MetaPathPredict

MetaPathPredict predicts the presence or absence of KEGG (Kyoto Encyclopedia of Genes and Genomes) modules in incomplete bacterial genomes using deep learning to reconstruct microbial metabolic pathways from environmental 'omics data.

Key Features:

• Machine Learning Integration: Uses advanced deep learning models to analyze gene annotation data for metabolic module prediction.
• KEGG Module Database: Leverages the KEGG (Kyoto Encyclopedia of Genes and Genomes) module database to map gene annotations to metabolic modules.
• Incomplete Genome Prediction: Predicts presence of complete KEGG metabolic modules even when bacterial genomes are highly incomplete.
• Environmental 'Omics Support: Operates on 'omics data derived from environmental samples to infer metabolic capabilities.

Scientific Applications:

• Metabolic Pathway Reconstruction: Reconstructs microbial metabolic pathways from environmental 'omics and incomplete genomes by predicting module presence.
• Functional Potential Inference: Provides insights into functional capabilities of microorganisms via predicted KEGG modules.
• Metagenomics and Microbial Ecology: Supports studies in metagenomics and microbial ecology where complete genome sequences are unavailable.

Methodology:

Applies deep learning algorithms to gene annotation data mapped to the KEGG (Kyoto Encyclopedia of Genes and Genomes) module database to predict presence or absence of KEGG modules in bacterial genomes.