A survey of best practices for RNA-seq data analysis

Conesa, Ana; Madrigal, Pedro; Tarazona, Sonia; Gómez-Cabrero, David; Cervera, Alejandra; McPherson, Andrew; Szcześniak, Michał Wojciech; Gaffney, Daniel J.; Elo, Laura L.; Zhang, Xuegong; Mortazavi, A

doi:10.1186/s13059-016-0881-8

reviewGenome biologyJan 26, 2016GOLD OA

A survey of best practices for RNA-seq data analysis

ACAna Conesa PMPedro Madrigal STSonia Tarazona DGDavid Gómez-Cabrero ACAlejandra Cervera

University of Florida · Centro de Investigacion Principe Felipe · +14 more institutions

PubMed

Indexed incrossrefdoajpubmed

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.

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2,863

total citations

FWCI: 159.11
Percentile: 100%
References: 214

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Authors

11

Topics & keywords

Topics

Keywords

RNA-Seq
Biology
Computational biology
Expression quantitative trait loci
Transcriptome
Functional genomics
Genomics
RNA

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