Auditing subtype inconsistencies among gene ontology concepts

Abeysinghe, Rashmie; Hinderer, Eugene W.; Moseley, Hunter N. B.; Cui, Licong

doi:10.1109/bibm.2017.8217835

Cited by 8 publications

(7 citation statements)

References 8 publications

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“…As GO continues to grow, automated methods to evaluate the structural organization of data will become necessary for curation and quality control. For instance, we recently collaborated in the evaluation of a method to automate auditing of potential subtype inconsistencies among terms in GO [26]. Because GOcats allows versatile interpretation of the GO DAG structure, it has many potential curation and quality control uses, especially for evaluating the high-level ontological organization of GO terms.…”

Section: Discussionmentioning

confidence: 99%

Section: Using Gocats For Curation and Quality Controlmentioning

confidence: 99%

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GOcats: A tool for categorizing Gene Ontology into subgraphs of user-defined concepts

Hinderer

Flight

Moseley

2018

Preprint

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results)relations are included in the analysis. Therefore, we developed a custom graph parsing scheme that allows these relations to be incorporated without resulting in erroneous term categorizations. This enables a more robust semantic scoping utilization of Gene Ontology, facilitating summarization of annotations in large data sets. We also demonstrate that GOcats is well suited for comparing results from separate annotation sources due to its ability to allow adjustment of categories to the appropriate annotation term granularity. GOcats thus facilitates more robust interpretation and comparison of experimental and knowledgebase annotation sources and provides new tools for semantic scoping utilization and development of Gene Ontology. Furthermore, when used alongside annotation enrichment tools such as categoryCompare2, GOcats' unique method for inferring category membership results in both improved enrichment statistics and the identification of enriched terms otherwise impossible-to-identify with statistical significance when compared to using conventional ontology inference rules. 8 has_part describe part-whole (mereological) correspondence. Therefore, we consider scoping relations to be comprised of is_a, part_of, and has_part, and mereological relations to be comprised of part_of and has_part.There are three versions of the GO database, each containing aspects of the CV with varying complexity: go-basic is filtered to exclude relations that span across multiple sub-ontologies and to include only relations that point toward the root of the ontology; go or go-core contains additional relations, such as has_part that may span sub-ontologies and which point both toward and away from the root of the ontology; and go-plus contains yet more relations in addition to cross-references to entries in external databases like the Chemical Entities of Biological Interest (ChEBI) ontology (Munoz-Torres & Carbon, 2017). The first and second versions are available in the Open Biomedical Ontology (OBO) flat text file formatting, while the third is available only in the Web Ontology Language (OWL) RDF/XML format. Categorization-relevant issues in GOOntological graphs are typically designed as directed graphs, meaning that every edge has directionality or directed acyclic graphs (DAGs), meaning that no path exists that leads back to a node already visited if one were to traverse the graph stepwise.This allows the graph to form a complex semantic model of biology containing both general concepts and more-specific (fine-grained) concepts. The "parent-child" relation hierarchy allows biological entities to be annotated at any level of specificity (granularity) with a single term code, as fine-grained terms intrinsically capture the meaning of every one of its parent and ancestor terms through the linking of relation-defining is_a edges in the graph. However, it is deceptively non-trivial to reverse the logic and organize 1 0 Author Contributions E.W.H. worked on the design of GOcats, implemented GOcats, performed all analyses,...

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Section: Discussionmentioning

confidence: 99%

Section: Using Gocats For Curation and Quality Controlmentioning

confidence: 99%

GOcats: A tool for categorizing Gene Ontology into subgraphs of user-defined concepts

Hinderer

Flight

Moseley

2018

Preprint

Self Cite

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“…Despite maintenance and standard policies for adding terms, ontological organization is still subject to human error and disagreement, necessitating quality assurance and revising, especially as ontologies evolve or merge. A recent review of current methods for biomedical ontology mapping highlights the importance in developing semi-automatic methods [18,19] to aid in ontology evolution efforts and reiterates the aforementioned concept of semantic correspondence in terms of scoping between terms [20]. Methods incorporating such correspondences have been published elsewhere, but these deal with issues of ontology evolution and merging, and not with categorizing terms into user-defined subsets [21,22].…”

Section: Maintenance Of Ontologiesmentioning

confidence: 99%

GOcats: A tool for categorizing Gene Ontology into subgraphs of user-defined concepts

Hinderer

Moseley

2020

PLoS ONE

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Gene Ontology is used extensively in scientific knowledgebases and repositories to organize a wealth of biological information. However, interpreting annotations derived from differential gene lists is often difficult without manually sorting into higher-order categories. To address these issues, we present GOcats, a novel tool that organizes the Gene Ontology (GO) into subgraphs representing user-defined concepts, while ensuring that all appropriate relations are congruent with respect to scoping semantics. We tested GOcats performance using subcellular location categories to mine annotations from GO-utilizing knowledgebases and evaluated their accuracy against immunohistochemistry datasets in the Human Protein Atlas (HPA). In comparison to term categorizations generated from UniProt's controlled vocabulary and from GO slims via OWLTools' Map2Slim, GOcats outperformed these methods in its ability to mimic human-categorized GO term sets. Unlike the other methods, GOcats relies only on an input of basic keywords from the user (e.g. biologist), not a manually compiled or static set of top-level GO terms. Additionally, by identifying and properly defining relations with respect to semantic scope, GOcats can utilize the traditionally problematic relation, has_part, without encountering erroneous term mapping. We applied GOcats in the comparison of HPA-sourced knowledgebase annotations to experimentallyderived annotations provided by HPA directly. During the comparison, GOcats improved correspondence between the annotation sources by adjusting semantic granularity. GOcats enables the creation of custom, GO slim-like filters to map fine-grained gene annotations from gene annotation files to general subcellular compartments without needing to handselect a set of GO terms for categorization. Moreover, GOcats can customize the level of semantic specificity for annotation categories. Furthermore, GOcats enables a safe and more comprehensive semantic scoping utilization of go-core, allowing for a more complete utilization of information available in GO. Together, these improvements can impact a variety of GO knowledgebase data mining use-cases as well as knowledgebase curation and quality control.

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“…Xing et al developed a scalable approach combining the algorithmic ideas of dynamic programming and topological sort to exhaustively identify redundant hierarchical is-a relations in large ontologies including GO [ 23 ]. In previous works, we investigated a lexical-based inference approach [ 24 ] and a subsumption-based sub-term inference framework [ 25 ] to identify missing and erroneous hierarchical is-a relations in GO.…”

Section: Introductionmentioning

confidence: 99%

An evidence-based lexical pattern approach for quality assurance of Gene Ontology relations

Abeysinghe

Yang

Bartels

et al. 2022

Briefings in Bioinformatics

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Gene Ontology (GO) is widely used in the biological domain. It is the most comprehensive ontology providing formal representation of gene functions (GO concepts) and relations between them. However, unintentional quality defects (e.g. missing or erroneous relations) in GO may exist due to the large size of GO concepts and complexity of GO structures. Such quality defects would impact the results of GO-based analyses and applications. In this work, we introduce a novel evidence-based lexical pattern approach for quality assurance of GO relations. We leverage two layers of evidence to suggest potentially missing relations in GO as follows. We first utilize related concept pairs (i.e. existing relations) in GO to extract relationship-specific lexical patterns, which serve as the first layer evidence to automatically suggest potentially missing relations between unrelated concept pairs. For each suggested missing relation, we further identify two other existing relations as the second layer of evidence that resemble the difference between the missing relation and the existing relation based on which the missing relation is suggested. Applied to the 15 December 2021 release of GO, this approach suggested a total of 866 potentially missing relations. Local domain experts evaluated the entire set of potentially missing relations, and identified 821 as missing relations and 45 indicate erroneous existing relations. We submitted these findings to the GO consortium for further validation and received encouraging feedback. These indicate that our evidence-based approach can be utilized to uncover missing relations and erroneous existing relations in GO.

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Auditing subtype inconsistencies among gene ontology concepts

Cited by 8 publications

References 8 publications

GOcats: A tool for categorizing Gene Ontology into subgraphs of user-defined concepts

GOcats: A tool for categorizing Gene Ontology into subgraphs of user-defined concepts

GOcats: A tool for categorizing Gene Ontology into subgraphs of user-defined concepts

An evidence-based lexical pattern approach for quality assurance of Gene Ontology relations

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