人类白细胞抗原
基因分型
生物信息学
DNA测序
外显子组测序
计算生物学
计算机科学
外显子组
生物
数据挖掘
基因型
遗传学
基因
突变
抗原
作者
András Szolek,Benjamin Schubert,Christopher Mohr,Marc Sturm,Magdalena Feldhahn,Oliver Kohlbacher
出处
期刊:Bioinformatics
[Oxford University Press]
日期:2014-08-20
卷期号:30 (23): 3310-3316
被引量:545
标识
DOI:10.1093/bioinformatics/btu548
摘要
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.
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