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