Motivation: Imbalances in the human gut microbiome have been linked to various conditions, including inflammatory bowel disease (IBD), diabetes, and mental health disorders. While metagenomics and amplicon sequencing are the most commonly used technologies to characterize microbial communities, they do not capture all layers of functional activity of the microbiome. Unfortunately, data from other meta-omics modalities is generally difficult to obtain, due to high costs and error-prone technologies, among other issues. 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. The aim is to enable the prediction of these other forms of meta-omics data from metagenomics data. To that end, we evaluated several machine learning model architectures on the task of predicting meta-omics features from various meta-omics inputs, and analyzed the robustness of these models, as well as potential use cases of artificially generated microbiome data. Results: Machine learning models, in particular simpler architectures such as elastic net regression models 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 proved more difficult, with correlations of at most 0.42. 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 IBD prediction. For instance, accuracy obtained using predicted metabolite abundances was 77%, compared to the 80% accuracy achieved using real metabolomics data.

Motivation: The development of automated protein function prediction models is essential in closing the gap between the large amount of protein sequence data available and the fraction of validly annotated data. Recent approaches to function prediction rely on unsupervised deep learning models, through which protein sequences are represented as real-valued embeddings that can be used as input to a machine-learning model. This study aims to evaluate embedding models in the context of protein function prediction on bacteria, which are organisms less commonly included in these types of benchmarks. To this end, we generated embeddings with four recently developed embedding models, and predicted protein function using a nearest-neighbor search in the embedding space. We evaluated these predictors on two query sets, with proteins from gram-positive B. subtilis and gram-negative E. coli. Results: Our nearest neighbor models outperformed BLAST sequence-based protein function annotation, according to the evaluation procedure outlined in the CAFA challenges. The results were also shown to be comparable, and at times better than DeepGOPlus predictions, thus highlighting the potential of embedding-based predictions as state-of-the art models. On the B. subtilis dataset, our nearest neighbor model from ESM1b embeddings scored an Fmax of 0.6 in molecular function predictions, and was able to predict GO terms with a high information content. Hence unsupervised embedding models were shown to encode information about a protein sequence that is useful in the task of function prediction. Availability: The scripts used in this project are available on GitHub.