PMID- 27802932
OWN - NLM
STAT- MEDLINE
DCOM- 20190228
LR  - 20191210
IS  - 1477-4054 (Electronic)
IS  - 1467-5463 (Print)
IS  - 1467-5463 (Linking)
VI  - 19
IP  - 2
DP  - 2018 Mar 1
TI  - Evaluation of computational programs to predict HLA genotypes from genomic 
      sequencing data.
PG  - 179-187
LID - 10.1093/bib/bbw097 [doi]
AB  - MOTIVATION: Despite being essential for numerous clinical and research 
      applications, high-resolution human leukocyte antigen (HLA) typing remains 
      challenging and laboratory tests are also time-consuming and labour intensive. 
      With next-generation sequencing data becoming widely accessible, on-demand in 
      silico HLA typing offers an economical and efficient alternative. RESULTS: In 
      this study we evaluate the HLA typing accuracy and efficiency of five 
      computational HLA typing methods by comparing their predictions against a curated 
      set of > 1000 published polymerase chain reaction-derived HLA genotypes on three 
      different data sets (whole genome sequencing, whole exome sequencing and 
      transcriptomic sequencing data). The highest accuracy at clinically relevant 
      resolution (four digits) we observe is 81% on RNAseq data by PHLAT and 99% 
      accuracy by OptiType when limited to Class I genes only. We also observed 
      variability between the tools for resource consumption, with runtime ranging from 
      an average of 5 h (HLAminer) to 7 min (seq2HLA) and memory from 12.8 GB 
      (HLA-VBSeq) to 0.46 GB (HLAminer) per sample. While a minimal coverage is 
      required, other factors also determine prediction accuracy and the results 
      between tools do not correlate well. Therefore, by combining tools, there is the 
      potential to develop a highly accurate ensemble method that is able to deliver 
      fast, economical HLA typing from existing sequencing data.
FAU - Bauer, Denis C
AU  - Bauer DC
AD  - CSIRO, Sydney, Australia.
FAU - Zadoorian, Armella
AU  - Zadoorian A
AD  - CSIRO, Sydney, Australia.
AD  - School of Biotechnology and Biomolecular Sciences, University of New South Wales, 
      Sydney, Australia.
FAU - Wilson, Laurence O W
AU  - Wilson LOW
AD  - CSIRO, Sydney, Australia.
CN  - Melbourne Genomics Health Alliance
FAU - Thorne, Natalie P
AU  - Thorne NP
AD  - Murdoch Childrens Research Institute, Royal Children's Hospital, Parkville, 
      Australia.
AD  - Department of Medical Biology, The University of Melbourne, Parkville, Australia.
AD  - Melbourne Genomics Health Alliance, Parkville, Australia.
AD  - Walter and Eliza Hall Institute, Parkville, Australia.
LA  - eng
PT  - Evaluation Study
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - England
TA  - Brief Bioinform
JT  - Briefings in bioinformatics
JID - 100912837
RN  - 0 (HLA Antigens)
SB  - IM
MH  - *Algorithms
MH  - Computational Biology/methods
MH  - Exome
MH  - Genotype
MH  - HLA Antigens/*genetics
MH  - Histocompatibility Testing/*methods
MH  - Humans
MH  - Sequence Analysis, DNA/*methods
PMC - PMC6019030
EDAT- 2016/11/03 06:00
MHDA- 2019/03/01 06:00
PMCR- 2016/10/31
CRDT- 2016/11/03 06:00
PHST- 2016/07/04 00:00 [received]
PHST- 2016/11/03 06:00 [pubmed]
PHST- 2019/03/01 06:00 [medline]
PHST- 2016/11/03 06:00 [entrez]
PHST- 2016/10/31 00:00 [pmc-release]
AID - 2571395 [pii]
AID - bbw097 [pii]
AID - 10.1093/bib/bbw097 [doi]
PST - ppublish
SO  - Brief Bioinform. 2018 Mar 1;19(2):179-187. doi: 10.1093/bib/bbw097.