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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Qi, W., Schlapbach, R., Rehrauer, H. (2017). RNA-Seq Data Analysis: From Raw Data Quality Control to Differential Expression Analysis. In: Schmidt, A. (eds) Plant Germline Development. Methods in Molecular Biology, vol 1669. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7286-9_23
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DOI: https://doi.org/10.1007/978-1-4939-7286-9_23
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Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-7285-2
Online ISBN: 978-1-4939-7286-9
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