scholarly journals Phylogenetic-based propagation of functional annotations within the Gene Ontology consortium

2011 ◽  
Vol 12 (5) ◽  
pp. 449-462 ◽  
Author(s):  
P. Gaudet ◽  
M. S. Livstone ◽  
S. E. Lewis ◽  
P. D. Thomas
Keyword(s):  
Gene Ontology ◽  
Functional Annotations ◽  
Gene Ontology Consortium

Brief Survey of Biological Network Alignment and a Variant with Incorporation of Functional Annotations

2018 ◽  
Vol 14 (1) ◽  
pp. 4-10
Author(s):  
Fang Jing ◽  
Shao-Wu Zhang ◽  
Shihua Zhang
Keyword(s):  
Gene Ontology ◽  
Topological Structure ◽  
Metabolic Networks ◽  
Biological Network ◽  
Genomic Sequence ◽  
Network Alignment ◽  
Future Directions ◽  
Functional Annotations ◽  
Protein Protein Interaction ◽  
Ppi Networks

Background:Biological network alignment has been widely studied in the context of protein-protein interaction (PPI) networks, metabolic networks and others in bioinformatics. The topological structure of networks and genomic sequence are generally used by existing methods for achieving this task.Objective and Method:Here we briefly survey the methods generally used for this task and introduce a variant with incorporation of functional annotations based on similarity in Gene Ontology (GO). Making full use of GO information is beneficial to provide insights into precise biological network alignment.Results and Conclusion:We analyze the effect of incorporation of GO information to network alignment. Finally, we make a brief summary and discuss future directions about this topic.

scholarly journals The Gene Ontology resource: enriching a GOld mine

2020 ◽  
Vol 49 (D1) ◽  
pp. D325-D334
Author(s):  
◽  
Seth Carbon ◽  
Eric Douglass ◽  
Benjamin M Good ◽  
Deepak R Unni ◽  
...  
Keyword(s):  
Experimental Data ◽  
Gene Ontology ◽  
Gold Mine ◽  
Gene Products ◽  
Gene Ontology Consortium ◽  
The Past ◽  
Historical Archive ◽  
File Structure

Abstract The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20 000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesigned for quick access to documentation, downloads and tools. To maintain an accurate resource and support traceability and reproducibility, we have made available a historical archive covering the past 15 years of GO data with a consistent format and file structure for both the ontology and annotations.

scholarly journals PANTHER version 7: improved phylogenetic trees, orthologs and collaboration with the Gene Ontology Consortium

2009 ◽  
Vol 38 (suppl_1) ◽  
pp. D204-D210 ◽  
Author(s):  
Huaiyu Mi ◽  
Qing Dong ◽  
Anushya Muruganujan ◽  
Pascale Gaudet ◽  
Suzanna Lewis ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Phylogenetic Trees ◽  
Gene Ontology Consortium

Gene multifunctionality scoring using gene ontology

2018 ◽  
Vol 16 (05) ◽  
pp. 1840018 ◽  
Author(s):  
Hisham Al-Mubaid
Keyword(s):  
Gene Ontology ◽  
Protein Interactions ◽  
Evaluation Criteria ◽  
Computational Method ◽  
Protein Protein Interactions ◽  
Validity And Reliability ◽  
Functional Annotations ◽  
Gene Sets ◽  
Human Genes ◽  
Disease Associations

Multifunctional genes are important genes because of their essential roles in human cells. Studying and analyzing multifunctional genes can help understand disease mechanisms and drug discovery. We propose a computational method for scoring gene multifunctionality based on functional annotations of the target gene from the Gene Ontology. The method is based on identifying pairs of GO annotations that represent semantically different biological functions and any gene annotated with two annotations from one pair is considered multifunctional. The proposed method can be employed to identify multifunctional genes in the entire human genome using solely the GO annotations. We evaluated the proposed method in scoring multifunctionality of all human genes using four criteria: gene-disease associations; protein–protein interactions; gene studies with PubMed publications; and published known multifunctional gene sets. The evaluation results confirm the validity and reliability of the proposed method for identifying multifunctional human genes. The results across all four evaluation criteria were statistically significant in determining multifunctionality. For example, the method confirmed that multifunctional genes tend to be associated with diseases more than other genes, with significance [Formula: see text]. Moreover, consistent with all previous studies, proteins encoded by multifunctional genes, based on our method, are involved in protein–protein interactions significantly more ([Formula: see text]) than other proteins.

scholarly journals Mining functional annotations across species

2018 ◽  
Author(s):  
Sven Warris ◽  
Steven Dijkxhoorn ◽  
Teije van Sloten ◽  
Bart van de Vossenberg
Keyword(s):  
Gene Ontology ◽  
Related Species ◽  
Functional Annotation ◽  
Graph Database ◽  
Functional Annotations ◽  
Novel Approach

AbstractMotivationNumerous tools and databases exist to annotate and interpret the functions encoded in genomes (InterProScan, KEGG, GO etc.). However, analyzing and comparing functionality across a number of genomes, for example of related species, is not trivial.ResultsWe present a novel approach, for which KEGG and Gene Ontology data are imported into a Neo4j graph database and InterProScan results from several species are added. Using the Neo4j plugin for Cytoscape, users can query this database and visualize functional annotations (sub)graphs, to compare and group functional annotation across species.

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