2018-06-21 -- g:Profiler was updated to Ensembl 91 and Ensembl Genomes 38. Due to changes in mirBase we do not have miRNA predictions in the current release but we are looking for alternative data source for the future. We have also stopped supporting the BioGRID enriched networks.
2017-10-25 -- g:Profiler was updated to Ensembl 90 and Ensembl Genomes 37.
2017-08-16 -- Removed scaling of p-values in the case of FDR and Bonferroni multiple testing correction for better clarity for the user. This change makes the web results identical to the results obtained by R phyper+correction.
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Welcome to g:GOSt!First time? See our welcome note.
g:GOSt performs functional profiling of gene lists using various kinds of biological evidence. The tool performs statistical enrichment analysis to find over-representation of information like Gene Ontology terms, biological pathways, regulatory DNA elements, human disease gene annotations, and protein-protein interaction networks. Its output is a tabular graphic where genes are shown in columns, functions in rows, and coloured table cells show functional associations. The basic input of g:GOSt is a list of genes.
Default output of g:GOSt is a PNG graphic. See its detailed description that uses INHBA and co-expressed genes 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).
The basic form of g:Gost input is a list of genes. g:GOSt accepts a simple whitespace-separated gene lists that consist of mixed types of gene IDs (proteins, transcripts, microarray IDs, etc). Single genes, ordered gene lists, GO, pathway or any other IDs of functional information and chromosomal regions may be presented as input. See help items [?] for further information.
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.