Workshop summary

2.6.1 Day 1: Raw sequence to gene counts

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On Day 1 we used the nf-core/rnaseq pipeline to convert raw RNAseq data into raw counts.
We discussed the following essential steps in a RNAseq analysis workflow:

  • Check the quality of raw-reads.
  • Trim the raw-reads to get rid of bad-quality read-regions and/or bad-quality reads.
  • Align the trimmed-reads to reference sequence to identify where they belong
  • Quantify the aligned reads to get gene-level read-counts.

Next:

  • We discussed the workflow management tool nextflow which is used for automated, reproducible, flexible and portable analysis.
  • We excecuted the nf-core/rnaseq pipeline and interpreted its outputs.

2.6.2 Day 2: Counts to genes and functional enrichments

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On Day 2 we used the raw counts to identify the differentially expressed genes and highlighted the relevant functions.
The following steps were identified to be essential:

  • Perform an exploratory analysis of the count data for quality control.
  • Analyse the count data to identify differentially expressed genes.
  • Identify functional enrichments from differentially expressed genes.
  • We discussed how to perform reproducible analysis in Rstudio with Rmarkdown using singularity containers.