Working in a Jupyter notebook

For this project, it will be easier to work in a jupyter notebook. Please write all sections of the report in markdown blocks in your notebook and create or display any plots there as well. Please create a single jupyter notebook.

Introduction (1 paragraph)

  • What is the biological background of the study?
  • Why was the study performed?
  • Why did the authors use the bioinformatic techniques they did?

Methods (as long as needed)

The methods section should concisely describe which steps were taken in the analysis of the data. Remember to adhere to our conventions for writing a methods section, including specifying the software versions used and the parameters for each step, if no parameters are changed, state that default parameters were used. Remember that a methods section should include any details that are necessary for someone else to replicate your analysis exactly. You can exclude any details that should not affect the results, such as the exact file paths used.

  • Write a methods section for the pipeline you developed for project 3

Quality Control Evaluation (1-2 paragraphs)

Use the MultiQC report or the individual logs from FASTQC and TRIMMOMATIC to evaluate the quality of the reads. Please make sure that at minimum you specifically mention the following (even if there’s no issue, state that there is no issue):

  • The range of the number of reads in all the samples
  • Any potential issues with the reads as flagged by FASTQC
  • Any overrepresented sequences, or adaptor contamination in the reads
  • The alignment rate of the reads to the reference genome
  • Based on your evaluation above, please state whether you believe the experiment was of high quality and was suitable for downstream analysis. If not, please state what you would do to improve the quality of the reads.

Signal Coverage Plot (1-2 paragraphs)

Display your signal coverage plot and please address the following questions:

  1. Explain briefly what the plot represents
  2. Provide a brief biological interpretation or hypothesis that the plot may support about the binding of the factor in question

Motif Finding (1-2 paragraphs)

Use the results from HOMER findMotifsGenome and address the following:

  1. Create a single figure or plot (you may take a screenshot) of the top results from the knownResults output from HOMER
  2. Comment briefly on the results you observe and why they may be interesting

Overlap your ChIPseq results with the original RNAseq data (3-4 paragraphs)

In their publication find the link to their GEO submission. Read the methods section of the paper and integrate your called ChIPSeq peaks with the results from their differential expression RNAseq experiment. Use your set of reproducible and filtered peaks, and use the publication’s listed significance thresholds for the RNAseq results. You may do all of these steps in python using pandas or R using tidyverse / ggplot. You may start by reading the RNAseq results and the annotated peak results as dataframes.

  1. Create a figure that displays the same information of figure 2F from the original publication using your annotated peaks and the RNAseq results. The figure does not have to be the same style but must convey the same information using your results.

  2. In figures 2D and 2E, the authors identify and highlight two specific genes that were identified in both experiments. Using your list of filtered and reproducible peaks, a genome browser of your choice, and your bigWig files, please re-create these figures with your own results (You do not need to include the RNAseq data, but you should re-create the genomic tracks from your ChIPseq results)

  3. In the notebook you created, please ensure you address the following questions:

  4. Focusing on your results for figure 2F: - Do you observe any differences in the number of overlapping genes from both analyses? - If you do observe a difference, explain at least two factors that may have contributed to these differences. - What is the rationale behind combining these two analyses in this way? What additional conclusions is it supposed to enable you to draw?

  5. Focusing on your results for figures 2D and 2E: - From your annotated peaks, do you observe statistically significant peaks in these same two genes? - How similar do your genomic tracks appear to those in the paper? If you observe any differences, comment briefly on why there may be discrepancies.

Comparing key findings to the original paper (3-4 paragraphs)

Find the supplementary information for the publication and focus on supplementary figure S2A, S2B, and S2C.

  1. Re-create the table found in supplementary figure S2A. Compare the results with your own findings. Address the following questions:

  • Do you observe differences in the reported number of raw and mapped reads?

  • If so, provide at least two explanations for the discrepancies.

  1. Compare your correlation plot with the one found in supplementary figure S2B.

  • Do you observe any differences in your calculated metrics?

  • What was the author’s takeaway from this figure? What is your conclusion from this figure regarding the success of the experiment?

  1. Create a venn diagram with the same information as found in figure S2C.

  • Do you observe any differences in your results compared to what you see?

  • If so, provide at least two explanations for the discrepancies in the number of called peaks.

Analyze the annotated peaks (1 paragraph)

Use your annotated peaks list and perform an enrichment method of your choice. This is purposefully open-ended so you may consider filtering your peaks by different categories before performing some kind of enrichment analysis. There are a few peak / region based enrichment methods (GREAT) in addition to standard methods used such as DAVID / Enrichr.

  1. In your created notebook, detail the methodology used to perform the enrichment.

  2. Create a single figure / plot / table that displays some of the top results from the analysis.

  3. Comment briefly in a paragraph about the results you observe and why they may be interesting.

REMINDER TO CLEAN UP YOUR WORKING DIRECTORY

When you have successfully run your project 3 pipeline, please ensure that you fully delete your work/ directory and any large files that you may have published to your results/ directory.

You may use the following command:

rm -rf work/

These samples are very large and we have limited disk space. I will be checking your working directories to ensure you do this.

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