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Bi-directional semantic similarity for gene ontology to optimize biological and clinical analyses.

ABSTRACT: BACKGROUND: Semantic similarity analysis facilitates automated semantic explanations of biological and clinical data annotated by biomedical ontologies. Gene ontology (GO) has become one of the most important biomedical ontologies with a set of controlled vocabularies, providing rich semantic annotations for genes and molecular phenotypes for diseases. Current methods for measuring GO semantic similarities are limited to considering only the ancestor terms while neglecting the descendants. One can find many GO term pairs whose ancestors are identical but whose descendants are very different and vice versa. Moreover, the lower parts of GO trees are full of terms with more specific semantics. METHODS: This study proposed a method of measuring semantic similarities between GO terms using the entire GO tree structure, including both the upper (ancestral) and the lower (descendant) parts. Comprehensive comparison studies were performed with well-known information content-based and graph structure-based semantic similarity measures with protein sequence similarities, gene expression-profile correlations, protein-protein interactions, and biological pathway analyses. CONCLUSION: The proposed bidirectional measure of semantic similarity outperformed other graph-based and information content-based methods.

SUBMITTER: Bien SJ

PROVIDER: S-EPMC3422825 | biostudies-literature | 2012 Sep-Oct

REPOSITORIES: biostudies-literature

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Bi-directional semantic similarity for gene ontology to optimize biological and clinical analyses.

Bien Sang Jay SJ Park Chan Hee CH Shim Hae Jin HJ Yang Woongcheol W Kim Jihun J Kim Ju Han JH

Journal of the American Medical Informatics Association : JAMIA 20120228 5

<h4>Background</h4>Semantic similarity analysis facilitates automated semantic explanations of biological and clinical data annotated by biomedical ontologies. Gene ontology (GO) has become one of the most important biomedical ontologies with a set of controlled vocabularies, providing rich semantic annotations for genes and molecular phenotypes for diseases. Current methods for measuring GO semantic similarities are limited to considering only the ancestor terms while neglecting the descendants ...[more]

PMID: 22374934

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Project description:BackgroundThe 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.ResultsHere 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.ConclusionsThe 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 .

Project description:BackgroundVarious measures of semantic similarity of terms in bio-ontologies such as the Gene Ontology (GO) have been used to compare gene products. Such measures of similarity have been used to annotate uncharacterized gene products and group gene products into functional groups. There are various ways to measure semantic similarity, either using the topological structure of the ontology, the instances (gene products) associated with terms or a mixture of both. We focus on an instance level definition of semantic similarity while using the information contained in the ontology, both in the graphical structure of the ontology and the semantics of relations between terms, to provide constraints on our instance level description.Semantic similarity of terms is extended to annotations by various approaches, either though aggregation operations such as min, max and average or through an extrapolative method. These approaches introduce assumptions about how semantic similarity of terms relates to the semantic similarity of annotations that do not necessarily reflect how terms relate to each other.ResultsWe exploit the semantics of relations in the GO to construct an algorithm called SSA that provides the basis of a framework that naturally extends instance based methods of semantic similarity of terms, such as Resnik's measure, to describing annotations and not just terms. Our measure attempts to correctly interpret how terms combine via their relationships in the ontological hierarchy. SSA uses these relationships to identify the most specific common ancestors between terms. We outline the set of cases in which terms can combine and associate partial order constraints with each case that order the specificity of terms. These cases form the basis for the SSA algorithm. The set of associated constraints also provide a set of principles that any improvement on our method should seek to satisfy.ConclusionWe derive a measure of semantic similarity between annotations that exploits all available information without introducing assumptions about the nature of the ontology or data. We preserve the principles underlying instance based methods of semantic similarity of terms at the annotation level. As a result our measure better describes the information contained in annotations associated with gene products and as a result is better suited to characterizing and classifying gene products through their annotations.

Project description:BackgroundExome sequencing is a promising method for diagnosing patients with a complex phenotype. However, variant interpretation relative to patient phenotype can be challenging in some scenarios, particularly clinical assessment of rare complex phenotypes. Each patient's sequence reveals many possibly damaging variants that must be individually assessed to establish clear association with patient phenotype. To assist interpretation, we implemented an algorithm that ranks a given set of genes relative to patient phenotype. The algorithm orders genes by the semantic similarity computed between phenotypic descriptors associated with each gene and those describing the patient. Phenotypic descriptor terms are taken from the Human Phenotype Ontology (HPO) and semantic similarity is derived from each term's information content.ResultsModel validation was performed via simulation and with clinical data. We simulated 33 Mendelian diseases with 100 patients per disease. We modeled clinical conditions by adding noise and imprecision, i.e. phenotypic terms unrelated to the disease and terms less specific than the actual disease terms. We ranked the causative gene against all 2488 HPO annotated genes. The median causative gene rank was 1 for the optimal and noise cases, 12 for the imprecision case, and 60 for the imprecision with noise case. Additionally, we examined a clinical cohort of subjects with hearing impairment. The disease gene median rank was 22. However, when also considering the patient's exome data and filtering non-exomic and common variants, the median rank improved to 3.ConclusionsSemantic similarity can rank a causative gene highly within a gene list relative to patient phenotype characteristics, provided that imprecision is mitigated. The clinical case results suggest that phenotype rank combined with variant analysis provides significant improvement over the individual approaches. We expect that this combined prioritization approach may increase accuracy and decrease effort for clinical genetic diagnosis.

Dataset Information

Bi-directional semantic similarity for gene ontology to optimize biological and clinical analyses.

Publications

Bi-directional semantic similarity for gene ontology to optimize biological and clinical analyses.

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