scHLAcount: allele-specific HLA expression from single-cell gene expression data

Charlotte A Darby; Michael J T Stubbington; Patrick J Marks; Álvaro Martínez Barrio; Ian T Fiddes

doi:10.1093/bioinformatics/btaa264

scHLAcount: allele-specific HLA expression from single-cell gene expression data

Bioinformatics. 2020 Jun 1;36(12):3905-3906. doi: 10.1093/bioinformatics/btaa264.

Authors

Charlotte A Darby¹, Michael J T Stubbington², Patrick J Marks², Álvaro Martínez Barrio², Ian T Fiddes²

Affiliations

¹ Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.
² 10x Genomics, Pleasanton, CA 94588, USA.

Abstract

Summary: Bulk RNA sequencing studies have demonstrated that human leukocyte antigen (HLA) genes may be expressed in a cell type-specific and allele-specific fashion. Single-cell gene expression assays have the potential to further resolve these expression patterns, but currently available methods do not perform allele-specific quantification at the molecule level. Here, we present scHLAcount, a post-processing workflow for single-cell RNA-seq data that computes allele-specific molecule counts of the HLA genes based on a personalized reference constructed from the sample's HLA genotypes.

Availability and implementation: scHLAcount is available under the MIT license at https://github.com/10XGenomics/scHLAcount.

Supplementary information: Supplementary data are available at Bioinformatics online.

MeSH terms

Alleles
Gene Expression
Humans
Sequence Analysis, RNA
Single-Cell Analysis*
Software*
Workflow