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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz235","type":"journal-article","created":{"date-parts":[[2019,4,2]],"date-time":"2019-04-02T07:25:11Z","timestamp":1554189911000},"page":"4394-4396","source":"Crossref","is-referenced-by-count":148,"title":["HLA*LA\u2014HLA typing from linearly projected graph 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