A survey of best practices for RNA-seq data analysis

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http://hdl.handle.net/10138/160250

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Conesa , A , Madrigal , P , Tarazona , S , Gomez-Cabrero , D , Cervera Taboada , A , McPherson , A , Szczesniak , M W , Gaffney , D J , Elo , L L , Zhang , X & Mortazavi , A 2016 , ' A survey of best practices for RNA-seq data analysis ' , Genome Biology , vol. 17 , 13 . https://doi.org/10.1186/s13059-016-0881-8

Title: A survey of best practices for RNA-seq data analysis
Author: Conesa, Ana; Madrigal, Pedro; Tarazona, Sonia; Gomez-Cabrero, David; Cervera Taboada, Alejandra; McPherson, Andrew; Szczesniak, Michal Wojciech; Gaffney, Daniel J.; Elo, Laura L.; Zhang, Xuegong; Mortazavi, Ali
Contributor: University of Helsinki, Research Programs Unit
Date: 2016-01-26
Language: eng
Number of pages: 19
Belongs to series: Genome Biology
ISSN: 1465-6906
URI: http://hdl.handle.net/10138/160250
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.
Subject: DIFFERENTIAL EXPRESSION ANALYSIS
INTEGRATED ANALYSIS MMIA
DEEP SEQUENCING DATA
LARGE GENE LISTS
WEB-BASED TOOL
SINGLE-CELL
DNA-METHYLATION
PROSTATE-CANCER
SYSTEMS BIOLOGY
GENOME BROWSER
318 Medical biotechnology
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