cKNOTs: Chromatin Knots

About

This software allows user to look for links in chromatin. It takes .bedpe files as an input and outputs files containing information about localizations of links.

This repository consist of three main components:

• cknots.py script: running the link finding algorithm. The best way to run it is by using Docker.
• preprocessing_cknots.py script: preprocessing files before running the link finding algorithm on them.
• cknots Python package: analyzing the results.

How to set it up?

To run the link finding algorithm, the only requirement is having Docker installed on your machine.

To run the preprocessing_cknots.py script and use cknots package, you need to have Python 3.7+ installed on your machine with packages specified in requirements.txt file.

How to use it?

First, create folder for data, and put the relevant files there (i.e. GM12878.bedpe file with contacts and GM12878.bed file with CCDs). The files in the cknots_data folder will be available in the /data folder inside the container.

mkdir /home/$USER/cknots_data/

Then, clone this repository in a location you prefer (for example /home/$USER/):

git clone https://github.com/SFGLab/cKNOTs

Looking for minors

Go to the repository:

cd cKNOTs

Build the Docker image and tag it as cknots:

docker build -t cknots .

Run the software in the Docker container:

docker run -v "/home/$USER/cknots_data":"/data" \
cknots \
/cknots-app/docker_cknots.sh \
/data/GM12878.bedpe \
/data/GM12878.bed \
/data/results/GM12878 \
23

The results will be available in the /home/$USER/cknots_data/results/GM12878 folder. You will be able to check the progress by looking into log file (named based on chromosome number, so for 23'd chromosome the file name will be cknots_x.log):

cat /home/$USER/cknots_data/results/GM12878/cknots_x.log

Preprocessing data

• preprocessing_cknots.py orientation: Create a new .bedpe file with two new columns containing motif orientation for first and second locus of contact.

  • <in_bedpe> Path to the .bedpe file containing information about contacts.
  • <in_motif> Path to the .jaspar file containing motif.
  • <in_ref> Path to the .fa file containing reference genome.
  • <out_bedpe> Path to the output .bedpe file.

• preprocessing_cknots.py pet_filter: Create a new .bedpe file in which rows with PET count smaller than <min_pet_count> are filtered out.

  • <in_bedpe> Path to the .bedpe file containing information about contacts.
  • <out_bedpe> Path to the output .bedpe file.
  • <min_pet_count> Minimal number of contacts that should be left in the output file.

Analyzing the results

The cnots.analysis module contains classes and functions that can be used to analyze the output of the algorithm. This includes visualization of the minors loci, comparing similarity of the results for different cell lines and generating tables of number of minors found in different chromosomes.