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ChIP-seq Practice Exercises
****NOTE: When working on O2, be sure to run this in /n/scratch2/ rather than your home directory .****
# ChIP-Seq Analysis Workflow
1. Create a directory called your eCommons ID within /n/scratch2/. Enter that directory and create a new directory called HCFC1_chipseq.
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- Run FASTQC
- Align reads with Bowtie2 using the parameters we used in class.
NOTE: For the Bowtie2 index you will need to point to the hg19 index files from:/n/groups/shared_databases/igenome/Homo_sapiens/UCSC/hg19/Sequence/Bowtie2Index/ - Change alignment file format from SAM to BAM (can be done using samtools or sambamba)
- Sort the BAM file by read coordinate locations (can be done using sambamba or with samtools)
- Filter to keep only uniquely mapping reads (this will also remove any unmapped reads and duplicates) using sambamba
**NOTE: The script will require positional parameters, and using basename will help with the naming of output files**
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c. Create a separate job submission script to run the shell script you created in b) on all four .fastq files. You have the option of running this in serial or parallel. Take a look at the lessons from the RNA-Seq session automation lesson to help with setting up the job submission script.
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e. Use phantompeakqualtools to check the quality of the ChIP signal for HCFC1-rep1 and HCFC1-rep2. Report the NSC, RSC, and QualityTag values. What can you conclude about the quality of the data based on these quality measures?f. Run ChIPQC and upload the
OPTIONAL: If you feel very ambitious get X11 setup on your personal account and try running ChIPQC to create a report for HCFC1. Discuss the quality of the data using these
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metrics. If you run into problems here please reach out to HMSRC folks, this is known to be tricky!
g. Sort each of the narrowPeak files using:
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**NOTE 2: Just perform the 1 step we did in class to generate the IDR stats; there is no need to do the remaining 2 steps in the IDR workflow. (However, you can do them optionally if you are interested and we are happy to help you troubleshoot if need be.)**
i. These high confidence peaks from IDR can be used to explore downstream tools for functional enrichment and motif discovery. Use ChIPseeker to do the following:
Plot the read count frequency relative to the TSS (use a window of +/- 1000 bp). Upload this figure.
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GREAT to annotate the peaks and write those annotations to file.
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Plot a pie chart using the genomic region annotations. Which genomic feature is the most represented?
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Take a look at binding site locations relative to the TSS, what does this tell us?
Evaluate the GO enrichment analysis from GREAT, what terms do you see over-represented?
h. **OPTIONAL:** Try motif analysis using the MEME suite and comment on the results you find.
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