Command-line

Make sure you satisfy the requirements provided below to ensure smooth working of the utility;

System Requirements

Hardware Requirements

A standard computer with around 16 GB RAM

Software Requirements

Python 3.8 or above

Installation

Follow the below steps to install linc2function in your computer.

Login

Upon login to a system and opening a terminal, the following prompt should appear, where the user is the user name and the hostname is the hostname of the system.

user@hostname:~$

Change directory

From the home directory which will be open by default, change to a suitable directory on your computer where the utility needs to be installed. For example, in this tutorial we have changed to workspace directory.

user@hostname:~$ cd workspace

Clone

In the workspace directory, clone the current version of linc2functionpipeline repository from the GitHub.

username@hostname:~/workspace$git clone https://gitlab.com/tyagilab/linc2functionpipeline.git

Open linc2function

Open the linc2function directory that is downloaded from GitHub after cloning.

username@hostname:~/workspace$cd linc2functionpipeline

Python virtual environment

The Python virtual environment encaptulates all the libraries required for the linc2function. All the necessary libraries listed in a requirements.txt file that can be found at the root of the repository. Below are the instructions to create and install dependancies in the Python virtual environment.

Note

linc2function requires Python version 3.8 or higher. For installing Python, please refer the below link: https://www.python.org/downloads/

Create virtual environment

Inside the linc2functionpipeline directory, create a new Python virtual enviroment to conveniently manage all the dependencies required for the utility.

username@hostname:~/workspace/linc2functionpipeline$virtualenv -p python3 .venv

Activate virtual environment

After creating the Python virtual enviroment, activate the virtual enviroment to start using it for subsequent commands. The prompt will change with (.venv) appearing in front of it as shown below;

username@hostname:~/workspace/linc2functionpipeline$source ./venv/bin/activate
(.venv) user@hostname:~/workspace/linc2functionpipeline$

Install dependencies

Install all the required dependencies listed in the requirements.txt file in the newly created Python virtual environment.

(.venv) user@hostname:~/workspace/linc2functionpipeline$pip install -r requirements.txt

Usage

Human Specific Basic (HSB) Model

Execute the following command to invoke Human Specific Basic (HSB) model to predict if a given sequence is a non-coding RNA.

(.venv) username@hostname:~$python3 main.py predict_hs_model <sequence> <mode> <model_path> <scalers_path>

For example;

(.venv) username@hostname:~$python3 main.py predict_hs_model 'ACUCCAGAAUGGGCUCCCUCAGUCGGAAGUCUCCCCGCUCCACCGCCCCCAGUGUAACCCCUCCAACCC' 'basic' /path/to/model.h5 path/to/scaler.pkl

Species Agnostic Basic (SAB) Model

Execute the following command to invoke Species Agnostic Basic (SAB) model to predict if a given sequence is a non-coding RNA.

(.venv) username@hostname:~$python3 main.py predict_sa_model  <sequence> <mode> <model_path> <scalers_path>

For example;

(.venv) username@hostname:~$python3 main.py predict_sa_model 'ACUCCAGAAUGGGCUCCCUCAGUCGGAAGUCUCCCCGCUCCACCGCCCCCAGUGUAACCCCUCCAACCC' 'basic' /path/to/model.h5 path/to/scaler.pkl

Human Specific Standard (HSS) Model

Execute the following command to invoke Human Specific Standard (HSS) model to predict if a given sequence is a non-coding RNA.

(.venv) username@hostname:~$python3 main.py predict_hs_model <sequence> <mode> <model_path> <scalers_path>

For example;

(.venv) username@hostname:~$python3 main.py predict_hs_model 'ACUCCAGAAUGGGCUCCCUCAGUCGGAAGUCUCCCCGCUCCACCGCCCCCAGUGUAACCCCUCCAACCC' 'standard' /path/to/model.h5 path/to/scaler.pkl

Species Agnostic Standard (SAS) Model

Execute the following command to invoke Species Agnostic Standard (SAS) model to predict if a given sequence is a non-coding RNA.

(.venv) username@hostname:~$python3 main.py predict_sa_model  <sequence> <mode> <model_path> <scalers_path>

For example;

(.venv) username@hostname:~$python3 main.py predict_sa_model 'ACUCCAGAAUGGGCUCCCUCAGUCGGAAGUCUCCCCGCUCCACCGCCCCCAGUGUAACCCCUCCAACCC' 'standard' /path/to/model.h5 path/to/scaler.pkl