Microbe Decoder uncovers functional traits of microbes in microbiome datasets.

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• Microbiome sequencing reveals numerous organisms, but their specific functional traits are often unknown.
• Current tools primarily identify microbial names or genes, lacking prediction of concrete biological functions like fermentation.
• Understanding microbial functions is crucial for diverse scientific fields.

Purpose of the Study:

• To introduce Microbe Decoder, a web server designed to predict functional traits of microbes in microbiome datasets.
• To bridge the gap in predicting specific biological functions beyond organism identification or gene annotation.
• To provide an accessible tool for researchers to explore microbial community functions.

Main Methods:

• Microbe Decoder integrates three complementary prediction tools: taxonomy-based, metabolic network-based, and machine learning-based.
• The tools accept microbial names or gene functions as input.
• An easy-to-use web application facilitates access and utilization of these prediction methods.

Main Results:

• Microbe Decoder demonstrated strong predictive performance on microbial isolate data, achieving a balanced accuracy of 0.85.
• Application to gut, sediment, and sea datasets revealed significant shifts in microbial functional traits across spatial and temporal scales.
• The server is currently optimized for prokaryotes, with future plans to include eukaryotes.

Conclusions:

• Microbe Decoder effectively predicts microbial functional traits, offering valuable insights into biological systems.
• The tool has the potential to advance research in biology, medicine, and environmental science by elucidating microbial roles.
• Accessible via https://www.microbe-decoder.org/, Microbe Decoder serves as a key resource for microbiome functional analysis.