PMID- 28166716
OWN - NLM
STAT- MEDLINE
DCOM- 20170825
LR  - 20181202
IS  - 1471-2105 (Electronic)
IS  - 1471-2105 (Linking)
VI  - 18
IP  - 1
DP  - 2017 Feb 6
TI  - PyHLA: tests for the association between HLA alleles and diseases.
PG  - 90
LID - 10.1186/s12859-017-1496-0 [doi]
LID - 90
AB  - BACKGROUND: Recently, several tools have been designed for human leukocyte 
      antigen (HLA) typing using single nucleotide polymorphism (SNP) array and 
      next-generation sequencing (NGS) data. These tools provide high-throughput and 
      cost-effective approaches for identifying HLA types. Therefore, tools for 
      downstream association analysis are highly desirable. Although several tools have 
      been designed for multi-allelic marker association analysis, they were designed 
      only for microsatellite markers and do not scale well with increasing data 
      volumes, or they were designed for large-scale data but provided a limited number 
      of tests. RESULTS: We have developed a Python package called PyHLA, which 
      implements several methods for HLA association analysis, to fill the gap. PyHLA 
      is a tailor-made, easy to use, and flexible tool designed specifically for the 
      association analysis of the HLA types imputed from genome-wide genotyping and NGS 
      data. PyHLA provides functions for association analysis, zygosity tests, and 
      interaction tests between HLA alleles and diseases. Monte Carlo permutation and 
      several methods for multiple testing corrections have also been implemented. 
      CONCLUSIONS: PyHLA provides a convenient and powerful tool for HLA analysis. 
      Existing methods have been integrated and desired methods have been added in 
      PyHLA. Furthermore, PyHLA is applicable to small and large sample sizes and can 
      finish the analysis in a timely manner on a personal computer with different 
      platforms. PyHLA is implemented in Python. PyHLA is a free, open source software 
      distributed under the GPLv2 license. The source code, tutorial, and examples are 
      available at https://github.com/felixfan/PyHLA.
FAU - Fan, Yanhui
AU  - Fan Y
AD  - School of Biomedical Sciences, The University of Hong Kong, 21 Sassoon Road, 
      Pokfulam, Hong Kong, Hong Kong.
AD  - Centre for Genomic Sciences, The University of Hong Kong, 5 Sassoon Road, 
      Pokfulam, Hong Kong, Hong Kong.
AD  - Department of Cancer Genomics, LemonData Biotech (Shenzhen) Ltd., Shenzhen, 
      China.
FAU - Song, You-Qiang
AU  - Song YQ
AD  - School of Biomedical Sciences, The University of Hong Kong, 21 Sassoon Road, 
      Pokfulam, Hong Kong, Hong Kong. songy@hku.hk.
AD  - Centre for Genomic Sciences, The University of Hong Kong, 5 Sassoon Road, 
      Pokfulam, Hong Kong, Hong Kong. songy@hku.hk.
LA  - eng
PT  - Journal Article
DEP - 20170206
PL  - England
TA  - BMC Bioinformatics
JT  - BMC bioinformatics
JID - 100965194
RN  - 0 (HLA Antigens)
SB  - IM
MH  - *Alleles
MH  - Computational Biology/*methods
MH  - Genome, Human
MH  - Genotyping Techniques
MH  - HLA Antigens/*genetics
MH  - High-Throughput Nucleotide Sequencing
MH  - Humans
MH  - Linear Models
MH  - Logistic Models
MH  - Microsatellite Repeats
MH  - Models, Theoretical
MH  - Polymorphism, Single Nucleotide
MH  - *Software
PMC - PMC5292802
OTO - NOTNLM
OT  - Association
OT  - HLA
OT  - Interaction
OT  - Multi-allelic
EDAT- 2017/02/09 06:00
MHDA- 2017/08/26 06:00
PMCR- 2017/02/06
CRDT- 2017/02/08 06:00
PHST- 2016/07/26 00:00 [received]
PHST- 2017/01/27 00:00 [accepted]
PHST- 2017/02/08 06:00 [entrez]
PHST- 2017/02/09 06:00 [pubmed]
PHST- 2017/08/26 06:00 [medline]
PHST- 2017/02/06 00:00 [pmc-release]
AID - 10.1186/s12859-017-1496-0 [pii]
AID - 1496 [pii]
AID - 10.1186/s12859-017-1496-0 [doi]
PST - epublish
SO  - BMC Bioinformatics. 2017 Feb 6;18(1):90. doi: 10.1186/s12859-017-1496-0.