cytolncpred 1.2

A computational method to predict the probability of lncRNA localizing to cytoplasm

CytoLNCpred

A computational method to predict the probability of lncRNA localizing to cytoplasm

Introduction

CytoLNCpred is a tool developed by Raghava-Lab in 2024. It is designed to predict the probability of lncRNA localizing to the cytoplasm. It utilizes a correlation-based features with machine learning to make predictions. CytoLNCpred is also available as web-server at https://webs.iiitd.edu.in/raghava/cytolncpred. Please read/cite the content about the CytoLNCpred for complete information including algorithm behind the approach.

PIP Installation

PIP version is also available for easy installation and usage of this tool. The following command is required to install the package

pip install cytolncpred

To know about the available option for the pip package, type the following command:

cytolncpred -h

Standalone

The Standalone version of CytoLNCpred is written in python3 and following libraries are necessary for the successful run:

• numpy 2.1.1
• pandas 2.2.3
• scikit-learn 1.5.2
• xgboost 2.1.1
• argparse

Minimum USAGE

To know about the available option for the stanadlone, type the following command:

python cytolncpred.py -h

To run the example, type the following command:

python cytolncpred.py -i example_input.fa

This will predict the probability whether a submitted sequence will localize to the cytoplasm or nucleus. It will use other parameters by default. It will save the output in "outfile.csv" in CSV (comma separated variables).

Full Usage

usage: cytolncpred.py [-h] -i INPUT [-o OUTPUT] -c {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15} [-t THRESHOLD] [-w WORKDIR] [-d {1,2,3}]

Provide the following inputs for a successful run

options:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        Input: nucleotide sequence in FASTA format
  -o OUTPUT, --output OUTPUT
                        Output: File for saving results; by default outfile.csv
  -c {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15}, --cell-line {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15}
                        Select cell-line: 1: A549 2: H1.hESC 3: HeLa.S3 4: HepG2 5: HT1080 6: HUVEC 7: MCF.7 8: NCI.H460 9: NHEK 10: SK.MEL.5 11: SK.N.DZ 12:
                        SK.N.SH 13: GM12878 14: K562 15: IMR.90
  -t THRESHOLD, --threshold THRESHOLD
                        Threshold: Value between 0 to 1; by default 0.5
  -w WORKDIR, --workdir WORKDIR
                        Working directory: Directory where all intermediate and final files will be created; by default .
  -d {1,2,3}, --display {1,2,3}
                        Display: 1:Cytoplasm-localized, 2: Nucleus-localized, 3: All; by default 3

Input File: It allow users to provide input in the FASTA format.

Output File: Program will save the results in the CSV format, in case user does not provide output file name, it will be stored in "outfile.csv".

Threshold: User should provide threshold between 0 and 1, by default its 0.5.

Cell-line: User should select the specific cell-line among the 15 cell-lines for which prediction will be done.

Working Directory: Directory where intermediate files will be saved

Display type: This option allow users to fetch either only lncRNA localizing to Cytoplasm by choosing option 1 or only lncRNA localizing to Nucleus by choosing option 2 or prediction for all lncRNAs by choosing option 2.

CytoLNCpred Package Files

It contains the following files, brief description of these files given below

INSTALLATION : Installations instructions

LICENSE : License information

README.md : This file provide information about this package

Nfeature_DNA.py : This file is used to compute the features

model : This folder contains the pickled models for each cell-line

cytolncpred.py : Main python program

example.fasta : Example file contain peptide sequences in FASTA format

sample_output.csv : Example output file for the program