Gene Semantic Similarity Analysis and Measurement Tools


G-SESAME is a set of on-line tools to measure the semantic similarities of Gene Ontology (GO) terms and the functional similarities of gene products, and to discover biomedical knowledge through GO database. These tools are originally based on the G-SESAME paper in 2007. They were developed using MariaDB, PHP and hosted by an Apache Web server running on a Linux operating system (CentOS 7). New methods taking into account the statistical distribution of the GO database are implemented as a new features. Other state-of-the-art methods were also implemented to allow researchers to choose the best methods on their own needs.

Visualization techniques are provided in these tools to allow users to inspect the locations of the GO terms within the GO graph and to visually determine the semantic similarity. A batch command interface is also provided for users to execute the tools to measure the semantic similarity of a group of GO terms or functional similarities of a group of genes. Web based APIs are also provided for advanced users.

G-SESAME tools have been used more than 70.2 million times by researchers from 252 organizations between October 2006 and February 2016 according to our web log records.. G-SESAME is currently using the gene ontology database published by the gene ontology consortium in Feb, 2016.

How to cite the G-SESAME tools?

Please cite the following paper published in Bioinformatics:

  • James Z. Wang, Zhidian Du, Rapeeporn Payattakool, Philip S. Yu and Chin-Fu Chen, A New Method to Measure the Semantic Similarity of GO Terms, Bioinformatics, 2007, 23: 1274-1281; doi: 10.1093/bioinformatics/btm087
  • James Z. Wang, Zhidian Du, Philip S. Yu and Chin-Fu Chen, An Effient Online Tool to Search Top-N Genes with Similar Biological Functions in Gene Ontology Database, IEEE International Conference on Bioinformatics and Biomedicine, 2007
  • Zhidian Du, Lin Li, Chin-Fu Chen, Philip S. Yu, and James Z. Wang. G-SESAME: web tools for go term based gene similarity analysis and knowledge discovery. Nucleic Acids Research, 37:W345-W349, 2009.
  • Xuebo Song, Lin Li, Pradip K. Srimani, Philip S. Yu, James Z. Wang, Measure the Seman\ tic Similarity of GO terms Using Aggregate Information Content, IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, VOL. 11, NO. 3, MAY-JUNE 2014

Sponsor Information

This project is supported by NSF grant DBI-0960586 and DBI-0960443.