Chiron3D: an interpretable deep learning framework for understanding the DNA code of chromatin looping

Chiron3D is a DNA-only deep learning model that predicts cell-type–specific CTCF HiChIP contact maps from 524,288 bp genomic windows. The model uses a frozen Borzoi backbone with lightweight adapters and a C.Origami-style pairwise head to output 105 × 105 contact matrices at 5 kb resolution.

This repository currently focuses on:

• Training Chiron3D on the A673 CTCF HiChIP dataset

• Evaluating trained checkpoints and reproducing the main quantitative and qualitative results from the figures

Data

Chiron3D is trained on CTCF HiChIP and matched CTCF ChIP-seq from the A673 wild-type Ewing sarcoma cell line on the hg19 reference genome.

All preprocessed inputs required to run the scripts in this repository are made available via Zenodo.

Setup

Download and unpack the Zenodo archive and have the following layout in the top level directory.

data/
  A673_WT_CTCF_5000.cool      # 5 kb binned CTCF HiChIP contact map (hg19)
  borzoi/                     # Weights of backbone from borzoi-pytorch
  chiron-model.ckpt           # pretrained checkpoint for evaluation + downstream 
  chromosomes/                # hg19 FASTA files per chromosome (e.g. chr1.fa, ...)
  ctcf/                       # CTCF feature track (ChIP-seq)
  extruding_loops.csv         # Dataset of extruding loops classified by Tweed
  stable_loops.csv            # Dataset of stable loops classified by FitHiChIP
  windows_hg19.bed            # 524,288 bp windows tiled with 50 kb stride

Create a conda environment and install all required packages. For a SLURM-cluster, there is an install script already provided in scripts/create_environment.sh. Please note the specific requirements of transformers==4.50.0 and peft==0.17.0.

Training and evaluation

There are two slurm scripts provided in the scripts directory: model-evaluation.sh and model-training.sh. For training, Chiron3D requires 4 × NVIDIA RTX 4090 or 3090 with 24GB of memory. Training takes about one day. For evaluation (across three test chromosomes), 1 × NVIDIA RTX 4090 or 3090 with 24GB finishes in 30 minutes.

Downstream Task: Loop editing

The notebooks folder showcases four examples of using the ledidi-based editing framework to suggest in silico edits. The outputs of the runs can be viewed in the respective notebook and the corresponding example folders. Please note, that the package must be installed in editable mode by running pip install -e . for all paths to work. On our SLURM cluster, the following command is used to run from within the notebooks directory:

srun --job-name jupyter -p gpu --gres=gpu:rtx4090:1 --time 01:00:00 --cpus-per-task 16 --mem 128G bash -c 'source ~/.bashrc && conda activate chiron && cd /path/to/main/folder/Chiron3D && pip install -e . && cd notebooks && jupyter lab --ip $(hostname -i) --no-browser'.