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

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

  • Qi W Functional Genomics Center Zurich, Winterthurerstr. 190, Y32H66, 8057, Zurich, Switzerland. Weihong.qi@fgcz.ethz.ch.
  • Schlapbach R Functional Genomics Center Zurich, Winterthurerstr. 190, Y32H52, 8057, Zurich, Switzerland.
  • Rehrauer H Functional Genomics Center Zurich, Winterthurerstr. 190, Y32H66, 8057, Zurich, Switzerland.
  • 2017-09-23
Published in:
  • Methods in molecular biology (Clifton, N.J.). - 2017
Differential gene expression
Gene expression quantification
Quality control
RNA-seq
Read alignment
Gene Expression Profiling
High-Throughput Nucleotide Sequencing
Quality Control
Sequence Analysis, RNA
Software
English 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.
Language
  • English
Open access status
closed
Identifiers
Persistent URL
https://sonar.ch/global/documents/293303
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