Published January 26, 2016
| Version v1
Journal article
Open
A survey of best practices for RNA-seq data analysis
Authors/Creators
- 1. Institute for Food and Agricultural Sciences, Department of Microbiology and Cell Science, University of Florida, Gainesville, FL, 32603, USA
- 2. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
- 3. Centro de Investigación Príncipe Felipe, Genomics of Gene Expression Laboratory, 46012, Valencia, Spain
- 4. Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, 171 77, Stockholm, Sweden
- 5. Systems Biology Laboratory, Institute of Biomedicine and Genome-Scale Biology Research Program, University of Helsinki, 00014, Helsinki, Finland
- 6. School of Computing Science, Simon Fraser University, Burnaby, V5A 1S6, BC, Canada
- 7. Department of Bioinformatics, Institute of Molecular Biology and Biotechnology, Adam Mickiewicz University in Poznań, 61-614, Poznań, Poland
- 8. Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland
- 9. Key Lab of Bioinformatics/Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University, Beijing, 100084, China
- 10. Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, 92697-2300, USA
Description
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.