In this webinar, the presenters describe a targeted sequencing workflow that combines Roche NimbleGen’s SeqCap EZ enrichment technology with PacBio’ SMRT Sequencing to provide a more comprehensive view of variants…
In this ASHG 2016 poster video, Martin Pollard from the Wellcome Trust Sanger Institute and the University of Cambridge describes an ambitious project to better represent natural variation in the…
In a talk at AGBT 2017, Histogenetics CEO Nezih Cereb reported on how SMRT Sequencing is allowing his team to produce full-length, phased sequences for HLA alleles, which are important…
This tutorial provides an overview of the Long Amplicon Analysis (LAA) application. The LAA algorithm generates highly accurate, phased and full-length consensus sequences from long amplicons. Applications of LAA include…
Human MHC class I genes HLA-A, -B, -C, and class II genes HLA -DR, -DQ, and -DP play a critical role in the immune system as primary factors responsible for…
PacBio SMRT Sequencing is fast changing the genomics space with its long reads and high consensus sequence accuracy, providing the most comprehensive view of the genome and transcriptome. In this…
Studying microbial genomics and infectious disease? Learn how the PacBio Sequel II System can help advance your research, with first-hand perspectives from scientists who are investigating SARS-CoV-2 and COVID-19. In…
In this Labroots webinar, Meredith Ashby, Director of Microbial Genomics at PacBio, describes the utility of highly accurate long-read sequencing, known as HiFi sequencing, to understand the SARs-CoV-2 viral genome….
Highly accurate long reads, known as HiFi reads, are a new tool in scientists’ sequencing toolbox. Hear PacBio users share how they are using HiFi reads to explore the genomes,…
Pacific Biosciences’ SMRT sequencing method was used to extend the sequence of HLA-A*02:13. © 2019 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
HLA*LA implements a new graph alignment model for HLA type inference, based on the projection of linear alignments onto a variation graph. It enables accurate HLA type inference from whole-genome (99% accuracy) and whole-exome (93% accuracy) Illumina data; from long-read Oxford Nanopore and Pacific Biosciences data (98% accuracy for whole-genome and targeted data); and from genome assemblies. Computational requirements for a typical sample vary between 0.7 and 14 CPU hours per sample.HLA*LA is implemented in C?++ and Perl and freely available as a bioconda package or from https://github.com/DiltheyLab/HLA-LA (GPL v3).Supplementary data are available online. © The Author(s) 2019. Published by Oxford University Press.
Next generation DNA sequencing is used to determine the HLA-A, -B, -C, -DRB1, -DRB3/4/5, and -DQB1 assignments of 1009 unrelated volunteers for the unrelated donor registry in The Netherlands. The analysis characterizes all HLA exons and introns for class I alleles; at least exons 2 to 3 for HLA-DRB1; and exons 2 to 6 for HLA-DQB1. Of the distinct alleles present, there are 229 class I and 71 class II; 36 of these alleles are novel. The majority (approximately 98%) of the cumulative allele frequency at each locus is contributed by alleles that appear three or more times. Alleles encoding protein variation outside of the antigen recognition domains are 0.6% of the class I assignments and 5.3% of the class II assignments. © 2019 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Extended molecular characterization of HLA genes in the IHWG reference B-lymphoblastoid cell lines (B-LCLs) was one of the major goals for the 17th International HLA and Immunogenetics Workshop (IHIW). Although reference B-LCLs have been examined extensively in previous workshops complete high-resolution typing was not completed for all the classical class I and class II HLA genes. To address this, we conducted a single-blind study where select panels of B-LCL genomic DNA samples were distributed to multiple laboratories for HLA genotyping by next-generation sequencing methods. Identical cell panels comprised of 24 and 346 samples were distributed and typed by at least four laboratories in order to derive accurate consensus HLA genotypes. Overall concordance rates calculated at both 2- and 4-field allele-level resolutions ranged from 90.4% to 100%. Concordance for the class I genes ranged from 91.7 to 100%, whereas concordance for class II genes was variable; the lowest observed at HLA-DRB3 (84.2%). At the maximum allele-resolution 78 B-LCLs were defined as homozygous for all 11 loci. We identified 11 novel exon polymorphisms in the entire cell panel. A comparison of the B-LCLs NGS HLA genotypes with the HLA genotypes catalogued in the IPD-IMGT/HLA Database Cell Repository, revealed an overall allele match at 68.4%. Typing discrepancies between the two datasets were mostly due to the lower-resolution historical typing methods resulting in incomplete HLA genotypes for some samples listed in the IPD-IMGT/HLA Database Cell Repository. Our approach of multiple-laboratory NGS HLA typing of the B-LCLs has provided accurate genotyping data. The data generated by the tremendous collaborative efforts of the 17th IHIW participants is useful for updating the current cell and sequence databases and will be a valuable resource for future studies.Copyright © 2019. Published by Elsevier Inc.
Our understanding of sequence variation in the HLA-DPB1 gene is largely restricted to the hypervariable antigen recognition domain (ARD) encoded by exon 2. Here, we employed a redundant sequencing strategy combining long-read and short-read data to accurately phase and characterise in full length the majority of common and well-documented (CWD) DPB1 alleles as well as alleles with an observed frequency of at least 0.0006% in our predominantly European sample set. We generated 664 DPB1 sequences, comprising 279 distinct allelic variants. This allows us to present the, to date, most comprehensive analysis of the nature and extent of DPB1 sequence variation. The full-length sequence analysis revealed the existence of two highly diverged allele clades. These clades correlate with the rs9277534 A???G variant, a known expression marker located in the 3′-UTR. The two clades are fully differentiated by 174 fixed polymorphisms throughout a 3.6?kb stretch at the 3′-end of DPB1. The region upstream of this differentiation zone is characterised by increasingly shared variation between the clades. The low-expression A clade comprises 59% of the distinct allelic sequences including the three by far most frequent DPB1 alleles, DPB1*04:01, DPB1*02:01 and DPB1*04:02. Alleles in the A clade show reduced nucleotide diversity with an excess of rare variants when compared to the high-expression G clade. This pattern is consistent with a scenario of recent proliferation of A-clade alleles. The full-length characterisation of all but the most rare DPB1 alleles will benefit the application of NGS for DPB1 genotyping and provides a helpful framework for a deeper understanding of high- and low-expression alleles and their implications in the context of unrelated haematopoietic stem-cell transplantation.Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.
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