ePlatypus: an ecosystem for computational analysis of immunogenomics data

Area of Science:

• Computational immunology
• Immunogenomics
• Systems immunology

Background:

• Maturing systems immunology requires robust computational frameworks for integrating diverse data modalities.
• Existing tools often lack transparency and reproducibility for complex immunogenomic analyses.

Purpose of the Study:

• Introduce the ePlatypus computational immunology ecosystem.
• Provide a transparent and reproducible framework for immunogenomics data analysis.
• Facilitate the study of adaptive immune repertoires and single-cell sequencing data.

Main Methods:

• Developed ePlatypus as an open-source, web-based platform.
• Integrated diverse bioinformatics pipelines for single-cell immune repertoires.
• Included programming tutorials and an integrative database.

Main Results:

• ePlatypus enables elucidation of B and T cell clonal selection signatures.
• The ecosystem links novel and established bioinformatics tools.
• Facilitates analysis of ligand-receptor interactions, structural modeling, simulations, machine learning, graph theory, pseudotime, spatial transcriptomics, and phylogenetics.

Conclusions:

• ePlatypus provides deeper insights into computational immunology and immunogenomics.
• Promotes open science principles in the field.
• Offers a comprehensive resource for researchers analyzing complex immune system data.