A survey of best practices for RNA-seq data analysis

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• 作者：Ana Conesa ; Pedro Madrigal ; Sonia Tarazona ; David Gomez-Cabrero…
• 刊名：Genome Biology
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：17
• 期：1
• 全文大小：1,095 KB
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• 作者单位：Ana Conesa (1) (2)
  Pedro Madrigal (3) (4)
  Sonia Tarazona (2) (5)
  David Gomez-Cabrero (6) (7) (8) (9)
  Alejandra Cervera (10)
  Andrew McPherson (11)
  Michał Wojciech Szcześniak (12)
  Daniel J. Gaffney (3)
  Laura L. Elo (13)
  Xuegong Zhang (14) (15)
  Ali Mortazavi (16) (17)
  
  1. Institute for Food and Agricultural Sciences, Department of Microbiology and Cell Science, University of Florida, Gainesville, FL, 32603, USA
  2. Centro de Investigación Príncipe Felipe, Genomics of Gene Expression Laboratory, 46012, Valencia, Spain
  3. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
  4. 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
  5. Department of Applied Statistics, Operations Research and Quality, Universidad Politécnica de Valencia, 46020, Valencia, Spain
  6. Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, 171 77, Stockholm, Sweden
  7. Center for Molecular Medicine, Karolinska Institutet, 17177, Stockholm, Sweden
  8. Unit of Clinical Epidemiology, Department of Medicine, Karolinska University Hospital, L8, 17176, Stockholm, Sweden
  9. Science for Life Laboratory, 17121, Solna, Sweden
  10. Systems Biology Laboratory, Institute of Biomedicine and Genome-Scale Biology Research Program, University of Helsinki, 00014, Helsinki, Finland
  11. School of Computing Science, Simon Fraser University, Burnaby, V5A 1S6, BC, Canada
  12. Department of Bioinformatics, Institute of Molecular Biology and Biotechnology, Adam Mickiewicz University in Poznań, 61-614, Poznań, Poland
  13. Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland
  14. Key Lab of Bioinformatics/Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University, Beijing, 100084, China
  15. School of Life Sciences, Tsinghua University, Beijing, 100084, China
  16. Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, 92697-2300, USA
  17. Center for Complex Biological Systems, University of California, Irvine, Irvine, CA, 92697, USA
  
• 刊物主题：Animal Genetics and Genomics; Human Genetics; Plant Genetics & Genomics; Microbial Genetics and Genomics; Fungus Genetics; Bioinformatics;
• 出版者：BioMed Central
• ISSN：1465-6906

文摘

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 materialThe online version of this article (doi:10.​1186/​s13059-016-0881-8) contains supplementary material, which is available to authorized users.