Publication Date:
2019-06-07
Description:
Motivation The human leukocyte antigen (HLA) locus plays a critical role in tissue compatibility and regulates the host response to many diseases, including cancers and autoimmune di3orders. Recent improvements in the quality and accessibility of next-generation sequencing have made HLA typing from standard short-read data practical. However, this task remains challenging given the high level of polymorphism and homology between HLA genes. HLA typing from RNA sequencing is further complicated by post-transcriptional modifications and bias due to amplification. Results Here, we present arcasHLA: a fast and accurate in silico tool that infers HLA genotypes from RNA-sequencing data. Our tool outperforms established tools on the gold-standard benchmark dataset for HLA typing in terms of both accuracy and speed, with an accuracy rate of 100% at two-field resolution for Class I genes, and over 99.7% for Class II. Furthermore, we evaluate the performance of our tool on a new biological dataset of 447 single-end total RNA samples from nasopharyngeal swabs, and establish the applicability of arcasHLA in metatranscriptome studies. Availability and implementation arcasHLA is available at https://github.com/RabadanLab/arcasHLA. Supplementary information Supplementary data are available at Bioinformatics online.
Print ISSN:
1367-4803
Electronic ISSN:
1460-2059
Topics:
Biology
,
Computer Science
,
Medicine
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