A Tensorflow DL framework for predicting Hi-C chromatin interactions using megabase scale DNA sequence.
This repository contains the core deepC python code, R scripts and functions for downstream analysis as well as tutorials and links to example data.
The core code is implemented in python (v3.5+) and tensorflow (v1). For downstream analysis and visualizations we use R and custom functions for handling HiC data and deepC predictions.
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python 3.5 +
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tensorflow (tensorflow-gpu)
- GPU support is preferable for predictions and essential for training
- developed under 1.8.0, use legacy version
- compatible with latest version 1.14.0, use current version
- the legacy versions should work with every tf version 1.8.0 or higher bearing with deprecation warnings
- the code was not yet tested on tensorflow 2
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additional python modules:
- numpy (v1.16.4 or above)
- pysam (v 0.15.2)
- pybedtools and a compatible version of bedtools installed
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R version 3.4.4 +
- packages:
- tidyverse (v1.2.1 or above)
- RColorBrewer (v1.1-2 or above)
- cowplot (v0.9.2 or above)
- for plotting 1D tracks (e.g. DNase, ChIP-seq) rtracklayer rtracklayer (v1.38.3 or above) and dependencies are required
- Rstudio (not required but recommended)
- packages:
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some processing helper scripts require perl (v5.26.0 or above)
- training deepC models requires running with GPU support for several hours (up to days depending on the dataset and epochs aimed for)
- running predictions is feasible without but runs significantly faster with a GPU
- for example predicting the impact of a variant as in the tutorial provided requires ~5 mins with GPU support and ~ 2h on CPU.
Clone the repository. Make sure all dependencies are available.
To use from within a python script import as import deepCregr.
Find tutorials here.
Download links to trained models are provided under ./models. See the README
file there for details.
Please refer to the bioRxiv preprint here
Implementation of dilated convolutions was adapted from wavenet.