Identifying and Predicting Novelty in Microbiome Studies.

Area of Science:

• Microbiome Research
• Bioinformatics
• Computational Biology

Background:

• Global expansion of microbiome sequencing presents challenges in relating new samples to existing data.
• A comprehensive reference database is crucial for contextualizing novel microbiome discoveries.

Purpose of the Study:

• Introduce the Microbiome Search Engine (MSE) for rapid searching of microbiome samples against a large reference database.
• Develop novel metrics (MNS, MAS, MFI) to quantify microbiome novelty, attention, and research focus.
• Analyze trends in microbiome research to predict future areas of interest.

Main Methods:

• Implemented MSE for whole-microbiome level searches based on taxonomic similarity.
• Calculated Microbiome Novelty Score (MNS) and Microbiome Attention Score (MAS) using over 100,000 global 16S rRNA gene amplicon samples.
• Derived Microbiome Focus Index (MFI) from MNS and MAS to track and compare sample/project novelty and attention.

Main Results:

• Human microbiome novelty is approaching saturation, while environmental microbiome novelty remains significantly higher (5x).
• Identified specific environmental habitats (marine, indoor) and interactions (mother-baby) as likely future research hotspots.
• Demonstrated the utility of MNS, MAS, and MFI as "alt-metrics" for evaluating microbiome research.

Conclusions:

• MSE provides a scalable solution for contextualizing new microbiome data within the growing global dataset.
• MNS, MAS, and MFI offer objective measures for assessing the novelty and scientific focus of microbiome studies.
• These metrics can guide future research directions toward generating fundamentally new microbiome insights.