PageMan 0.12 – Annotates, Investigates, and Condenses Microarray data in the Context of Functional Ontologies

PageMan 0.12

:: DESCRIPTION

PageMan is a tool to get a quick overview of multiparallel experiments. PageMan also helps comparing experiments from different organisms.

::DEVELOPER

Max Planck Institute for Molecular Plant Physiology

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Java

:: DOWNLOAD

 PageMan

:: MORE INFORMATION

Citation

Usadel B, Nagel A, Steinhauser D, Gibon Y, Blaesing OE, Redestig H, Sreenivasulu N, Krall L, Hannah MA, Poree F, Fernie AR, Stitt M (2006)
PageMan an interactive ontology tool to generate, display, and annotate overview graphs for profiling experiments,
BMC Bioinformatics 18:7:535

ArrayMining – Online Microarray Data Mining

ArrayMining

:: DESCRIPTION

ArrayMining is a server for automating statistical analysis of gene and protein expression microarray data, designed as a supporting tool for investigation of the genetic components of diseases.

::DEVELOPER

ArrayMining team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2009 Oct 28;10:358. doi: 10.1186/1471-2105-10-358.
ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization.
Glaab E, Garibaldi JM, Krasnogor N.

ReMoDiscovery – Inferring Transcriptional Module networks from ChIP-chip-, motif- and microarray data

ReMoDiscovery

:: DESCRIPTION

ReMoDiscovery is an intuitive algorithm to correlate regulatory programs with regulators and corresponding motifs to a set of co-expressed genes. It exploits in a concurrent way three independent data sources: ChIP-chip data, motif information and gene expression profiles.

::DEVELOPER

Kathleen Marchal 

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux /  Windows / MacOsX
  • Java
:: DOWNLOAD

 ReMoDiscovery

:: MORE INFORMATION

Citation

Genome Biol. 2006;7(5):R37. Epub 2006 May 5.
Inferring transcriptional modules from ChIP-chip, motif and microarray data.
Lemmens K, Dhollander T, De Bie T, Monsieurs P, Engelen K, Smets B, Winderickx J, De Moor B, Marchal K.

GoSurfer 2.0 – Graphical Data Mining tool for Microarray data using Gene Ontology Information

GoSurfer 2.0

:: DESCRIPTION

GoSurfer uses Gene Ontology (GO) information to analyze gene sets obtained from genome-wide computations or microarray analyses. GoSurfer is a graphical interactive data mining tool. It associates user input genes with GO terms and visualizes such GO terms as a hierarchical tree. Users can manipulate the tree output by various means, like setting heuristic thresholds or using statistical tests. Significantly important GO terms resulted from a statistical test can be highlighted. All related information are exportable either as texts or as graphics.

::DEVELOPER

Zhong Lab

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

 GoSurfer

:: MORE INFORMATION

Citation

Zhong S, Storch F, Lipan O, Kao MJ, Weitz C, Wong WH.
GoSurfer: a graphical interactive tool for comparative analysis of large gene sets in Gene Ontology space.
Applied Bioinformatics 2004, 3(4): 1-5.

RPS 1.0 – Reproducibility Probability Score for Microarray Data

RPS 1.0

:: DESCRIPTION

RPS (Reproducibility Probability Score) computes reproducibility probability score  to select differentially expressed genes. The Reproducibility Probability Score (RPS), takes into consideration both the replicated data in a particular lab and the measurement variability across labs. The measurement variability is assessed by utilizing the reference gene expression data generated in the Microarray Quality Control (MAQC) project. Specifically, we applied the data generated across replicate gene expression analysis that was conducted in multiple facilities as part of this effort.  A larger RPS means a gene is more likely to be differentially expressed; and if similar transcription profiling measurements are made in other laboratories, it is highly likely to be confirmed.

::DEVELOPER

Zhong Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

  RPS

:: MORE INFORMATION

Citation

Guixian Lin, Xuming He, Hanlee Ji, Leming Shi, Ronald W. Davis, and Sheng Zhong.
Reproducibility Probability Score: Incorporating Measurement Variability across Laboratories for Gene Selection,
Nature Biotechnology 24, 1476 – 1477 (2006)

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

GeneTrail / GeneTrailExpress

:: DESCRIPTION

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

GeneTrailExpress provides comprehensive normalization and scoring functions for pre-processing microarray data. The processed data is directly passed to GeneTrail for statistical evaluation in an extensive gene set analysis.

::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.

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)

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.

CALIB 1.34.0 – Estimate absolute Expression levels from two color Microarray data

CALIB 1.34.0

:: DESCRIPTION

CALIB (Calibration model) is a BioConductor package for estimating absolute expression levels from two color microarray data

::DEVELOPER

Bioinformatics Research Group, Belgium

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 CALIB

:: MORE INFORMATION

Citation

CALIB: a Bioconductor package for estimating absolute expression levels from two-color microarray data.
Zhao H, Engelen K, De Moor B, Marchal K.
Bioinformatics. 2007 Jul 1;23(13):1700-1. Epub 2007 May 7.

PeakFinder 1.0 – Find Cohesin Binding Sites in Yeast ChIP Microarray Data

PeakFinder 1.0

:: DESCRIPTION

PeakFinder program was developed to find cohesin binding sites represented by the peaks in yeast chromatin immunoprecipitation (ChIP) microarray data, but can be applied to plot any measurement against a parameter such as genome coordinate, to interactively analyze the measurement plot, and to annotate the peaks on the basis of local properties of the curve.

::DEVELOPER

Earl F. Glynn

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

PeakFinder

:: MORE INFORMATION

Citation

Glynn EF, Megee PC, Yu H-G, Mistrot C, Unal E, et al. (2004)
Genome-wide mapping of the cohesin complex in the yeast Saccharomyces cerevisiae.
PLoS Biol 2(9): e259. doi:10.1371/journal.pbio.0020259