Alevin efficiently estimates accurate gene abundances from dscRNA-seq data View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-12

AUTHORS

Avi Srivastava, Laraib Malik, Tom Smith, Ian Sudbery, Rob Patro

ABSTRACT

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. More... »

PAGES

65

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13059-019-1670-y

DOI

http://dx.doi.org/10.1186/s13059-019-1670-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1113041739

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30917859


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