Preparing the environment

We recommend setting up conda environment named sc_dros with all the requirements.

List of dependencies:

• snHi-C and Hi-C data processing:

  bwa

  cooler

  pairtools, version https://github.com/agalitsyna/pairtools.git

  lavaburst

• Pyhon 3.7, libraries:

  pandas

  numpy

  seaborn

Preparing the dataset

The files needed:

• Files with information about the genome:

  • Reduced chromosomes sizes,

    Path: data/GENOME

  • FASTA file with genome and bwa index,

    Path: data/GENOME

    Run: bash script scripts/00_prepare_data/001_prepare_genome.sh

  • FASTQ files with snHi-C data,

    PATH: data/FASTQ

    Run python code: scripts/00_prepare_data/002_download_data.py

    Note. The GEO dataset is currently private and the script won't work until it's public. The result of this step should be a set of FASTQ files with names formatted as follows:

    {Cell_name}_{Replicate}_R1.fastq.gz
    {Cell_name}_{Replicate}_R2.fastq.gz
    

    Dependencies: GEOparse with parallel download option: https://github.com/agalitsyna/GEOparse.git To install GEOparse, run:

    git clone https://github.com/agalitsyna/GEOparse.git
    cd GEOparse
    pip install -e .

Raw data processing

Raw data processing includes the steps from raw FASTQ files to processed COOL files for both snHi-C and bulk Hi-C.

• Reads mapping and BAM files to PAIR parse

bash scripts/01_data_mapping/010_run_mapping.sh
bash scripts/01_data_mapping/011_parse_population.sh

• PAIR files to COOL conversion

bash 012_run_pairsam2cooler.sh

This should result in a set of COOL files: data/COOL/

TAD calling

TAD calling is performed with lavaburst package and includes the steps of scanning a wide range of gamma parameter and selection of a single set of TADs and sub-TADs.

Working directory: scripts/02_tad_calling/

Script example: 021_run_FindOptimalGamma.sh

Remarks

This folder is maintained for demonstration of initial steps of snHi-C data processing.