FastTagger 1.0 – Genome-Wide Tag SNP selection

FastTagger 1.0

:: DESCRIPTION

FastTagger is a software to calculate multi-marker tagging rules and select tag SNPs based on multi-marker LD. FastTagger uses several techniques to reduce running time and memory consumption.

::DEVELOPER

Limsoon Wong Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  •  Linux/ Windows
  • C++ Compiler

:: DOWNLOAD

 FastTagger

:: MORE INFORMATION

Citation:

Guimei Liu, Yue Wang, Limsoon Wong.
FastTagger: An Efficient Algorithm for Genome-Wide Tag SNP selection using multi-marker linkage disequilibrium
BMC Bioinformatics, 11:66, February 2010.

SNPRuler – Predictive Rule Inference for Epistatic Interaction Detection in Genome-wide Association studies

SNPRuler

:: DESCRIPTION

SNPRuler finds epistatic interactions in GWASs. SNPRuler first uses the predictive rule learning to narrow down possible interactions among SNPs and then captures true interactions using χ2 statistic test. The rule-based strategy in our non-parametric learning approach enables our new method to search for interaction patterns more efficiently than existing methods. We conduct extensive experiments on both simulated data and real genome-wide data. The experimental results demonstrate that our new learning method is a powerful tool in handling large-scale SNP data both in terms of speed and detection of potential interactions that were not identified before.

::DEVELOPER

Laboratory for Bioinformatics and Computational Biology, HKUST

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Java

:: DOWNLOAD

  SNPRuler

:: MORE INFORMATION

Citation:

Xiang Wan et al.
Predictive rule inference for epistatic interaction detection in genome-wide association studies
Bioinformatics (2010) 26 (1): 30-37.

SNPHarvester – Detect Epistatic Interactions in Genome-wide Association studies

SNPHarvester

:: DESCRIPTION

SNPHarvester detects SNP–SNP interactions in GWA studies. SNPHarvester creates multiple paths in which the visited SNP groups tend to be statistically associated with diseases, and then harvests those significant SNP groups which pass the statistical tests. It greatly reduces the number of SNPs. Consequently, existing tools can be directly used to detect epistatic interactions.

::DEVELOPER

Laboratory for Bioinformatics and Computational Biology, HKUST

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Java

:: DOWNLOAD

 SNPHarvester

:: MORE INFORMATION

Citation:

SNPHarvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies
Can Yang, Zengyou He, Xiang Wan, Qiang Yang, Hong Xue and Weichuan Yu
Bioinformatics (2009) 25 (4): 504-511.

MegaSNPHunter – Detect Disease Predisposition SNPs and High Level Interactions

MegaSNPHunter

:: DESCRIPTION

MegaSNPHunter takes case-control genotype data as input and produces a ranked list of multi-SNP interactions. In particular, the whole genome is first partitioned into multiple short subgenomes and a boosting tree classifier is built for each subgenomes based on multi-SNP interactions and then used to measure the importance of SNPs. The method keeps relatively more important SNPs from all subgenomes and let them compete with each other in the same way at the next level. The competition terminates when the number of selected SNPs is less than the size of a subgenome.

::DEVELOPER

Laboratory for Bioinformatics and Computational Biology, HKUST

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 MegaSNPHunter

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2009 Jan 9;10:13.
MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome wide association study.
Wan X, Yang C, Yang Q, Xue H, Tang NL, Yu W.

SNPAssociation – Detecting Two-locus Associations allowing for Interactions in GWAS

SNPAssociation

:: DESCRIPTION

SNPAssociation is a code for testing associations allowing for interactions in genome-wide association studies.

::DEVELOPER

Laboratory for Bioinformatics and Computational Biology, HKUST

:: SCREENSHOTS

n/a

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

 SNPAssociation

:: MORE INFORMATION

Citation:

Bioinformatics. 2010 Oct 15;26(20):2517-25. doi: 10.1093/bioinformatics/btq486. Epub 2010 Aug 24.
Detecting two-locus associations allowing for interactions in genome-wide association studies.
Wan X1, Yang C, Yang Q, Xue H, Tang NL, Yu W.

BOOST 20101230 / GBOOST 20130821 – Detect Gene-gene Interactions

BOOST 20101230 / GBOOST 20130821

:: DESCRIPTION

BOOST (BOolean Operation based Screening and Testing) is a method for detecting gene-gene interactions. It allows examining all pairwise interactions in genome-wide case-control studies in a remarkably fast manner. Interaction analyses on seven data sets from the Wellcome Trust Case Control Consortium were carried out.

GBOOST is a GPU-implementation of BOOST based on the CUDA technology by Nvidia.

::DEVELOPER

Laboratory for Bioinformatics and Computational Biology, HKUST

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 BOOST , GBOOST

:: MORE INFORMATION

Citation:

Wan, Yang, Yang, Xue, Fan, Tang, Yu (2010),
BOOST: a Boolean representation-based method for detecting SNP-SNP interactions in genome-wide association studies“”,
American Journal of Human Genetics, 87:325-340.

L. S. Yung, C. Yang, X. Wan, and W. Yu
GBOOST : A GPU-based tool for detecting gene-gene interactions in genome-wide case control studies“,
Bioinformatics, 27:1309-1310, 2011.

PBOOST – Parallel Permutation Tests in Genome-wide Association Studies

PBOOST

:: DESCRIPTION

PBOOST is a GPU based tool for parallel permutation tests in genome-wide association studies.

::DEVELOPER

Laboratory for Bioinformatics and Computational Biology, HKUST

:: SCREENSHOTS

PBOOST

:: REQUIREMENTS

:: DOWNLOAD

  PBOOST 

:: MORE INFORMATION

Citation:

PBOOST: a GPU-based tool for parallel permutation tests in genome-wide association studies.
Yang G, Jiang W, Yang Q, Yu W.
Bioinformatics. 2014 Dec 21. pii: btu840.

PLA – Piecewise-constant and Low-rank Approximation for Multi-sample aCGH Data Analysis

PLA

:: DESCRIPTION

PLA – Piecewise-constant and Low-rank Approximation for Multi-sample aCGH Data Analysis

::DEVELOPER

Laboratory for Bioinformatics and Computational Biology, HKUST

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux
  • MatLab

:: DOWNLOAD

 PLA

:: MORE INFORMATION

Citation:

Bioinformatics. 2014 Mar 31. [Epub ahead of print]
Piecewise-constant and low-rank approximation for identification of recurrent copy number variations.
Zhou X1, Liu J, Wan X, Yu W.

GeneLoc 4.8 – Exon-based Integration of Human Genome Maps

GeneLoc 4.8

:: DESCRIPTION

GeneLoc (Gene Location) presents an integrated map for each human chromosome, based on data integrated by the GeneLoc algorithm

::DEVELOPER

GeneLoc team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Bioinformatics. 2003;19 Suppl 1:i222-4.
GeneLoc: exon-based integration of human genome maps.
Rosen N, Chalifa-Caspi V, Shmueli O, Adato A, Lapidot M, Stampnitzky J, Safran M, Lancet D.

PREDA 1.32.0 – Detecting Regional Variations in Genomics data

PREDA 1.32.0

:: DESCRIPTION

PREDA (Position RElated Data Analysis) is an R package for detecting regional variations in genomics data.

::DEVELOPER

Bicciato Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • R
  • BioConductor

:: DOWNLOAD

 PREDA

:: MORE INFORMATION

Citation

PREDA: an R-package to identify regional variations in genomic data.
Ferrari F, Solari A, Battaglia C, Bicciato S.
Bioinformatics. 2011 Sep 1;27(17):2446-7. doi: 10.1093/bioinformatics/btr404.