title: A fault-tolerant method for HLA typing with PacBio data.
abstract: BACKGROUND
  Human leukocyte antigen (HLA) genes are critical genes involved in important biomedical aspects, including organ transplantation, autoimmune diseases and infectious diseases. The gene family contains the most polymorphic genes in humans and the difference between two alleles is only a single base pair substitution in many cases. The next generation sequencing (NGS) technologies could be used for high throughput HLA typing but in silico methods are still needed to correctly assign the alleles of a sample. Computer scientists have developed such methods for various NGS platforms, such as Illumina, Roche 454 and Ion Torrent, based on the characteristics of the reads they generate. However, the method for PacBio reads was less addressed, probably owing to its high error rates. The PacBio system has the longest read length among available NGS platforms, and therefore is the only platform capable of having exon 2 and exon 3 of HLA genes on the same read to unequivocally solve the ambiguity problem caused by the "phasing" issue.
  
  
  RESULTS
  We proposed a new method BayesTyping1 to assign HLA alleles for PacBio circular consensus sequencing reads using Bayes' theorem. The method was applied to simulated data of the three loci HLA-A, HLA-B and HLA-DRB1. The experimental results showed its capability to tolerate the disturbance of sequencing errors and external noise reads.
  
  
  CONCLUSIONS
  The BayesTyping1 method could overcome the problems of HLA typing using PacBio reads, which mostly arise from sequencing errors of PacBio reads and the divergence of HLA genes, to some extent.
authors.forename: Chia-Jung
authors.initials: CJ
authors.lastname: Chang
authors.forename: Pei-Lung
authors.initials: PL
authors.lastname: Chen
authors.forename: Wei-Shiung
authors.initials: WS
authors.lastname: Yang
authors.affiliation: Department of Computer Science and Information Engineering, National Taiwan University, No,1, Sec,4, Roosevelt Road, Taipei 10617, Taiwan. kmchao@csie.ntu.edu.tw.
authors.forename: Kun-Mao
authors.initials: KM
authors.lastname: Chao
mesh_terms: D000483:Alleles; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D005091:Exons / Q000235:genetics; D060005:Genotyping Techniques / Q000379:methods*; D006680:HLA Antigens / Q000235:genetics*; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D011110:Polymorphism, Genetic; D017422:Sequence Analysis, DNA
chemical_list: D006680:HLA Antigens