Background: Human leukocyte antigen (HLA) genes are critical genes involved in important bio medical aspects,\nincluding organ transplantation, autoimmune diseases and infectious diseases. The gene family contains the most\npolymorphic genes in humans and the difference between two alleles is only a single base pair substitution in many\ncases. The next generation sequencing (NGS) technologies could be used for high throughput HLA typing but in silico\nmethods are still needed to correctly assign the alleles of a sample. Computer scientists have developed such\nmethods for various NGS platforms, such as Illumina, Roche 454 and Ion Torrent, based on the characteristics of the\nreads they generate. However, the method for PacBio reads was less addressed, probably owing to its high error rates.\nThe PacBio system has the longest read length among available NGS platforms, and therefore is the only platform\ncapable of having exon 2 and exon 3 of HLA genes on the same read to unequivocally solve the ambiguity problem\ncaused by the ââ?¬Å?phasingââ?¬Â issue.\nResults: We proposed a new method Bayes Typing1 to assign HLA alleles for Pac Bio circular consensus sequencing\nreads using Bayesââ?¬â?¢ theorem. The method was applied to simulated data of the three loci HLA-A, HLA-B and HLA-DRB1.\nThe experimental results showed its capability to tolerate the disturbance of sequencing errors and external noise\nreads.\nConclusions: The Bayes Typing1 method could overcome the problems of HLA typing using PacBio reads, which\nmostly arise from sequencing errors of Pac Bio reads and the divergence of HLA genes, to some extent.
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