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

Biology

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

A. Hoarfrost, A. Aptekmann, et al.

Discover the groundbreaking work of A. Hoarfrost, A. Aptekmann, G. Farfañuk, and Y. Bromberg as they unveil LookingGlass, a deep learning model that provides crucial insights into uncultured microbial genomes. This innovative approach identifies novel enzymes and predicts their optimal conditions, illuminating the mysteries of microbial dark matter.

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Playback language: English
Abstract
A significant portion of microbial genomes remain uncultured, and many proteins lack functional annotations. This research introduces LookingGlass, a deep learning model that creates contextually aware representations of short DNA reads, distinguishing reads based on function, homology, and origin. LookingGlass, through transfer learning, successfully identifies novel oxidoreductases, predicts enzyme optimal temperatures, and recognizes DNA sequence reading frames. This model provides functionally relevant representations of unknown sequences, shedding light on 'microbial dark matter'.
Publisher
Nature Communications
Published On
Nov 16, 2022
Authors
A. Hoarfrost, A. Aptekmann, G. Farfañuk, Y. Bromberg
Tags
deep learning
microbial genomes
oxidoreductases
transfer learning
functional annotations
DNA sequences
enzyme prediction

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