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      edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

      1 , 2 , * , , 2 , 2

      Bioinformatics

      Oxford University Press

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          Abstract

          Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data.

          Availability: The package is freely available under the LGPL licence from the Bioconductor web site ( http://bioconductor.org).

          Contact: mrobinson@ 123456wehi.edu.au

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          Most cited references 7

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          Small-sample estimation of negative binomial dispersion, with applications to SAGE data.

          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.

            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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              Comparative Analysis of Human Gut Microbiota by Barcoded Pyrosequencing

              Humans host complex microbial communities believed to contribute to health maintenance and, when in imbalance, to the development of diseases. Determining the microbial composition in patients and healthy controls may thus provide novel therapeutic targets. For this purpose, high-throughput, cost-effective methods for microbiota characterization are needed. We have employed 454-pyrosequencing of a hyper-variable region of the 16S rRNA gene in combination with sample-specific barcode sequences which enables parallel in-depth analysis of hundreds of samples with limited sample processing. In silico modeling demonstrated that the method correctly describes microbial communities down to phylotypes below the genus level. Here we applied the technique to analyze microbial communities in throat, stomach and fecal samples. Our results demonstrate the applicability of barcoded pyrosequencing as a high-throughput method for comparative microbial ecology.
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                Author and article information

                Journal
                Bioinformatics
                bioinformatics
                bioinfo
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                1 January 2010
                11 November 2009
                11 November 2009
                : 26
                : 1
                : 139-140
                Affiliations
                1 Cancer Program, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW 2010 and 2 Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia
                Author notes
                * To whom correspondence should be addressed

                The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.

                Associate Editor: Joaquin Dopazo

                Article
                btp616
                10.1093/bioinformatics/btp616
                2796818
                19910308
                © The Author(s) 2009. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Categories
                Applications Note
                Gene Expression

                Bioinformatics & Computational biology

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