Predicted meta-omics: A potential solution to multi-omics data scarcity in microbiome studies

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Correlations up to 0.77 for transcripts, 0.74 for metabolites

• Primary Outcome: Prediction accuracy of meta-omics features from metagenomics data

• Nnt Or Sensitivity: Comparable performance to experimental data for IBD classification

• Confidence Interval: Not reported

Clinical Application

Requires computational expertise and access to paired training datasets Could reduce costs for Australian microbiome research initiatives, relevant to NHMRC-funded studies and hospital-based microbiome programs Researchers studying gut microbiome in inflammatory bowel disease, diabetes, and cancer

Abstract

Imbalances in the gut microbiome have been linked to conditions such as inflammatory bowel disease, diabetes, and cancer. While metagenomics and amplicon sequencing are commonly used to study the microbiome, they do not capture all layers of microbial functions. Other meta-omics data can provide more insights, but these are more costly and laborious to procure. The growing availability of paired meta-omics data offers an opportunity to develop machine learning models that can infer connections between metagenomics data and other forms of meta-omics data, enabling the prediction of these other forms of meta-omics data from metagenomics. We evaluated several machine learning models for predicting meta-omics features from various meta-omics inputs. Simpler architectures such as elastic net regression and random forests generated reliable predictions of transcript and metabolite abundances, with correlations of up to 0.77 and 0.74, respectively, but predicting protein profiles was more challenging. We also identified a core set of well-predicted features for each meta-omics output type, and showed that multi-output regression neural networks performed similarly when trained using fewer output features. Lastly, our experiments demonstrated that predicted features can be used for the downstream task of inflammatory bowel disease classification, with performance comparable to that of experimental data.

References

1. 1.Cosma, B. M., Pillay, S., Calderón-Franco, D., & Abeel, T. (2026). Predicted meta-omics: A potential solution to multi-omics data scarcity in microbiome studies. PLoS One. https://doi.org/10.1371/journal.pone.0345919