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.


What is GS2?

GS2 (GO-based similarity of gene sets) is a novel GO-based measure of gene set similarity that is computable in linear time in the size of the gene set. The measure quantifies the similarity of a gene set with GO annotations by averaging the contribution of each gene’s GO terms and their ancestor terms with respect to the GO vocabulary graph. Our measure manages to preserve semantic relationships without the use of weighting factors.

Motivation

The growing availability of genome-scale data sets has attracted increasing attention to the development of computational methods for automated inference of functional similarities among genes and their products. One class of such methods measures the functional similarity of genes based on their distance in the Gene Ontology (GO). To measure the functional relatedness of a gene set, these measures are applied to every pair of genes in the set, and the average of all pairwise distances is calculated. However, as more data becomes available and gene sets become larger, such pair-based calculation becomes prohibitive.

Services

We provide an online similarity calculation available here. This web tool takes as input a list of GO terms and lets the user choose the GO tree version. Within seconds, GS2 returns a result.


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

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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.