MegaR

MegaR applies machine learning to taxonomic profiles derived from whole metagenome and 16S rRNA sequencing to classify metagenomic samples and predict phenotypes.

Key Features:

• Sequencing support: Uses taxonomic profiles derived from whole metagenome sequencing and 16S rRNA sequencing data.
• Taxonomic-profile-based modeling: Builds predictive models from taxonomic profiles to classify samples into multiple categories.
• Machine learning techniques: Supports various machine learning techniques for model training and phenotype prediction.
• Model development and validation: Provides data processing, model fine-tuning, selection of machine learning techniques, and model validation options.
• Unknown sample prediction: Applies trained models to predict properties of unknown samples.

Scientific Applications:

• Human health and disease characterization: Characterizes microbiome composition effects on human health and disease through sample classification and phenotype prediction.
• Diagnostic application: Enables identification of microbe-related human diseases by classifying metagenomic samples and predicting phenotypes.
• Ecosystem and evolutionary studies: Analyzes microbiome–environment relationships to inform biogeochemical processes and evolutionary dynamics in ecosystems.

Methodology:

Constructs machine learning models from taxonomic profiles obtained from whole metagenome and 16S rRNA sequencing data, supporting various machine learning techniques along with data processing, model fine-tuning, selection of methods, and model validation, and uses trained models to predict unknown sample properties.