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      Gene ontology analysis for RNA-seq: accounting for selection bias

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      Author(s): 1 , 1 , 1 , 1 ,
      Publication date (Electronic):
      Journal: Genome Biology
      Publisher: BioMed Central

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          Abstract

          GOseq is a method for GO analysis of RNA-seq data that takes into account the length bias inherent in RNA-seq

          Abstract

          We present GOseq, an application for performing Gene Ontology (GO) analysis on RNA-seq data. GO analysis is widely used to reduce complexity and highlight biological processes in genome-wide expression studies, but standard methods give biased results on RNA-seq data due to over-detection of differential expression for long and highly expressed transcripts. Application of GOseq to a prostate cancer data set shows that GOseq dramatically changes the results, highlighting categories more consistent with the known biology.

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          Gene Ontology: tool for the unification of biology

          Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)
          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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            Small-sample estimation of negative binomial dispersion, with applications to SAGE data.

            Mark Robinson, Gordon K. Smyth (2008)
            We derive a quantile-adjusted conditional maximum likelihood estimator for the dispersion parameter of the negative binomial distribution and compare its performance, in terms of bias, to various other methods. Our estimation scheme outperforms all other methods in very small samples, typical of those from serial analysis of gene expression studies, the motivating data for this study. The impact of dispersion estimation on hypothesis testing is studied. We derive an "exact" test that outperforms the standard approximate asymptotic tests.
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              Moderated statistical tests for assessing differences in tag abundance.

              Mark Robinson, Gordon K. Smyth (2007)
              Digital gene expression (DGE) technologies measure gene expression by counting sequence tags. They are sensitive technologies for measuring gene expression on a genomic scale, without the need for prior knowledge of the genome sequence. As the cost of sequencing DNA decreases, the number of DGE datasets is expected to grow dramatically. Various tests of differential expression have been proposed for replicated DGE data using binomial, Poisson, negative binomial or pseudo-likelihood (PL) models for the counts, but none of the these are usable when the number of replicates is very small. We develop tests using the negative binomial distribution to model overdispersion relative to the Poisson, and use conditional weighted likelihood to moderate the level of overdispersion across genes. Not only is our strategy applicable even with the smallest number of libraries, but it also proves to be more powerful than previous strategies when more libraries are available. The methodology is equally applicable to other counting technologies, such as proteomic spectral counts. An R package can be accessed from http://bioinf.wehi.edu.au/resources/
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                Author and article information

                Journal
                Journal ID (nlm-ta): Genome Biol
                Title: Genome Biology
                Publisher: BioMed Central
                ISSN (Print): 1465-6906
                ISSN (Electronic): 1465-6914
                Publication date (Print): 2010
                Publication date (Electronic): 4 February 2010
                Volume: 11
                Issue: 2
                Page: R14
                Affiliations
                [1 ]Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville 3052, Australia
                Article
                Publisher ID: gb-2010-11-2-r14
                DOI: 10.1186/gb-2010-11-2-r14
                PMC ID: 2872874
                PubMed ID: 20132535
                SO-VID: 35f88178-5bc2-401f-aa25-3d7578c484a1
                Copyright © Copyright ©2010 Young et al.; licensee BioMed Central Ltd.
                License:

                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 cited.

                History
                Date received : 1 December 2009
                Date revision received : 14 January 2010
                Date accepted : 4 February 2010
                Categories
                Subject: Method

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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