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kundajelab / chrombpnet

Bias factorized, base-resolution deep learning models of chromatin accessibility (chromBPNet)
https://github.com/kundajelab/chrombpnet/wiki
MIT License
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Bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants

Chromatin profiles (DNASE-seq and ATAC-seq) exhibit multi-resolution shapes and spans regulated by co-operative binding of transcription factors (TFs). This complexity is further difficult to mine because of confounding bias from enzymes (DNASE-I/Tn5) used in these assays. Existing methods do not account for this complexity at base-resolution and do not account for enzyme bias correctly, thus missing the high-resolution architecture of these profiles. Here we introduce ChromBPNet to address both these aspects.

ChromBPNet (shown in the image as Bias-Factorized ChromBPNet) is a fully convolutional neural network that uses dilated convolutions with residual connections to enable large receptive fields with efficient parameterization. It also performs automatic assay bias correction in two steps, first by learning simple model on chromatin background that captures the enzyme effect (called Frozen Bias Model in the image). Then we use this model to regress out the effect of the enzyme from the ATAC-seq/DNASE-seq profiles. This two step process ensures that the sequence component of the ChromBPNet model (called TF Model) does not learn enzymatic bias.

ChromBPNet

Table of contents

Installation

This section will discuss the packages needed to train a ChromBPNet model. Firstly, it is recommended that you use a GPU for model training and have the necessary NVIDIA drivers and CUDA already installed. You can verify that your machine is set up to use GPU's properly by executing the nvidia-smi command and ensuring that the command returns information about your system GPU(s) (rather than an error). Secondly there are two ways to ensure you have the necessary packages to train ChromBPNet models which we detail below,

1. Running in docker

Download and install the latest version of Docker for your platform. Here is the link for the installers -Docker Installers. Run the docker run command below to open an environment with all the packages installed and do cd chrombpnet to start running the tutorial.

Note: To access your system GPU's from within the docker container, you must have NVIDIA Container Toolkit installed on your host machine.

docker run -it --rm --memory=100g --gpus device=0  kundajelab/chrombpnet:latest

2. Local installation

Create a clean conda environment with python >=3.8 

conda create -n chrombpnet python=3.8
conda activate chrombpnet

Install non-Python requirements via conda

conda install -y -c conda-forge -c bioconda samtools bedtools ucsc-bedgraphtobigwig pybigwig meme

Install from pypi

pip install chrombpnet

Install from source

git clone https://github.com/kundajelab/chrombpnet.git
pip install -e chrombpnet

QuickStart

Bias-factorized ChromBPNet training

The command to train ChromBPNet with pre-trained bias model will look like this:

chrombpnet pipeline \
  -ibam /path/to/input.bam \ # only one of ibam, ifrag or itag is accepted
  -ifrag /path/to/input.tsv \ # only one of ibam, ifrag or itag is accepted
  -itag /path/to/input.tagAlign \ # only one of ibam, ifrag or itag is accepted
  -d "ATAC" \
  -g /path/to/hg38.fa \
  -c /path/to/hg38.chrom.sizes \ 
  -p /path/to/peaks.bed \
  -n /path/to/nonpeaks.bed \
  -fl /path/to/fold_0.json \
  -b /path/to/bias.h5 \ 
  -o path/to/output/dir/ \

Input Format

Please find scripts and best practices for preprocssing here.

Output Format

The ouput directory will be populated as follows -

models\
    bias_model_scaled.h5
    chrombpnet.h5
    chrombpnet_nobias.h5 (TF-Model i.e model to predict bias corrected accessibility profile) 
logs\
    chrombpnet.log (loss per epoch)
    chrombpnet.log.batch (loss per batch per epoch)
    (..other hyperparameters used in training)

auxilary\
    filtered.peaks
    filtered.nonpeaks
    ...

evaluation\
    overall_report.pdf
    overall_report.html
    bw_shift_qc.png 
    bias_metrics.json 
    chrombpnet_metrics.json
    chrombpnet_only_peaks.counts_pearsonr.png
    chrombpnet_only_peaks.profile_jsd.png
    chrombpnet_nobias_profile_motifs.pdf
    chrombpnet_nobias_counts_motifs.pdf
    chrombpnet_nobias_max_bias_response.txt
    chrombpnet_nobias.....footprint.png
    ...

Detailed usage guide with more information on input arguments and the output file formats and how to work with them are provided here and here.

For more information, also see:

Bias Model training

The command to train a custom bias bias model will look like this:

chrombpnet bias pipeline \
  -ibam /path/to/input.bam \ # only one of ibam, ifrag or itag is accepted
  -ifrag /path/to/input.tsv \ # only one of ibam, ifrag or itag is accepted
  -itag /path/to/input.tagAlign \ # only one of ibam, ifrag or itag is accepted
  -d "ATAC" \
  -g /path/to/hg38.fa \
  -c /path/to/hg38.chrom.sizes \ 
  -p /path/to/peaks.bed \
  -n /path/to/nonpeaks.bed \
  -fl /path/to/fold_0.json \
  -b 0.5 \ 
  -o path/to/output/dir/ \

Input Format

Please find scripts and best practices for preprocessing here.

Output Format

The output directory will be populated as follows -

models\
    bias.h5
logs\
    bias.log (loss per epoch)
    bias.log.batch (loss per batch per epoch)
    (..other hyperparameters used in training)

intermediates\
    ...

evaluation\
        overall_report.html
        overall_report.pdf
    pwm_from_input.png
        k562_epoch_loss.png 
    bias_metrics.json
    bias_only_peaks.counts_pearsonr.png
    bias_only_peaks.profile_jsd.png
    bias_only_nonpeaks.counts_pearsonr.png
    bias_only_nonpeaks.profile_jsd.png
        bias_predictions.h5
    bias_profile.pdf
    bias_counts.pdf
    ...

Detailed usage guide with more information on the input arguments and output file formats and how to work with them are provided here and here.

For more information, also see:

How to Cite

If github citations are not allowed in your submissions please reach out to Anusri Pampari and Anshul Kundaje. If you're using ChromBPNet in your work, please cite as follows:

@software{Pampari_Bias_factorized_base-resolution_2023,
author = {Pampari, Anusri and Shcherbina, Anna and Nair, Surag and Schreiber, Jacob and Patel, Aman and Wang, Austin and Kundu, Soumya and Shrikumar, Avanti and Kundaje, Anshul},
doi = {10.5281/zenodo.7567627},
month = {1},
title = {{Bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants.}},
url = {https://github.com/kundajelab/chrombpnet},
version = {0.1.1},
year = {2023}
}