A novel framework for human leukocyte antigen (HLA) genotyping using probe capture-based targeted next-generation sequencing and computational analysis

Human leukocyte antigen (HLA) genes play pivotal roles in numerous immunological applications. Given the immense number of polymorphisms, achieving accurate high-throughput HLA typing remains challenging. This study aimed to harness the human pan-genome reference consortium (HPRC) resources as a potential benchmark for HLA reference materials. We meticulously annotated specific four field-resolution alleles for 11 HLA genes (HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, -DRB1, -DRB3, -DRB4 and -DRB5) from 44 high-quality HPRC personal genome assemblies. For sequencing, we crafted HLA-specific probes and conducted capture-based targeted sequencing of the genomic DNA of the HPRC cohort, ensuring focused and comprehensive coverage of the HLA region of interest. We used publicly available short-read whole-genome sequencing (WGS) data from identical samples to offer a comparative perspective. To decipher the vast amount of sequencing data, we employed seven distinct software tools: OptiType, HLA-VBseq, HISAT genotype, SpecHLA, T1K, QzType, and DRAGEN. Each tool offers unique capabilities and algorithms for HLA genotyping, allowing comprehensive analysis and validation of the results. We then compared these results with benchmarks derived from personal genome assemblies. Our findings present a comprehensive four-field-resolution HLA allele annotation for 44 HPRC samples. Significantly, our innovative targeted next-generation sequencing (NGS) approach for HLA genes showed superior accuracy compared with conventional short-read WGS. An integrated analysis involving QzType, T1K, and DRAGEN was developed, achieving 100% accuracy for all 11 HLA genes. In conclusion, our study highlighted the combination of targeted short-read sequencing and astute computational analysis as a robust approach for HLA genotyping. Furthermore, the HPRC cohort has emerged as a valuable assembly-based reference in this realm.

BRAF Mutations in Papillary Thyroid Carcinoma: A Genomic Approach Using Probe-Based DNA Capture for Next-Generation Sequencing

The BRAF V600E mutation in papillary thyroid carcinoma is the major mutation in classical subtype of papillary thyroid carcinoma and other cancers. It is the most studied predictor of clinical and pathological characteristics as well as molecular targets for cancer therapy. On the other hand, there is potential for many more forms of activating mutation in BRAF that are not detectable by simple assays to detect V600E, or even simple polymerase chain reaction (PCR)-based sequencing for full-length BRAF. Such activating mutations could arise from larger-scale rearrangements which may apparently leave no sequence change to BRAF while causing increased expression or activation by unusual means, such as gene fusion. Detection of these kinds of changes can take place using a variety of methods, though capture-based sequencing can identify the existence of such forms of mutant BRAF without needing foreknowledge of the loci involved in these kinds of mutation. In this chapter, we detail a method for capture of specific DNA sequences and their amplification to prepare for massively parallel sequencing.

GmoDetector: An accurate and efficient GMO identification approach and its applications

The rapid increase of genetically modified organisms (GMOs) entering the food and feed markets, and the contamination of donor (micro)organisms of transgenic elements make it more challenging for the existing GMO detection. In this study, we developed a high-throughput and contamination-removal GMO detection approach named as GmoDetector. GmoDetector targeted 64 common transgenic elements and 76 GMO-specific events collected from 251 singular GM events, and combined with next generation sequencing (NGS) and target enrichment technology to detect various GMOs. As a result, GmoDetector was able to exclude the donor (micro)organism contamination, and detect the authorized and unauthorized GMOs (UGMOs) in any forms of food or feed, such as processed or unprocessed. The sensitivity of GmoDetector is as low as 0.1% (GMO content), which has met the GMO labeling threshold for all countries. Therefore, GmoDetector is a robust tool for accurate and efficient detection of the authorized and UGMOs.