Nm-Nano: A framework for predicting 2´-O-Methylation (Nm) Sites in Nanopore RNA Sequencing Data
The following softwares and modules should be installed before using Nm-Nano
python 3.7.13
minimpa2 (https://github.com/lh3/minimap2)
Nanopolish (https://github.com/jts/nanopolish)
samtools (http://www.htslib.org/)
numpy 1.19.5
pandas 1.2.4
scikit-learn 1.0.2
tensorflow 2.6.0
keras 2.6.0 (using Tensorflow backend)
gensim 4.2.0
The easiest way to get all python packages installed and running without root access is through conda. create the conda environment with the provided yml file and activate the environment
conda env create -f Nm-nano.yml
conda activate Nm-nano
In order to run Nm-Nano, the user has to choose one of Nm-nano machine learning models (xgboost or Random forest with embeeding) and run one of following python commands:
python test_xgboost.py #To run the xgboost model
Or
python RF_embedding.py #To run the RF with embeeding model
-
The user should include the benchmark dataset in the same path of test_xgboost.py and RF_embedding.py
-
All files required to generate the benchmark dataset are included in the generate_benchmark folder. To allow users to practice with Nm-nano predictors, we include a small benchmark dataset sample for Hela cell line. However, the user is free to generate a benchmark benchmark dataset for any other cell lines based on the instructions mentioned in README file in generate_benchmark folder.