Deep embeddings to comprehend and visualize microbiome protein space.

ABSTRACT: Understanding the function of microbial proteins is essential to reveal the clinical potential of the microbiome. The application of high-throughput sequencing technologies allows for fast and increasingly cheaper acquisition of data from microbial communities. However, many of the inferred protein sequences are novel and not catalogued, hence the possibility of predicting their function through conventional homology-based approaches is limited, which indicates the need for further research on alignment-free methods. Here, we leverage a deep-learning-based representation of proteins to assess its utility in alignment-free analysis of microbial proteins. We trained a language model on the Unified Human Gastrointestinal Protein catalogue and validated the resulting protein representation on

SUBMITTER: Odrzywolek K 

PROVIDER: S-EPMC9209496 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature