schedule

Workshop Schedule

Pre-requisite for this workshop: The Basic Data Skills Introduction to the command-line interface workshop or a working knowledge of the command line and cluster computing.

Pre-reading

• Shell basics review
• Best Practices in Research Data Management (RDM)
• Introduction to RNA-seq

Day 1

Time	Topic	Instructor
09:30 - 09:45	Workshop Introduction	Meeta
09:45 - 10:25	Working in an HPC environment - Review	Emma
10:25 - 11:05	Project Organization (using Data Management best practices)	Meeta
11:05 - 11:45	Quality Control of Sequence Data: Running FASTQC	Emma
11:45 - 12:00	Overview of self-learning materials and homework submission	Meeta

Before the next class:

1. Please study the contents and work through all the code within the following lessons:

• Experimental design considerations

• Quality Control of Sequence Data: Running FASTQC on multiple samples

• Quality Control of Sequence Data: Evaluating FASTQC reports

  NOTE: To run through the code above, you will need to be logged into O2 and working on a compute node (i.e. your command prompt should have the word compute in it).

  1. Log in using ssh rc_trainingXX@o2.hms.harvard.edu and enter your password (replace the "XX" in the username with the number you were assigned in class).
  2. Once you are on the login node, use srun --pty -p interactive -t 0-2:30 --mem 1G /bin/bash to get on a compute node or as specified in the lesson.
  3. Proceed only once your command prompt has the word compute in it.
  4. If you log out between lessons (using the exit command twice), please follow points 1. and 2. above to log back in and get on a compute node when you restart with the self learning.

2. Complete the exercises:
   • Each lesson above contain exercises; please go through each of them.
   • Add your answers to the questions to Google forms the day before the next class.

Questions?

• If you get stuck due to an error while runnning code in the lesson, email us

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Day 2

Time	Topic	Instructor
09:30 - 10:30	Self-learning lessons review	All
10:30 - 11:10	Expression quantification: Theory and Tools	Meeta
11:10 - 11:50	Quantifying expression using alignment-free methods (Salmon)	Emma
11:50 - 12:00	Review of workflow	Emma

Before the next class:

1. Please study the contents and work through all the code within the following lessons:

• Quantifying expression using alignment-free methods (Salmon on multiple samples)

  Click here for a preview of this lesson

  
  Now that we know how to run the quantification of one sample with Salmon, this lesson will guide you to run multiple samples by creating a job submission script
  
  

• QC with Alignment Data

  Click here for a preview of this lesson

  
  Besides transcript-level quantification, we also want to understand the quality of the mapping, which is not provided in Salmon output.
  
  This lesson will cover:
  - Aligning the reads with an aligner, STAR
  - Assessing QC metrics among samples
  
  

• Documenting Steps in the Workflow with MultiQC

  Click here for a preview of this lesson

  
  It would be great to have a summary document of all QC results from the previous analysis.
  
  This lesson will cover:
  - Generating such a summary report with multiQC
  - Generating alignment metric with Qualimap
  
  

  NOTE: To run through the code above, you will need to be logged into O2 and working on a compute node (i.e. your command prompt should have the word compute in it).

  1. Log in using ssh rc_trainingXX@o2.hms.harvard.edu and enter your password (replace the "XX" in the username with the number you were assigned in class).
  2. Once you are on the login node, use srun --pty -p interactive -t 0-2:30 --mem 8G /bin/bash to get on a compute node or as specified in the lesson.
  3. Proceed only once your command prompt has the word compute in it.
  4. If you log out between lessons (using the exit command twice), please follow points 1. and 2. above to log back in and get on a compute node when you restart with the self learning.

2. Complete the exercises:
   • Each lesson above contain exercises; please go through each of them.
   • Add your answers to the questions to Google forms the day before the next class.

Questions?

• If you get stuck due to an error while runnning code in the lesson, email us

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Day 3

Time	Topic	Instructor
09:30 - 10:10	Self-learning lessons review	All
10:10 - 11:10	Automating the RNA-seq workflow	Will
11:10 - 11:45	Troubleshooting RNA-seq Data Analysis	Emma
11:45 - 12:00	Wrap up	Will

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• Downloadable Answer Keys (Day 2 exercises):

  • Experimental design (one possible solution)
  • sbatch script
  • .out file
  • .err file.

• Downloadable Answer Keys (Day 3 exercises):

  • sbatch script to run salmon for all samples

• Automation Script

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Resources

• Getting an O2 account
• Video about statistics behind salmon quantification
• Advanced bash for working on O2:
  • Creating shortcuts or aliases
  • Copying files from other remote locations to O2
  • Creating symbolic links
• Obtaining reference genomes or transcriptomes
• Youtube videos
  • Hash tables - Paul Programming
  • Suffix arrays - William Fiset

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Building on this workshop

• Introduction to R workshop materials
• Introduction to Differential Gene Expression analysis (bulk RNA-seq) workshop materials

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These materials have been developed by members of the teaching team at the Harvard Chan Bioinformatics Core (HBC). These are open access materials distributed under the terms of the Creative Commons Attribution license (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.