. Conclusion Overall, 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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Section: Introductionmentioning

confidence: 99%

Comparative analysis of common alignment tools for single-cell RNA sequencing

et al. 2022

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“…Barcode reads were trimmed to exclude the intersub-barcode PCR adapters using a mawk script. Reads were then mapped and cell-demultiplexed using STARsolo 40 in CB_UMI_Complex mode. The resulting STARsolo-filtered count matrices were further analysed using Scanpy 41 .…”

Section: Methodsmentioning

confidence: 99%

HyDrop: droplet-based scATAC-seq and scRNA-seq using dissolvable hydrogel beads

Rop

Ismail

et al. 2021

Preprint

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Single-cell RNA-seq and single-cell ATAC-seq technologies are being used extensively to create cell type atlases for a wide range of organisms, tissues, and disease processes. To increase the scale of these atlases, lower the cost, and allow for more specialized multi-ome assays, custom droplet microfluidics may provide complementary solutions to commercial setups. We developed HyDrop, a flexible and generic droplet microfluidic platform encompassing three protocols. The first protocol involves creating dissolvable hydrogel beads with custom oligos that can be released in the droplets. In the second protocol, we demonstrate the use of these beads for HyDrop-ATAC, a low-cost non-commercial scATAC-seq protocol in droplets. After validating HyDrop-ATAC, we applied it to flash-frozen mouse cortex and generated 8,502 high-quality single-cell chromatin accessibility profiles in a single run. In the third protocol, we adapt both the reaction chemistry and the capture sequence of the barcoded hydrogel bead to capture mRNA, and demonstrate a significant improvement in throughput and sensitivity compared to previous open-source droplet-based scRNA-seq assays (Drop-seq and inDrop). Similarly, we applied HyDrop-RNA to flash-frozen mouse cortex and generated 9,508 single-cell transcriptomes closely matching reference single-cell gene expression data. Finally, we leveraged HyDrop-RNA's high capture rate to analyse a small population of FAC-sorted neurons from the Drosophila brain, confirming the protocol's applicability to low-input samples and small cells. HyDrop is currently capable of generating single-cell data in high throughput and at a reduced cost compared to commercial methods, and we envision that HyDrop can be further developed to be compatible with novel (multi-) omics protocols.

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“…The targeted exome and the whole genome sequencing reads were aligned to the latest version of the human genome reference (GRCh38, Dec 2013) using BWA v.0.7.17 default settings [20]. The pooled sequencing reads from the scRNA-seq datasets were aligned using the STARsolo module of STAR v.2.7.7a in 2-pass mode, with transcript annotations from the assembly GRCh38.79 [12,21]. To generate individual cell alignments we adopted a publicly available python script that splits the pooled scRNA-seq alignments, based on cellular barcode [22].…”

Section: Data Processing: Alignment Processing Generation Of Individual Scrna-seq Alignmentsmentioning

confidence: 99%

“…For this analysis, we processed the scRNA-seq datasets as we have previously described [11,18]. Briefly, after alignment with STARsolo [12] and quality filtering, the gene-expression matrices were processed using Seurat [25] to normalize gene expression, and corrected for batch-and cell-cycle effects. The normalized gene expression values were then used to assign likely cell types using SingleR and to provide context for cells carrying particular SNVs [26] (Methods).…”

Section: Scesnvs Expressionmentioning

confidence: 99%

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Improved SNV Discovery in Barcode-Stratified scRNA-seq Alignments

Prashant

Liu

Dillard

et al. 2021

Genes

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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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STARsolo: accurate, fast and versatile mapping/quantification of single-cell and single-nucleus RNA-seq data

Cited by 258 publications

References 49 publications

Comparative analysis of common alignment tools for single-cell RNA sequencing

Comparative analysis of common alignment tools for single-cell RNA sequencing

HyDrop: droplet-based scATAC-seq and scRNA-seq using dissolvable hydrogel beads

Improved SNV Discovery in Barcode-Stratified scRNA-seq Alignments

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