Alevin Efficiently Estimates Accurate Gene Abundances from DscRNA-seq Data

Overview

Journal Genome Biol

Specialties Biology
Genetics

Date 2019 Mar 29

PMID 30917859

Citations 118

Authors

Avi Srivastava

Laraib Malik

Tom Smith

Ian Sudbery

Rob Patro

Affiliations

Soon will be listed here.

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.

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