MetaBioME

MetaBioME identifies novel homologs of Commercially Useful Enzymes (CUEs) within metagenomic datasets and completed bacterial genomes to expand enzyme repertoires for biotechnological, agricultural, and medical applications.

Key Features:

• Curated CUE catalogue: A manually curated catalogue of 510 Commercially Useful Enzymes (CUEs) compiled from text mining of PubMed abstracts and other publicly accessible information sources.
• Text mining: Extraction of candidate CUE entries via text mining of PubMed abstracts and public information sources.
• Manual curation: Expert curation applied to the text-mined dataset to ensure accuracy and relevance.
• Metagenomic ORF collection: Potential open reading frames (ORFs) identified from metagenomic datasets derived from ten diverse environmental sources.
• Completed bacterial genomes: Inclusion of completed bacterial genomes alongside metagenomic ORFs for homology searches.
• Homology-based analysis: Application of homology-based computational methods to detect novel homologous CUEs.
• Integrated database and framework: Integration of the curated CUE database with metagenomic ORFs and bacterial genome data to support homology identification.

Scientific Applications:

• Enzyme discovery: Identification of novel homologs of known CUEs from metagenomic datasets and bacterial genomes.
• Biotechnology, agriculture, and medical use: Expansion of enzyme repertoires relevant to biotechnology, agriculture, and medical industries.
• Functional variant discovery: Uncovering improved functional variants of known CUEs for potential biocatalytic applications.
• Experimental candidate generation: Provision of homologous enzyme candidates for downstream experimental validation and potential commercial exploitation.

Methodology:

Text mining of PubMed abstracts and other public information sources followed by manual curation produced a catalogue of 510 CUEs; potential open reading frames (ORFs) were identified from metagenomic datasets from ten diverse environmental sources and from completed bacterial genomes; homology-based computational methods were applied to identify novel homologues.