BioinformaticsBioinformaticsPeer-reviewed journal article
HLA*PRG:LA - HLA typing from linearly projected graph alignments
Alexander DiltheyiD, Alexander J. MentzeriD, Raphaël CarapitoiD, Clare CutlandiD, Nezih CerebiD, Shabir Ahmed MadhiiD, Arang RhieiD, Sergey KoreniD, Seiamak BahramiD, Gil McVeaniD, Adam M. PhillippyiD
Abstract
SummaryHLA*PRG: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.\n\nAvailability and ImplementationHLA*PRG:LA is implemented in C++ and Perl and freely available from https://github.com/DiltheyLab/HLA-PRG-LA (GPL v3).\n\nContactalexander.dilthey@med.uni-duesseldorf.de\n\nSupplementary informationSupplementary data are available online.
- assay: whole-genome sequencing
- assay: long-read sequencing
- research focus: variant calling
- approach: graph neural network
- approach: statistical model
- Citations
- 157
- FWCI
- 4.80
- Field percentile
- top 3%
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