HLA*LA-HLA typing from linearly projected graph alignments

Alexander T Dilthey; Alexander J Mentzer; Raphael Carapito; Clare Cutland; Nezih Cereb; Shabir A Madhi; Arang Rhie; Sergey Koren; Seiamak Bahram; Gil McVean; Adam M Phillippy

doi:10.1093/bioinformatics/btz235

HLA*LA-HLA typing from linearly projected graph alignments

Bioinformatics. 2019 Nov 1;35(21):4394-4396. doi: 10.1093/bioinformatics/btz235.

Authors

Alexander T Dilthey^{1

2

3}, Alexander J Mentzer^{3

4}, Raphael Carapito^{5

6}, Clare Cutland^{7

8}, Nezih Cereb⁹, Shabir A Madhi^{7

8}, Arang Rhie², Sergey Koren², Seiamak Bahram^{5

6}, Gil McVean^{3

4}, Adam M Phillippy²

Affiliations

¹ Institute of Medical Microbiology, University Hospital of Dusseldorf, Dusseldorf, North Rhine-Westphalia, Germany.
² Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, Bethesda, MD, USA.
³ Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
⁴ Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
⁵ Laboratoire d'ImmunoRhumatologie Moléculaire, Plateforme GENOMAX, INSERM UMR_S 1109, LabEx TRANSPLANTEX, Fédération de Médecine Translationnelle de Strasbourg (FMTS), Faculté de Médecine, Université de Strasbourg, France.
⁶ Service d'Immunologie Biologique, Nouvel Hôpital Civil, Strasbourg, France.
⁷ Medical Research Council: Respiratory and Meningeal Pathogens Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
⁸ Department of Science/National Research Foundation: Vaccine Preventable Diseases, Faculty of Health Science, University of the Witwatersrand, Johannesburg, South Africa.
⁹ Histogenetics, Ossining, NY, USA.

Abstract

Summary: HLA*LA implements a new graph alignment model for human leukocyte antigen (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.

Availability and implementation: 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 information: Supplementary data are available at Bioinformatics online.

Publication types

Research Support, N.I.H., Intramural
Research Support, Non-U.S. Gov't

MeSH terms

Genome
High-Throughput Nucleotide Sequencing*
Histocompatibility Testing
Humans
Sequence Analysis, DNA
Software*

Abstract

Publication types

MeSH terms

Grants and funding