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High-sensitivity HLA typing by Saturated Tiling Capture Sequencing (STC-Seq)

Overview of attention for article published in BMC Genomics, January 2018
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Title
High-sensitivity HLA typing by Saturated Tiling Capture Sequencing (STC-Seq)
Published in
BMC Genomics, January 2018
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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
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 %
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%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 July 2018.
All research outputs
#20,459,801
of 23,016,919 outputs
Outputs from BMC Genomics
#9,326
of 10,697 outputs
Outputs of similar age
#406,032
of 473,646 outputs
Outputs of similar age from BMC Genomics
#195
of 218 outputs
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We're also able to compare this research output to 218 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.