Skip to content

Latest commit

 

History

History
181 lines (116 loc) · 4.21 KB

chp09.md

File metadata and controls

181 lines (116 loc) · 4.21 KB

Exercises and solutions for Chapter 9

Quality control

  1. Apply the fragment size estimation procedure to all ChIP and Input available datasets. [Difficulty: Beginner]

solution:

#coming soon
  1. Visualize the resulting distributions. [Difficulty: Beginner]

solution:

#coming soon
  1. How does the Input sample distribution differ from the ChIP samples? [Difficulty: Beginner]

solution:

#coming soon
  1. Write a function which converts the bam files into bigWig files. [Difficulty: Beginner]

solution:

#coming soon
  1. Apply the function to all files, and visualize them in the genome browser. Observe the signal profiles. What can you notice, about the similarity of the samples? [Difficulty: Beginner]

solution:

#coming soon
  1. Use GViz to visualize the profiles for CTCF, SMC3 and ZNF143. [Difficulty: Beginner/Intermediate]

solution:

#coming soon
  1. Calculate the cross correlation for both CTCF replicates, and the input samples. How does the profile look for the control samples? [Difficulty: Intermediate]

solution:

#coming soon
  1. Calculate the cross correlation coefficients for all samples and visualize them as a heatmap. [Difficulty: Intermediate]

solution:

#coming soon

Peak calling

  1. Use normR to call peaks for all SMC3, CTCF, and ZNF143 samples. [Difficulty: Beginner]

solution:

#coming soon
  1. Calculate the percentage of reads in peaks for the CTCF experiment. [Difficulty: Intermediate]

solution:

#coming soon
  1. Download the blacklisted regions corresponding to the hg38 human genome, and calculate the percentage of CTCF peaks falling in such regions. [Difficulty: Advanced]

solution:

#coming soon
  1. Unify the biological replicates by taking an intersection of peaks. How many peaks are specific to each biological replicate, and how many peaks overlap. [Difficulty: Intermediate]

solution:

#coming soon
  1. Plot a scatter plot of signal strengths for biological replicates. Do intersecting peaks have equal signal strength in both samples? [Difficulty: Intermediate]

solution:

#coming soon
  1. Quantify the combinatorial binding of all three proteins. Find the number of places which are bound by all three proteins, by a combination of two proteins, and exclusively by one protein. Annotate the different regions based on their genomic location. [Difficulty: Advanced]

solution:

#coming soon
  1. Correlate the normR enrichment score for CTCF with peak presence/absence (create boxplots of enrichment for peaks which contain and do not contain CTCF motifs). [Difficulty: Advanced]

solution:

#coming soon
  1. Explore the co-localization of CTCF and ZNF143. Where are the co-bound regions located? Which sequence motifs do they contain? Download the ChIA-pet data for the GM12878 cell line, and look at the 3D interaction between different classes of binding sites. [Difficulty: Advanced]

solution:

#coming soon

Motif discovery

  1. Repeat the motif discovery analysis on peaks from the ZNF143 transcription factor. How many motifs do you observe? How do the motifs look (visualize the motif logs)? [Difficulty: Intermediate]

solution:

#coming soon
  1. Scan the ZNF143 peaks with the top motifs found in the previous exercise. Where are the motifs located? [Difficulty: Advanced]

solution:

#coming soon
  1. Scan the CTCF peaks with the top motifs identified in the ZNF143 peaks. Where are the motifs located? What can you conclude from the previous exercises? [Difficulty: Advanced]

solution:

#coming soon