Download Examples of online analysis tools for gene expression data

Examples of online analysis tools for gene expression data
Tools integrated in data repositories
Tools for raw data analysis (cel files, or other scanner output)
Processed data analysis tools
Tools linking gene expression with gene function
Tools linking gene expression with sequence analysis
Tools from the data repositories
Advantages :
Fast
Done for a huge amount of public data
Allow quick & dirty overview of “what's already known”
Drawbacks
Not usable for custom data
Not flexible, poor tuning
Examples
GEO
ArrayExpress
SAGEmap
GEO Tools
Raw data retrieval (soft or matrix­formatted objects)
GEO DataSet Cluster Analysis : a visualization tool for displaying precomputed cluster heat maps
GEO Profiles : expression profiles per each gene/spot of one selected dataset
GEO DataSet cluster analysis : example
GEO DataSet cluster analysis : example
GEO DataSet cluster analysis : example
GEO differential expression analysis : example
ArrayExpress Tools
Processed (matrix) or Raw data retrieval
Expression Profiles (per gene and per experiment)
SAGE Anatomic Viewer (SAV)
Displays gene expression results based on SAGE tags counts in human normal and malignant tissues
Tools for raw files transformation
Input : Affymetrix cel files
Genepix or Scanalyze output files
Functions :
Standard microarray corrections and normalization
Background correction
Spot filtering
Intra­ and Interchip normalization
Replicate scaling
Data quality assessment and scoring
Tools for raw files transformation : Express Yourself
Processed data analysis tools
Drawbacks Can be quite slow
Input data format is very important
Need to know well your data before using them
Advantages
Usually contains lots of functionalities
Usable for custom data
Can be very flexible
Examples
CIMminer
GEDA
Expression Profiler
GEPAS
CIMminer
Generates color­coded Clustered Image Maps (CIMs) ("heat maps")
Easy to use, but few tuning possibilities
Good start for online clustering tools
GEDA
Specifically designed for the integrated analysis of global gene expression patterns in cancer
Easy to use BUT : careful with the results interpretation
GEDA : A few Screenshots
GEDA : A few Screenshots
GEDA : A few Screenshots
GEDA : A few Screenshots
GEDA : A few Screenshots
GEDA : A few Screenshots
Expression Profiler at EBI
Expression Profiler at EBI
Expression Profiler at EBI
GEPAS
GEPAS
GEPAS
GEPAS
GEPAS
GEPAS
Tools to retrieve gene functions and annotations
Goals
Link Gene Ontology information to co­expressed genes
Find pathways specificities under certain biological conditions
Find promoter elements common in co­expressed genes
Input files
Expression data matrix with classes AND gene names
Gene lists to compare
Promoter sequences in FASTA format
Examples
Carrie
Babelomics
DAVID : Database for Annotation, Visualization and Integrated Discovery
Inclusive : MotifSampler
SSA
CARRIE
Computational Ascertainment of Regulatory Relationships Inferred from Expression
Input Expression data matrix with gene Ids and sample classes
Associated promoter sequences
Output
Known transcription factors associated with co­expressed genes
KEGG pathways associated with genes
Gene Ontology for selected genes
CARRIE
CARRIE
Babelomics : FatiGO
Linked to the GEPAS gene expression analysis tools
Web­tools for functional annotation and analysis of group of genes in high­
throughput experiments.
Babelomics : FatiGO
Input :
Two gene lists to compare (differentially expressed genes)
Different gene IDs supported (Entrez, HUGO, RefSeq, Affy...)
Uses GO (Gene Ontology) database
Output : Summary with the input parameters
Summary input data: Initial number of genes, number of genes have ensembl correspondence and number of genes that have been used for the analysis.
Links with the results for each repository that has been selected and the number of genes for which gene ontology annotation exist.
Graphical view of GO terms represented in gene lists Babelomics : FatiGO
Babelomics : FatiGO
Babelomics : FatiGO
MotifSampler
Description
Part of the INCLUSive suite which also contains gene expression data analysis
Tries to find motifs in a given list of sequences
Input Sequences in FASTA format
An organism­specific background model (given)
Motif length
Number of motifs to retrieve
Output
A list of motifs instances for each input sequence
Other online Tool : ArrayQuest
Applies to data from GEO or custom data
Contains Bioconductor methods, BioPerl and C++ based scripts
Accepts new analysis method submission