PINC

A powerful tool for identifying non-coding RNAs in plants by analysing k-mer frequency, cds-related features, sequence length and GC content to distinguish between the growing number of non-coding RNAs and coding RNAs in plants.

Features

• High precision (ensemble learning)
• Multiple high-performance base models
• Convenience of use
• Automated Forecasting
• Web Online

Documents

Documentation

Get Start

There are multiple ways to run this tool, feel free to choose one of the following method.

Run PINC from Web Online (Fastest)

http://www.pncrna.com/

Run PINC from docker (Locally、Simply)

1. Download the PINC and Add the data file to the project directory.

git clone https://github.com/midisec/PINC
cd PINC
# upload the data file (example: data.fasta)

All input data must be in fasta format

2. Pull and build the environment image. (Time required)

sudo docker build -t pinc_images .

3. Create and Enter a new container.

sudo docker run -it pinc_images bash

4. Execute PINC for prediction

python pinc.py -f data.fasta

Run PINC from source code (Complex)

1. Installation Environment(Autogluon、kentUtils)

2. Clone project, install related dependencies

git clone https://github.com/midisec/PINC
cd PINC
pip3 install -r requirements.txt

3. Execute PINC for prediction

python pinc.py -f data.fasta

Usage

Command line version

Prediction

python pinc.py -f <data.fasta>

Website online version

Prediction

After this, you will get a task page address with the uuid.

After that you can also check the history of the task by the uuid, usually it will be saved for one month.

View Results and Download results

Contributors