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OptiType: precision HLA typing from next-generation sequencing data.

Szolek A, Schubert B, Mohr C, Sturm M, Feldhahn M, Kohlbacher O - Bioinformatics (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: 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.

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Performance comparison of HLA typing algorithms. OptiType’s average prediction accuracy for major HLA-I loci was compared with four other published HLA typing methods capable of four-digit typing on publicly available datasets previously used to evaluate these methods
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btu548-F2: Performance comparison of HLA typing algorithms. OptiType’s average prediction accuracy for major HLA-I loci was compared with four other published HLA typing methods capable of four-digit typing on publicly available datasets previously used to evaluate these methods

Mentions: OptiType outperforms comparable methods on all datasets by 4 to 15% accuracy, corresponding to a 65 to 83% lower rate of incorrect allele predictions (Fig. 2, Supplementary Table S1). Statistical significance was confirmed in each case by a sign test at an α-level of 0.05. Only ATHLATES showed comparable performance on their benchmark dataset consisting of 11 samples.Fig. 2.


OptiType: precision HLA typing from next-generation sequencing data.

Szolek A, Schubert B, Mohr C, Sturm M, Feldhahn M, Kohlbacher O - Bioinformatics (2014)

Performance comparison of HLA typing algorithms. OptiType’s average prediction accuracy for major HLA-I loci was compared with four other published HLA typing methods capable of four-digit typing on publicly available datasets previously used to evaluate these methods
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4441069&req=5

btu548-F2: Performance comparison of HLA typing algorithms. OptiType’s average prediction accuracy for major HLA-I loci was compared with four other published HLA typing methods capable of four-digit typing on publicly available datasets previously used to evaluate these methods
Mentions: OptiType outperforms comparable methods on all datasets by 4 to 15% accuracy, corresponding to a 65 to 83% lower rate of incorrect allele predictions (Fig. 2, Supplementary Table S1). Statistical significance was confirmed in each case by a sign test at an α-level of 0.05. Only ATHLATES showed comparable performance on their benchmark dataset consisting of 11 samples.Fig. 2.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: 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.

Show MeSH