Alevin efficiently estimates accurate gene abundances from dscRNA-seq data

We introduce alevin, a fast end-to-end pipeline to process droplet-based single-cell RNA sequencing data, performing cell barcode detection, read mapping, unique molecular identifier (UMI) deduplication, gene count estimation, and cell barcode whitelisting. Alevin’s approach to UMI deduplication con...

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Vydáno v:Genome Biology Ročník 20; číslo 1; s. 65
Hlavní autoři: Srivastava, Avi, Malik, Laraib, Smith, Tom, Sudbery, Ian, Patro, Rob
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 27.03.2019
Springer Nature B.V
BMC
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ISSN:1474-760X, 1474-7596, 1474-760X
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Shrnutí:We introduce alevin, a fast end-to-end pipeline to process droplet-based single-cell RNA sequencing data, performing cell barcode detection, read mapping, unique molecular identifier (UMI) deduplication, gene count estimation, and cell barcode whitelisting. Alevin’s approach to UMI deduplication considers transcript-level constraints on the molecules from which UMIs may have arisen and accounts for both gene-unique reads and reads that multimap between genes. This addresses the inherent bias in existing tools which discard gene-ambiguous reads and improves the accuracy of gene abundance estimates. Alevin is considerably faster, typically eight times, than existing gene quantification approaches, while also using less memory.
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ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-019-1670-y