OptiType: precision HLA typing from next-generation sequencing data

András Szolek; Benjamin Schubert; Christopher Mohr; Marc Sturm; Magdalena Feldhahn; Oliver Kohlbacher

doi:10.1093/bioinformatics/btu548

OptiType: precision HLA typing from next-generation sequencing data

Bioinformatics. 2014 Dec 1;30(23):3310-6. doi: 10.1093/bioinformatics/btu548. Epub 2014 Aug 20.

Authors

András Szolek¹, Benjamin Schubert², Christopher Mohr², Marc Sturm¹, Magdalena Feldhahn¹, Oliver Kohlbacher¹

Affiliations

¹ Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, and Department of Computer Science, University of Tübingen, Institute of Medical Genetics and Applied Genomics, University of Tübingen, and CeGaT GmbH, 72076 Tübingen, Germany.
² Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, and Department of Computer Science, University of Tübingen, Institute of Medical Genetics and Applied Genomics, University of Tübingen, and CeGaT GmbH, 72076 Tübingen, Germany Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center, and Department of Computer Science, University of Tübingen, Institute of Medical Genetics and Applied Genomics, University of Tübingen, and CeGaT GmbH, 72076 Tübingen, Germany.

Abstract

Motivation: The human leukocyte antigen (HLA) gene cluster plays a crucial role in adaptive immunity and is thus relevant in many biomedical applications. While next-generation sequencing data are often available for a patient, deducing the HLA genotype is difficult because of substantial sequence similarity within the cluster and exceptionally high variability of the loci. Established approaches, therefore, rely on specific HLA enrichment and sequencing techniques, coming at an additional cost and extra turnaround time.

Result: We present OptiType, a novel HLA genotyping algorithm based on integer linear programming, capable of producing accurate predictions from NGS data not specifically enriched for the HLA cluster. We also present a comprehensive benchmark dataset consisting of RNA, exome and whole-genome sequencing data. OptiType significantly outperformed previously published in silico approaches with an overall accuracy of 97% enabling its use in a broad range of applications.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Exome
Genotyping Techniques
HLA Antigens / genetics
High-Throughput Nucleotide Sequencing / methods*
Histocompatibility Testing / methods*
Humans
Introns
Sequence Analysis, DNA / methods*

Substances

HLA Antigens