isn-tractor 0.2.1

Interactome based Individual Specific Networks

ISN-tractor

Interactome based Individual Specific Networks (Ib-ISN)

About the project: Interactome Based Individual Specific Networks (Ib-ISN) Computation and its relevance

An individual-specific network in biology is a sort of network that depicts the relationships between the genes, proteins, or other biological molecules of a particular individual.

It is sometimes referred to as a "personalised network" or "individual network".

These networks can be computed using a range of data types, including genetic information, details on protein expression, and other omics data.

One of the top aims of individual-specific networks is to comprehend how interactions between different biological molecules affect an individual's overall function and behaviour. For example, an individual-specific network can be used to identify the proteins that are essential for maintaining a certain biological activity or the critical regulatory networks that control a person's gene expression. It is also possible to forecast how genetic or environmental changes may affect a person's biology by using individual-specific networks. For instance, they can be used to foretell how a specific mutation or environmental exposure may impact the way a certain gene or pathway functions.

The entire range of interactions between biological macromolecules in a cell, including as those mediated by protein-ligand binding or solely functional connections between proteins, are referred to as the interactome. As a result, it offers a summary of the functional activity within a particular cell. Extracellular protein-protein interaction (PPI) networks are particularly significant to illness causation, diagnosis, and treatment due to a number of features. Their functional diversity, chaos, and complexity are a few of these.

Luck et al. introduced HuRI, a human "all-by-all" reference interactome map of human binary protein interactions, which has been demonstrated to have over 53,000 protein-protein interactions.

HuRI, as

a systematic proteome-wide reference that connects genetic variation to phenotypic outcomes,

was the impetus for our decision to create a novel approach for computing interactome-based ISN, starting from SNP data and ending with a gene-based network.

Getting started

Installation

pip install isn-tractor

Usage

1. Data preprocessing and imputation

import pandas as pd
import isn_tractor.ibisn as it

snps = pd.read_csv("snp_dataset.csv")
snp_meta = pd.read_csv("snp_metadata.csv")
interact = pd.read_csv("interactome_interactions.csv")
gtf = pd.read_csv("human_genes.csv")

# returns 
gene_info = it.preprocess_gtf(gtf)

# returns 
it.preprocess_snp(snp_meta)

# returns 
snps = it.impute(snps)

2. Mapping

# returns 
it.positional_mapping(snp_meta, gene_info, neighborhood=5)

3. Features mapping and interaction

# returns 
(interact_snp, interact_gene) = it.snp_interaction(interact, gene_info, snp_info)

4. Individual Specific Network (ISN) computation

isn = it.compute_isn(df, interact_snp, interact_gene, "spearman", "max")

For more examples, please refer to the Documentation.

Roadmap

• Complete the Usage section
• Add documentation with examples
• Consider a new function for functional mapping
• Add:
  • Imputation with file saving
  • Function isn_calculation_per_edge
  • Progressbar

Contributing

Contributions are what make the open source community such a wonderful place to learn, be inspired, and create. Your contributions will be greatly appreciated.

If you have an idea for how to improve this, please fork the repository and submit a pull request. You can alternatively open a new issue with the tag "improvement". Don't forget to :star: the project! Thank you once more!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Contact

Giada Lalli - [e-mail]

Zuqi Li - [e-mail]

Federico Melograna - [e-mail]

Project Link:

Acknowledgments