BALSA 2.0 – GPU Accelerated Integrated WGS and WES Secondary Analysis

BALSA 2.0

:: DESCRIPTION

BALSA is an integrated solution for the secondary analysis of next generation sequencing data; it exploits the computational power of GPU and an intricate memory management to give a fast and accurate analysis.

::DEVELOPER

L3 Bioinformatics Limited

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • GPU

:: DOWNLOAD

 BALSA

:: MORE INFORMATION

Citation

BALSA: integrated secondary analysis for whole-genome and whole-exome sequencing, accelerated by GPU.
Luo R, Wong YL, Law WC, Lee LK, Cheung J, Liu CM, Lam TW.
PeerJ. 2014 Jun 3;2:e421. doi: 10.7717/peerj.421.

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

GPU-BLAST 1.1 – Sequence Alignment with GPUs

GPU-BLAST 1.1

:: DESCRIPTION

GPU-BLAST is an accelerated version of the popular NCBI-BLAST using a general-purpose graphics processing unit (GPU). The implementation is based on the source code of NCBI-BLAST, thus maintaining the same input and output interface while producing identical results. In comparison to the sequential NCBI-BLAST, the speedups achieved by GPU-BLAST range mostly between 3 and 4.

::DEVELOPER

Panagiotis D. Vouzis and Nikolaos V. Sahinidis

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C++
  • CUDA

:: DOWNLOAD

GPU-BLAST

:: MORE INFORMATION

Citation

Panagiotis D. Vouzis and Nikolaos V. Sahinidis,
GPU-BLAST: using graphics processors to accelerate protein sequence alignment,”
Bioinformatics, 2011 Vol. 27, no. 2, pages 182-188,