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Course Catch Up Day (CCU)

Instructions for Monday, February 24th

For today’s class, we will use this session as a course catch-up day. If you can answer “yes” to all the following questions, you do not need to attend class as no new materials will be discussed:

  1. Were you able to download the publicly available dataset for your final project? (Note: not required yet but many have tried and are having trouble)
  2. Do you feel confident copying data from the shared course directory during class?
  3. Are you confident submitting scripts using the SLURM job submission system?
  4. Can you transfer files between the VACC and your local desktop?
  5. Are you able to read the sections below (before Wednesday's class) to understand what conda and RSeQC are and how these programs are used?
  6. Can you follow the instructions provided in the next lesson (L14) to independently install the program RSeQC using conda?

In Wednesday’s class, we will continue using RSeQC and reviewing outputs from HISAT2. Since RSeQC is not available via module load, you must install the program beforehand. You will not be able to participate in Wednesday class tutorial without installing the program first.

Below is background information on the RSeQC and conda programs. Step-by-step instructions are provided in the next lesson (L14). Do not attempt to run the code shown below; it is for reading purposes only.

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Introducing Conda

Conda Logo

Conda is an open-source package management system and environment management system that runs on Windows, macOS, and Linux. Conda quickly installs, runs, and updates packages and their dependencies. Conda also easily creates, saves, loads, and switches between environments on your local computer. Conda was created for Python programs but it can package and distribute software for any language.

More information can be found here.

Managing environments with Conda

Conda allows you to create separate environments containing files, packages, and their dependencies, ensuring they do not interfere with one another. By default, you already have a base environment called base. This environment serves as your "home base," and any packages installed here will always be accessible, regardless of your location on the server.

However, installing all programs in the base environment can lead to software conflicts. Instead, it is best practice to create a separate environment for each program. For example, RSeQC recommends using Python 3.10, but your default Python version may be different. To avoid compatibility issues, we will create a dedicated environment for RSeQC.

Creating a Conda Environment

To create a new environment with Conda, use the following command:

conda create -n new-env
  • create - Tells Conda to create a new environment.
  • -n new-env - Specifies the name of the environment (new-env in this case). You can change "new-env" to any descriptive name (e.g., rseqc_env) for each environment you create.

Example shown below:

conda create -n rseqc_env python=3.10

Handling Dependencies

Conda will check if additional packages ("dependencies") are required. If prompted, type 'y' to proceed:

Proceed ([y]/n)? y
  • y confirms the installation of required dependencies
  • n cancels the installation

Activating the Environment

After creating the environment, activate it with: 

conda activate new-env
  • activate - Switches from the base environment to the newly created one
  • new-env - Name of environment to activate (must match what you used in -n new-env)

Once activated, your prompt will change from (base) to (new-env), indicating that you are now working inside the new environment.

Installing a Package

Now you will be able to install the desired package inside the environment you just created. 

conda install -c bioconda multiqc
  • install installs a package
  • -c bioconda specifies the channel. bioconda hosts many bioinformatics tools.
  • multiqc is the name of package being installed in this example.

Deactivating the Environment

When you are done using the environment, deactivate it using:

conda deactivate
  • deactivate exits the current environment and returns to the base environment

Introducing RSeQC

RSeQC (RNA-Seq Quality Control) is a widely used toolkit designed to evaluate the quality of RNA sequencing (RNA-Seq) data. It provides a collection of scripts to assess the integrity of sequencing reads, alignment quality, gene body coverage, GC content distribution, and other essential metrics. By identifying potential biases or errors in RNA-Seq data,RSeQC helps ensure accurate downstream analysis and interpretation.

This toolkit is particularly useful for verifying the quality of mapped reads from aligners such as HISAT2, STAR, or TopHat.

Commonly used features of RSeQC include:

  • Read Distribution Analysis: Determines the proportion of reads mapping to different genomic features (exons, introns, intergenic regions).
  • Gene Body Coverage: Evaluates uniformity of read distribution across genes.
  • Junction Annotation: Identifies and annotates splice junctions.
  • GC Content and Read Duplication Metrics: Helps detect sequencing biases.

Since RSeQC is not typically available as a module on high-performance computing (HPC) systems, users need to install it manually using Conda and pip. The following tutorial, will guide you through installing RSeQC.

More information on RseQC can be found here.