RSLpred 2.0: Deep neural network based Oryza sativa Subcellular Localization Prediction

RSLpred 2.0 predicts the deep learning-based subcellular localization(s) of Oryza sativa. Currently it performs a multi-level prediction. First it differentiate between single and dual location proteins. Then it differentiate between 12 different single localizations: 'Vacuole', 'Cytoplasm', 'Peroxisome', 'Golgi', 'Endoplasmic', 'Memebrane', 'Secreted', 'Cell', 'Mitochondria', 'Endosome', 'Plastid','Nucleus'. Additionally, it differentiate predicted Membrane loclization into 2 different categories: Single-Pass, Multi-Pass. Similarly dual localizations are classified into 9 different localizations: 'Cell-memebrane_Membrane', 'Cytoplasm_Membrane','ER_Membrane','Golgi_Membrane','Membrane_Mitochondria','Membrane_Plastid','Plastid_Mitochondria','Nucleus_Chromosome','Nucleus_Cytoplasm'.

Input

The RSLpred 2.0 server requires amino acid sequence(s) in fasta format, and does not support nucleic acid sequences.

Prediction approach

The RSLpred2 can be excuted using two different approaches using different models: Sensitive and Fast

The 'Sensitive' approach uses Tripeptide amino acid composition. This model provides a more accurate prediction at the cost of longer computation time. This approach is useful in annotationg small number of proteins with high-quality prediction.

Development Environment and Prerequisite

This source code was developed in Linux, and has been tested on Linux and OS X. The only prerequisite is to have Python 3.7 or above installed.

Installation

The installation of RSLpred 2.0 can be done in two ways:

1. Create a dedicated miniconda3 environment

    Download RSLpred 2.0 from:
        
        https://bioinfo.usu.edu/RSLpred2/download/RSLpred-2.0.tar.gz

    Download the Miniconda installer: 
        
        (https://docs.conda.io/en/latest/miniconda.html#linux-installers)

    Extract the downloaded file:

        tar -xvzf RSLpred-2.0.tar.gz

    cd RSLpred2
    
    Create and activate a conda environment

        conda env create -f environment.yml

        conda activate RSLpred-2.0

        pip3 install .

2. Intall using system Python3

    Download RSLpred 2.0 from: 

    https://bioinfo.usu.edu/RSLpred2/download/RSLpred-2.0.tar.gz

    Extract the downloaded file:

        tar -xvzf RSLpred-2.0.tar.gz

    cd RSLpred2

        pip3 install .

Example

Run RSLpred 2.0

python RSLpred2.py -i ./example/test.fasta -o output -m fast

Queries and Contact

Written by Naveen Duhan (naveen.duhan@usu.edu),

Kaundal Bioinformatics Lab, Utah State University,

Released under the terms of GNU General Public Licence v3

In case of technical problems (bugs etc.) please contact Naveen Duhan naveen.duhan@usu.edu.

For any Questions on the scientific aspects of the RSLpred 2.0 method please contact:

Rakesh Kaundal, rkaundal@usu.edu

Naveen Duhan, naveen.duhan@usu.edu