EPIBLASTER 1.0 – Two-locus Epistasis Detection Strategy using GPU

EPIBLASTER 1.0

:: DESCRIPTION

The purpose of EPIBLASTER is to compute the differences of correlation coefficients between Controls and Cases as a mean to isolate for significant SNPs Interactions using gpuCor function of the gputools package on a CUDA enabled graphic card

::DEVELOPER

Machine Learning and Computational Biology Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
R package
gputools R package
CUDA developer driver and Toolkit
GENABLE

:: DOWNLOAD

EPIBLASTER

:: MORE INFORMATION

Citation

Eur J Hum Genet. 2011 Apr;19(4):465-71. doi: 10.1038/ejhg.2010.196.
EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units.
Kam-Thong T, Czamara D, Tsuda K, Borgwardt K, Lewis CM, Erhardt-Lehmann A, Hemmer B, Rieckmann P, Daake M, Weber F, Wolf C, Ziegler A, Pütz B, Holsboer F, Sch?lkopf B, Müller-Myhsok B.

mendel-gpu – GPU enabled Haplotying and Genotype Imputation

mendel-gpu

:: DESCRIPTION

mendel-gpu uses OpenCL kernels to rapidly impute genotypes using linkage disequilibrium patterns in unrelated subjects. It is appropriate for resequencing data.

::DEVELOPER

Gary K. Chen, Ph.D.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
C++ compiler

:: DOWNLOAD

mendel-gpu

:: MORE INFORMATION

Citation

Bioinformatics. 2012 Nov 15;28(22):2979-80. doi: 10.1093/bioinformatics/bts536. Epub 2012 Sep 5.
Mendel-GPU: haplotyping and genotype imputation on graphics processing units.
Chen GK1, Wang K, Stram AH, Sobel EM, Lange K.

NMF-mGPU 1.0 – Non-negative Matrix Factorization on multi-GPU Systems

NMF-mGPU 1.0

:: DESCRIPTION

NMF-mGPU implements the Non-negative Matrix Factorization (NMF) algorithm by making use of Graphics Processing Units (GPUs).

::DEVELOPER

Biocomputing Unit – CNB

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
CUDA Toolkit and CUDA Driver:

:: DOWNLOAD

NMF-mGPU

:: MORE INFORMATION

Citation:

NMF-mGPU: non-negative matrix factorization on multi-GPU systems.
Mejía-Roa E, Tabas-Madrid D, Setoain J, García C, Tirado F, Pascual-Montano A.
BMC Bioinformatics. 2015 Feb 13;16:43. doi: 10.1186/s12859-015-0485-4.

GPUmotif – GPU-accelerated ultra-fast and Energy-efficient Motif analysis

GPUmotif

:: DESCRIPTION

GPUmotif is a GPU-accelerated ultra-fast and energy-efficient motif analysis program.

::DEVELOPER

GPUmotif team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
NVIDIA graphics cards

:: DOWNLOAD

GPUmotif

:: MORE INFORMATION

Citation

GPUmotif: an ultra-fast and energy-efficient motif analysis program using graphics processing units.
Zandevakili P, Hu M, Qin Z.
PLoS One. 2012;7(5):e36865. doi: 10.1371/journal.pone.0036865.

GPU-FAN 1.0 – GPU-based Fast Analysis of Networks

GPU-FAN 1.0

:: DESCRIPTION

GPU-FAN is a project to enable fast network analysis on GPUs. It provides a significant performance improvement for centrality computation in large-scale networks.

::DEVELOPER

the Zhang Lab

:: SCREENSHOTS

n/a

:: REQUIREMENTS

Linux

:: DOWNLOAD

GPU-FAN

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2011 May 12;12:149. doi: 10.1186/1471-2105-12-149.
Fast network centrality analysis using GPUs.
Shi Z1, Zhang B.

gCUP 1.0 – Rapid GPU-based HIV-1 Coreceptor Usage Prediction for Next-Generation Sequencing

gCUP 1.0

:: DESCRIPTION

gCUP: Rapid GPU-based HIV-1 Coreceptor Usage Prediction for Next-Generation Sequencing

::DEVELOPER

Heider Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows / Linux / Mac OsX
R
NVIDIA

:: DOWNLOAD

gCUP

:: MORE INFORMATION

Citation

gCUP: Rapid GPU-based HIV-1 Co-Receptor Usage Prediction for Next-Generation Sequencing.
Olejnik M, Steuwer M, Gorlatch S, Heider D.
Bioinformatics. 2014 Aug 13. pii: btu535.

MetaBinG 0.4 / MetaBinG2 – ultra-fast Metagenomic Sequence Classification system using GPUs

MetaBinG 0.4 / MetaBinG2

:: DESCRIPTION

MetaBinG is an ultra-fast metagenomic sequence classification system using graphic processing units (GPUs).

MetaBinG2 is a fast and accurate metagenomic sequence classification system for samples with many unknown organisms

::DEVELOPER

Dr. Chaochun Wei

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

MetaBinG / MetaBinG2

:: MORE INFORMATION

Citation:

Biol Direct. 2018 Aug 22;13(1):15. doi: 10.1186/s13062-018-0220-y.
MetaBinG2: a fast and accurate metagenomic sequence classification system for samples with many unknown organisms.
Qiao Y, Jia B, Hu Z, Sun C, Xiang Y, Wei C.

PLoS One. 2011;6(11):e25353. doi: 10.1371/journal.pone.0025353. Epub 2011 Nov 23.
MetaBinG: using GPUs to accelerate metagenomic sequence classification.
Jia P1, Xuan L, Liu L, Wei C.

TSP/TST (GPU) 1.1 – Top-Scoring Pair and Top-Scoring Triple on the GPU

TSP/TST (GPU) 1.1

:: DESCRIPTION

TSP/TST (GPU) (the top-scoring pair and top-scoring triplet) is the relative expression classification algorithms for the graphics processing unit (GPU).

::DEVELOPER

The Hood-Price Lab for Systems Biomedicine

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux/ WIndows/ MacOsX
Matlab

:: DOWNLOAD

TSP/TST (GPU)

:: MORE INFORMATION

QuickProbs 1.02 – Multiple Sequence Alignment designed especially for GPU

QuickProbs 1.02

:: DESCRIPTION

QuickProbs is a fast multiple sequence alignment algorithm designed for graphics processors.

::DEVELOPER

REFRESH Bioinformatics Group

:: SCREENSHOTS

n/a

:: REQUIREMENTS

Linux / Windows

:: DOWNLOAD

QuickProbs

:: MORE INFORMATION

Citation

PLoS One. 2014 Feb 25;9(2):e88901. doi: 10.1371/journal.pone.0088901. eCollection 2014.
QuickProbs–a fast multiple sequence alignment algorithm designed for graphics processors.
Gudyś A, Deorowicz S

cuda-sim 0.08 – CUDA GPU accelerated Biochemical Network Simulation

cuda-sim 0.08

:: DESCRIPTION

cuda-sim is a python package providing CUDA GPU accelerated biochemical network simulation

::DEVELOPER

Yanxiang Zhou and Chris Barnes, the Theoretical Systems Biology group at Imperial College London

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux/ WIndows/ MacOsX
Python

:: DOWNLOAD

cuda-sim

:: MORE INFORMATION

Citation

“GPU accelerated biochemical network simulation.”
Y. Zhou, J. Liepe, X. Sheng, M.P.H. Stumpf, C. Barnes
Bioinformatics. 2011 Mar 15;27(6):874-6.