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PyHLA: tests for the association between HLA alleles and diseases

Overview of attention for article published in BMC Bioinformatics, February 2017
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Title
PyHLA: tests for the association between HLA alleles and diseases
Published in
BMC Bioinformatics, February 2017
DOI 10.1186/s12859-017-1496-0
Pubmed ID
Authors

Yanhui Fan, You-Qiang Song

Abstract

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. 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. 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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 67 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 21%
Researcher 9 13%
Student > Master 9 13%
Student > Bachelor 6 9%
Other 6 9%
Other 8 12%
Unknown 15 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 21 31%
Immunology and Microbiology 7 10%
Agricultural and Biological Sciences 6 9%
Medicine and Dentistry 5 7%
Computer Science 3 4%
Other 9 13%
Unknown 16 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 February 2017.
All research outputs
#21,264,673
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#7,049
of 7,454 outputs
Outputs of similar age
#361,569
of 424,541 outputs
Outputs of similar age from BMC Bioinformatics
#125
of 149 outputs
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