J. Reimand, M. Kull, H. Peterson, J. Hansen, J. Vilo: g:Profiler -- a web-based toolset for functional profiling of gene lists from large-scale experiments (2007) NAR 35 W193-W200 [PDF
J. Reimand, T. Arak, J. Vilo: g:Profiler -- a web server for functional interpretation of gene lists (2011 update) Nucleic Acids Research 2011; doi: 10.1093/nar/gkr378 [PDF
30.07.2015 -- g:Profiler was updated to Ensembl 80, Ensembl Genomes 27, and Reactome 53. All upstream data sources are now versioned, information on each is available via the "Version info" link above.
27.04.2015 -- g:Profiler was updated to Ensembl 79, Ensembl Genomes 26, and Reactome 52. Graphical output has been changed to a more compact format with textual information on the left and gene-process annotation matrix on the right. In addition to PNG files shown on the website, graphical output can be also downloaded in PDF format.
20.02.2015 -- g:Profiler was updated to Ensembl 78 and Ensembl Genomes 25. The user now has the oppurtunity to semi-automatically resolve ambiguous incoming gene identifiers in the graphical interface of g:GOSt. A BioJS component is available for developers to visualize g:Profiler results in an external tool.
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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.
- Query 1: nine core cell cycle transcription factors (TF) in yeast (plain, unordered query).
- Query 2: same as above, but considering only the set of all yeast TFs as background (custom statistical background).
- Query 3: human INHBA, a member of the TGF-beta pathway, and 29 microarray probesets with highest correlation in gene expression (ordered query).
- Query 4: Same as above, but electronic annotations [IEA] excuded (no IEA).
- Query 5: Recurrent mutations in the PI3K/AKT signalling pathway from the TCGA pancancer dataset (5+ SNVs).
- Query 6: Same as above, but output stored as Excel file (alternative output type).
- Query 7: Textual list of all known annotations of human gene PAX6 (query with single gene, alternative output type, all significant and insignificant annotations).
- Query 8: Fully numeric EntrezGene IDs and appropriate prefixes added automatically (query with numeric IDs).
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.
gProfileR R package is available on CRAN.
- There is a BioJS component available for visualizing g:Profiler results in your web application.
- Check out gProfiler Beta for most recent developments and data updates.
- Previous versions of g:Profiler software and data are available in the archives.
- g:Profiler web service API documentation is available here.