EPI_2023_module5_galaxy.md

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tutorial_page	/EPI_2023_module5_galaxy	Epigenomics Analysis 2023 Module 5 extra Galaxy Lab	Workshop Pages for Students	Downstream Analysis and Online Tools	/site_images/CBW_Epigenome-data_icon.jpg	https://bioinformaticsdotca.github.io/EPI_2023

Module 5: Downstream analyses & integrative tools

by David Bujold, M.Sc.

Extra Lab: Galaxy

We will now explore and learn how to use the Galaxy interface. In this short exercise, we will load a FASTQ dataset, run FastQC on it, and trim it to improve overall quality of reads.

• For this exercise, we will use the main Galaxy server. Using a web browser, open the following URL: https://usegalaxy.org/

The types of jobs you can run without registering on the main Galaxy instance are limited. We should therefore create an account.

• On the top menu, click on "Login or Register".

• Click on "Don't have an account? Register here.", fill your account information, and validate your email address.

• Once you are logged in as a Galaxy user, you're ready to go. For this exercise, we’ll use subsets of data from the Illumina BodyMap 2.0 project, from human adrenal gland tissues. The sampled reads are paired-end 50bp that map mostly to a 500Kb region of chromosome 19, positions 3-3.5 million (chr19:3000000:3500000). (source: https://usegalaxy.org/u/jeremy/p/galaxy-rna-seq-analysis-exercise)

• Import the following two FASTQ files in your user space. To do so:

  • Click the Upload Data button on the upper left of the screen
  • In the popup, click on the Paste/Fetch data button
  • You can provide both URLs in the same text box
    • https://raw.githubusercontent.com/bioinformatics-ca/EPI_2021/master/files/adrenal_1.fastq
    • https://raw.githubusercontent.com/bioinformatics-ca/EPI_2021/master/files/adrenal_2.fastq
  • Under Genome (set all), specify Human Feb. 2009 (GRCh37/hg19) (hg19)
  • Click on Start.

• After it finished uploading (green state), you can rename the two imported files, for better organization.

  • From the history column, click on the Pen icon for the first imported item. Note that you can enter a new name to replace “adrenal_1.fastq” in the dialog if desired.
  • Examine the adrenal_1.fastq file content using the Eye icon.

• Run the tool FastQC: Comprehensive QC for adrenal_1.

  • To find it, use the search window at the top of the Tools column (left panel).
  • From the FastQC tool interface, for the field Raw read data from your current history, choose adrenal_1.
  • Click on Run Tool.
  • Pay attention to the green notice, which provides details about the input and output of the job you just launched.
  • Once the job is completed, examine the Webpage results from the history bar using the Eye icon.
  • Raw output statistics are also available, and can also be seen with the Eye icon.
  • Repeat the same operations for adrenal_2. As a shortcut, you can click on the FastQC history item, then click on the Run this job again icon and simply change the input file to automatically reuse the same parameters.

• If desired, you can run a sanity check on your FASTQ files in Galaxy, to ensure they meet the expected standards. To groom our FASTQ files, we will use the tool FASTQ Groomer with default parameters.

  • When we don’t know which quality score type to provide, we can extract that information from the FastQC report that we already generated. Can you find the information in the FastQC report? (Answer: It’s in Sanger format)
  • Leave the other options as-is.
  • Run this for both of our paired-end files, adrenal_1 and adrenal_2.

• You will now trim the reads, to improve the quality of the dataset by removing bad quality bases, clipping adapters and so on. Launch the Trimmomatic tool with default parameters, except:

  • Set the input as Paired-end (two separate input files)
  • Give the groomed adrenal_1 file for direction 1, and groomed adrenal_2 for direction 2.
  • Sliding window size: 4
  • Average quality required: 30

• Run FastQC again on both paired files, and compare results with pre-trimming FastQC output.

• As an exercise after the workshop, or if you have time remaining, you can now try to use this dataset in one of the aligners provided by Galaxy, such as HISAT2 or RNA STAR. Once the alignment is completed, try
  • downloading the BAM using the floppy disk icon and visualizing it in IGV
  • visualizing it in the UCSC Genome Browser, using the display at UCSC main option
  • creating a bigWig coverage track using the bamCoverage tool

End of Galaxy lab