links-to-lessons.md

Understanding chromatin biology using high throughput sequencing workshop

Learning Objectives

• Understand the necessity for, and use of, the command line interface (bash) and HPC for analyzing high-throughput sequencing data.
• Understand best practices for designing a ChIP-seq / CUT&RUN / ATAC-seq experiment.
• Perform the steps involved in going from raw FASTQ files to peak calls for an individual sample.
• Review qualitative ways to assess peak calls and if they support the hypothesis

Installations

All:

• FileZilla Client (make sure you get ‘FileZilla Client')

Mac users:

• Plain text editor like Sublime text or similar

Windows users:

• GitBash
• Plain text editor like Notepad++ or similar

Notes

• These materials focus on the use of local computational resources at Harvard, which are only accessible to Harvard affiliates
• Non-Harvard folks can download the data and set up to work on their local clusters (with the help of local system administrators)

Instructions for Harvard researchers with access to HMS-RC's O2 cluster

To run through the code in the lessons below, 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 [email protected] and enter your password.
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.

Lessons

Part 1

1. Shell basics review
2. Working in an HPC environment - Review
3. Best Practices in Research Data Management (RDM)
4. Dataset overview and Project organization)

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Part II

1. A review of high-throughput sequencing methods for understanding chromatin biology
2. Experimental design considerations for HTS of chromatin
3. Quality Control of Sequence Data: Running FASTQC and evaluating results
4. Alignment using Bowtie2

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Part III

1. Filtering BAM files
2. Peak calling
3. Handling peak files using bedtools

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Part IV

1. File formats for peak visualization
2. Qualitative assessment of peak enrichment using deepTools
3. Troubleshooting your ChIP-seq analysis

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Part V

1. Automating the ChIP-seq workflow

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Answer Keys

• Data Management and project organization
• QC and Alignment questions
• Handling peak calls
• Automation shell script
• Parallelization script

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Building on this workshop

• Integration of ChIP-seq and RNA-seq
• Advanced bash commands (aliases, copying files, and symlinks)
• Introduction to R workshop materials

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Resources

• ENCODE Data Standards and Processing Pipeline Information for Histone and Transcription Factors
• ENCODE guidelines and practices for ChIP-seq. An older paper, but a good outline of general best practices.
• Experimental design considerations:
  • Thermofisher Step-by-step guide to a successful ChIP experiment
  • "Chromatin Immunoprecipitation (ChIP) Principles and How to Obtain Quality Results", BenchSci Blog
  • O’Geen et al (2011), Methods Mol Biol - A focus on performing ChIP assays to characterize histone modifications
• Jung et al (2014). NAR. - Impact of sequencing depth in ChIP-seq experiments

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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.