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Comprehensive Functional Annotation of Metagenomes and Microbial Genomes Using a Deep Learning-Based Method.

Mary Maranga1, Pawel Szczerbiak1, Valentyn Bezshapkin1

  • 1Malopolska Centre of Biotechnology, Jagiellonian University, Krakow, Poland.

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Summary

We developed a new workflow for deep learning-based protein function annotation in the human gut microbiome. This approach significantly improves gene annotation coverage, aiding the understanding of microbiome functions in health and disease.

Keywords:
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Area of Science:

  • Metagenomics
  • Computational Biology
  • Microbiome Research

Background:

  • Functional annotation of human gut microbial proteins is crucial for understanding host-microbiome interactions and disease mechanisms.
  • A significant portion of microbial genes lack functional characterization, limiting insights into microbiome roles.
  • Existing annotation methods often have low coverage, hindering comprehensive analysis.

Purpose of the Study:

  • To develop and validate a novel metagenome analysis workflow integrating deep learning for enhanced functional protein annotation.
  • To assess the performance of deep learning-based functional annotations (DeepFRI) against traditional orthology-based methods (eggNOG).
  • To improve the functional understanding of the human gut microbiome by increasing gene annotation coverage.

Main Methods:

  • A new workflow combining de novo genome reconstruction, taxonomic profiling, and DeepFRI for functional annotation was developed.
  • DeepFRI annotations were validated against eggNOG orthology-based annotations using 1,070 infant metagenomes from the DIABIMMUNE cohort.
  • Pangenomes were constructed in a reference-free manner using metagenome-assembled genomes (MAGs) for annotation analysis.

Main Results:

  • The workflow generated a catalogue of 1.9 million nonredundant microbial genes with 99% Gene Ontology (GO) molecular function annotation coverage using DeepFRI.
  • A 70% concordance was observed between DeepFRI and eggNOG GO annotations, with DeepFRI providing broader, albeit less specific, coverage.
  • DeepFRI demonstrated improved annotation sensitivity across various taxa compared to eggNOG, offering additional insights beyond previous studies.

Conclusions:

  • The developed workflow significantly enhances functional annotation coverage for metagenomic data, surpassing traditional methods.
  • Deep learning-based functional prediction offers a valuable complementary approach to orthology-based methods for microbiome research.
  • This enhanced annotation capability will advance the understanding of gut microbiome functions in health and disease and guide future studies.