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Applied Genomics

The Golden Snidget

GitHub Assignment Link

A mash up of RNA-seq by Example, nf-core, and Snakemake: Integrating foreign workflow management systems.

In order to clean up the output from nf-core/rnaseq into a format that can be used by the biostar script we’re going to use Jupyter Notebooks.

Input files

Goals

  1. Reproduce the analysis in the Biostar handbook for the counts.csv(1 point each)

    • results.csv for deseq2
    • heatmap for deseq2
    • results.csv for edgeR
    • heatmap for edgeR
    • heatmap for the example.csv
    • A step to compare edgeR results to deseq2 using compare_results.R
  1. Reanalyze the Golden Snidget data using nf-core/rnaseq(1 point each)
    • Download the references
    • Run the nf-core/rnaseq pipeline on the provided data
      • Create a samplesheet for the workflow
      • Pass the samplesheet, and reference files to the workflow
    • Clean the results from the pipeline to a format that matches the results.csv created for deseq2 and edgeR in the Biostar Handbook using a Jupyter Notebook.
    • Produce the heatmap for the nf-core results.
    • Using the compare_results.r script compare the results between nf-core and the Biostar Handbook methods. Write the best method in the README.md.

Bonus Points:

  • The workflow is reproducible. The command ran will be snakemake --cores 4(Unless you state otherwise in the README.md) (2 Points)
  • The workflow follows Snakefmt and passes snakefmt --check.(2 Points)

Useful Resources