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Deep embeddings to comprehend and visualize microbiome protein space
- Source :
- Scientific reports. 12(1)
- Publication Year :
- 2022
-
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 the bacterial part of the SwissProt database. Finally, we present a use case on proteins involved in SCFA metabolism. Results indicate that the deep learning model manages to accurately represent features related to protein structure and function, allowing for alignment-free protein analyses. Technologies that contextualize metagenomic data are a promising direction to deeply understand the microbiome.
- Subjects :
- Multidisciplinary
Bacteria
Computer science
Microbiota
Representation (systemics)
High-Throughput Nucleotide Sequencing
Proteins
Computational biology
Space (commercial competition)
Metagenomics
Leverage (statistics)
Humans
Metagenome
Microbiome
Language model
Swissprot database
Function (biology)
Subjects
Details
- ISSN :
- 20452322
- Volume :
- 12
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Scientific reports
- Accession number :
- edsair.doi.dedup.....ceb31b2dd008c0564529df2be305abac