README.md

# task0 - metabolic pathways - processing mapping results

## Description
The aim of this project is to process the data obtained after mapping metabolites against biological pathways, in particular with the [ConsensusPathDb](http://cpdb.molgen.mpg.de/), MetExplore and RaMP tools.


## Usage
This library is designed to give biological contextualization to a list of metabolite identifiers, querying metabolite networks and metabolic pathways.


### Requirements

You will need:
- Python >= 3.8


### Installation

Clone repository: 
```
git clone https://unh-pfem-gitlab.ara.inrae.fr/mth/mth2-wp1/task0-metabolic-pathways-processing-mapping-results.git
```

Create environment with conda:
```
conda create --name mapping_metabo --file requirements.txt
```
Or using pip:
```
pip install -r requirements.txt
```

Then you can test the installation following the [example](http://example.fr).


## Begin

 
The first file is designed to format the mapping results from these 3 tools in the form of a column for each metabolic pathway, with its name in the first line and the metabolites of interest found below. The second part of the project involves extracting information from this mapping, and the third part involves visualizing this information.

To begin with, you can use the main program to process your data in the best possible way.
To use the main program, you need to have your data in an Excel file with four columns:
- first: the current name of the metabolites
- second: a column with all the exact IDs for that metabolite.
(The ID must be in full form, e.g. chebi:15354, hmdb:HMDB0000097).
-The third column is the case value for this metabolite.
-The last column is the control value for this metabolite.

![Input file figure example](src/processing_mapping_results/static/input_file_main.png "Example of input file")

And you need a folder for to save the generated data

## Support
If you have any problems, please write to me at mathieu.umec@inrae.fr

## Roadmap
At present, this library needs to be improved on a number of points:
- Adjust value with data for all out file
- Always give the actual name and not the ID 
- Automate mapping to MetExplore and then automate Voronoi tesselation visualization and Biosource modification through Met4J: see (https://github.com/MetExplore/met4j/)
- Automate report generation with all information and contextualization

## Authors and acknowledgment
* **Mathieu UMEC**
* **Clement Frainay**