admirepred 1.0.0
A tool to predict abundant miRNA in exosomes
License: MIT
Requires Python: >=3.9

# **AdmirePred**
A method for predicting abundant miRNAs in Exosomes
## Introduction
AdmirePred is a tool for the prediction of miRNA found abundantly in exosomes under normal conditions. It uses similarity-based methods (Basic Local Alignment Search Tool) combined with Extra Tree Classifier built on the best performing composition-based features extracted using One hot encoding and Term Frequency - Inverse Document Frequency. AdmirePred is also available as a web-server at https://webs.iiitd.edu.in/raghava/admirepred. Please read/cite the content about AdmirePred for complete information including algorithm behind the approach.

## Python Package
```
pip install admirepred
```
```
import admirepred
```
It can also be downloaded from - https://pypi.org/project/admirepred/


## Requirements
- scikit-learn=1.6.1
- Pandas
- Numpy
- Joblib
- Argparse


No additional package/tool is required for model = 1 (default model), however for model = 2, please download blast (version - blast: 2.12.0+) from https://blast.ncbi.nlm.nih.gov/doc/blast-help/downloadblastdata.html


## Minimum USAGE
To know about the available option for the standlone, type the following command:
```
admirepred -h
```
To run the example, type the following command:
```
admirepred -f example_seq.fa -o output
```
Here, -f argument is to enter the input file in Fasta format and -o argument is for giving the path to the output directory. By default, the package uses model (-m) = 1 which employs only ML algorithm (Extra Tree Classifier) to classify the miRNA sequences, which generates a prediction file "classification_ML.csv" in the specified output directory. If model (-m) = 2 is selected, then the hybrid model is employed (ML + BLAST) to classify the miRNA sequences, which generates a prediction file "classification_hybrid.csv" in the specified output directory.

## Full Usage
```
usage: admirepred [-h] --file FILE --output OUTPUT [--model MODEL] [--threshold THRESHOLD]
```
```
Please provide following arguments for successful run
required arguments:
  --file FILE, -f FILE                   Path to fasta file
  --output OUTPUT, -o OUTPUT             Path to output

optional arguments:

  --model MODEL, -m MODEL                Model selection: 1 for ML only, 2 for ML + BLAST + MERCI (By default model = 1)
  --threshold THRESHOLD, -t THRESHOLD    Threshold for classification (can be any value between 0-1 for model = 1 (by default = 0.5) and 0-2 for model = 2 (by default = 0.52))

For help:
  -h, --help            show this help message and exit

```

## Standalone minimum usage
```
python3 admirepred.py -f example_seq.fa -o output
```

## Arguments description

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

**Output File:** Program will save the results to this folder

**Model:** User can pick which model to run, model = 1 runs only ML model (ET classifier), whereas model = 2 runs hybrid model (ML + BLAST), by default the tool runs model = 1

**Threshold:** User can provide threshold for classification (can be any value between 0-1 for model = 1 (by default = 0.51) and 0-2 for model = 2 (by default = 0.50))


AdmirePred Package Files
=======================
It contantain following files, brief description of these files given below

INSTALLATION                    : Installations instructions

LICENSE                         : License information

README.md                       : This file provide information about this package

admirepred_et_model.pkl           : This file contains the pickled version of model

admirepred.py                     : Main python program

example_input.fa                : Example file contain nucleotide sequences in FASTA format

blast_db                        : Database for BLAST search