ArticleBMC bioinformatics2020
RainDrop: Rapid activation matrix computation for droplet-based single-cell RNA-seq reads.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- kallisto, bustools, and kb-python for quantifying bulk, single-cell, and single-nucleus RNA-seq.bioRxiv : the preprint server for biology · 2024Article
- Deep learning in next-generation sequencing.Drug discovery today · 2021Review
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Authors and funding
4 authors.
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Abstract
backgroundObtaining data from single-cell transcriptomic sequencing allows for the investigation of cell-specific gene expression patterns, which could not be addressed a few years ago. With the advancement of droplet-based protocols the number of studied cells continues to increase rapidly. This establishes the need for software tools for efficient processing of the produced large-scale datasets. We address this need by presenting RainDrop for fast gene-cell count matrix computation from single-cell RNA-seq data produced by 10x Genomics Chromium technology.
resultsRainDrop can process single-cell transcriptomic datasets consisting of 784 million reads sequenced from around 8.000 cells in less than 40 minutes on a standard workstation. It significantly outperforms the established Cell Ranger pipeline and the recently introduced Alevin tool in terms of runtime by a maximal (average) speedup of 30.4 (22.6) and 3.5 (2.4), respectively, while keeping high agreements of the generated results.
conclusionsRainDrop is a software tool for highly efficient processing of large-scale droplet-based single-cell RNA-seq datasets on standard workstations written in C++. It is available at https://gitlab.rlp.net/stnieble/raindrop .
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