Examples of online analysis tools for gene expression data Tools - - PowerPoint PPT Presentation

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examples of online analysis tools for gene expression data
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Examples of online analysis tools for gene expression data Tools - - PowerPoint PPT Presentation

Dec 27, 2023 12 likes •416 views

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

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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

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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

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GEO Tools

Raw data retrieval (soft or matrix-formatted 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

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GEO DataSet cluster analysis : example

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GEO DataSet cluster analysis : example

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GEO DataSet cluster analysis : example

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GEO differential expression analysis : example

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ArrayExpress Tools

Processed (matrix) or Raw data retrieval Expression Profiles (per gene and per experiment)

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SAGE Anatomic Viewer (SAV)

Displays gene expression results based on SAGE tags counts in human normal and malignant tissues

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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

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Tools for raw files transformation : Express Yourself

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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

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CIMminer

Generates color-coded Clustered Image Maps (CIMs) ("heat maps") Easy to use, but few tuning possibilities Good start for online clustering tools

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GEDA

Specifically designed for the integrated analysis of global gene expression patterns in cancer Easy to use BUT : careful with the results interpretation

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GEDA : A few Screenshots

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GEDA : A few Screenshots

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GEDA : A few Screenshots

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GEDA : A few Screenshots

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GEDA : A few Screenshots

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GEDA : A few Screenshots

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Expression Profiler at EBI

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Expression Profiler at EBI

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Expression Profiler at EBI

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GEPAS

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GEPAS

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GEPAS

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GEPAS

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GEPAS

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GEPAS

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Tools to retrieve gene functions and annotations

Goals Link Gene Ontology information to co-expressed genes Find pathways specificities under certain biological conditions Find promoter elements common in co-expressed 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

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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 co-expressed genes KEGG pathways associated with genes Gene Ontology for selected genes

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CARRIE

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CARRIE

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Babelomics : FatiGO

Linked to the GEPAS gene expression analysis tools Web-tools for functional annotation and analysis of group of genes in high- throughput experiments.

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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

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Babelomics : FatiGO

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Babelomics : FatiGO

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Babelomics : FatiGO

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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 organism-specific background model (given) Motif length Number of motifs to retrieve Output A list of motifs instances for each input sequence

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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