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      Differential expression analysis for sequence count data

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

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          Abstract

          High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.

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          The transcriptional landscape of the yeast genome defined by RNA sequencing.

          U Nagalakshmi, Z. Wang, K. Waern … (2008)
          The identification of untranslated regions, introns, and coding regions within an organism remains challenging. We developed a quantitative sequencing-based method called RNA-Seq for mapping transcribed regions, in which complementary DNA fragments are subjected to high-throughput sequencing and mapped to the genome. We applied RNA-Seq to generate a high-resolution transcriptome map of the yeast genome and demonstrated that most (74.5%) of the nonrepetitive sequence of the yeast genome is transcribed. We confirmed many known and predicted introns and demonstrated that others are not actively used. Alternative initiation codons and upstream open reading frames also were identified for many yeast genes. We also found unexpected 3'-end heterogeneity and the presence of many overlapping genes. These results indicate that the yeast transcriptome is more complex than previously appreciated.
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            HITS-CLIP yields genome-wide insights into brain alternative RNA processing

            Donny D. Licatalosi, Aldo Mele, John Fak … (2008)
            Summary Protein-RNA interactions play critical roles in all aspects of gene expression. Here we develop a genome-wide means of mapping protein-RNA binding sites in vivo, by high throughput sequencing of RNA isolated by crosslinking immunoprecipitation (HITS-CLIP). HITS-CLIP analysis of the neuron-specific splicing factor Nova2 revealed extremely reproducible RNA binding maps in multiple mouse brains. These maps provide genome-wide in vivo biochemical footprints confirming the previous prediction that the position of Nova binding determines the outcome of alternative splicing; moreover, they are sufficiently powerful to predict Nova action de novo. HITS-CLIP revealed a large number of Nova-RNA interactions in 3′ UTRs, leading to the discovery that Nova regulates alternative polyadenylation in the brain. HITS-CLIP, therefore, provides a robust, unbiased means to identify functional protein-RNA interactions in vivo.
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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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                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): 27 October 2010
                Volume: 11
                Issue: 10
                Page: R106
                Affiliations
                [1 ]European Molecular Biology Laboratory, Mayerhofstraße 1, 69117 Heidelberg, Germany
                Article
                Publisher ID: gb-2010-11-10-r106
                DOI: 10.1186/gb-2010-11-10-r106
                PMC ID: 3218662
                PubMed ID: 20979621
                SO-VID: 519ddc53-8f7f-49b6-ac3e-c209dcbce6f6
                Copyright © Copyright ©2010 Anders et al
                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 : 20 April 2010
                Date revision received : 22 July 2010
                Date accepted : 27 October 2010
                Categories
                Subject: Method

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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