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      A shortcut for multiple testing on the directed acyclic graph of gene ontology

      research-article
      , ,
      BMC Bioinformatics
      BioMed Central
      Bonferroni, Holm, Gene ontology, Multiple testing

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          Abstract

          Background

          Gene set testing has become an important analysis technique in high throughput microarray and next generation sequencing studies for uncovering patterns of differential expression of various biological processes. Often, the large number of gene sets that are tested simultaneously require some sort of multiplicity correction to account for the multiplicity effect. This work provides a substantial computational improvement to an existing familywise error rate controlling multiplicity approach (the Focus Level method) for gene set testing in high throughput microarray and next generation sequencing studies using Gene Ontology graphs, which we call the Short Focus Level.

          Results

          The Short Focus Level procedure, which performs a shortcut of the full Focus Level procedure, is achieved by extending the reach of graphical weighted Bonferroni testing to closed testing situations where restricted hypotheses are present, such as in the Gene Ontology graphs. The Short Focus Level multiplicity adjustment can perform the full top-down approach of the original Focus Level procedure, overcoming a significant disadvantage of the otherwise powerful Focus Level multiplicity adjustment. The computational and power differences of the Short Focus Level procedure as compared to the original Focus Level procedure are demonstrated both through simulation and using real data.

          Conclusions

          The Short Focus Level procedure shows a significant increase in computation speed over the original Focus Level procedure (as much as ∼15,000 times faster). The Short Focus Level should be used in place of the Focus Level procedure whenever the logical assumptions of the Gene Ontology graph structure are appropriate for the study objectives and when either no a priori focus level of interest can be specified or the focus level is selected at a higher level of the graph, where the Focus Level procedure is computationally intractable.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12859-014-0349-3) contains supplementary material, which is available to authorized users.

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          Most cited references25

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Ontological analysis of gene expression data: current tools, limitations, and open problems.

            Independent of the platform and the analysis methods used, the result of a microarray experiment is, in most cases, a list of differentially expressed genes. An automatic ontological analysis approach has been recently proposed to help with the biological interpretation of such results. Currently, this approach is the de facto standard for the secondary analysis of high throughput experiments and a large number of tools have been developed for this purpose. We present a detailed comparison of 14 such tools using the following criteria: scope of the analysis, visualization capabilities, statistical model(s) used, correction for multiple comparisons, reference microarrays available, installation issues and sources of annotation data. This detailed analysis of the capabilities of these tools will help researchers choose the most appropriate tool for a given type of analysis. More importantly, in spite of the fact that this type of analysis has been generally adopted, this approach has several important intrinsic drawbacks. These drawbacks are associated with all tools discussed and represent conceptual limitations of the current state-of-the-art in ontological analysis. We propose these as challenges for the next generation of secondary data analysis tools.
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              Conspectus florae Graecae / auctore E. de Halácsy.

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                Author and article information

                Contributors
                saundersg@byui.edu
                john.r.stevens@usu.edu
                clay.isom@usu.edu
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                1 November 2014
                1 November 2014
                2014
                : 15
                : 1
                : 349
                Affiliations
                [ ]Utah State University, Department of Mathematics & Statistics, Logan, Utah USA
                [ ]Utah State University, Department of Animal, Dairy, and Veterinary Sciences, Logan, Utah USA
                [ ]Brigham Young University-Idaho, Department of Mathematics, Rexburg, Idaho USA
                Article
                349
                10.1186/s12859-014-0349-3
                4232707
                25366961
                acd0b855-c0f5-40d7-b8ea-1cc52db7e322
                © Saunders et al.; licensee BioMed Central Ltd. 2014

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 16 May 2014
                : 9 October 2014
                Categories
                Methodology Article
                Custom metadata
                © The Author(s) 2014

                Bioinformatics & Computational biology
                bonferroni,holm,gene ontology,multiple testing
                Bioinformatics & Computational biology
                bonferroni, holm, gene ontology, multiple testing

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