ArticleBMC research notes2011
GO Trimming: Systematically reducing redundancy in large Gene Ontology datasets.
Article in BMC research notes, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
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- Benchmarking multiple gene ontology enrichment tools reveals high biological significance, ranking, and stringency heterogeneity among datasets.Frontiers in bioinformatics · 2026Article
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- Vision-Dependent and -Independent Molecular Maturation of Mouse Retinal Ganglion Cells.Neuroscience · 2023Article
- Immunological modifications following chemotherapy are associated with delayed recurrence of ovarian cancer.Frontiers in immunology · 2023Article
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- Computational analyses of mechanism of action (MoA): data, methods and integration.RSC chemical biology · 2022Review
- Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata.PeerJ. Computer science · 2022Article
- VarSAn: associating pathways with a set of genomic variants using network analysis.Nucleic acids research · 2021Article
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- Pathway analysis for genome-wide genetic variation data: Analytic principles, latest developments, and new opportunities.Journal of genetics and genomics = Yi chuan xue bao · 2021Review
- Genome-Wide Expression Difference of MicroRNAs in Basal Cell Carcinoma.Journal of immunology research · 2021Article
- Genome-Wide Transcriptional Changes ofFrontiers in microbiology · 2021Article
- Understanding the Transcriptional Changes During Infection ofFrontiers in microbiology · 2021Article
- Use of Chou's 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment.Journal of integrative bioinformatics · 2020Article
- Prior Biological Knowledge Improves Genomic Prediction of Growth-Related Traits inFrontiers in genetics · 2020Article
- GOTrapper: a tool to navigate through branches of gene ontology hierarchy.BMC bioinformatics · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe increased accessibility of gene expression tools has enabled a wide variety of experiments utilizing transcriptomic analyses. As these tools increase in prevalence, the need for improved standardization in processing and presentation of data increases, as does the need to guard against interpretation bias. Gene Ontology (GO) analysis is a powerful method of interpreting and summarizing biological functions. However, while there are many tools available to investigate GO enrichment, there remains a need for methods that directly remove redundant terms from enriched GO lists that often provide little, if any, additional information.
findingsHere we present a simple yet novel method called GO Trimming that utilizes an algorithm designed to reduce redundancy in lists of enriched GO categories. Depending on the needs of the user, this method can be performed with variable stringency. In the example presented here, an initial list of 90 terms was reduced to 54, eliminating 36 largely redundant terms. We also compare this method to existing methods and find that GO Trimming, while simple, performs well to eliminate redundant terms in a large dataset throughout the depth of the GO hierarchy.
conclusionsThe GO Trimming method provides an alternative to other procedures, some of which involve removing large numbers of terms prior to enrichment analysis. This method should free up the researcher from analyzing overly large, redundant lists, and instead enable the concise presentation of manageable, informative GO lists. The implementation of this tool is freely available at: http://lucy.ceh.uvic.ca/go_trimming/cbr_go_trimming.py.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.