Fast and accurate matching of cellular barcodes across short-reads and long-reads of single-cell RNA-seq experiments

Ghazal Ebrahimi; Baraa Orabi; Meghan Robinson; Cedric Chauve; Ryan Flannigan; Faraz Hach

doi:10.1016/j.isci.2022.104530

Fast and accurate matching of cellular barcodes across short-reads and long-reads of single-cell RNA-seq experiments

iScience. 2022 Jun 7;25(7):104530. doi: 10.1016/j.isci.2022.104530. eCollection 2022 Jul 15.

Authors

Ghazal Ebrahimi¹, Baraa Orabi², Meghan Robinson³, Cedric Chauve⁴, Ryan Flannigan^{3

5}, Faraz Hach^{2

3

5}

Affiliations

¹ Bioinformatics Program, the University of British Columbia, Vancouver, BC, Canada.
² Computer Science Department, the University of British Columbia, Vancouver, BC, Canada.
³ Vancouver Prostate Centre, Vancouver, BC, Canada.
⁴ Department of Mathematics, Simon Fraser University, Burnaby, BC, Canada.
⁵ Department of Urologic Sciences, the University of British Columbia, Vancouver, BC, Canada.

Abstract

Single-cell RNA sequencing allows for characterizing the gene expression landscape at the cell type level. However, because of its use of short-reads, it is severely limited at detecting full-length features of transcripts such as alternative splicing. New library preparation techniques attempt to extend single-cell sequencing by utilizing both long-reads and short-reads. These techniques split the library material, after it is tagged with cellular barcodes, into two pools: one for short-read sequencing and one for long-read sequencing. However, the challenge of utilizing these techniques is that they require matching the cellular barcodes sequenced by the erroneous long-reads to the cellular barcodes detected by the short-reads. To overcome this challenge, we introduce scTagger, a computational method to match cellular barcodes data from long-reads and short-reads. We tested scTagger against another state-of-the-art tool on both real and simulated datasets, and we demonstrate that scTagger has both significantly better accuracy and time efficiency.

Keywords: Bioinformatics; Genomics; Sequence analysis.