[] IntRNA first introduced a dual-path classification model to enable a systematic interpretation of the RNA universe. (1) providing a dual-path classification model was proposed to enable systematic interpretation of the RNA universe, (2) a novel encoding strategy was introduced by incorporating the most comprehensive set of features. Therefore, users can get the encoding results and the classification of RNA from IntRNA.

Contents

• Contents
• Requirements and installment
• Basic usage
• Study demo
  • IntRNA
• Question && issue

Requirements and installment

This software is developed with Python 3.X, Python 3.X is required as runtime environment. we recommemd user to install anaconda3 which is well-known as a scientific Python.

git clone https://github.com/idrblab/intrna
cd intrna
# virtual environment are recommended
conda create -n intrna python=3.8
pip install -r requirements.txt

Basic usage

Users can use IntRNA to (1) encode RNA image and (2) classification of RNA.

python IntRNA.py --help

usage: IntRNA.py [-h] [-i INPUTFASTA] [-o OUTPUTPATH] [-m {1,2,3}]

Your program description here

optional arguments:
  -h, --help            show this help message and exit
  -i INPUTFASTA, --inputfasta INPUTFASTA
                        The RNA data needed to predict
  -o OUTPUTPATH, --outputpath OUTPUTPATH
                        The folder to save encoding features and predict result
  -m {1,2,3}, --model_type {1,2,3}
                        The model will be used to predict. 1: mRNA and ncRNA; 2: 13 classes linear ncRNA; 3: circRNA and linear RNA

Study demo

IntRNA

Users can run tasks like following shell script.

python IntRNA.py -i ./demo/sense_intronic_sample.fa \
    -o ./demo/output_sense_intronic_sample \
    -m 1 

Question && issue

If there are any usage problems, welcome to submit issues to this repository or contact author with email lfwyx@zju.edu.cn