Biological interpretation of gene or protein lists resulting from omics experiments can be a complex task. A common approach consists of reviewing Gene Ontology (GO) annotations for entries in such lists and searching for enrichment patterns and analysis. Unfortunately, there is a gap between machine-readable output of GO software and its human-interpretable form. This gap can be bridged by allowing users to simultaneously visualize and interact with term-term and gene-term relationships.
The open-source GOnet web-application has created which takes a list of gene or protein entries from human or mouse data and performs GO term annotation analysis (mapping of provided entries to GO subsets) or GO term enrichment analysis (scanning for GO categories overrepresented in the input list). The application is capable of producing parsable data formats and importantly, interactive visualizations of the GO analysis results. The interactive results allow exploration of genes and GO terms as a graph that depicts the natural hierarchy of the terms and retains relationships between terms and genes/proteins. As a result, GOnet provides insight into the functional interconnection of the submitted entries.
The output of genome-wide studies is typically a list of genes (or their protein products) exhibiting a shared pattern. Making scientific sense out of such results is a complicated task requiring biological knowledge of the involved genes/proteins and their functions.
However, the output of current GO analysis web applications (like AmiGO or DAVID) does not fully convey the hierarchical structure of the terms. Tools like GOrilla and NaviGO allow visualization of GO terms’ hierarchy but they in turn lose the relation of GO terms to the genes or proteins being analyzed. Addressing both visualization of term hierarchy and gene-term relations was the main motivation for creating the open source web-application, GOnet. It is achieved by generating a fully interactive graph with gene and term nodes. The graph supports different layouts making it possible to extend analyses based on graph topology.
The output of the analysis is typically a ranked list of GO terms, each associated with a p-value.
Since its inception, the Gene Ontology (GO)1 has empowered analyses of high-throughput molecular data. Researchers often perform statistical tests using the GO to determine functional enrichments within their discoveries and recently it has also been used to improve model architectures in machine learning applications.
Data visualizations, by illustrating the number of terms, rendering the relations between them and displaying term annotations, can alleviate some of the aforementioned issues. The information in GO is organized as a directed acyclic graph (DAG), where each node corresponds to a GO term and edges indicate relations such as “part of”, “a type of”, etc. It is then natural to rely on the DAG structure of the GO for information. Many existing GO tools focus on displaying a small subset of terms to reveal variation of the hierarchical relationships.
Our company, BioinfoLytics, is affliated with BioCode and is a project, where we are providing many topics on Genomics, Proteomics, their analysis using many tools in a cool way, Sequence Alignment & Analysis, Bioinformatics Scripting & Software Development, Phylogenetic and Phylogenomic Analysis, Functional Analysis, Biological Data Analysis & Visualization, Custom Analysis, Biological Database Analysis, Molecular Docking, Protein Structure Prediction and Molecular Dynamics etc. for the seekers of Biocode to further develop their interest to take part in these services to fulfill their requirements and obtain their desired results. We are providing such a platform where one can find opportunity to learn, research projects analysis and get help and huge knowledge based on molecular, computational and analytical biology.
We are providing “Gene Ontology Analysis” service to unify the representation of gene and gene product attributes across all species and make computational analysis of large-scale molecular biology and genetics experiments in biomedical research.