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RNA-seq workflow (Salmon)

Salmon is an open-source tool to quantify the expression of transcripts from RNA-seq data [1, 2]. We have chosen to use a Salmon workflow for RNA-seq data because it has been shown to be very fast without using a lot of memory.

This workshop assumes basic familiarity with RNA-seq analysis. If you are unfamiliar with RNA-seq analysis, please refer to RNA-seq resources outside of this training portal, such as Youtube videos [3], review papers (e.g. [4] or [5]) and/or tutorials (e.g. [6]) before proceeding.

Salmon RNA-seq workflow

The following demo video:

  1. Shows how to load an example Salmon workflow that download 1 sample of data
  2. Discusses the basics of the Salmon RNA-seq workflow.

  3. Explains the widgets in the example workflow, consisting of the following widgets:

    a. Download the sequence data. This demo uses example Arabidopsis thaliana data (DRR016125) [7] from the official Salmon tutorial [6].

    b. Download the reference transcriptome for Arabidopsis.

    c. Build an index on the reference transcriptome. This index helps to map reads to the reference.

    d. Quantification to generate counts representing gene expression levels.

    e. Visualization of the resulting counts in a spreadsheet.

  4. Show how to run the example workflow. Click the start button to download data, run Salmon, and show the table of counts in a spreadsheet in Bwb.

After walking through the workflow, the next video goes through how to change input parameters to run the analysis on another sample. Using the copy-paste clipboard in Bwb is covered in this video.

To learn more about Salmon’s different input parameters, please refer to the official Salmon documentation at https://salmon.readthedocs.io/en/latest/salmon.html#description-of-some-important-options

References

[1] Patro, Rob, et al. "Salmon provides fast and bias-aware quantification of transcript expression." Nature methods 14.4 (2017): 417-419. doi: 10.1038/nmeth.4197

[2] https://github.com/COMBINE-lab/Salmon

[3] StatQuest: A gentle introduction to RNA-seq https://youtu.be/tlf6wYJrwKY?si=Kudph4cULo_3okB1

[4] Deshpande, Dhrithi, et al. "RNA-seq data science: from raw data to effective interpretation." Frontiers in Genetics 14 (2023): 997383.

[5] Conesa, Ana, et al. "A survey of best practices for RNA-seq data analysis." Genome biology 17.1 (2016): 1-19.

[6] https://combine-lab.github.io/salmon/getting_started/

[7] ENA project PRJDB2508 https://www.ebi.ac.uk/ena/browser/view/PRJDB2508