GO Trimming: Systematically reducing redundancy in large Gene Ontology datasets
By Stuart G. Jantzen, Ben J. G. Sutherland, D. Minkley, B. Koop. 2011
91 citations recorded by the source corpus.
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
Subfields: Molecular Biology
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In Molecular Biology
- Björn Usadel
- G. Ponce-Soto
- K. Krause
- J. Hanna
- Mark Stitt
- Kristina Gruden
- Alexey A. Sergushichev
- Anthony M. Bolger
Citations in the corpus
- FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, methodology): “We shall also use the ontological terms children and parents, as widely used in ontology-related genome research [127].”
- MapMan4: A Refined Protein Classification and Annotation Framework Applicable to Multi-Omics Data Analysis. (2019, background): “While GO is beneficial for a rich annotation, it can pose difficulties when it is used to visualize omics data because the multiple annotations lead to a strong redundancy (Jantzen et al., 2011).”
- SOX17 is a Critical Specifier of Human Primordial Germ Cell Fate (2014, methodology): “Gene ontology analysis was performed byDAVID (Huang et al., 2009) followed byGO trimming (Jantzen et al., 2011).”
- Fast gene set enrichment analysis (2016, methodology): “The procedure is similar to GO Trimming method [7] but is based on the Bayesian network construction approaches.”