QTXNetwork
Multi-GPU Accelerated Software for Analyses of Epistasis and Environment Interaction of Complex Traits Based on Omics Genotypes
Futao Zhang, Zhihong Zhu, Xiaoran Tong, Zhixiang Zhu, Ting Qi , Haiming Xu and Jun Zhu*
Institute of Bioinformatics, Zhejiang University, Hangzhou, China
QTXNetwork is a GPU computation software for
linkage and association analyses of epistasis and environment interaction of
complex traits based on omics genotypes. It contains four functional modules:
quantitative trait locus (QTL) for linkage analysis, quantitative trait SNP (QTS)
for genome-wide association analysis, quantitative trait
transcript/protein/metabolite (QTT/P/M) for transcriptome, proteome, metabolome
association analysis, and GMDR for data filtering which will be used in
Genome-Wide Association Studies (GWAS). By using the massive parallel nature of
multi-GPUs, QTXNetwork can perform association analyses on large-scale omics
data for complex traits.
QTXNetwork requires that your system be equipped with a NVIDIA CUDA-compatible device. The NVIDIA drivers must be version CUDA 5.5 (or greater) for your systems.
If Visual Studio 2010 is not installed in your Windows system, you should install visual c++ 2010 redistributable package to run QTXNetwork. For x64 systems, download and install vcredist_x64.exe . For x86 system, download and install vcredist_x86.exe .
User Manual: Latest release (04/07/2014) QTXNetwork Manual.pdf
Software for Windows: Current Version v1.0 (03/22/2016) QTXNetwork_Win.rar
Software for RHEL6.5: Current Version v1.0 (04/07/2014) QTXNetwork_RHEL6.5_v1.0.zip
Note: If you want to use terminal to run this software, the executable files are located in the directory "exe" of the package.
Futao Zhang, Zhihong Zhu , Xiaoran Tong, Zhixiang Zhu, Ting Qi, and Jun Zhu* (2015) Mixed Linear Model Approaches of Association Mapping for Complex Traits Based on Omics Variants. Scientific Reports 5, Article Number: 10298
If you have any question or comment with our software, please contact to Prof. jzhu@zju.edu.cn.
This research
is supported in part by grants from:
1. The National Basic Research Program of China (973) (2011CB109306,
2010CB126006)
2. National Natural Science Foundation of China (30470916)
3. Microsoft Research Asia, UR project
This research
is supported in part by technique from:
1. Software Analytics Group, Microsoft Research Asia
2. HPC Developer Technology, Greater China - NVIDIA