A survey of best practices for RNA-seq data analysis

Ana Conesa; Pedro Madrigal; Sonia Tarazona; David Gomez-Cabrero; Alejandra Cervera; Andrew McPherson; Michal Wojciech Szcześniak; Daniel J. Gaffney; Laura L. Elo; Xuegong Zhang; Ali Mortazavi

doi:10.1186/s13059-016-0881-8

A survey of best practices for RNA-seq data analysis

Ana Conesa, Pedro Madrigal, Sonia Tarazona, David Gomez-Cabrero, Alejandra Cervera, Andrew McPherson, Michal Wojciech Szcześniak, Daniel J. Gaffney, Laura L. Elo, Xuegong Zhang, Ali Mortazavi

Research output: Contribution to journal › Article › peer-review

1785 Scopus citations

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.

Original language	English (US)
Journal	Genome biology
Volume	17
Issue number	1
DOIs	https://doi.org/10.1186/s13059-016-0881-8
State	Published - Jan 26 2016
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2021-02-16

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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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AU - Conesa, Ana

AU - Madrigal, Pedro

AU - Tarazona, Sonia

AU - Gomez-Cabrero, David

AU - Cervera, Alejandra

AU - McPherson, Andrew

AU - Szcześniak, Michal Wojciech

AU - Gaffney, Daniel J.

AU - Elo, Laura L.

AU - Zhang, Xuegong

AU - Mortazavi, Ali

N1 - Generated from Scopus record by KAUST IRTS on 2021-02-16

PY - 2016/1/26

Y1 - 2016/1/26

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AB - 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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A survey of best practices for RNA-seq data analysis

Abstract

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