Abstract
Algorithms for measuring semantic similarity between Gene Ontology (GO) terms has become a popular area of research in bioinformatics as it can help to detect functional associations between genes and potential impact to the health and well-being of humans, animals, and plants. While the focus of the research is on the design and improvement of GO semantic similarity algorithms, there is still a need for implementation of such algorithms before they can be used to solve actual biological problems. This can be challenging given that the potential users usually come from a biology background and they are not programmers. A number of implementations exist for some well-established algorithms but these implementations are not generic enough to support any algorithm other than the ones they are designed for. The aim of this paper is to shift the focus away from implementation, allowing researchers to focus on algorithm’s design and execution rather than implementation. This is achieved by an implementation approach capable of understanding and executing user defined GO semantic similarity algorithms. Questions and answers were used for the definition of the user defined algorithm. Additionally, this approach understands any direct acyclic digraph in an Open Biomedical Ontologies (OBO)-like format and its annotations. On the other hand, software developers of similar applications can also benefit by using this as a template for their applications.
Highlights
Many methods have been developed for the execution of Gene Ontology Semantic Similarities algorithms, but none of them allows the user to define and execute user-defined algorithms
It can classify genes based on functional similarities. It does support user defined algorithms. Both ProteInOn and FunSimMat were very useful for the researchers that were working on standard datasets, but on the other hand, they were found unable to perform semantic similarity using user-provided annotation files
The focus of this paper is the implementation of user-defined Gene Ontology (GO) semantic similarity algorithm, in this paragraph we are going to describe a generic parser, which can accept any type of Open Biomedical Ontologies (OBO)
Summary
Many methods have been developed for the execution of Gene Ontology Semantic Similarities algorithms, but none of them allows the user to define and execute user-defined algorithms Instead they offer to the user the ability to choose from a static list of pre-implemented algorithms. Res. Public Health 2020, 17, 267 we propose an implementation approach (see Supplementary Materials) to allow users to define GO semantic similarity algorithms, load any annotation file, and execute it. As part of this research we have developed a tool (see Supplementary Files) that implements the proposed approaches In this tool, we follow a question/answer approach where the user will “teach” the program how to use the new algorithm by answering a number of questions. In the seventh section, we summarize the contribution of our work to GO semantic similarity and propose future perspectives
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