OptiType: precision HLA typing from next-generation sequencing data.
作者： András Szolek , Benjamin Schubert , Christopher Mohr , Marc Sturm , Magdalena Feldhahn
DOI: 10.1093/BIOINFORMATICS/BTU548
关键词: DNA sequencing 、 In silico 、 Exome sequencing 、 Biology 、 Human leukocyte antigen 、 Genetics 、 Genotyping Techniques 、 Sequence analysis 、 Exome 、 Computational biology 、 Gene cluster
摘要: Motivation: The human leukocyte antigen (HLA) gene cluster plays a crucial role in adaptive immunity and is thus relevant many biomedical applications. While next-generation sequencing data are often available for patient, deducing the HLA genotype difficult because of substantial sequence similarity within exceptionally high variability loci. Established approaches, therefore, rely on specific enrichment techniques, coming at an additional cost extra turnaround time. Result: We present OptiType, novel genotyping algorithm based integer linear programming, capable producing accurate predictions from NGS not specifically enriched cluster. also comprehensive benchmark dataset consisting RNA, exome whole-genome data. OptiType significantly outperformed previously published silico approaches with overall accuracy 97% enabling its use broad range
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