As a revolutionary technology for life sciences, RNA-seq has many applications and the computation pipeline has also many variations. Researchers from the Functional Genomics Center Zurich 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.
Open source software packages used in this protocol
Name |
Hyperlink to the project home |
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UCSC utility scripts |
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NCBI SRA Toolkit |
https://trace-ncbi-nlm-nih-gov.ezproxyhost.library.tmc.edu/Traces/sra/sra.cgi?view=software |
FastQC |
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Trimmomatic |
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STAR |
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SAMtools |
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RSeQC |
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featureCounts |
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R |
|
ezRun |
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SUSHI |