Computational Tools for Parsimony Phylogenetic Analysis of Omics Data

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• High-throughput omics assays generate vast datasets (genomics, proteomics, metabolomics, next-generation sequencing) that are difficult to analyze in biological or clinical settings.
• Existing bioinformatics tools lack standardized methods for converting quantitative omics data into formats compatible with phylogenetic analysis programs.

Purpose of the Study:

• To develop and introduce OmicsTract and SynpExtractor, the first publicly available Windows-based programs designed to bridge the gap in omics data conversion for phylogenetic analysis.
• To enable advanced phylogenetic parsimony analysis and extraction of shared aberrations from omics data, enhancing the understanding of biological and clinical contexts.

Main Methods:

• OmicsTract converts comma-delimited omics data tables into binary values (0 for normal, 1 for abnormal) and formats them for phylogenetic programs MIX (PHYLIP) or TNT.
• SynpExtractor processes output files from MIX and TNT to identify and extract shared aberrations at cladogram nodes, linking them to dataset labels (e.g., gene identifiers, m/z values).
• Both programs automate the conversion and extraction processes, facilitating rapid and standardized phylogenetic analyses.

Main Results:

• Successful creation and validation of OmicsTract and SynpExtractor for phylogenetic analysis of omics data.
• Demonstrated capability to convert diverse omics data into phylogenetic input formats and extract key biological information from cladogram nodes.
• The developed software facilitates the application of phylogenetic cladograms to reveal relationships between healthy and diseased individuals and disease-specific aberrations.

Conclusions:

• OmicsTract and SynpExtractor provide a novel and automated solution for phylogenetic analysis of large-scale omics datasets, including next-generation sequencing data.
• These freely available tools empower researchers to conduct rapid and standardized phylogenetic analyses, advancing the understanding of complex biological systems and diseases.
• The developed model supports the broader adoption of phylogenetic approaches in omics research for biological and clinical applications.