HLAreporter: a tool for HLA typing from next generation sequencing data

Huang, Youhua; Yang, Jing; Ying, Dingge; Zhang, Yan; Shotelersuk, Vorasuk; Hirankarn, Nattiya; Sham, Pak C.; Lau, Yu Lung; Yang, Wanling

doi:10.1186/s13073-015-0145-3

Cited by 73 publications

(73 citation statements)

References 15 publications

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“…We compare the performance of HLA*PRG with PHLAT [14] and HLAreporter [13], two state-of-the-art algorithms that support HLA class I and class II (Table 1). For the Platinum samples, we find that PHLAT also correctly infers all 36 alleles, whereas HLAreporter only reports 16 alleles (of which 14 are correct).…”

Section: Resultsmentioning

confidence: 99%

“…The current gold standard for high resolution typing of HLA alleles, sequence-based typing (SBT), uses Sanger sequencing or targeted amplification of the HLA genes followed by next-generation sequencing. With the growth of high throughput genomic technologies, methods for inferring HLA genotype have been developed that use SNP genotyping [9–12] or next-generation sequencing [13–19]. These approaches, to date, are either limited to a subset of HLA loci, require targeted capture / amplification, or do not achieve the same degree of accuracy as SBT.…”

Section: Introductionmentioning

confidence: 99%

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High-Accuracy HLA Type Inference from Whole-Genome Sequencing Data Using Population Reference Graphs

et al. 2016

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Genetic variation at the Human Leucocyte Antigen (HLA) genes is associated with many autoimmune and infectious disease phenotypes, is an important element of the immunological distinction between self and non-self, and shapes immune epitope repertoires. Determining the allelic state of the HLA genes (HLA typing) as a by-product of standard whole-genome sequencing data would therefore be highly desirable and enable the immunogenetic characterization of samples in currently ongoing population sequencing projects. Extensive hyperpolymorphism and sequence similarity between the HLA genes, however, pose problems for accurate read mapping and make HLA type inference from whole-genome sequencing data a challenging problem. We describe how to address these challenges in a Population Reference Graph (PRG) framework. First, we construct a PRG for 46 (mostly HLA) genes and pseudogenes, their genomic context and their characterized sequence variants, integrating a database of over 10,000 known allele sequences. Second, we present a sequence-to-PRG paired-end read mapping algorithm that enables accurate read mapping for the HLA genes. Third, we infer the most likely pair of underlying alleles at G group resolution from the IMGT/HLA database at each locus, employing a simple likelihood framework. We show that HLA*PRG, our algorithm, outperforms existing methods by a wide margin. We evaluate HLA*PRG on six classical class I and class II HLA genes (HLA-A, -B, -C, -DQA1, -DQB1, -DRB1) and on a set of 14 samples (3 samples with 2 x 100bp, 11 samples with 2 x 250bp Illumina HiSeq data). Of 158 alleles tested, we correctly infer 157 alleles (99.4%). We also identify and re-type two erroneous alleles in the original validation data. We conclude that HLA*PRG for the first time achieves accuracies comparable to gold-standard reference methods from standard whole-genome sequencing data, though high computational demands (currently ~30–250 CPU hours per sample) remain a significant challenge to practical application.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-Accuracy HLA Type Inference from Whole-Genome Sequencing Data Using Population Reference Graphs

et al. 2016

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“…Next-generation sequencing strategies are enabling the effective interrogation of the HLA genes and the MHC region 59–62 , although these protocols have yet to be widely adopted in clinical practice or research. Recently, Dilthey et al .…”

Section: Genetic Factors Associated With Autoimmunitymentioning

confidence: 99%

Autoimmune diseases — connecting risk alleles with molecular traits of the immune system

2016

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Genome-wide strategies have driven the discovery of more than 300 susceptibility loci for autoimmune diseases. However, for almost all loci, understanding of the mechanisms leading to autoimmunity remains limited, and most variants that are likely to be causal are in non-coding regions of the genome. A critical next step will be to identify the in vivo and ex vivo immunophenotypes that are affected by risk variants. To do this, key cell types and cell states that are implicated in autoimmune diseases will need to be defined. Functional genomic annotations from these cell types and states can then be used to resolve candidate genes and causal variants. Together with longitudinal studies, this approach may yield pivotal insights into how autoimmunity is triggered.

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“…21 The field has tried to overcome these challenges, and numerous of HLA typing tools that use NGS data have been developed during the last decade. 19,[22][23][24][25][26][27][28][29][30][31] All HLA typing algorithms can be divided into two major groups: those that perform de novo assembly of small reads into longer contigs (such as HLAreporter, 23 HLAminer, 24 and ATHLATES 25 ) and those methods that directly align reads against the reference HLA sequences (HLA*PRG, 19 OptiType, 26 Polysolver, 27 HLAscan, 28 HLA-HD, 29 xHLA, 30 and ALPHLARD 31 ). Each method has its own advantages and limitations, and recent studies have benchmarked the different tools in order to characterize their performance.…”

Section: Introductionmentioning

confidence: 99%

Improved HLA typing of Class I and Class II alleles from next‐generation sequencing data

Sverchkova

Anzar

Stratford

et al. 2019

HLA

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Precise HLA genotyping is of great clinical importance, albeit a challenging bioinformatics endeavor because of the hyper polymorphism of the HLA region. The ever‐increasing availability of next‐generation sequencing (NGS) solutions has spurred the development of several computational methods for predicting HLA genotypes from NGS data. Although some of these tools genotype HLA Class I alleles reasonably well, there is a need to incorporate integrative parameters related to ethnicity frequency information, in order to improve performance for both Class I and Class II alleles. Here, we present a bioinformatics method that addresses some of the current shortfalls in HLA genotyping from NGS. First, reads that map to the HLA region is aligned against a comprehensive library of reference HLA alleles. The allele type was then subsequently determined on the basis of the distribution of aligned reads, and the prior probabilities of the ethnic frequencies of alleles. Three public NGS datasets were used to benchmark the approach against six similar tools. The method outlined in this manuscript displayed an overall accuracy of 98.73% for Class I and 96.37% for Class II alleles. We illustrate an improved integrative approach that outperforms existing tools and is able to predict HLA alleles with improved fidelity for both Class I and Class II alleles.

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HLAreporter: a tool for HLA typing from next generation sequencing data

Cited by 73 publications

References 15 publications

High-Accuracy HLA Type Inference from Whole-Genome Sequencing Data Using Population Reference Graphs

High-Accuracy HLA Type Inference from Whole-Genome Sequencing Data Using Population Reference Graphs

Autoimmune diseases — connecting risk alleles with molecular traits of the immune system

Improved HLA typing of Class I and Class II alleles from next‐generation sequencing data

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