Dataset Information

Monitoring changes in the Gene Ontology and their impact on genomic data analysis.

ABSTRACT: Background:The Gene Ontology (GO) is one of the most widely used resources in molecular and cellular biology, largely through the use of "enrichment analysis." To facilitate informed use of GO, we present GOtrack (https://gotrack.msl.ubc.ca), which provides access to historical records and trends in the GO and GO annotations. Findings:GOtrack gives users access to gene- and term-level information on annotations for nine model organisms as well as an interactive tool that measures the stability of enrichment results over time for user-provided "hit lists" of genes. To document the effects of GO evolution on enrichment, we analyzed more than 2,500 published hit lists of human genes (most older than 9 years ); 53% of hit lists were considered to yield significantly stable enrichment results. Conclusions:Because stability is far from assured for any individual hit list, GOtrack can lead to more informed and cautious application of GO to genomics research.

SUBMITTER: Jacobson M

PROVIDER: S-EPMC6113503 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Monitoring changes in the Gene Ontology and their impact on genomic data analysis.

Jacobson Matthew M Sedeño-Cortés Adriana Estela AE Pavlidis Paul P

GigaScience 20180801 8

<h4>Background</h4>The Gene Ontology (GO) is one of the most widely used resources in molecular and cellular biology, largely through the use of "enrichment analysis." To facilitate informed use of GO, we present GOtrack (https://gotrack.msl.ubc.ca), which provides access to historical records and trends in the GO and GO annotations.<h4>Findings</h4>GOtrack gives users access to gene- and term-level information on annotations for nine model organisms as well as an interactive tool that measures ...[more]

PMID: 30107399

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Project description:MotivationRecently, a shift was made from using Gene Ontology (GO) to evaluate molecular network data to using these data to construct and evaluate GO. Dutkowski et al. provide the first evidence that a large part of GO can be reconstructed solely from topologies of molecular networks. Motivated by this work, we develop a novel data integration framework that integrates multiple types of molecular network data to reconstruct and update GO. We ask how much of GO can be recovered by integrating various molecular interaction data.ResultsWe introduce a computational framework for integration of various biological networks using penalized non-negative matrix tri-factorization (PNMTF). It takes all network data in a matrix form and performs simultaneous clustering of genes and GO terms, inducing new relations between genes and GO terms (annotations) and between GO terms themselves. To improve the accuracy of our predicted relations, we extend the integration methodology to include additional topological information represented as the similarity in wiring around non-interacting genes. Surprisingly, by integrating topologies of bakers' yeasts protein-protein interaction, genetic interaction (GI) and co-expression networks, our method reports as related 96% of GO terms that are directly related in GO. The inclusion of the wiring similarity of non-interacting genes contributes 6% to this large GO term association capture. Furthermore, we use our method to infer new relationships between GO terms solely from the topologies of these networks and validate 44% of our predictions in the literature. In addition, our integration method reproduces 48% of cellular component, 41% of molecular function and 41% of biological process GO terms, outperforming the previous method in the former two domains of GO. Finally, we predict new GO annotations of yeast genes and validate our predictions through GIs profiling.Availability and implementationSupplementary Tables of new GO term associations and predicted gene annotations are available at http://bio-nets.doc.ic.ac.uk/GO-Reconstruction/.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Monitoring changes in the Gene Ontology and their impact on genomic data analysis.

Publications

Monitoring changes in the Gene Ontology and their impact on genomic data analysis.

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