HLAProfiler utilizes k-mer profiles to improve HLA calling accuracy for rare and common alleles in RNA-seq data.
HLAProfiler utilizes k-mer profiles to improve HLA calling accuracy for rare and common alleles in RNA-seq data.
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DOI：
10.1186/s13073-017-0473-6
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发表时间：
2017-09-27
期刊：
影响因子：
12.3
通讯作者：
Powers JG
中科院分区：
文献类型：
--
作者：
Buchkovich ML;Brown CC;Robasky K;Chai S;Westfall S;Vincent BG;Weimer ET;Powers JG
人类白细胞抗原（HLA）系统是通过编码负责自我识别的细胞膜主要组织相容性复合体（MHC）蛋白而参与调节人类免疫系统的基因组区域。了解这一区域的变化为自身免疫性疾病、疾病易感性、肿瘤免疫治疗、再生医学、移植排斥和毒理基因组学提供了重要的见解。HLA分型的传统方法是低通量的，仅靶向少数基因，劳动密集且昂贵，或者需要专门的方案。RNA测序有望为所有基因的HLA调用提供一种相对便宜、高通量的解决方案，并提供完整的转录组信息和广泛的历史数据。现有的工具在从RNA-seq数据中准确和全面地调用HLA基因的能力方面受到限制。我们创建了HLAProfiler（https：//github.com/ExpressionAnalysis/HLAProfiler），这是一种用于RNA-seq数据中HLA调用的基于k-mer概况的方法，其可以在生物和模拟数据中以两个字段的精度以> 99%的准确度鉴定罕见和常见的HLA等位基因。对于参考数据库中不存在的68%的新等位基因，HLAProfiler可以正确识别两个字段的精度或确切的编码序列，这是对现有算法的重大进步。HLAProfiler允许在RNA-seq数据中进行准确的HLA调用，可靠地扩展了这些数据在HLA相关研究中的实用性，并在广泛的学科中取得进展。此外，通过使用观察到的数据来识别潜在的新等位基因和更新部分等位基因，HLAProfiler将有助于进一步改进现有的参考HLA等位基因数据库。HLAProfiler可在https://expressionanalysis.github.io/HLAProfiler/上获得。本文的在线版本（doi：10.1186/s13073-017-0473-6）包含补充材料，可供授权用户使用。
The human leukocyte antigen (HLA) system is a genomic region involved in regulating the human immune system by encoding cell membrane major histocompatibility complex (MHC) proteins that are responsible for self-recognition. Understanding the variation in this region provides important insights into autoimmune disorders, disease susceptibility, oncological immunotherapy, regenerative medicine, transplant rejection, and toxicogenomics. Traditional approaches to HLA typing are low throughput, target only a few genes, are labor intensive and costly, or require specialized protocols. RNA sequencing promises a relatively inexpensive, high-throughput solution for HLA calling across all genes, with the bonus of complete transcriptome information and widespread availability of historical data. Existing tools have been limited in their ability to accurately and comprehensively call HLA genes from RNA-seq data. We created HLAProfiler (https://github.com/ExpressionAnalysis/HLAProfiler), a k-mer profile-based method for HLA calling in RNA-seq data which can identify rare and common HLA alleles with > 99% accuracy at two-field precision in both biological and simulated data. For 68% of novel alleles not present in the reference database, HLAProfiler can correctly identify the two-field precision or exact coding sequence, a significant advance over existing algorithms. HLAProfiler allows for accurate HLA calls in RNA-seq data, reliably expanding the utility of these data in HLA-related research and enabling advances across a broad range of disciplines. Additionally, by using the observed data to identify potential novel alleles and update partial alleles, HLAProfiler will facilitate further improvements to the existing database of reference HLA alleles. HLAProfiler is available at https://expressionanalysis.github.io/HLAProfiler/. The online version of this article (doi:10.1186/s13073-017-0473-6) contains supplementary material, which is available to authorized users.
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DOI：
10.1093/bioinformatics/btu548
发表时间：
2014-12-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
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作者：
Szolek A;Schubert B;Mohr C;Sturm M;Feldhahn M;Kohlbacher O
通讯作者：
Kohlbacher O
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
2.6
作者：
Gough SC;Simmonds MJ
通讯作者：
Simmonds MJ
影响因子：
7
作者：
Harrow J;Frankish A;Gonzalez JM;Tapanari E;Diekhans M;Kokocinski F;Aken BL;Barrell D;Zadissa A;Searle S;Barnes I;Bignell A;Boychenko V;Hunt T;Kay M;Mukherjee G;Rajan J;Despacio-Reyes G;Saunders G;Steward C;Harte R;Lin M;Howald C;Tanzer A;Derrien T;Chrast J;Walters N;Balasubramanian S;Pei B;Tress M;Rodriguez JM;Ezkurdia I;van Baren J;Brent M;Haussler D;Kellis M;Valencia A;Reymond A;Gerstein M;Guigó R;Hubbard TJ
通讯作者：
Hubbard TJ
影响因子：
--
作者：
Mack SJ;Cano P;Hollenbach JA;He J;Hurley CK;Middleton D;Moraes ME;Pereira SE;Kempenich JH;Reed EF;Setterholm M;Smith AG;Tilanus MG;Torres M;Varney MD;Voorter CE;Fischer GF;Fleischhauer K;Goodridge D;Klitz W;Little AM;Maiers M;Marsh SG;Müller CR;Noreen H;Rozemuller EH;Sanchez-Mazas A;Senitzer D;Trachtenberg E;Fernandez-Vina M
通讯作者：
Fernandez-Vina M