A survey of best practices for RNA-seq data analysis PMID: 26813401

RQC and Longevity Regulation

2026-09-03

A survey of best practices for RNA-seq data analysis

Genome Biol. 2016 Jan 26:17:13. doi: 10.1186/s13059-016-0881-8.

Ana Conesa ¹ ² , Pedro Madrigal ³ ⁴ , Sonia Tarazona ⁵ ⁶ , David Gomez-Cabrero ⁷ ⁸ ⁹ ¹⁰ , Alejandra Cervera ¹¹ , Andrew McPherson ¹² , Michał Wojciech Szcześniak ¹³ , Daniel J Gaffney ¹⁴ , Laura L Elo ¹⁵ , Xuegong Zhang ¹⁶ ¹⁷ , Ali Mortazavi ¹⁸ ¹⁹

Affiliations

1. Institute for Food and Agricultural Sciences, Department of Microbiology and Cell Science, University of Florida, Gainesville, FL, 32603, USA. [email protected].
2. Centro de Investigación Príncipe Felipe, Genomics of Gene Expression Laboratory, 46012, Valencia, Spain. [email protected].
3. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. [email protected].
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. [email protected].
5. Centro de Investigación Príncipe Felipe, Genomics of Gene Expression Laboratory, 46012, Valencia, Spain.
6. Department of Applied Statistics, Operations Research and Quality, Universidad Politécnica de Valencia, 46020, Valencia, Spain.
7. Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, 171 77, Stockholm, Sweden.
8. Center for Molecular Medicine, Karolinska Institutet, 17177, Stockholm, Sweden.
9. Unit of Clinical Epidemiology, Department of Medicine, Karolinska University Hospital, L8, 17176, Stockholm, Sweden.
10. Science for Life Laboratory, 17121, Solna, Sweden.
11. Systems Biology Laboratory, Institute of Biomedicine and Genome-Scale Biology Research Program, University of Helsinki, 00014, Helsinki, Finland.
12. School of Computing Science, Simon Fraser University, Burnaby, V5A 1S6, BC, Canada.
13. Department of Bioinformatics, Institute of Molecular Biology and Biotechnology, Adam Mickiewicz University in Poznań, 61-614, Poznań, Poland.
14. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.
15. Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland.
16. Key Lab of Bioinformatics/Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University, Beijing, 100084, China.
17. School of Life Sciences, Tsinghua University, Beijing, 100084, China.
18. Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, 92697-2300, USA. [email protected].
19. Center for Complex Biological Systems, University of California, Irvine, Irvine, CA, 92697, USA. [email protected].

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.