IgemRNA

IgemRNA integrates transcriptome data with genome-scale metabolic models to perform statistical analyses linking gene expression to metabolic network topology for genotype–phenotype interpretation.

Key Features:

• Integration with Genome-Scale Metabolic Models (GSM): Analyzes transcriptome data both independently and in conjunction with GSMs to support genotype–phenotype interpretation.
• Transcriptome Data Pre-processing: Implements novel pre-processing approaches addressing compatibility issues in transcriptomics datasets.
• Flexible Analysis Options: Supports different thresholding and gene mapping techniques to tailor analyses to specific research needs.
• Enhanced Data Validation: Incorporates validation steps that ensure minimal metabolic network connectivity and flux requirements, extending beyond standard Gene Set Enrichment Analysis.
• MATLAB and Cobra Toolbox 3.0 Integration: Developed within MATLAB and leverages functions from Cobra Toolbox 3.0 for genome-scale metabolic model optimization.
• Spreadsheet File Management: Provides spreadsheet file management capabilities for input and output of transcriptome and model data.
• Statistical Transcriptome Analysis: Performs comprehensive statistical analysis of transcriptome datasets.

Scientific Applications:

• Genotype–Phenotype Analysis: Links gene expression changes to metabolic network behavior to support genotype–phenotype studies.
• Comparative Transcriptome Analysis: Enables comparison of transcriptome datasets across different phenotypes using integrated metabolic constraints.
• Research in Biotechnology, Medicine, and Life Sciences: Supports investigations into how transcriptomic variation affects organismal properties in biotechnology, medicine, and broader life sciences research.

Methodology:

The toolbox implementation was informed by analysis and classification of existing transcriptome analysis tools, includes refined pre-processing and analysis methods, and uses Cobra Toolbox 3.0 functions within MATLAB for genome-scale metabolic model optimization.