Package: exopropred
###ExoProPredA computational approach to predict the subcellular localisation of exosomal proteins using the sequence information of the proteins.================================================================================###IntroductionExoProPred is a web server to predict exosomal proteins based on a hybrid model that combines a machine learning model with a motif-search approach. The models are trained on a dataset comprising 2831 exosomal proteins and 2831 non-exosomal proteins. The performance of the models was evaluated using 5-fold cross-validation. The models were trained on the top 70 best features comprising of composition-based and evolutionary information-based features as well as on hybrid features(Top 70 features + Motif-search) by implementing a random-forest classifier from the scikit library of Python. In the standalone version, a random-forest classifier-based model is implemented along with the motif search using the MERCI tool, named as a hybrid approach.ExoProPred is also available as a web server athttps://webs.iiitd.edu.in/raghava/exopropred. Please read/cite the content about the ExoProPred for complete information, including the algorithm behind the approach.================================================================================###StandaloneThe Standalone version of exopropred is written in python3, and the following libraries are necessary for the successful run:scikit-learnPandasNumpy================================================================================###nstallationTo install the package, type the following command:pip install exopropred================================================================================###Minimum usageTo know about the available option for the standalone, type the following command:exopropred -h================================================================================###Getting startedTo run the example, type the following command:exopropred. -i example_input.faThis will predict if the submitted sequences are exosomal proteins or non-exosomal proteins. It will use other parameters by default. It will save the output in "outfile.csv" in CSV (comma-separated variables).================================================================================###Full Usageusage: exopropred [-h] -i INPUT [-o OUTPUT] [-m {1,2}] [-t THRESHOLD]
[-d {1,2}]Please provide the following argumentsOptional arguments:-h, --help            show this help message and exit-i INPUT, --input INPUTInput: protein or peptide sequence(s) in FASTA format
                    or single sequence per line in single letter code-o OUTPUT, --output OUTPUTOutput: File for saving results by default outfile.csv-m {1,2}, --model {1,2}Model Type: 1: Composition-based model, 2: Hybrid
                    Model, by default 1-t THRESHOLD, --threshold THRESHOLDThreshold: Value between 0 to 1 by default 0.51-d {1,2}, --display {1,2}Display: 1:Exosomal Proteins only, 2: All Proteins, by
                    default 1================================================================================###File descriptionsInput File: It allows users to provide input in the FASTA format.Output File: The program will save the results in the CSV format; in case the user does not provide the output file name, it will be stored in "outfile.csv".Threshold: User should provide a threshold between 0 and 1; by default, its 0.51.Model: User is allowed to choose between two different models, such as 1 for the composition-based model and 2 for the hybrid model; by default it's 1.Display type: This option allow users to fetch either only exosomal proteins by choosing option 1 or prediction against all proteins by choosing option 2.================================================================================###ExoProPred Package FilesIt contains the following files; a brief description of these files is given belowINSTALLATION                              : Installations instructionsLICENSE                                   : License informationREADME.md                                 : This file provides information about this packagemodel.zip                                 : This zipped file contains the compressed version of the modelexopropred.py                             : Main Python programMERCI_motif_locator.pl                    : Perl script for locating motifs using MERCIswissprot                                 : Swiss-Prot database for calculating PSSM profilemotifs                                    : Folder containing the motif filesextra                                      : Folder containing the Python scripts for PSSM-based composition featuresData                                      : Folder containing the files to calculate the features using Pfeatureexample_input.fa                          : Example file contain peptide sequences in FASTA formatexample_composition_model_output.csv      : Example output file for composition-based modelexample_hybrid_model_output.csv           : Example output file for the hybrid model