Modular, efficient and constant-memory single-cell RNA-seq preprocessing

Melsted, Páll; Booeshaghi, A. Sina; Liu, Lauren; Gao, Fan; Moses, Lambda; Min, Kyung Hoi; Beltrame, Eduardo da Veiga; Hjorleifsson, Kristjan E; Gehring, Jase; Pachter, Lior

doi:10.1038/s41587-021-00870-2

Cited by 332 publications

(321 citation statements)

References 50 publications

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“…We compared STARsolo performance on simulated and real data with several existing tools: CellRanger [14], Alevin/Alevin-fry [7,10,21] and Kallisto/Bustools [8,9,22]. CellRanger is a de facto standard for analyzing 10X Genomics scRNA-seq data, while Kallisto and Alevin use light-weight alignment-to-transcriptome algorithms which are profoundly different from STAR's aligment to the full genome.…”

Section: Resultsmentioning

confidence: 99%

“…To include the multi-gene reads, the expectation-maximization algorithm was introduced in Alevin, and its impact on gene detection and quantification was investigated [7,20]. A similar option was also implemented in Kallisto [8].…”

Section: Simulation With Multi-gene Readsmentioning

confidence: 99%

“…FASTQ and BAM), this approach typically results in low processing speed and overtaxing of computing infrastructure. The next wave of tools, Alevin [7] and Kallisto-bustools [8], significantly increased the overall computational efficiency owing to a tight integration between alignment and CB/UMI processing steps. To achieve high performance without sacrificing modularity, these tools utilize efficient intermediate file formats [9,10].…”

Section: Introductionmentioning

confidence: 99%

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

Kaminow

Yunusov

Dobin

2021

Preprint

257

219

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We present STARsolo, a comprehensive turnkey solution for quantifying gene expression in single-cell/nucleus RNA-seq data, built into RNA-seq aligner STAR. Using simulated data that closely resembles realistic scRNA-seq, we demonstrate that STARsolo is highly accurate and significantly outperforms pseudoalignment-to-transcriptome tools. STARsolo can replicate the results of, but is considerably faster than CellRanger, currently the most widely used tool for pre-processing scRNA-seq data. In addition to uniquely mapped reads, STARsolo takes account of multi-gene reads, necessary to detect certain classes of biologically important genes. It has a flexible cell barcode processing scheme, compatible with many established scRNA-seq protocols, and extendable to emerging technologies. STARsolo can quantify transcriptomic features beyond gene expression, which we illustrate by analyzing cell-type-specific alternative splicing in the Tabula Muris project.

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Section: Resultsmentioning

confidence: 99%

Section: Simulation With Multi-gene Readsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Kaminow

Yunusov

Dobin

2021

Preprint

257

219

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“…Coupled with memory efficient methods, like Kallisto Bustools 55 , for generating cell-gene count matrices, Scarf represents an end-to-end solution for the analysis of single-cell RNA-Seq datasets. During the preparation of this manuscript an R-based memory efficient tool, ArchR 56 , was published for analysis of scATAC-Seq data.…”

Section: Discussionmentioning

confidence: 99%

Scarf: A toolkit for memory efficient analysis of large-scale single-cell genomics data

Dhapola

Rodhe

Olofzon

et al. 2021

Preprint

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The increasing capacity to perform large-scale single-cell genomic experiments continues to outpace the ability to efficiently handle growing datasets. Herein we present Scarf, a modularly designed Python package that seamlessly interoperates with other single-cell toolkits and allows for memory efficient single-cell analysis of millions of cells on a laptop or low-cost devices like single board computers. We demonstrate Scarf's memory and compute-time efficiency by applying it to the largest existing single-cell RNA-Seq and ATAC-Seq datasets. Scarf wraps memory efficient implementations of a graph-based t-stochastic neighbour embedding and hierarchical clustering algorithm. Moreover, Scarf performs accurate reference-anchored mapping of datasets while maintaining memory efficiency. By implementing a novel data downsampling algorithm, Scarf additionally has the capacity to generate representative sampling of cells from a given dataset wherein rare cell populations and lineage differentiation trajectories are conserved. Together, Scarf provides a framework wherein any researcher can perform advanced processing, downsampling, reanalysis and integration of atlas-scale datasets on standard laptop computers.

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“…After demultiplexing the sequencing reads, we generated UMI count matrices for the libraries using the kallisto indexing and tag extraction (kite) workflow with the hg38 reference genome (Ensembl v96), followed by the kallisto | bustools scRNA-seq pipeline (74). We then analyzed the UMI count matrices in R v.4.0.2 with Seurat v.3.0.0 (41) and custom scripts for differential gene expression testing within the SCEPTRE framework (26).…”

Section: Single Cell Data Processingmentioning

confidence: 99%

Discovery of target genes and pathways of blood trait loci using pooled CRISPR screens and single cell RNA sequencing

Morris

Daniloski

Domingo

et al. 2021

Preprint

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The majority of variants associated with complex traits and common diseases identified by genome-wide association studies (GWAS) map to noncoding regions of the genome with unknown regulatory effects in cis and trans. By leveraging biobank-scale GWAS data, massively parallel CRISPR screens and single cell transcriptome sequencing, we discovered target genes of noncoding variants for blood trait loci. The closest gene was often the target gene, but this was not always the case. We also identified trans-effects networks of noncoding variants when cis target genes encoded transcription factors, such as GFI1B and NFE2. We observed that GFI1B trans-target genes were enriched for GFI1B binding sites and fine-mapped GWAS variants, and expressed in human bone marrow progenitor cells, suggesting that GFI1B acts as a master regulator of blood traits. This platform will enable massively parallel assays to catalog the target genes of human noncoding variants in both cis and trans.

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Modular, efficient and constant-memory single-cell RNA-seq preprocessing

Cited by 332 publications

References 50 publications

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

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

Scarf: A toolkit for memory efficient analysis of large-scale single-cell genomics data

Discovery of target genes and pathways of blood trait loci using pooled CRISPR screens and single cell RNA sequencing

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