Services



Features

GS2 provides the fastest set similarity measure for GO annotations. We can handle thousands of genes in a fraction of a second. All you have to do is provide the annotations. Interested? Try it out now.

If you'd like to learn more, check out the description of our method.

Analysis

This online tool calculates the functional similarity of a set of genes using their GO annotations. Our online tool takes as input Ensembl gene identifiers. To input a set of genes, first select the appropriate organism dataset. Next, enter gene identifiers using one line per gene (see an example). For 500 genes, there should be 500 lines of text. It is a good idea to prepare this list externally, then paste the data into the textbox below.

We retrieve the GO annotations directly from the Ensembl biomart so that the annotations are up to date. For large gene sets (1000+) it may take around a minute to retrieve the annotation data, but the similarity calculation will take only a second! We also provide several versions of the Gene Ontology, including the Slim subsets.

Set Similarity

Clear Gene Set Example Data
GO Tree version:
Organism dataset:
Ensembl Gene IDs:


Download

We also provide a Python implementation of our method. This library only requires Python to be installed on the machine, making this code platform independent. pyGS2 is distributed under the LPGL. You can right click and download pyGS2. It's only one file, and the usage instructions are at the top of the document.

Annotation Links

Right now we compute similarity only on GO terms; however, it's easy to retrieve GO annotations for any gene list with the tools below.

Feedback

Found GS2 useful? We appreciate any and all feedback, or, just drop us a line and we'll add you to our list of satisfied customers.

Acknowledgement

GS2 was made possible in part by DOE grant DE-FG02-06ER25734, NSF grants CCF-0622037 and CCF-0829276, and grant R01LM009494 from the National Library of Medicine. The contents are solely the responsibility of the authors and do not necessarily represent the official views of the DOE, NSF, National Library of Medicine or the National Institutes of Health.