GenoMap 1.0 – Graphical Representation of Microarray data

GenoMap 1.0

:: DESCRIPTION

GenoMap is a viewer for genome-wide map of microarray expression data within a circular bacterial genome

::DEVELOPER

Sato Lab

:: SCREENSHOTS

GenoMap

:: REQUIREMENTS

  • Linux / Windows/MacOsX
  • TCL/Tk

:: DOWNLOAD

 GenoMap

:: MORE INFORMATION

Citation

Bioinformatics. 2003 Aug 12;19(12):1583-4.
GenoMap, a circular genome data viewer.
Sato N, Ehira S.

TightClust 1.0 – Resampling Based Clustering Method for Microarray data

TightClust 1.0

:: DESCRIPTION

TightClust applies K-means clustering as an intermediate clustering engine. Early truncation of a hierarchical clustering tree is used to overcome the local minimum problem in K-means clustering. The tightest and most stable clusters are identified in a sequential manner through an analysis of the tendency of genes to be grouped together under repeated resampling.

::DEVELOPER

George C. Tseng 

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 TightClust

:: MORE INFORMATION

Citation

George C. Tseng and Wing H. Wong. (2005)
Tight Clustering: A Resampling-based Approach for Identifying Stable and Tight Patterns in Data.
Biometrics.61:10-16.

ErmineJ 3.2 – Analysis of Gene sets in Expression Microarray data

ErmineJ 3.2

:: DESCRIPTION

ErmineJ performs analyses of gene sets in expression microarray data. A typical goal is to determine whether particular biological pathways are “doing something interesting” in the data. The software is designed to be used by biologists with little or no informatics background.

::DEVELOPER

Pavlidis lab

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Linux / Mac OsX
  • java

:: DOWNLOAD

 ErmineJ

:: MORE INFORMATION

Citation

Lee H.K., Braynen W., Keshav K. and Pavlidis P. (2005)
ErmineJ: Tool for functional analysis of gene expression data sets.
BMC Bioinformatics 6:269.

LIMMA 3.50.0 – Linear Models for Microarray Data

LIMMA 3.50.0

:: DESCRIPTION

LIMMA is a software package for the analysis of gene expression microarray data, especially the use of linear models for analysing designed experiments and the assessment of differential expression. The package includes pre-processing capabilities for two-colour spotted arrays. The differential expression methods apply to all array platforms and treat Affymetrix, single channel and two channel experiments in a unified way.

::DEVELOPER

WEHI Bioinformatics

:: REQUIREMENTS

:: DOWNLOAD

 LIMMA

:: MORE INFORMATION

Citation

Smyth, G. K. (2005).
Limma: linear models for microarray data.
Bioinformatics and Computational Biology Solutions using R and Bioconductor, R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds.),
Springer, New York, pages 397-420

GENECLUST 1.0.2 – Exploratory Analysis of Gene Expression Microarray data

GENECLUST 1.0.2

:: DESCRIPTION

GeneClust is a piece of computer software which can be used as a tool for exploratory analysis of gene expression microarray data. The development of GeneClust was motivated by surging interest to search for interpretable biological structure in gene expression microarray data.

::DEVELOPER

Kim-Anh Do, Ph.D.

:: SCREENSHOTS

:: REQUIREMENTS

:: DOWNLOAD

 GENECLUST

:: MORE INFORMATION

Citation

Cancer Inform. 2007;5:25-43. Epub 2007 Apr 2.
Application of gene shaving and mixture models to cluster microarray gene expression data.
Do KA, McLachlan GJ, Bean R, Wen S.

CpGassoc 2.60 – Analysis of DNA Methylation Microarray data

CpGassoc 2.60

:: DESCRIPTION

CpGassoc – An R Function for Analysis of DNA Methylation Microarray Data The analysis of DNA methylation data has recently garnered attention among researchers from a variety of backgrounds, due to the availability of high-throughput methylation microarrays. The number of CpG sites that can be analyzed is growing rapidly – for example, the latest Illumina Infinium BeadChip interrogates ~450,000 CpG sites. With the growing interest in DNA methylation and the growing volume of data analyzed, there is a need for software to perform these types of analyses.

::DEVELOPER

R Barfield <barfieldrichard8 at gmail.com>

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

CpGassoc

:: MORE INFORMATION

Citation

R. T. Barfield et al.
CpGassoc – An R Function for Analysis of DNA Methylation Microarray Data.
American Society of Human Genetics/ICHG 2011

MethLAB 2.1 – Analysis of DNA Methylation Microarray data

MethLAB 2.1

:: DESCRIPTION

MethLAB provides a graphical user interface (GUI) to facilitate analysis of DNA methylation microarray data, allowing users with no experience using statistical software to implement flexible and powerful analyses of array-based DNA methylation data.

::DEVELOPER

Lab of Alicia Smith, PhD, Emory University School of Medicine

:: SCREENSHOTS

:: REQUIREMENTS

:: DOWNLOAD

MethLAB

:: MORE INFORMATION

Citation

Varun Kilaru, Richard T. Barfield, James W. Schroeder, Alicia K. Smith, Karen N. Conneely.
MethLAB: A GUIpackage for the analysis of array-based DNA methylation data (2012).
Epigenetics, Volume 7, Issue 3

Genesis 1.8.1 / 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)

SVA 3.40.0 – Surrogate Variable Analysis

SVA 3.40.0

:: DESCRIPTION

The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment.

::DEVELOPER

 Johnson lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • R
  • BioConductor

:: DOWNLOAD

 sva

:: MORE INFORMATION

Citation

Bioinformatics. 2012 Mar 15;28(6):882-3. doi: 10.1093/bioinformatics/bts034. Epub 2012 Jan 17.
The sva package for removing batch effects and other unwanted variation in high-throughput experiments.
Leek JT1, Johnson WE, Parker HS, Jaffe AE, Storey JD.

Johnson, WE, Rabinovic, A, and Li, C (2007).
Adjusting batch effects in microarray expression data using Empirical Bayes methods.
Biostatistics 8(1):118-127.

GeneTrail 3.0 – Gene Set Analysis tool / for pre-processing Microarray data

GeneTrail 3.0

:: DESCRIPTION

GeneTrail is a comprehensive and efficient gene set analysis tool that offers a rich functionality and is easy to use.

::DEVELOPER

Chair for clinical bioinformatics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2008 Dec 22;9:552. doi: 10.1186/1471-2105-9-552.
GeneTrailExpress: a web-based pipeline for the statistical evaluation of microarray experiments.
Keller A1, Backes C, Al-Awadhi M, Gerasch A, Küntzer J, Kohlbacher O, Kaufmann M, Lenhof HP.

Backes C, Keller A, Kuentzer J, Kneissl B, Comtesse N, Elnakady YA, Müller R, Meese E, Lenhof HP.
GeneTrail–advanced gene set enrichment analysis.
Nucleic Acids Res. 2007 Jul;35(Web Server issue):W186-92.