Types of binding events in the cell

Proteins and nucleic acids are the most important components of the cell. Their interactions are necessary for functioning and regulation. Types of interactions:

• DNA-protein interactions, examples:
  • replication/reparation/recombination
  • transcription
  • chromatin modification and folding
• RNA-protein:
  • RNA metabolism regulation
  • processing
  • translation
  • degradation: microRNA, NMD
• RNA-DNA interactions:
  • expression regulation
  • chromatin structure maintenance

Wasserman & Sandelin, 2004

Types of molecular interactions

• Direct:
  • protein-protein
  • RNA-protein
  • DNA-protein
  • RNA-DNA (R-loops)
• Indirect:
  • Protein-mediated
  • RNA-mediated
• Colocalization:
  • Compartments
  • Hubs

RNA-DNA interactions

Engreitz et al. 2016

ChIP-Seq

Chromatin-immunoprecipitation followed by sequencing:

ChIP-Seq: input experiment

Unwanted factors contributing to sequencing & processing output:

• DNA accessibility
• DNA amplification efficiency
• Mappability

"Input" experiment is a ChIP-Seq control without precipitation step. It is used for normalisation of ChIP-Seq.

Park, Nat Reviews Genetics, 2009

ChIP-Seq: peak calling

In order to predict binging events we need to call peaks in ChIP-Seq data:

Mahony & Pugh 2015

ChIP-Seq: peak calling

https://www.strand-ngs.com/features/chip-seq

Example output of the procedure:

What is the next step?

ChIP-Seq: binding pattern

• As an output from ChIP-Seq we have a list of peaks.
• In theory, each peak should have a binding event.
• Imagine you have the peaks with the following sequences:
  
  
  
  
  
  
  
  
  
   
• What is the binding pattern?

ChIP-Seq: binding pattern

• Seems to be easy to find the binding pettern:

Question: Can you propose a computational approach to find it?

ChIP-Seq: motifs search

• Let's introduce some variety, which can arise as:
  • Suboptimal binding of the protein​
  • Genome variability
  • Errors during the sequencing

ChIP-Seq: motifs search

• With possible substitutions, finding the correct pattern is not easy anymore:

We need some advanced algorithms to find binding patterns.

Binding pattern representation

• Binding motif is a DNA sequence pattern that has biological significance (e.g. it is a target for protein binding).
• Consider following pattern of sequences:

TATAAT
TAAAAT
TAATAT
TGTAAT
TATACT

Consensus is the sequence of the most frequent letters:

T[AG][AT][AT][AC]T

With the consensus sequence, we lose the information:

• about suboptimal binding,
• of background nucleotides frequencies.

 

Binding motif representation

Other representations: 

Frequency matrix

Probability matrix

Matrix normalized by background

Position Weight Matrix (PWM)

Position Weight Matrix (PWM)

Binding motifs search 

Approaches:

•  Scanning of the motifs from the database
  (e.g. JASPAR http://jaspar.genereg.net/ )

Binding motifs search 

Approaches:

•  Scanning of the motifs from the database
  (e.g. JASPAR http://jaspar.genereg.net/ )
• Motifs discovery (de novo search)

http://meme-suite.org/tools/meme

ATAC-Seq data processing

• ENCODE data processing standards:
  https://www.encodeproject.org/data-standards/

Bioinformatics pipelines for ATAC-Seq data processing

Pipeline is a set of processing steps, usually wrapped in a single script, container or tool.

Pipelines aim to be:

• High-performance
• Reproducible
• Scalable

Some web-server based pipelines:

• GALAXY
• DNA Nexus

Some command-line-based pipelines:

• ENCODE ATAC-Seq pipeline based on Caper/Cromwell
• Nextflow-core ATAC-Seq pipeline
• Snakemake ATAC-Seq pipelines (e.g., pyflow-ATACseq etc.)

Let's run our first pipeline

1. Log in to the cluster
    
2. ​Activate the environment:
   
   
    
3. Setup the working directory:

Task 1 (no points, but if absent, the practice won't be counted as done):
Please, store all the obtained files and results in your home folder under EpiPract2 path. I will collect them in a week and check it.

Running the pipeline example

1. Go to ATAC-Seq nextflow-core GitHub repository:
   https://github.com/nf-core/atacseq
    
2. Learn how to run a test on a minimal dataset with a single command (suggested by the authors in the manual).
   Task 2 (1 point): What is the command that you use for running the pipeline? Add it to the report.
3. Check if nextflow is accessible in your environment on the cluster. Try to run the command from p. 2. What's changed?
   Task 3 (0.5 point): List the content of the directory with results.
4. Nextflow names each run by a unique and catchy name (e.g. berserk_crick). What is the name of your run?
   Task 4 (0.5 point): Add unique name of your run to the report.
5. Copy the folder ./results/multiQC to your local computer. Open the .html from there in your browser. Inspect.

Inspecting the data

The test dataset that authors provide is ATAC-Seq on Saccharomyces cerevisiae cells (yeast) from the Shep et al. paper.
You can find the details on it in GEO database: GSE66386

 

Open the link and read about it. 

Inspecting 

• Your FASTQ file is listed in a table. Is it a forward or reverse part of the library? To what experiment and library does it correspond?
• Is the corresponding experiment merged during the run of the pipeline with other replicates? At what steps?

Task 5 (0.5 points): Describe the details of your experiment and answer the questions above. Is it similar to others or is it outlier? By what criteria? (List at least one)

Parameters tuning

We aim to study the binding of transcription factors in them. Thus we should explicitly state that we need the narrow peaks originating from TF binding.

1. Set the parameter --narrow_peak and run the pipeline again (on a cluster).
2. Copy the folder ./results/multiQC to your local computer.
3. Open multiQC reports for both narrow and broad peaks mode on your browser. Inspect.

Task 6 (1.5 point): What is the Fraction of Reads in Peaks for your experiment with and without --narow_peak option?
Why?

Is it consistent with the number of peaks for your experiment?

ATAC-Seq

Buenrostro et al., 2013

• A memo from the last lesson:

ATAC-Seq

Hsu et al. 2018

paired-end sequencing and mapping

ATAC-Seq: insertion size distribution

Buenrostro et al., 2013

• Distance between forward and reverse mapping positions:

ATAC-Seq: insertion size distribution

Buenrostro et al., 2013

• Distance between forward and reverse mapping positions:

ATAC-Seq: insertion size distribution

Buenrostro et al., 2013

• Distance between forward and reverse mapping positions:

ATAC-Seq around gene starts

Buenrostro et al., 2013

• Average plot around
  Transcription Start Sites (TSS): 

ATAC-Seq around TF binding sites

 Albanus et al., 2019

We can plot V-plot around positions of factors binding sites:

ATAC-Seq around TF binding sites

 Albanus et al., 2019

ATAC-Seq around nucleosomes

Schep et al., 2015

We can also plot V-plot around positions of nucleosomes:

Motifs de novo search

1. Activate the environment for motifs search:
    
2. Download the annotation and the genome:
    
3. Run the motifs search in your narrowPeak file:
   (follow the manual, if needed)
   
    
4. Download the resulting HTML report.

Task 9 (2 points): What are the found motifs? Report the logo of the best hit. What is the significance of the hit? What might be the factor that binds this motif (given the experimental setup and analysis)?

Motifs scanning

1. Run motifs scanning against CIS-BP database:
   
    
2. Download tehre resulting report. Inspect.

Task 10 (2 points): What are the found motifs? Report the logo of the best hit. What is the significance of the hit? Is it similar to the one that was found de novo?