FunCluster 1.07 – Functional Analysis of Gene Expression data

FunCluster 1.07

:: DESCRIPTION

FunCluster is a genomic data analysis tool designed to perform a functional analysis of gene expression data obtained from cDNA microarray experiments. Besides automated functional annotation of gene expression data, FunCluster functional analysis allows to detect co-regulated biological processes (i.e. represented by annotating genomic themes) through a specifically designed co-clustering procedure involving biological annotations and gene expression data. FunCluster’s functional analysis relies on Gene Ontology and KEGG annotations and is currently available for three organisms: Homo sapiens, Mus musculus and Saccharomyces cerevisiae.

::DEVELOPER

Corneliu Henegar

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / MacOsX / Windows
R package

:: DOWNLOAD

FunCluster

:: MORE INFORMATION

Citation

Clustering biological annotations and gene expression data to identify putatively co-regulated biological processes
Henegar C, Cancello R, Rome S, Vidal H, Clement K, Zucker JD
J Bioinform Comput Biol. 2006 Aug;4(4):833-52.

Prf-browser 2.6 – Browse and Display Gene Expression Profiles

Prf-browser 2.6

:: DESCRIPTION

Prf browser is the program to plot expression profiles and to browse Peak-mapper, Event-mapper data, profiles for extracted genes from directory “genes” created by Glob-mapper or profiles by the unique gene identifiers (i.e. d2378). Prf-browser requires profile data (*.rsz or *aln) and the ID file with gene names as well.

::DEVELOPER

Dmitri Papatsenko

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows

:: DOWNLOAD

Prf browser

:: MORE INFORMATION

Citation

Goltsev Y, Papatsenko D.
“Time warping of evolutionary distant temporal gene expression data based on noise suppression”
BMC Bioinformatics. 2009 Oct 26;10:353.

Peak-mapper 2.7 – Find Disconcordances in Gene Expression, shared by many gene pair orthologs

Peak-mapper 2.7

:: DESCRIPTION

Peak-mapper is the program to identify local differences (disconcordances) in gene expression after aligning orthologous data sets. The program takes two aligned microarray datasets (*.aln) as an input and returns a map similar to that of Event-mapper, where peaks (hot spots) correspond to gene batteries having similar disconcordances between two organisms.

::DEVELOPER

Dmitri Papatsenko

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows

:: DOWNLOAD

Peak-mapper

:: MORE INFORMATION

Citation

Goltsev Y, Papatsenko D.
“Time warping of evolutionary distant temporal gene expression data based on noise suppression”
BMC Bioinformatics. 2009 Oct 26;10:353.

AVF-filter 2.4 – Find Noisy Datasets, Measure Periodicity of Gene Expression

AVF-filter 2.4

:: DESCRIPTION

AVF-filter is designed for the removal of noisy/low-variance expression profiles and non-periodic profiles (optional, for cell culture data).

::DEVELOPER

Dmitri Papatsenko

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows

:: DOWNLOAD

AVF-filter

:: MORE INFORMATION

Citation

Goltsev Y, Papatsenko D.
“Time warping of evolutionary distant temporal gene expression data based on noise suppression”
BMC Bioinformatics. 2009 Oct 26;10:353.

GSIM – Microarray Gene Expression Simulator

GSIM

:: DESCRIPTION

GSIM (Gene Expression Simulator) is a C program which simulates gene expression microarray data. It will generate distributions for any number of genes and use those distributions to generate intensities for any number of replicates. It generates two-class data and any number of the genes can be chosen to be differentially expressed. The program is very flexible and the parameters of the differentially expressed genes can be set by the user.

::DEVELOPER

the Computational Biology and Informatics Laboratory (in the Center for Bioinformatics at the University of Pennsylvania)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux/ MacOsX / Windows
Perl
C Compiler

:: DOWNLOAD

GSIM

:: MORE INFORMATION

PaGE 5.1.6 – Patterns from Gene Expression

PaGE 5.1.6

:: DESCRIPTION

PaGE (Patterns from Gene Expression) is free downloadable software for microarray analisys. PaGE can be used to produce sets of differentially expressed genes with confidence measures attached.

::DEVELOPER

the Computational Biology and Informatics Laboratory (in the Center for Bioinformatics at the University of Pennsylvania)

:: SCREENSHOTS

:: REQUIREMENTS

Linux/ MacOsX / Windows
Perl / Java

:: DOWNLOAD

PaGE

:: MORE INFORMATION

Citation:

Grant G.R., Liu J., Stoeckert C.J.Jr. (2005)
A practical false discovery rate approach to identifying patterns of differential expression in microarray data,
Bioinformatics, Vol 21 no 11, 2684-2690.

EMOGEE 20070704 – Estimator for MOdels of Gene Expression Evolution

EMOGEE 20070704

:: DESCRIPTION

EMOGEE (Estimator for MOdels of Gene Expression Evolution) implements neutral models for evolution of gene expression to analyze or simulate microarray data.

::DEVELOPER

the Center of Integrative Bioinformatics Vienna (CIBIV) headed by Arndt von Haeseler.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

EMOGEE

:: MORE INFORMATION

Citation:

M. Rosskopf (2007)
Development and Applications of Neutral Models for Evolution of Gene Expression,
Inaugural-Dissertation zur Erlangung des Doktorgrades der Mathematisch-Naturwissenschaftlichen Fakultät der Heinrich-Heine-Universität Düsseldorf

CIST – Analysis of Gene Expression data Generated using SAGE

CIST

:: DESCRIPTION

CIST (Confidence Intervals for SAGE Tags )is a Windows program for the analysis of gene expression data generated using SAGE (Serial Analysis of Gene Expression).

::DEVELOPER

Mark P. Miller

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows

:: DOWNLOAD

CIST

:: MORE INFORMATION

SVN – Remove Systematic Variation in Microarray Gene Expression data

SVN

:: DESCRIPTION

SVN (Systematic variation normalization) is a procedure for removing systematic variation in microarray gene expression data. Based on an analysis of how systematic variation contributes to variability in microarray data sets, the SVN procedure includes background subtraction determined from the distribution of pixel intensity values and log conversion, linear or non-linear regression, restoration or transformation, and multiarray normalization.

::DEVELOPER

Pierre R. Bushel, Ph.D.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows /MacOsX
Java

:: DOWNLOAD

SVN

:: MORE INFORMATION

Citation:

Chou JW, Paules RS, Bushel PR.
Systematic variation normalization in microarray data to get gene expression comparison unbiased.
J Bioinform Comput Biol. 2005 Apr;3(2):225-41.

PAGE – Phase-shifted Analysis of Gene Expression

PAGE

:: DESCRIPTION

PAGE (Phase-shifted Analysis of Gene Expression) is a Java-based software for phase-shifted analysis of gene expression developed along the lines of the original q-Clustering algorithm (Ji and Tan, 2005) to analyze gene expression from multiple biological conditions across dose and time series experiments. Grouping of gene expression patterns is performed in q-intervals of the measurements using phase-shifts to find clusters of genes which share trends of expression profiles within the dataset.

::DEVELOPER

Pierre R. Bushel, Ph.D.

:: SCREENSHOTS

:: REQUIREMENTS

Linux / Windows /MacOsX
Java

:: DOWNLOAD

PAGE

:: MORE INFORMATION

Citation:

Bioinformatics. 2006 Feb 1;22(3):367-8. Epub 2005 Dec 1.
PAGE: phase-shifted analysis of gene expression.
Leung E, Bushel PR.