BioComet is an advanced bioinformatics tool designed to facilitate the analysis of protein-protein interactions (PPIs) and the exploration of biological networks. Built as an object-oriented Python module, BioComet leverages the STRING database to provide users with powerful capabilities for community detection, functional annotation, and network visualization.
Large gene lists or gene lists emerging from the analysis of complex conditions can functionally point to a variety of underlying biological pathways. When analyzing all of these data as one input in a functional enrichment analysis, this can lead to a facilitation of the unspecific top-level terms obscuring more detailed underlying findings. E.g., this set of 139 genes emerging as commonly appearing in several neurodegenerative diseases (Ruffini et al., Cells 2020) shows a functional enrichment that is most significantly associated with the vague top-level term "Disease".
If assuming that the transcriptomic commonalities between these neurodegenerative diseases is derived from a variety of processes, a further observation of this large gene set as subnetworks might shed more light into the fine underlying pathways.
- 🔍 Community Detection: Utilize the Louvain and Leiden algorithms for robust community detection in PPI networks.
- 📖 Functional Annotation: Perform comprehensive functional annotation using databases like KEGG, Reactome, MetaCyc, the EBI Complex Portal, and Gene Ontology Complexes.
- 🎨 Network Visualization: Generate visual representations of PPI networks, including specific communities, with support for various visualization methods.
- 🌐 Integrated Hub Node Detection: Seamlessly identify hub nodes within communities or across the entire PPI graph.
BioComet is distributed via PyPi, so it can easily be installed with pip.
However, in case problems might arise, we also shortly showcase how to install it in a fresh conda environment.
BioComet can be easily installed via pip:
pip install biocometIf any issues arise, trying creating a new conda environment with Pyton >=3.6 and installing biocomet in that fresh environment. This could e.g. look like this:
conde create -n biocomet_env python=3.10
conda activate biocomet_env
pip install biocometAfter installation, you can start using BioComet by importing it into your Python projects:
import pandas as pd
import biocomet as bc
# either draw data from our example section or use your own data
# loading your data could e.g. look like this
my_DEG_results = pd.read('path/to/my/DEG_results.csv')
# Example usage
ppi_graph = bc.PPIGraph(gene_list=my_DEG_results['GeneID'], reg_list=my_DEG_results['logFC'])For more details see the example section in which we offer three different use-cases to browse through.
We welcome contributions to BioComet! If you have suggestions, bug reports, or contributions, please submit them as issues or pull requests on GitHub.
If you use BioComet in your research, please cite our paper:
tbd