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RNA-Seq Data Analysis: From Raw Data Quality Control to Differential Expression Analysis


Qi, Weihong; Schlapbach, Ralph; Rehrauer, Hubert (2017). RNA-Seq Data Analysis: From Raw Data Quality Control to Differential Expression Analysis. Methods in Molecular Biology, 1669:295-307.

Abstract

As a revolutionary technology for life sciences, RNA-seq has many applications and the computation pipeline has also many variations. Here, we describe a protocol to perform RNA-seq data analysis where the aim is to identify differentially expressed genes in comparisons of two conditions. The protocol follows the recently published RNA-seq data analysis best practice and applies quality checkpoints throughout the analysis to ensure reliable data interpretation. It is written to help new RNA-seq users to understand the basic steps necessary to analyze an RNA-seq dataset properly. An extension of the protocol has been implemented as automated workflows in the R package ezRun, available also in the data analysis framework SUSHI, for reliable, repeatable, and easily interpretable analysis results.

Abstract

As a revolutionary technology for life sciences, RNA-seq has many applications and the computation pipeline has also many variations. Here, we describe a protocol to perform RNA-seq data analysis where the aim is to identify differentially expressed genes in comparisons of two conditions. The protocol follows the recently published RNA-seq data analysis best practice and applies quality checkpoints throughout the analysis to ensure reliable data interpretation. It is written to help new RNA-seq users to understand the basic steps necessary to analyze an RNA-seq dataset properly. An extension of the protocol has been implemented as automated workflows in the R package ezRun, available also in the data analysis framework SUSHI, for reliable, repeatable, and easily interpretable analysis results.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Functional Genomics Center Zurich
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Language:English
Date:2017
Deposited On:26 Jan 2018 12:30
Last Modified:19 Feb 2018 10:35
Publisher:Springer
ISSN:1064-3745
OA Status:Closed
Publisher DOI:https://doi.org/10.1007/978-1-4939-7286-9_23
PubMed ID:28936667

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