MUI: A New Functional Similarity Measure for Gene Products Based on Gene Ontology

2009 ◽  
Author(s):  
Qiang Hu ◽  
Zheng-Guo Zhang
Keyword(s):  
Gene Ontology ◽  
Similarity Measure ◽  
Functional Similarity ◽  
Gene Products

scholarly journals A new measure for functional similarity of gene products based on Gene Ontology

2006 ◽  
Vol 7 (1) ◽  
Author(s):  
Andreas Schlicker ◽  
Francisco S Domingues ◽  
Jörg Rahnenführer ◽  
Thomas Lengauer
Keyword(s):  
Gene Ontology ◽  
Functional Similarity ◽  
Gene Products

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 Term Matrix: a novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns

Open Biology ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 200149 ◽  
Author(s):  
Valerie Wood ◽  
Seth Carbon ◽  
Midori A. Harris ◽  
Antonia Lock ◽  
Stacia R. Engel ◽  
...  
Keyword(s):  
Quality Control ◽  
Gene Ontology ◽  
Biological Processes ◽  
Gene Products ◽  
Model Species ◽  
Ontology Term ◽  
Protein Coding ◽  
Ontology Structure ◽  
Exclusive Processes ◽  
Annotation Quality

Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionally, temporally and/or spatially distant may have few gene products in common, and co-annotation to unrelated processes probably reflects errors in literature curation, ontology structure or automated annotation pipelines. We have developed an annotation quality control workflow that uses rules based on mutually exclusive processes to detect annotation errors, based on and validated by case studies including the three we present here: fission yeast protein-coding gene annotations over time; annotations for cohesin complex subunits in human and model species; and annotations using a selected set of GO biological process terms in human and five model species. For each case study, we reviewed available GO annotations, identified pairs of biological processes which are unlikely to be correctly co-annotated to the same gene products (e.g. amino acid metabolism and cytokinesis), and traced erroneous annotations to their sources. To date we have generated 107 quality control rules, and corrected 289 manual annotations in eukaryotes and over 52 700 automatically propagated annotations across all taxa.

scholarly journals Unifying Themes in Microbial Associations with Animal and Plant Hosts Described Using the Gene Ontology

2010 ◽  
Vol 74 (4) ◽  
pp. 479-503 ◽  
Author(s):  
Trudy Torto-Alalibo ◽  
Candace W. Collmer ◽  
Michelle Gwinn-Giglio ◽  
Magdalen Lindeberg ◽  
Shaowu Meng ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Effector Proteins ◽  
Biological Processes ◽  
Gene Products ◽  
Host Defenses ◽  
Host Interactions ◽  
Microbial Associations ◽  
Bioinformatic Approaches ◽  
Go Terms ◽  
Diverse Plant

SUMMARY Microbes form intimate relationships with hosts (symbioses) that range from mutualism to parasitism. Common microbial mechanisms involved in a successful host association include adhesion, entry of the microbe or its effector proteins into the host cell, mitigation of host defenses, and nutrient acquisition. Genes associated with these microbial mechanisms are known for a broad range of symbioses, revealing both divergent and convergent strategies. Effective comparisons among these symbioses, however, are hampered by inconsistent descriptive terms in the literature for functionally similar genes. Bioinformatic approaches that use homology-based tools are limited to identifying functionally similar genes based on similarities in their sequences. An effective solution to these limitations is provided by the Gene Ontology (GO), which provides a standardized language to describe gene products from all organisms. The GO comprises three ontologies that enable one to describe the molecular function(s) of gene products, the biological processes to which they contribute, and their cellular locations. Beginning in 2004, the Plant-Associated Microbe Gene Ontology (PAMGO) interest group collaborated with the GO consortium to extend the GO to accommodate terms for describing gene products associated with microbe-host interactions. Currently, over 900 terms that describe biological processes common to diverse plant- and animal-associated microbes are incorporated into the GO database. Here we review some unifying themes common to diverse host-microbe associations and illustrate how the new GO terms facilitate a standardized description of the gene products involved. We also highlight areas where new terms need to be developed, an ongoing process that should involve the whole community.

GENE ONTOLOGY SIMILARITY MEASURES BASED ON LINEAR ORDER STATISTICS

2006 ◽  
Vol 14 (06) ◽  
pp. 639-661 ◽  
Author(s):  
JAMES M. KELLER ◽  
JAMES C. BEZDEK ◽  
MIHAIL POPESCU ◽  
NIKHIL R. PAL ◽  
JOYCE A. MITCHELL ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Order Statistics ◽  
Gene Product ◽  
Linear Order ◽  
Similarity Measures ◽  
Product Family ◽  
Amino Acid Sequences ◽  
Gene Products ◽  
Multiple Sources ◽  
Similarity Relations

The standard method for comparing gene products (proteins or RNA) is to compare their DNA or amino acid sequences. Additional information about some gene products may come from multiple sources, including the set of Gene Ontology (GO) annotations and the set of journal abstracts related to each gene product. Gene product similarity measures can be based on evaluating sets of descriptor terms found in the GO taxonomy, and/or the index term sets of the related documents (MeSH annotations). While our techniques can be applied to term sets from any taxonomy, we restrict our examples in this article to GO annotations. We investigate the use of linear order statistics (LOS) to build similarity relations on pairs of terms that are used in the GO as linguistic descriptors of genes and gene products. One of our objectives is to investigate the construction and utility of visual assessments of relational data (in this case, dissimilarity matrices) for discovering tendencies of groups of gene products to "cluster together". We use gene product data derived from a group of 194 gene products representing three protein families extracted from ENSEMBL. Our examples suggest that LOS similarity measures are more effective than traditional sequence-based similarity measures at capturing relationships between pairs of gene products in ENSEMBL families when annotation information is available. We show examples of how these similarity measures can assist in knowledge discovery and gene product family validation.

A GRAPH-BASED SEMANTIC SIMILARITY MEASURE FOR THE GENE ONTOLOGY

2011 ◽  
Vol 09 (06) ◽  
pp. 681-695 ◽  
Author(s):  
MARCO A. ALVAREZ ◽  
CHANGHUI YAN
Keyword(s):  
Gene Ontology ◽  
Semantic Similarity ◽  
Common Ancestor ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Similarity Score ◽  
Gene Products ◽  
Semantic Similarity Measure ◽  
Similarity Algorithm ◽  
Go Terms

Existing methods for calculating semantic similarities between pairs of Gene Ontology (GO) terms and gene products often rely on external databases like Gene Ontology Annotation (GOA) that annotate gene products using the GO terms. This dependency leads to some limitations in real applications. Here, we present a semantic similarity algorithm (SSA), that relies exclusively on the GO. When calculating the semantic similarity between a pair of input GO terms, SSA takes into account the shortest path between them, the depth of their nearest common ancestor, and a novel similarity score calculated between the definitions of the involved GO terms. In our work, we use SSA to calculate semantic similarities between pairs of proteins by combining pairwise semantic similarities between the GO terms that annotate the involved proteins. The reliability of SSA was evaluated by comparing the resulting semantic similarities between proteins with the functional similarities between proteins derived from expert annotations or sequence similarity. Comparisons with existing state-of-the-art methods showed that SSA is highly competitive with the other methods. SSA provides a reliable measure for semantics similarity independent of external databases of functional-annotation observations.

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