PMID- 31173059
OWN - NLM
STAT- MEDLINE
DCOM- 20200828
LR  - 20200828
IS  - 1367-4811 (Electronic)
IS  - 1367-4803 (Print)
IS  - 1367-4803 (Linking)
VI  - 36
IP  - 1
DP  - 2020 Jan 1
TI  - arcasHLA: high-resolution HLA typing from RNAseq.
PG  - 33-40
LID - 10.1093/bioinformatics/btz474 [doi]
AB  - MOTIVATION: The human leukocyte antigen (HLA) locus plays a critical role in 
      tissue compatibility and regulates the host response to many diseases, including 
      cancers and autoimmune di3orders. Recent improvements in the quality and 
      accessibility of next-generation sequencing have made HLA typing from standard 
      short-read data practical. However, this task remains challenging given the high 
      level of polymorphism and homology between HLA genes. HLA typing from RNA 
      sequencing is further complicated by post-transcriptional modifications and bias 
      due to amplification. RESULTS: Here, we present arcasHLA: a fast and accurate in 
      silico tool that infers HLA genotypes from RNA-sequencing data. Our tool 
      outperforms established tools on the gold-standard benchmark dataset for HLA 
      typing in terms of both accuracy and speed, with an accuracy rate of 100% at 
      two-field resolution for Class I genes, and over 99.7% for Class II. Furthermore, 
      we evaluate the performance of our tool on a new biological dataset of 447 
      single-end total RNA samples from nasopharyngeal swabs, and establish the 
      applicability of arcasHLA in metatranscriptome studies. AVAILABILITY AND 
      IMPLEMENTATION: arcasHLA is available at https://github.com/RabadanLab/arcasHLA. 
      SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
      online.
CI  - (c) The Author(s) 2019. Published by Oxford University Press.
FAU - Orenbuch, Rose
AU  - Orenbuch R
AD  - Department of Systems Biology, Columbia University, New York, NY 10032, USA.
AD  - Department of Computer Science, Columbia University, New York, NY 10027, USA.
FAU - Filip, Ioan
AU  - Filip I
AD  - Department of Systems Biology, Columbia University, New York, NY 10032, USA.
FAU - Comito, Devon
AU  - Comito D
AD  - Department of Environmental Health Sciences, Mailman School of Public Health, 
      Columbia University, New York, NY 10032, USA.
FAU - Shaman, Jeffrey
AU  - Shaman J
AD  - Department of Environmental Health Sciences, Mailman School of Public Health, 
      Columbia University, New York, NY 10032, USA.
FAU - Pe'er, Itsik
AU  - Pe'er I
AD  - Department of Computer Science, Columbia University, New York, NY 10027, USA.
FAU - Rabadan, Raul
AU  - Rabadan R
AD  - Department of Systems Biology, Columbia University, New York, NY 10032, USA.
LA  - eng
GR  - R01 GM117591/GM/NIGMS NIH HHS/United States
GR  - U54 CA193313/CA/NCI NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
PT  - Research Support, U.S. Gov't, Non-P.H.S.
PL  - England
TA  - Bioinformatics
JT  - Bioinformatics (Oxford, England)
JID - 9808944
RN  - 0 (HLA Antigens)
RN  - 0 (Histocompatibility Antigens Class I)
SB  - IM
MH  - Alleles
MH  - *HLA Antigens/genetics
MH  - High-Throughput Nucleotide Sequencing
MH  - *Histocompatibility Antigens Class I/classification
MH  - Histocompatibility Testing/methods
MH  - Humans
MH  - *Sequence Analysis, RNA/methods
PMC - PMC6956775
EDAT- 2019/06/08 06:00
MHDA- 2020/08/29 06:00
PMCR- 2019/06/07
CRDT- 2019/06/08 06:00
PHST- 2019/02/11 00:00 [received]
PHST- 2019/05/13 00:00 [revised]
PHST- 2019/06/03 00:00 [accepted]
PHST- 2019/06/08 06:00 [pubmed]
PHST- 2020/08/29 06:00 [medline]
PHST- 2019/06/08 06:00 [entrez]
PHST- 2019/06/07 00:00 [pmc-release]
AID - 5512361 [pii]
AID - btz474 [pii]
AID - 10.1093/bioinformatics/btz474 [doi]
PST - ppublish
SO  - Bioinformatics. 2020 Jan 1;36(1):33-40. doi: 10.1093/bioinformatics/btz474.