OptiType: precision HLA typing from next-generation sequencing data
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 wi...
Saved in:
Published in | Bioinformatics Vol. 30; no. 23; pp. 3310 - 3316 |
---|---|
Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
England
Oxford University Press
01.12.2014
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | 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.
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. |
---|---|
Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Associate Editor: Michael Brudno The authors wish it to be known that, in their opinion, the first three authors should be regarded as Joint First Authors. |
ISSN: | 1367-4803 1367-4811 1460-2059 |
DOI: | 10.1093/bioinformatics/btu548 |