4.8 Article

Deep learning of a bacterial and archaeal universal language of life enables transfer learning and illuminates microbial dark matter

Journal

NATURE COMMUNICATIONS
Volume 13, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41467-022-30070-8

Keywords

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Funding

  1. NASA Astrobiology Postdoctoral Fellowship within the NAI [80NSSC18M0093]
  2. NSF (National Science Foundation) [1553289]
  3. Div Of Biological Infrastructure
  4. Direct For Biological Sciences [1553289] Funding Source: National Science Foundation

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Computational methods for analyzing microbial systems often rely on reference databases that do not fully capture the functional diversity of these systems. In this study, the authors develop a deep learning model that is capable of transferring its learned knowledge to multiple tasks, resulting in biologically relevant models. The model, called LookingGlass, can provide useful representations of unknown and unannotated sequences, shedding light on the microbial dark matter that dominates life on Earth.
Computational methods to analyse microbial systems rely on reference databases which do not capture their full functional diversity. Here the authors develop a deep learning model and apply it using transfer learning, creating biologically useful models for multiple different tasks. The majority of microbial genomes have yet to be cultured, and most proteins identified in microbial genomes or environmental sequences cannot be functionally annotated. As a result, current computational approaches to describe microbial systems rely on incomplete reference databases that cannot adequately capture the functional diversity of the microbial tree of life, limiting our ability to model high-level features of biological sequences. Here we present LookingGlass, a deep learning model encoding contextually-aware, functionally and evolutionarily relevant representations of short DNA reads, that distinguishes reads of disparate function, homology, and environmental origin. We demonstrate the ability of LookingGlass to be fine-tuned via transfer learning to perform a range of diverse tasks: to identify novel oxidoreductases, to predict enzyme optimal temperature, and to recognize the reading frames of DNA sequence fragments. LookingGlass enables functionally relevant representations of otherwise unknown and unannotated sequences, shedding light on the microbial dark matter that dominates life on Earth.

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