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本文引用的文献

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Collection and storage of HLA NGS genotyping data for the 17th International HLA and Immunogenetics Workshop.第17届国际HLA与免疫遗传学研讨会HLA二代测序基因分型数据的收集与存储
Hum Immunol. 2018 Feb;79(2):77-86. doi: 10.1016/j.humimm.2017.12.004. Epub 2017 Dec 14.
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HLA typing using next generation sequencing: An overview.使用下一代测序技术进行HLA分型:综述。
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HLA typing by SSO and SSP methods.采用序列特异性寡核苷酸探针(SSO)和序列特异性引物(SSP)方法进行人类白细胞抗原(HLA)分型。
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第17届国际HLA与免疫遗传学研讨会的二代测序HLA基因分型质量控制项目

Quality control project of NGS HLA genotyping for the 17th International HLA and Immunogenetics Workshop.

作者信息

Osoegawa Kazutoyo, Vayntrub Tamara A, Wenda Sabine, De Santis Dianne, Barsakis Konstantinos, Ivanova Milena, Hsu Susan, Barone Jonathan, Holdsworth Rhonda, Diviney Mary, Askar Medhat, Willis Amanda, Railton Dawn, Laflin Sophie, Gendzekhadze Ketevan, Oki Arisa, Sacchi Nicoletta, Mazzocco Michela, Andreani Marco, Ameen Reem, Stavropoulos-Giokas Catherine, Dinou Amalia, Torres Margareth, Dos Santos Francisco Rodrigo, Serra-Pages Carles, Goodridge Damian, Balladares Sandra, Bettinotti Maria P, Iglehart Brian, Kashi Zahra, Martin Russell, Saw Chee Loong, Ragoussis Jiannis, Downing Jonathan, Navarrete Cristina, Chong Winnie, Saito Katsuyuki, Petrek Martin, Tokic Stana, Padros Karin, Beatriz Rodriguez Ma, Zakharova Viktoria, Shragina Olga, Marino Susana R, Brown Nicholas K, Shiina Takashi, Suzuki Shingo, Spierings Eric, Zhang Qiuheng, Yin Yuxin, Morris Gerald P, Hernandez Ana, Ruiz Phillip, Khor Seik-Soon, Tokunaga Katsushi, Geretz Aviva, Thomas Rasmi, Yamamoto Fumiko, Mallempati Kalyan C, Gangavarapu Sridevi, Kanga Uma, Tyagi Shweta, Marsh Steven G E, Bultitude Will P, Liu Xiangjun, Cao Dajiang, Penning Maarten, Hurley Carolyn K, Cesbron Anne, Mueller Claudia, Mytilineos Joannis, Weimer Eric T, Bengtsson Mats, Fischer Gottfried, Hansen John A, Chang Chia-Jung, Mack Steven J, Creary Lisa E, Fernandez-Viña Marcelo A

机构信息

Histocompatibility, Immunogenetics, and Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA.

Histocompatibility, Immunogenetics, and Disease Profiling Laboratory, Stanford Blood Center, Palo Alto, CA, USA.

出版信息

Hum Immunol. 2019 Apr;80(4):228-236. doi: 10.1016/j.humimm.2019.01.009. Epub 2019 Feb 6.

DOI:10.1016/j.humimm.2019.01.009
PMID:30738112
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6446570/
Abstract

The 17th International HLA and Immunogenetics Workshop (IHIW) organizers conducted a Pilot Study (PS) in which 13 laboratories (15 groups) participated to assess the performance of the various sequencing library preparation protocols, NGS platforms and software in use prior to the workshop. The organizers sent 50 cell lines to each of the 15 groups, scored the 15 independently generated sets of NGS HLA genotyping data, and generated "consensus" HLA genotypes for each of the 50 cell lines. Proficiency Testing (PT) was subsequently organized using four sets of 24 cell lines, selected from 48 of 50 PS cell lines, to validate the quality of NGS HLA typing data from the 34 participating IHIW laboratories. Completion of the PT program with a minimum score of 95% concordance at the HLA-A, HLA-B, HLA-C, HLA-DRB1 and HLA-DQB1 loci satisfied the requirements to submit NGS HLA typing data for the 17th IHIW projects. Together, these PS and PT efforts constituted the 17th IHIW Quality Control project. Overall PT concordance rates for HLA-A, HLA-B, HLA-C, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DRB1, HLA-DRB3, HLA-DRB4 and HLA-DRB5 were 98.1%, 97.0% and 98.1%, 99.0%, 98.6%, 98.8%, 97.6%, 96.0%, 99.1%, 90.0% and 91.7%, respectively. Across all loci, the majority of the discordance was due to allele dropout. The high cost of NGS HLA genotyping per experiment likely prevented the retyping of initially failed HLA loci. Despite the high HLA genotype concordance rates of the software, there remains room for improvement in the assembly of more accurate consensus DNA sequences by NGS HLA genotyping software.

摘要

第17届国际HLA与免疫遗传学研讨会(IHIW)的组织者开展了一项试点研究(PS),13个实验室(15个小组)参与其中,以评估研讨会之前使用的各种测序文库制备方案、二代测序(NGS)平台及软件的性能。组织者向15个小组各发送了50个细胞系,对15组独立生成的NGS HLA基因分型数据进行评分,并为50个细胞系中的每一个生成“一致性”HLA基因型。随后,利用从50个PS细胞系中的48个选出的四组、每组24个细胞系组织了能力验证(PT),以验证来自34个参与第17届IHIW的实验室的NGS HLA分型数据的质量。在HLA - A、HLA - B、HLA - C、HLA - DRB1和HLA - DQB1位点达到最低95%一致性的PT计划完成情况满足了为第17届IHIW项目提交NGS HLA分型数据的要求。这些PS和PT工作共同构成了第17届IHIW质量控制项目。HLA - A、HLA - B、HLA - C、HLA - DPA1、HLA - DPB1、HLA - DQA1、HLA - DQB1、HLA - DRB1、HLA - DRB3、HLA - DRB4和HLA - DRB5的总体PT一致性率分别为98.1%、97.0%、98.1%、99.0%、98.6%、98.8%、97.6%、96.0%、99.1%、90.0%和91.7%。在所有位点中,大多数不一致是由于等位基因缺失。每次实验的NGS HLA基因分型成本高昂,这可能阻碍了对最初失败的HLA位点进行重新分型。尽管软件的HLA基因型一致性率很高,但NGS HLA基因分型软件在组装更准确的一致性DNA序列方面仍有改进空间。