A survey of best practices for RNA-seq data analysis

被引：0
作者：
Ana Conesa
Pedro Madrigal
Sonia Tarazona
David Gomez-Cabrero
Alejandra Cervera
Andrew McPherson
Michał Wojciech Szcześniak
Daniel J. Gaffney
Laura L. Elo
Xuegong Zhang
Ali Mortazavi
机构：
[1] University of Florida,Institute for Food and Agricultural Sciences, Department of Microbiology and Cell Science
[2] Centro de Investigación Príncipe Felipe,Wellcome Trust
[3] Genomics of Gene Expression Laboratory,Medical Research Council Cambridge Stem Cell Institute, Anne McLaren Laboratory for Regenerative Medicine, Department of Surgery
[4] Wellcome Trust Sanger Institute,Department of Applied Statistics, Operations Research and Quality
[5] Wellcome Trust Genome Campus,Unit of Computational Medicine, Department of Medicine, Karolinska Institutet
[6] University of Cambridge,Center for Molecular Medicine
[7] Universidad Politécnica de Valencia,Unit of Clinical Epidemiology
[8] Karolinska University Hospital,Systems Biology Laboratory, Institute of Biomedicine and Genome
[9] Karolinska Institutet,Scale Biology Research Program
[10] Department of Medicine,School of Computing Science
[11] Karolinska University Hospital,Department of Bioinformatics, Institute of Molecular Biology and Biotechnology
[12] Science for Life Laboratory,Turku Centre for Biotechnology
[13] University of Helsinki,Key Lab of Bioinformatics/Bioinformatics Division, TNLIST and Department of Automation
[14] Simon Fraser University,School of Life Sciences
[15] Adam Mickiewicz University in Poznań,Department of Developmental and Cell Biology
[16] University of Turku and Åbo Akademi University,Center for Complex Biological Systems
[17] Tsinghua University,undefined
[18] Tsinghua University,undefined
[19] University of California,undefined
[20] Irvine,undefined
[21] University of California,undefined
[22] Irvine,undefined
来源：
Genome Biology | / 17卷
关键词：
Differential Expression Analysis; Reference Transcriptome; Transcript Identification; Transcript Discovery; Gene Transfer Format;
D O I：
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中图分类号：
学科分类号：
摘要：
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.
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[1]  
Mortazavi A(2008)Mapping and quantifying mammalian transcriptomes by RNA-Seq Nat Methods 5 1-8
[2]  
Williams BA(2010)Comprehensive comparative analysis of strand-specific RNA sequencing methods Nat Methods 7 709-15
[3]  
McCue K(2009)Transcriptome analysis by strand-specific sequencing of complementary DNA Nucleic Acids Res 37 e123-15
[4]  
Schaeffer L(2010)Analysis and design of RNA sequencing experiments for identifying isoform regulation Nat Methods 7 1009-77
[5]  
Wold B(2011)Computational methods for transcriptome annotation and quantification using RNA-seq Nat Methods 8 469-91
[6]  
Levin JZ(2011)Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling Bioinformatics 27 i383-32
[7]  
Yassour M(2014)Sequencing depth and coverage: key considerations in genomic analyses Nat Rev Genet 15 121-8
[8]  
Adiconis X(2014)Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex Nat Biotechnol 32 1053-9
[9]  
Nusbaum C(2014)Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types Science 343 776-23
[10]  
Thompson DA(2011)Differential expression in RNA-seq: a matter of depth Genome Res 21 2213-20
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