Mining GO Annotations for Improving Annotation Consistency

Faria, Daniel; Schlicker, Andreas; Pesquita, Cátia; Bastos, Hugo; Ferreira, António E. N.; Albrecht, Mario; Falcão, André O.

doi:10.1371/journal.pone.0040519

Cited by 37 publications

(33 citation statements)

References 22 publications

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“…The E. coli GO term assignments at Amigo are compiled from several sources including EcoCyc (26), EcoliWiki (27), UniProtKB (11,28) and InterPro (29). We made two observations consistent with problems reported elsewhere (30). First, the GO annotations are incomplete and many proteins lack GO annotations consistent with their molecular function as currently understood.…”

Section: Introductionsupporting

confidence: 91%

EcoGene 3.0

Zhou¹,

Rudd²

2012

Nucleic Acids Research

188

181

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EcoGene (http://ecogene.org) is a database and website devoted to continuously improving the structural and functional annotation of Escherichia coli K-12, one of the most well understood model organisms, represented by the MG1655(Seq) genome sequence and annotations. Major improvements to EcoGene in the past decade include (i) graphic presentations of genome map features; (ii) ability to design Boolean queries and Venn diagrams from EcoArray, EcoTopics or user-provided GeneSets; (iii) the genome-wide clone and deletion primer design tool, PrimerPairs; (iv) sequence searches using a customized EcoBLAST; (v) a Cross Reference table of synonymous gene and protein identifiers; (vi) proteome-wide indexing with GO terms; (vii) EcoTools access to >2000 complete bacterial genomes in EcoGene-RefSeq; (viii) establishment of a MySql relational database; and (ix) use of web content management systems. The biomedical literature is surveyed daily to provide citation and gene function updates. As of September 2012, the review of 37 397 abstracts and articles led to creation of 98 425 PubMed-Gene links and 5415 PubMed-Topic links. Annotation updates to Genbank U00096 are transmitted from EcoGene to NCBI. Experimental verifications include confirmation of a CTG start codon, pseudogene restoration and quality assurance of the Keio strain collection.

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Section: Introductionsupporting

confidence: 91%

EcoGene 3.0

Zhou¹,

Rudd²

2012

Nucleic Acids Research

188

181

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“…First, we recall that GO-WAR is slightly different from the method described in [9], because the authors are interested in finding implicit relationships regarding aspects of a single function (e.g. AT P aseactivity → AT P binding).…”

Section: Discussionmentioning

confidence: 99%

“…The methodology developed by Faria et al [9] is based on the use of association rules to discover possible inconsistent or redundant annotations. In particular, they are interested in finding implicit relationships regarding aspects of a single function (e.g.…”

Section: Related Workmentioning

confidence: 99%

“…Literature reports different uses of AR mining from annotated data that are usually tailored to some specific aims as reported by Naulaerts et al [8]. For instance, Faria et al [9] proposed association rules to support GO curators by evaluating the annotation consistency in order to avoid possible inconsistent or redundant annotations. Diversely, Benites et al [10], proposed a data mining algorithm focused on mining rare associations from pairwise associations among multiple categories, useful to describe relations that are not obvious.…”

Section: Introductionmentioning

confidence: 99%

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Extracting Cross-Ontology Weighted Association Rules from Gene Ontology Annotations

Agapito

Milano

Guzzi

et al. 2016

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Gene Ontology (GO) is a structured repository of concepts (GO Terms) that are associated to one or more gene products through a process referred to as annotation. The analysis of annotated data is an important opportunity for bioinformatics. There are different approaches of analysis, among those, the use of association rules (AR) which provides useful knowledge, discovering biologically relevant associations between terms of GO, not previously known. In a previous work, we introduced GO-WAR (Gene Ontology-based Weighted Association Rules), a methodology for extracting weighted association rules from ontology-based annotated datasets. We here adapt the GO-WAR algorithm to mine cross-ontology association rules, i.e., rules that involve GO terms present in the three sub-ontologies of GO. We conduct a deep performance evaluation of GO-WAR by mining publicly available GO annotated datasets, showing how GO-WAR outperforms current state of the art approaches.

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“…Alterovitz et al proposed an information theory-based approach to automatically organize the structure of GO and optimize the distribution of the information within it [32]. Faria et al proposed an association rule-based algorithm for identifying implicit relationships between molecular function terms [33]. Other works focused on the quality of terms definitions [29] and on the detection of semantic inconsistencies of gene annotations [34].…”

Section: Discussionmentioning

confidence: 99%

Measuring the Evolution of Ontology Complexity: The Gene Ontology Case Study

2013

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Ontologies support automatic sharing, combination and analysis of life sciences data. They undergo regular curation and enrichment. We studied the impact of an ontology evolution on its structural complexity. As a case study we used the sixty monthly releases between January 2008 and December 2012 of the Gene Ontology and its three independent branches, i.e. biological processes (BP), cellular components (CC) and molecular functions (MF). For each case, we measured complexity by computing metrics related to the size, the nodes connectivity and the hierarchical structure.The number of classes and relations increased monotonously for each branch, with different growth rates. BP and CC had similar connectivity, superior to that of MF. Connectivity increased monotonously for BP, decreased for CC and remained stable for MF, with a marked increase for the three branches in November and December 2012. Hierarchy-related measures showed that CC and MF had similar proportions of leaves, average depths and average heights. BP had a lower proportion of leaves, and a higher average depth and average height. For BP and MF, the late 2012 increase of connectivity resulted in an increase of the average depth and average height and a decrease of the proportion of leaves, indicating that a major enrichment effort of the intermediate-level hierarchy occurred.The variation of the number of classes and relations in an ontology does not provide enough information about the evolution of its complexity. However, connectivity and hierarchy-related metrics revealed different patterns of values as well as of evolution for the three branches of the Gene Ontology. CC was similar to BP in terms of connectivity, and similar to MF in terms of hierarchy. Overall, BP complexity increased, CC was refined with the addition of leaves providing a finer level of annotations but decreasing slightly its complexity, and MF complexity remained stable.

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Mining GO Annotations for Improving Annotation Consistency

Cited by 37 publications

References 22 publications

EcoGene 3.0

EcoGene 3.0

Extracting Cross-Ontology Weighted Association Rules from Gene Ontology Annotations

Measuring the Evolution of Ontology Complexity: The Gene Ontology Case Study

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