CAPReSE version 1

Introduction

The regulatory effect of non-coding large-scale structural variations (SVs) on proto-oncogene activation remains unclear. In this study, we investigated SV-mediated gene dysregulation by profiling 3D cancer genome maps from 40 colorectal cancer (CRC) patients. To systematically identify such de novo chromatin contacts, we developed a new machine-learning method named ‘Chromatin Anomaly Pattern Recognition and Size Estimation,’ (CAPReSE) comprising a deep neural network (DNN)-based feature extractor combined with an XGBoost classifier. CAPReSE utilized a unique chromatin contact signature that shows enriched contact frequencies at the break-ends of SVs and a gradual decrease in contact frequencies along the rearranged genomic regions. The input tumor Hi-C contact map was normalized against a pan-normal Hi-C contact map, and a series of image processing algorithms were applied to identify the unique chromatin contact signatures. The SVs supported by both WGS and Hi-C data were used as a ground truth set for the final classifier.

Note: current version is optimized for the 40 CRC patient cohort used in the citation paper. Upgraded version avaialble for general cases will be released as version 2.

Publication and Citation

Under review in Cell Reports.

License

Copyright (c) YEAR: 2020 COPYRIGHT HOLDER: KAIST (Corporation registration number: 114471-0000668). Will be registered at the Korea Copyright Commission in accordance with Article 53 of the Copyright Act. Developed by Kyukwang Kim & Inkyung Jung, KAIST Dept. of Biological Sciences. For commercial use of the software, please contact the authors.

Requirements

Following software/packages are required.

• Python 2.7
• numpy
• scipy
• cv2
• PIL
• torch
• xgboost
• sklearn

Sample Data and Input Format

Example input image and model binary files can be downloaded with:

wget http://junglab.kaist.ac.kr/Dataset/SNUCRC_16-178T_chr8_pannormdiv.png #Example input image

wget http://junglab.kaist.ac.kr/Dataset/mnist_cnn.pt #Pretrained CNN weights
wget http://junglab.kaist.ac.kr/Dataset/xg_model_cis.pkl #XGBoost weight for cis- SV
wget http://junglab.kaist.ac.kr/Dataset/xg_model_trans.pkl #XGBoost weight for trans- SV

Following example codes shows how input image files are generated.

wget http://junglab.kaist.ac.kr/Dataset/SNUCRC_16-178T_chr8_cis20kb.pkl.gz # Tumor Hi-C contact map
wget http://junglab.kaist.ac.kr/Dataset/pannorm_chr8_example.pkl.gz # Pan-Normal Hi-C contact map
# pickle files generated using python 2.7

Tumor Hi-C contact map was divided by the control (pan-normal) Hi-C contact map (pseudocount added). Depth of the contact maps were pre-scaled by adjusting their mean and standard devidations to average values. Input image was scaled to n-fold and converted to np.uint8 array for OpenCV processing.

import numpy as np
import cv2

import pickle
import gzip
import sys

tumormap = sys.argv[1] 
pannorm  = sys.argv[2]
output_imgname = sys.argv[3]

target_map_raw  = pickle.load(gzip.open(tumormap))
pannorm_map = pickle.load(gzip.open(pannorm))

st_mean = np.mean(pannorm_map)
st_std  = np.std(pannorm_map)
target_map_zs = (target_map_raw - np.mean(target_map_raw))/np.std(target_map_raw) # z-score conversion

target_map_rs = (target_map_zs * st_std) + st_mean
target_map_div = target_map_rs/(pannorm_map + 1)
target_map_div[target_map_div <= 0] = 0 # re-scale z-score map with standard mean/std

target_map = np.clip(target_map_div, 0, 5)
target_map = (target_map * 51).astype(np.uint8)
cv2.imwrite(output_imgname,target_map) # 0-255 scale python cv2 image conversion

Running the example script will generate output image for the SV detection.

python example_script.py SNUCRC_16-178T_chr8_cis20kb.pkl.gz pannorm_chr8_example.pkl.gz test.png

For the overall Hi-C data processing procedures, please refer to the following papers.

Kyukwang Kim and Inkyung Jung,
covNorm: an R package for coverage based normalization of Hi-C and capture Hi-C data,
Computational and Structural Biotechnology Journal, Volume 19, pages 3149-3159(2021).
doi: https://doi.org/10.1016/j.csbj.2021.05.041

Kim, K., et al, 3DIV update for 2021: a comprehensive resource of 3D genome and 3D cancer genome
Nucleic Acids Research, Volume 49, Issue D1, pages D38–D46(2020).
doi: https://doi.org/10.1093/nar/gkaa1078

Note

• Please modify/fill the "FIX PATH" (downloaded weight files, WGS SV list, and info files) in the code for the deployment.
• We recommend to use new version of the CAPReSE (will be released soon) for practical purpose.
• Comment WGS coordinate list to obtain Hi-C only SV detection result.

python deploy_cis.py SNUCRC_16-178T_chr8_pannormdiv.png SVcall.png #Example run