Yu-Huai Yu+, Wei-Cheng Dai+, Zhi-Hao Jiang+ and Tzu-Hsien Yang* "CRMIPred: identifying the spatial interactions among Drosophila cis-regulatory modules via considering their cross-attended epigenetic profiles", (submitting).
+: These authors contributed equally.
Suggested running environments: Linux Ubuntu 16.04.6, Python 3.8.13
We recommend that you can use the conda package to create a new environment. This will automatically install the required python packages.
Here is an example:
-
Install the Conda package for you system. The installation of the package can be found here.
-
Create the CRMIPred Conda environment. This may take a while, depending on the network status.
conda create -n "CRMIPred" python=3.8.13
- Activate your CRMIPred Conda environment.
conda activate CRMIPred
- Download the codes from the following link and unzip the file. Please skip it if you have done this step.
wget https://cobis-fs.bme.ncku.edu.tw/CRMIPred/CRMIPred.tar.gz
- Unzip the file.
tar -zxvf CRMIPred.tar.gz
- Change the working directory.
cd CRMIPred
- Download the processed epigenetic datasets from the following link.
wget https://cobis-fs.bme.ncku.edu.tw/CRMIPred/Data.tar.gz
- Unzip the file.
tar -zxvf CRMIPred_Dataset.tar.gz
- If this is the first time you use CRMIPred, run the following command to install necessary packages.
pip install -r requirements.txt
CRMIPred can also support GPU acceleration. If you want to utilize GPU, please run the following command instead:
pip install -r requirements_gpu.txt
-
Prepare the input Drosophila CRM region pairs (ver. dm6).
The input CRM pair format MUST followed the following formats:
CRM_[chromosome]_[start1]_[end1]@CRM_[chromosome]_[start2]_[end2]
Important Rule: The start position of the first CRM must be smaller than the start position of the second CRM (start1 < start2).
For example: (as the input file named input_Test.csv)
Note: The input chromosomal regions start from the 5' end.
- Predict the probability if the given CRM pairs are interacting pairs.
python main.py -i <input_txt_file> -o <output_file_name> -mode <normal|reduced>
Required arguments:
-i: The input file for CRMIPred.
-o: The output prediction results.
Optional arguments:
- -mode: 'normal' (default model) or 'reduced' (reduced version model).
If we use the following as our inputs with the example command:
python main.py -i input_test.txt -o output_test
** output_test:**
Output format explanation:
- CRM1@CRM2 [interacting probability].


