Title |
High-sensitivity HLA typing by Saturated Tiling Capture Sequencing (STC-Seq)
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Published in |
BMC Genomics, January 2018
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DOI | 10.1186/s12864-018-4431-5 |
Pubmed ID | |
Authors |
Yang Jiao, Ran Li, Chao Wu, Yibin Ding, Yanning Liu, Danmei Jia, Lifeng Wang, Xiang Xu, Jing Zhu, Min Zheng, Junling Jia |
Abstract |
Highly polymorphic human leukocyte antigen (HLA) genes are responsible for fine-tuning the adaptive immune system. High-resolution HLA typing is important for the treatment of autoimmune and infectious diseases. Additionally, it is routinely performed for identifying matched donors in transplantation medicine. Although many HLA typing approaches have been developed, the complexity, low-efficiency and high-cost of current HLA-typing assays limit their application in population-based high-throughput HLA typing for donors, which is required for creating large-scale databases for transplantation and precision medicine. Here, we present a cost-efficient Saturated Tiling Capture Sequencing (STC-Seq) approach to capturing 14 HLA class I and II genes. The highly efficient capture (an approximately 23,000-fold enrichment) of these genes allows for simplified allele calling. Tests on five genes (HLA-A/B/C/DRB1/DQB1) from 31 human samples and 351 datasets using STC-Seq showed results that were 98% consistent with the known two sets of digitals (field1 and field2) genotypes. Additionally, STC can capture genomic DNA fragments longer than 3 kb from HLA loci, making the library compatible with the third-generation sequencing. STC-Seq is a highly accurate and cost-efficient method for HLA typing which can be used to facilitate the establishment of population-based HLA databases for the precision and transplantation medicine. |
X Demographics
Geographical breakdown
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Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 31 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 7 | 23% |
Student > Ph. D. Student | 5 | 16% |
Student > Master | 4 | 13% |
Other | 3 | 10% |
Professor > Associate Professor | 3 | 10% |
Other | 3 | 10% |
Unknown | 6 | 19% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 7 | 23% |
Agricultural and Biological Sciences | 6 | 19% |
Economics, Econometrics and Finance | 3 | 10% |
Computer Science | 2 | 6% |
Immunology and Microbiology | 2 | 6% |
Other | 4 | 13% |
Unknown | 7 | 23% |