OC 2.1a – Cluster Analysis Program

OC 2.1a

:: DESCRIPTION

OC implements single, complete and means linkage cluster analysis. It does not have software limits on the number of entities that can be clustered. OC was developed to cluster large sets of protein sequences, but it is general and can be applied to any type of data.

::DEVELOPER

The Barton Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Perl

:: DOWNLOAD

OC

:: MORE INFORMATION

Citation:

Barton, G.J. (1993, 2002) “OC – A cluster analysis program”, University of Dundee, Scotland,
UK; www.compbio.dundee.ac.uk/downloads/oc.

Genesis 1.7.7 / GenesisServer 1.1.0 – Cluster Analysis of Microarray data

Genesis 1.7.7 / GenesisServer 1.1.0

:: DESCRIPTION

Genesis integrates various tools for microarray data analysis such as filters, normalization and visualization tools, distance measures as well as common clustering algorithms including hierarchical clustering, self-organizing maps, k-means, principal component analysis, and support vector machines.

Genesis Server is an application server for computation of Hierarchical Clustering, Self Organizing Maps (SOM), k-means Clustering and Support Vector Machines (SVM).

::DEVELOPER

Genomics & Bioinformatics Graz, Graz University of Technology

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • Java

:: DOWNLOAD

 Genesis , GenesisServer

:: MORE INFORMATION

Citation

Sturn A, Quackenbush J, Trajanoski Z.
Genesis: Cluster analysis of microarray data.
Bioinformatics. 2002 Jan;18(1):207-8.

Sturn A, Mlecnik B, Pieler R, Rainer J, Truskaller T, Trajanoski Z.
Client-Server environment for high-performance gene expression data analysis.
Bioinformatics. 19: 772-773 (2003)

VISDA 1.0 – Visualization, and Discovery for Cluster Analysis of Genomic data

VISDA 1.0

:: DESCRIPTION

VISDA (VIsual and Statistical Data Analyzer) is a software for cluster modeling, visualization, and discovery in genomic data. VISDA performs progressive, coarse-to-fine (divisive) hierarchical clustering and visualization, supported by hierarchical mixture modeling, supervised/unsupervised informative gene selection, supervised/unsupervised data visualization, and user/prior knowledge guidance, to discover hidden clusters within complex, high-dimensional genomic data.

::DEVELOPER

Computational Bioinformatics & Bio-imaging Laboratory (CBIL)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 VISDA

:: MORE INFORMATION

Citation:

caBIG VISDA: modeling, visualization, and discovery for cluster analysis of genomic data.
Zhu Y, Li H, Miller DJ, Wang Z, Xuan J, Clarke R, Hoffman EP, Wang Y.
BMC Bioinformatics. 2008 Sep 18;9:383.

3DCA – 3D Cluster Analysis

3DCA

:: DESCRIPTION

3DCA (3D Cluster Analysis)offers a method for the prediction of functional residue clusters in proteins. This method requires a representative structure and a multiple sequence alignment as input data. Individual residues are represented in terms of regional alignments that reflect both their structural environment and their evolutionary variation, as defined by the alignment of homologous sequences.

::DEVELOPER

Ralf Landgraf (rlandgraf@mednet.ucla.edu )

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX / Windows
  • Perl 

:: DOWNLOAD

 3DCA

:: MORE INFORMATION

Citation:

Landgraf R., Xenarios I., Eisenberg D.,
Three-dimensional Cluster Analysis Identifies Interfaces and Functional residue Clusters in Proteins “,
Journal of Molecular Biology, 307 , 1487-1502 (2001)

JustClust – Analysing Biological Data with Cluster Analysis

JustClust

:: DESCRIPTION

JustClust is a tool for analysing biological data with cluster analysis. JustClust can handle many formats of data and cluster the data with many state-of-the-art techniques. The aim of JustClust is to provide an easy-to-use application which can perform any analysis on any data.

::DEVELOPER

Paccanaro Lab

:: SCREENSHOTS

JustClust

::REQUIREMENTS

  • Linux / Windows/ MacOsX
  • Java

:: DOWNLOAD

 JustClust

:: MORE INFORMATION

GIMM 3.8 – Gaussian Infinite Mixture Models for Cluster Analysis of Genomics Data

GIMM 3.8

:: DESCRIPTION

GIMM implements Bayesian infinite mixtures clustering procedures. The software consists of the R package gimmR and the self-standing software WinGimm.

:: DEVELOPER

Laboratory for Statistical Genomics, Univ. Cincinnati

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux/MacOsX/Windows
  • R Package

:: DOWNLOAD

 GIMM

:: MORE INFORMATION

Citation:

Bioinformatics. 2006 Jul 15;22(14):1737-44. Epub 2006 May 18.
Context-specific infinite mixtures for clustering gene expression profiles across diverse microarray dataset.
Liu X, Sivaganesan S, Yeung KY, Guo J, Bumgarner RE, Medvedovic M.

BMC Bioinformatics. 2007 Aug 3;8:283.
Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data.
Liu X, Jessen WJ, Sivaganesan S, Aronow BJ, Medvedovic M.

WebGimm – Cluster Analysis Server

WebGimm

:: DESCRIPTION

WebGimm is a Java Web Start based tool for performing gene and sample clustering analysis of microarray data and enrichment analysis on the results of clustering. WebGimm operates on the client-server paradigm and offloads the burden of computation from the user’s machine to our servers.

:: DEVELOPER

Laboratory for Statistical Genomics, Univ. Cincinnati

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux/MacOsX/Windows
  • Java

:: DOWNLOAD

 WebGimm

:: MORE INFORMATION

Citation:

Source Code Biol Med. 2011 Jan 17;6:3.
WebGimm: An integrated web-based platform for cluster analysis, functional analysis, and interactive visualization of results.
Joshi VK, Freudenberg JM, Hu Z, Medvedovic M.

CAGED 1.1 – Cluster Analysis of Gene Expression Dynamics

CAGED 1.1

:: DESCRIPTION

CAGED (Cluster Analysis of Gene Expression Dynamics) is a model based,Bayesian clustering procedure developed by Ramoni et al. to cluster gene expression profiles measured with microarrays in temporal experiments. Contrary to popular clustering methods, CAGED takes into account explicitly the fact that expression profiles in temporal experiments may be serially correlated and uses a model-based, Bayesian procedure to identify the best grouping of the gene expression data in an automated way.

CAGED implements a Bayesian clustering method designed to handle temporal experiments and subsuming standard independent experiments as a special case.

::DEVELOPER

Marco Ramoni & Paola Sebastiani

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

CAGED

:: MORE INFORMATION

You will need a password to use this program. Please email sebas@bu.edu for a password.