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      From RNA-seq reads to differential expression results

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      Author(s): 1 , , 1 , 2 , 1
      Publication date (Electronic):
      Journal: Genome Biology
      Publisher: BioMed Central

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          Abstract

          Many methods and tools are available for preprocessing high-throughput RNA sequencing data and detecting differential expression.

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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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              De novo assembly and analysis of RNA-seq data.

              Gordon Robertson, Jacqueline Schein, Readman Chiu … (2010)
              We describe Trans-ABySS, a de novo short-read transcriptome assembly and analysis pipeline that addresses variation in local read densities by assembling read substrings with varying stringencies and then merging the resulting contigs before analysis. Analyzing 7.4 gigabases of 50-base-pair paired-end Illumina reads from an adult mouse liver poly(A) RNA library, we identified known, new and alternative structures in expressed transcripts, and achieved high sensitivity and specificity relative to reference-based assembly methods.
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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): 22 December 2010
                Publication date PMC-release: 22 December 2011
                Volume: 11
                Issue: 12
                Page: 220
                Affiliations
                [1 ]Bioinformatics Division, Walter and Eliza Hall Institute, 1G Royal Parade, Parkville 3052, Australia
                [2 ]Epigenetics Laboratory, Cancer Program, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW 2010, Australia
                Article
                Publisher ID: gb-2010-11-12-220
                DOI: 10.1186/gb-2010-11-12-220
                PMC ID: 3046478
                PubMed ID: 21176179
                SO-VID: efd71f92-bc25-4419-b604-116a2ce55b10
                Copyright © Copyright ©2010 BioMed Central Ltd
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                Subject: Review

                ScienceOpen disciplines: Genetics
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                ScienceOpen disciplines: Genetics

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