The Gene Ontology (GO) is a dynamic, controlled vocabulary that describes the cellular function of genes and proteins according to tree major categories: biological process, molecular function and cellular component. It has become widely used in many bioinformatics applications for annotating genes and measuring their semantic similarity, rather than their sequence similarity. Generally speaking, semantic similarity measures involve the GO tree topology, information content of GO terms, or a combination of both. Here we present a new semantic similarity measure called TopoICSim (Topological Information Content Similarity) which uses information on the specific paths between GO terms based on the topology of the GO tree, and the distribution of information content along these paths. The TopoICSim algorithm was evaluated on two human benchmark datasets based on KEGG pathways and Pfam domains grouped as clans, using GO terms from either the biological process or molecular function. The performance of the TopoICSim measure compared favorably to five existing methods. Furthermore, the TopoICSim similarity was also tested on gene/protein sets defined by correlated gene expression, using three human datasets, and showed improved performance compared to two previously published similarity measures. Finally we used an online benchmarking resource which evaluates any similarity measure against a set of 11 similarity measures in three tests, using gene/protein sets based on sequence similarity, Pfam domains, and enzyme classifications. The results for TopoICSim showed improved performance relative to most of the measures included in the benchmarking, and in particular a very robust performance throughout the different tests. The TopoICSim similarity measure provides a competitive method with robust performance for quantification of semantic similarity between genes and proteins based on GO annotations. An R script for TopoICSim is available at http://bigr.medisin.ntnu.no/tools/TopoICSim.R.
TopoICSim: a new semantic similarity measure based on gene ontology
Published 2016 in BMC Bioinformatics
ABSTRACT
PUBLICATION RECORD
- Publication year
2016
- Venue
BMC Bioinformatics
- Publication date
2016-07-29
- Fields of study
Biology, Medicine, Computer Science
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
CITATION MAP
EXTRACTION MAP
CLAIMS
CONCEPTS
- benchmark datasets
Human evaluation datasets based on KEGG pathway groupings and Pfam clan groupings used to compare similarity methods.
Aliases: human benchmark datasets
- correlated gene expression datasets
Human gene and protein sets derived from correlated expression patterns and used as another evaluation setting.
Aliases: correlated expression datasets
- gene ontology
A controlled vocabulary for annotating gene and protein function across biological process, molecular function, and cellular component.
Aliases: GO
- go topology
The tree structure and connecting paths among Gene Ontology terms used in the similarity calculation.
Aliases: Gene Ontology topology
- information content
A term-specific specificity score used here to weight how informative GO terms are along a path.
Aliases: IC
- online benchmarking resource
A web-based evaluation resource that compares a similarity measure against a panel of existing measures in multiple tests.
Aliases: benchmarking resource
- semantic similarity measure
A scoring approach that quantifies functional relatedness between genes or proteins from their GO annotations.
Aliases: similarity measure
- topoicsim
A semantic similarity measure for GO-annotated genes and proteins that uses GO-term path structure and information content along those paths.
Aliases: Topological Information Content Similarity
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