Alevin efficiently estimates accurate gene abundances from dscRNA-seq data

Abstract

We introduce alevin, a fast end-to-end pipeline to process droplet-based single cell RNA sequencing data, which performs cell barcode detection, read mapping, unique molecular identifier deduplication, gene count estimation, and cell barcode whitelisting. Alevins approach to UMI deduplication 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.