Welcome to g:Cocoa!g:Cocoa performs functional analysis of multiple gene lists. The compact output of g:Cocoa allows a condensed and minimal view of functional enrichments in dozens of gene lists. Its output is a tabular graphic similar to g:Gost where each column stands for a gene list, each row represents a functional term, and cell colour-coding highlights significant enrichments. The tool naturally works on a single gene list and may be preferred over g:Gost for compact visuals.
Default output of g:Cocoa is a PNG graphic. See its detailed description that applies Neanderthal chromosomal regions as example. Three alternative options are available in the output type dropdown menu: textual, spreadsheet (XLS) and minimal. The minimal option shows output without HTML header and is useful for programmatic access. All these options present enrichment information in a common format (see help item [?] at the dropdown).
g:Cocoa accepts an input format that resembles FASTA. Lists in the multi-query are separated by lines that start with a > symbol and optionally contain a name. Any number of following lines until the next > belong to the query and should contain gene names, chromosomal regions, etc. Note that genes and chromosomal regions cannot be mixed. A toy example of a g:Cocoa query is shown below:
> list A gene 1 gene5 gene25 > list B gene3 gene4 gene17 # this is a comment gene25 > list C gene3 gene4
Ordered gene lists
g:Profiler gene lists may be interpreted as ordered lists where elements (genes, proteins, probesets) are in order of decreasing importance. The ordered query option is useful when the genes are placed in some biologically meaningful order, for instance according to differential expression in a given microarray experiment. g:Profiler then performs incremental enrichment analysis with increasingly larger numbers of genes from the top of the list. This optimisation procedure identifies specific functional terms that associate to most dramatic changes in gene expression, as well as broader terms that characterise the gene set as a whole.
g:Sorter is a convenient method for producing examples of sorted lists from microarray co-expression searches.
Electronic annotations [IEA]
A significant proportion of functional annotations of Gene Ontology are assigned using in silico curation methods and have the IEA evidence code (Inferred from Electronic Annotation). While IEA annotations are an invaluable resource in mapping gene functions, manually curated annotations of experimental and computational studies are generally of higher confidence. Therefore it is sometimes advisable to exclude electronically inferred annotations from enrichment analysis and focus on annotations with stronger evidence. Excluding IEA annotations may also help reduce bias towards abundant and ubiquitous housekeeping genes like the ribosomes. Our IEA filter is enabled via a single checkbox no electronic GO annotations and corresponding enrichment analyses account for altered structure of GO annotations.
Besides various gene names, symbols and accessions, queries may be constructed from collections of chromosomal regions. ENSG genes from these regions are retrieved automatically. Genes need not fit the region fully, and hence one may even study single nucleotides (SNPs). g:Profiler uses a chromosome:start:end format for chromosomal regions, e.g. X:1:2000000. To activate chromosomal queries, check the checkbox chromosome ranges. Note that genes and chromosome regions cannot be mixed in a single query.
Multiple testing correction
Multiple testing problem is a statistical concept that relates to the increased chance of getting significant-looking false positive results, when evaluating a large number of alternative hypotheses simultaneously. It is an important issue in functional enrichment analysis, since each input query is compared against hundreds of Gene Ontology terms, pathways, regulatory motifs, etc. Multiple testing correction systematically reduces the significance of detected p-values to discard false positives.
g:Profiler uses multiple testing correction by default and applies our tailor-made algorithm g:SCS for reducing significance scores. Alternatively, one may select Bonferroni correction (BC) or Benjamini-Hochberg FDR (False Discovery Rate) -- these two standard solutions to the multiple testing problem are available under Advanced options. In comparison to the latter two, our algorithm takes into account the unevenly distributed structure of functionally annotated gene sets. Our simulations in (Nucleic Acids Research, 2007) show that g:SCS provides a better threshold between significant and non-significant results than FDR or BC.