- Functional profiling documentation
- Single enrichment analysis
- Set enrichment analysis
- Module enrichment analysis
- Network enrichment analysis (SNOW)
- Tissue phenotype based profiling
- De novo annotations (Blast2GO)
Functional profiling documentation¶
Single enrichment analysis¶
This is the conventional enrichment test. FatiGO takes two lists of genes (ideally a group of interest and the rest of the genes in the experiment, although any two groups, formed in any way, can be tested against each other) and convert them into two lists of annotations using the corresponding gene-annotation association table. Annotations can be GO terms, pathways (KEGG, Biocarta, reactome), regulatory annotations (TFBSs, miRNA targets, etc.) Then a Fisher's exact test for 2×2 contingency tables is used to check for significant over-representation of annotations in one of the sets with respect to the other one. Multiple test correction to account for the multiple hypothesis tested (one for each annotation) is applied.
Marmite. Enrichment analysis using text-mining derived annotations¶
Marmite extends the concept of functional enrichment to annotations derived from text-mining methods. Such methods allow extracting informative annotations (bioentities) with different functional, chemical, clinical, etc. meanings, that can be associated to genes. In this case, the association of an annotation to a gene has a strength derived from the number of times that the gene and the annotation are co-cite in a PubMed abstract. A Kolmogorov-Smirnov test is used instead the conventional Fisher's exact test. Multiple test correction to account for the multiple hypothesis tested (one for each annotation) is applied. Marmite stands for My Accurate Resource for MIning TExt.
Set enrichment analysis¶
Gene set analysis¶
Gene set methods are much more sensitive than single enrichment methods in detecting gene sets (defined as sets of genes with a common annotation) with a collective behaviour in a genomic experiment. These methods very efficiently detect gene sets (annotations) that are consistently associated to high or low values in a ranked list of genes.
Here two methods have been implemented: the FatiScan, a segmentation test, and the logistic regression, which detect asymmetrical distributions of annotations within ranked lists of genes.
MarmiteScan expands the concept of gene set analysis from gene sets defined with conventional annotations (GO, KEGG, etc.) to text-mining derived annotations (bioentities).
This is a novel web-based resource to check for pathway (or GO terms) associations to diseases (or any other trait) in genome-wide association analysis (GWAS) with SNPs or CNVs.
Module enrichment analysis¶
Genecodis method searchs for annotations that frequently co-occur in a set of genes and rank them by statistical significance. The analysis of concurrent annotations provides significant information for the biologic interpretation of genomic data and provide a perspective complementary to the conventional enrichment methods.
Network enrichment analysis (SNOW)¶
The SNOW tool introduces protein-protein interaction data into the functional profiling of genomic data. It extracts from a list of pre-selected proteins or genes the minimal connected network (smallest network that connects all the elements of the list) that they conform in terms of physical interactions and then it evaluates its topological parameters comparing them versus same-size networks generated from random lists of genes/proteins.
Methods - Input form - Output results
Tissue phenotype based profiling¶
Resource to extract differences between the distributions of the expression values of two groups of genes in a set of tissues. In order to improve the posibilities of your analysis and to cover most of the scope of the possible experiments users are interested in, we provide data from two type of platforms, SAGE Tags and Microarray (Affymetrix) expression data.
Tissue phenotype profiling based on SAGE Tags.
Tissue phenotype profiling based on Microarray Affymetrix expression data.
De novo annotations (Blast2GO)¶
Tool for the functional annotation of (novel) sequence data. The annotations produced can be used for further functional interpretation of genomic data by the different enrichment methods or gene-set methods described above.