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      A survey of best practices for RNA-seq data analysis

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          Abstract

          RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13059-016-0881-8) contains supplementary material, which is available to authorized users.

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          Most cited references 109

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          Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

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            Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences

            Increased reliance on computational approaches in the life sciences has revealed grave concerns about how accessible and reproducible computation-reliant results truly are. Galaxy http://usegalaxy.org, an open web-based platform for genomic research, addresses these problems. Galaxy automatically tracks and manages data provenance and provides support for capturing the context and intent of computational methods. Galaxy Pages are interactive, web-based documents that provide users with a medium to communicate a complete computational analysis.
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              Improving RNA-Seq expression estimates by correcting for fragment bias

              The biochemistry of RNA-Seq library preparation results in cDNA fragments that are not uniformly distributed within the transcripts they represent. This non-uniformity must be accounted for when estimating expression levels, and we show how to perform the needed corrections using a likelihood based approach. We find improvements in expression estimates as measured by correlation with independently performed qRT-PCR and show that correction of bias leads to improved replicability of results across libraries and sequencing technologies.
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                Author and article information

                Contributors
                aconesa@ufl.edu
                pm12@sanger.ac.uk
                ali.mortazavi@uci.edu
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                26 January 2016
                26 January 2016
                2016
                : 17
                Affiliations
                [ ]Institute for Food and Agricultural Sciences, Department of Microbiology and Cell Science, University of Florida, Gainesville, FL 32603 USA
                [ ]Centro de Investigación Príncipe Felipe, Genomics of Gene Expression Laboratory, 46012 Valencia, Spain
                [ ]Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA UK
                [ ]Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, Anne McLaren Laboratory for Regenerative Medicine, Department of Surgery, University of Cambridge, Cambridge, CB2 0SZ UK
                [ ]Department of Applied Statistics, Operations Research and Quality, Universidad Politécnica de Valencia, 46020 Valencia, Spain
                [ ]Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, 171 77 Stockholm, Sweden
                [ ]Center for Molecular Medicine, Karolinska Institutet, 17177 Stockholm, Sweden
                [ ]Unit of Clinical Epidemiology, Department of Medicine, Karolinska University Hospital, L8, 17176 Stockholm, Sweden
                [ ]Science for Life Laboratory, 17121 Solna, Sweden
                [ ]Systems Biology Laboratory, Institute of Biomedicine and Genome-Scale Biology Research Program, University of Helsinki, 00014 Helsinki, Finland
                [ ]School of Computing Science, Simon Fraser University, Burnaby, V5A 1S6 BC Canada
                [ ]Department of Bioinformatics, Institute of Molecular Biology and Biotechnology, Adam Mickiewicz University in Poznań, 61-614 Poznań, Poland
                [ ]Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
                [ ]Key Lab of Bioinformatics/Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University, Beijing, 100084 China
                [ ]School of Life Sciences, Tsinghua University, Beijing, 100084 China
                [ ]Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA 92697-2300 USA
                [ ]Center for Complex Biological Systems, University of California, Irvine, Irvine, CA 92697 USA
                Article
                881
                10.1186/s13059-016-0881-8
                4728800
                26813401
                8b00f775-e522-4bee-98eb-9fbdc32ddb9d
                © Conesa et al. 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004963, Seventh Framework Programme;
                Award ID: 36000 - STATegra
                Award Recipient :
                Funded by: National Basic Research Program of China
                Award ID: 2012CB316504
                Award Recipient :
                Funded by: JDRF
                Award ID: 2-2013-32
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/http://dx.doi.org/10.13039/501100006306, Sigrid Juséliuksen Säätiö;
                Categories
                Review
                Custom metadata
                © The Author(s) 2016

                Genetics

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