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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| Veröffentlicht in: | Genome Biology Jg. 20; H. 1; S. 65 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
London
BioMed Central
27.03.2019
Springer Nature B.V BMC |
| Schlagworte: | |
| ISSN: | 1474-760X, 1474-7596, 1474-760X |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | 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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| Bibliographie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Undefined-1 ObjectType-Feature-3 content type line 23 |
| ISSN: | 1474-760X 1474-7596 1474-760X |
| DOI: | 10.1186/s13059-019-1670-y |