scholarly journals arcasHLA: high resolution HLA typing from RNA seq

2018 ◽  
Author(s):  
Rose Orenbuch ◽  
Ioan Filip ◽  
Devon Comito ◽  
Jeffrey Shaman ◽  
Itsik Pe'er ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Mhc Class Ii ◽  
Critical Role ◽  
Human Leukocyte ◽  
Hla Typing ◽  
Sequencing Data ◽  
Leukocyte Antigen ◽  
Hla Genes ◽  
Biological Dataset ◽  
High Level

Human leukocyte antigen (HLA) locus makes up the major compatibility complex (MHC) and plays a critical role in host response to disease, including cancers and autoimmune disorders. In the clinical setting, HLA typing is necessary for determining tissue compatibility. Recent improvements in the quality and accessibility of next-generation sequencing have made HLA typing from standard short-read data practical. However, this task remains challenging given the high level of polymorphism and homology between the HLA genes. HLA typing from RNA sequencing is further complicated by post-transcriptional splicing and bias due to amplification. Here, we present arcasHLA: a fast and accurate in silico tool that infers HLA genotypes from RNA sequencing data. Our tool outperforms established tools on the gold-standard benchmark dataset for HLA typing in terms of both accuracy and speed, with an accuracy rate of 100% at two field precision for MHC class I genes, and over 99.7% for MHC class II. Importantly, arcasHLA takes as its input pre-aligned BAM files, and outputs three-field resolution for all HLA genes in less than 2 minutes. Finally, we discuss evaluate the performance of our tool on a new biological dataset of 447 single-end total RNA samples from nasopharyngeal swabs, and establish the applicability of arcasHLA in metatranscriptome studies. arcasHLA is available at https://github.com/RabadanLab/arcasHLA.

scholarly journals arcasHLA: high-resolution HLA typing from RNAseq

2019 ◽  
Vol 36 (1) ◽  
pp. 33-40 ◽  
Author(s):  
Rose Orenbuch ◽  
Ioan Filip ◽  
Devon Comito ◽  
Jeffrey Shaman ◽  
Itsik Pe’er ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Critical Role ◽  
Human Leukocyte ◽  
Hla Typing ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Leukocyte Antigen ◽  
Biological Dataset ◽  
Hla Genotypes ◽  
High Level

Abstract Motivation The human leukocyte antigen (HLA) locus plays a critical role in tissue compatibility and regulates the host response to many diseases, including cancers and autoimmune di3orders. Recent improvements in the quality and accessibility of next-generation sequencing have made HLA typing from standard short-read data practical. However, this task remains challenging given the high level of polymorphism and homology between HLA genes. HLA typing from RNA sequencing is further complicated by post-transcriptional modifications and bias due to amplification. Results Here, we present arcasHLA: a fast and accurate in silico tool that infers HLA genotypes from RNA-sequencing data. Our tool outperforms established tools on the gold-standard benchmark dataset for HLA typing in terms of both accuracy and speed, with an accuracy rate of 100% at two-field resolution for Class I genes, and over 99.7% for Class II. Furthermore, we evaluate the performance of our tool on a new biological dataset of 447 single-end total RNA samples from nasopharyngeal swabs, and establish the applicability of arcasHLA in metatranscriptome studies. Availability and implementation arcasHLA is available at https://github.com/RabadanLab/arcasHLA. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Familial Pemphigus Vulgaris Occured in a Father and Son as the First Confirmed Cases

2016 ◽  
Vol 2016 ◽  
pp. 1-4
Author(s):  
Ali Haydar Eskiocak ◽  
Birgul Ozkesici ◽  
Soner Uzun
Keyword(s):  
Genetic Predisposition ◽  
Human Leukocyte ◽  
Pemphigus Vulgaris ◽  
Hla Typing ◽  
Enzyme Linked Immunosorbent Assay ◽  
Leukocyte Antigen ◽  
Hla Genes ◽  
Mother And Daughter ◽  
First Occurrence ◽  
Father And Son

Pemphigus vulgaris (PV) is a chronic autoimmune bullous disease of the skin and mucous membranes. Although there is some evidence pointing towards a genetic predisposition by some human leukocyte antigen (HLA) genes, familial occurrence of PV is very rare. Most of the familial PV cases so far reported have been in mother and daughter and in siblings. PV in father and son, as presented here, has not been reported in the literature before, except an unconfirmed report. The diagnosis of PV was established by histologic, cytologic studies and enzyme linked immunosorbent assay (ELISA) in Case1and by ELISA and BIOCHIP indirect immunofluorescence test in Case2. The son was responsive to moderate doses of methylprednisolone, with the treatment continuing with tapered doses. The father was in a subclinic condition; consequently, only close follow-up was recommended. HLA typing studies revealed identical HLA alleles of HLA-DR4 (DRB1⁎04) and HLA-DQB1⁎03in both of our cases; this had been found to be associated with PV in prior studies. Familial occurrences of PV and related HLA genes indicate the importance of genetic predisposition. The first occurrence of confirmed familial PV in father and son is reported here.

scholarly journals Accuracy of programs for the determination of HLA alleles from NGS data

2017 ◽  
Author(s):  
Antti Larjo ◽  
Robert Eveleigh ◽  
Elina Kilpeläinen ◽  
Tony Kwan ◽  
Tomi Pastinen ◽  
...  
Keyword(s):  
Human Leukocyte ◽  
Hla Typing ◽  
Ensemble Prediction ◽  
Hla Alleles ◽  
Leukocyte Antigen ◽  
Peptide Antigens ◽  
Hla Genes ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data

AbstractThe human leukocyte antigen (HLA) genes code for proteins that play a central role in the function of the immune system by presenting peptide antigens to T cells. As HLA genes show extremely high genetic polymorphism, HLA typing on the allele level is demanding and is based on DNA sequencing. Determination of HLA alleles is warranted as many HLA alleles are major genetic factors that confer susceptibility to autoimmune diseases and is important for the matching of HLA alleles in transplantation. Here, we compared the accuracy of several published HLA-typing algorithms that are based on next generation sequencing (NGS) data. As genome screens are becoming increasingly routine in research, we wanted to test how well HLA alleles can be deduced from genome screens not designed for HLA typing. The accuracies were assessed using datasets consisting of NGS data produced using the ImmunoSEQ platform, including the full 4 Mbp HLA segment, from 94 stem cell transplantation patients and exome sequences from the 1000 Genomes collection. When used with the default settings none of the methods gave perfect results for all the genes and samples. However, we found that ensemble prediction of the results or modifications of the settings could be used to improve accuracy. Most of the algorithms did not perform very well for the exome-only data. The results indicate that the use of these algorithms for accurate HLA allele determination based on NGS data is not straightforward.

scholarly journals SweHLA: the high confidence HLA typing bio-resource drawn from 1000 Swedish genomes

2019 ◽  
Vol 28 (5) ◽  
pp. 627-635 ◽  
Author(s):  
Jessika Nordin ◽  
Adam Ameur ◽  
Kerstin Lindblad-Toh ◽  
Ulf Gyllensten ◽  
Jennifer R. S. Meadows
Keyword(s):  
Human Leukocyte ◽  
Class Ii ◽  
Hla Typing ◽  
Class I ◽  
Sequencing Data ◽  
High Confidence ◽  
Leukocyte Antigen ◽  
Hla Dqa1 ◽  
Inference Methods ◽  
Ngs Data

AbstractThere is a need to accurately call human leukocyte antigen (HLA) genes from existing short-read sequencing data, however there is no single solution that matches the gold standard of Sanger sequenced lab typing. Here we aimed to combine results from available software programs, minimizing the biases of applied algorithm and HLA reference. The result is a robust HLA population resource for the published 1000 Swedish genomes, and a framework for future HLA interrogation. HLA 2nd-field alleles were called using four imputation and inference methods for the classical eight genes (class I: HLA-A, HLA-B, HLA-C; class II: HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DRB1). A high confidence population set (SweHLA) was determined using an n−1 concordance rule for class I (four software) and class II (three software) alleles. Results were compared across populations and individual programs benchmarked to SweHLA. Per gene, 875 to 988 of the 1000 samples were genotyped in SweHLA; 920 samples had at least seven loci called. While a small fraction of reference alleles were common to all software (class I = 1.9% and class II = 4.1%), this did not affect the overall call rate. Gene-level concordance was high compared to European populations (>0.83%), with COX and PGF the dominant SweHLA haplotypes. We noted that 15/18 discordant alleles (delta allele frequency >2) were previously reported as disease-associated. These differences could in part explain across-study genetic replication failures, reinforcing the need to use multiple software solutions. SweHLA demonstrates a way to use existing NGS data to generate a population resource agnostic to individual HLA software biases.

The HLA system: immunobiology, HLA typing, antibody screening and crossmatching techniques

2010 ◽  
Vol 63 (5) ◽  
pp. 387-390 ◽  
Author(s):  
W M Howell ◽  
V Carter ◽  
B Clark
Keyword(s):  
Critical Role ◽  
Human Leukocyte ◽  
Hla Typing ◽  
Antibody Screening ◽  
Leukocyte Antigen ◽  
Hla System ◽  
Clinical Transplantation ◽  
Cross Matching ◽  
Immunological Barrier ◽  
Introductory Review

The Human Leukocyte Antigen (HLA) system plays a critical role in regulating the immune response. As a consequence of its role in immune regulation and exquisite polymorphism, the HLA system also constitutes an immunological barrier which must be avoided or otherwise overcome in clinical transplantation. This introductory review provides a brief summary of the immunobiology of the HLA system and methodology for HLA typing, antibody screening and patient-donor cross-matching. This constitutes a basis for consideration of the importance of these procedures in the system-specific reviews which follow.

scholarly journals SweHLA: the high confidence HLA typing bio-resource drawn from 1 000 Swedish genomes

2019 ◽  
Author(s):  
Jessika Nordin ◽  
Adam Ameur ◽  
Kerstin Lindblad-Toh ◽  
Ulf Gyllensten ◽  
Jennifer R.S. Meadows
Keyword(s):  
Human Leukocyte ◽  
Class Ii ◽  
Hla Typing ◽  
Class I ◽  
Sequencing Data ◽  
High Confidence ◽  
Leukocyte Antigen ◽  
Gene Level ◽  
Inference Methods ◽  
Ngs Data

AbstractThere is a need to accurately call human leukocyte antigen (HLA) genes from existing short-read sequencing data, however there is no single solution that matches the gold standard of lab typing. Here we aimed to combine results from available software, minimising the biases of applied algorithm and HLA reference. The result is a robust HLA population resource for the published 1 000 Swedish genomes, and a framework for future HLA interrogation. HLA 2-field alleles were called using four imputation and inference methods for the classical eight genes (class I: HLA-A, -B, -C; class II: HLA-DPA1, -DPB1, -DQA1, -DQB1, -DRB1). A high confidence population set (SweHLA) was determined using an n-1 concordance rule for class I (four software) and class II (three software) alleles. Results were compared across populations and individual programs benchmarked to SweHLA. Per allele, 875 to 988 of the 1 000 samples were genotyped in SweHLA; 920 samples had at least seven loci. While a small fraction of reference alleles were common to all software (class I=1.9% and class II=4.1%), this did not affect the overall call rate. Gene-level concordance was high compared to European populations (>0.83%), with COX and PGF the dominant SweHLA haplotypes. We noted that 15/18 discordant alleles (delta allele frequency > 2) were previously reported as disease-associated. These differences could in part explain across-study genetic replication failures, reinforcing the need to use multiple software. SweHLA demonstrates a way to use existing NGS data to generate a population resource agnostic to individual HLA software biases.

scholarly journals Rigorous Benchmarking of HLA Callers for RNA Sequencing Data

2021 ◽  
Author(s):  
Ram Ayyala ◽  
Junghyun Jung ◽  
Sergey Knyazev ◽  
SERGHEI MANGUL
Keyword(s):  
Rna Sequencing ◽  
Gold Standard ◽  
Large Scale ◽  
Human Leukocyte ◽  
Tissue Expression ◽  
Hla Typing ◽  
Read Length ◽  
Evaluation Metrics ◽  
Rna Seq ◽  
Sequencing Data

Although precise identification of the human leukocyte antigen (HLA) allele is crucial for various clinical and research applications, HLA typing remains challenging due to high polymorphism of the HLA loci. However, with Next-Generation Sequencing (NGS) data becoming widely accessible, many computational tools have been developed to predict HLA types from RNA sequencing (RNA-seq) data. However, there is a lack of comprehensive and systematic benchmarking of RNA-seq HLA callers using large-scale and realist gold standards. In order to address this limitation, we rigorously compared the performance of 12 HLA callers over 50,000 HLA tasks including searching 30 pairwise combinations of HLA callers and reference in over 1,500 samples. In each case, we produced evaluation metrics of accuracy that is the percentage of correctly predicted alleles (two and four-digit resolution) based on six gold standard datasets spanning 650 RNA-seq samples. To determine the influence of the relationship of the read length over the HLA region on prediction quality using each tool, we explored the read length effect by considering read length in the range 37-126 bp, which was available in our gold standard datasets. Moreover, using the Genotype-Tissue Expression (GTEx) v8 data, we carried out evaluation metrics by calculating the concordance of the same HLA type across different tissues from the same individual to evaluate how well the HLA callers can maintain consistent results across various tissues of the same individual. This study offers crucial information for researchers regarding appropriate choices of methods for an HLA analysis.

scholarly journals NeoFuse: predicting fusion neoantigens from RNA sequencing data

2019 ◽  
Vol 36 (7) ◽  
pp. 2260-2261 ◽  
Author(s):  
Georgios Fotakis ◽  
Dietmar Rieder ◽  
Marlene Haider ◽  
Zlatko Trajanoski ◽  
Francesca Finotello
Keyword(s):  
Rna Sequencing ◽  
Human Leukocyte ◽  
Computational Prediction ◽  
Supplementary Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Leukocyte Antigen ◽  
Computational Workflow ◽  
Hla Binding ◽  
Proteins And Peptides

Abstract Summary Gene fusions can generate immunogenic neoantigens that mediate anticancer immune responses. However, their computational prediction from RNA sequencing (RNA-seq) data requires deep bioinformatics expertise to assembly a computational workflow covering the prediction of: fusion transcripts, their translated proteins and peptides, Human Leukocyte Antigen (HLA) types, and peptide-HLA binding affinity. Here, we present NeoFuse, a computational pipeline for the prediction of fusion neoantigens from tumor RNA-seq data. NeoFuse can be applied to cancer patients’ RNA-seq data to identify fusion neoantigens that might expand the repertoire of suitable targets for immunotherapy. Availability and implementation NeoFuse source code and documentation are available under GPLv3 license at https://icbi.i-med.ac.at/NeoFuse/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Human Leukocyte Antigen and Red Blood Cells Impact Umbilical Cord Blood CD34+ Cell Viability after Thawing

2019 ◽  
Vol 20 (19) ◽  
pp. 4875 ◽  
Author(s):  
Vanegas ◽  
Galindo ◽  
Páez-Gutiérrez ◽  
González-Acero ◽  
Medina-Valderrama ◽  
...  
Keyword(s):  
Human Leukocyte Antigen ◽  
Cell Viability ◽  
Cord Blood ◽  
Red Blood Cells ◽  
Umbilical Cord ◽  
Umbilical Cord Blood ◽  
Blood Cells ◽  
Human Leukocyte ◽  
Hla Typing ◽  
Leukocyte Antigen

Hematopoietic progenitor cell (HPC) transplantation is a treatment option for malignant and nonmalignant diseases. Umbilical cord blood (UCB) is an important HPC source, mainly for pediatric patients. It has been demonstrated that human leukocyte antigen (HLA) matching and cell dose are the most important features impacting clinical outcomes. However, UCB matching is performed using low resolution HLA typing and it has been demonstrated that the unnoticed mismatches negatively impact the transplant. Since we found differences in CD34+ viability after thawing of UCB units matched for two different patients (p = 0.05), we presumed a possible association between CD34+ cell viability and HLA. We performed a multivariate linear model (n = 67), comprising pre-cryopreservation variables and high resolution HLA genotypes separately. We found that pre-cryopreservation red blood cells (RBC), granulocytes, and viable CD34+ cell count significantly impacted CD34+ viability after thawing, along with HLA-B or -C (R2 = 0.95, p = 0.01; R2 = 0.56, p = 0.007, respectively). Although HLA-B*40:02 may have a negative impact on CD34+ cell viability, RBC depletion significantly improves it.

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