ArticleNature communications2022
Deep learning of a bacterial and archaeal universal language of life enables transfer learning and illuminates microbial dark matter.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed, 69 citations in OpenAlex.
- Artificial intelligence in human microbiome research: from data integration to explainable translation.Archives of microbiology · 2026Review
- Decoding extremophiles: insights from bioinformatics, machine learning, and data-driven approaches.Briefings in bioinformatics · 2026Review
- 16S rRNA sequence captures microbial functional potential.bioRxiv : the preprint server for biology · 2026Article
- MGM as a Large-Scale Pretrained Foundation Model for Microbiome Analyses in Diverse Contexts.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Quantifying uncertainty in protein representations across models and tasks.Nature methods · 2026Article
- The DNA dialect: a comprehensive guide to pretrained genomic language models.Molecular systems biology · 2026Review
- Personalized gene expression prediction in the era of deep learning: a review.Briefings in bioinformatics · 2026Review
- Beyond sequence similarity: toward function-based screening of nucleic acid synthesis.Frontiers in bioengineering and biotechnology · 2026Article
- FGeneBERT: function-driven pre-trained gene language model for metagenomics.Briefings in bioinformatics · 2025Article
- MetaflowX: a scalable and resource-efficient workflow for multi-strategy metagenomic analysis.Nucleic acids research · 2025Article
- Deciphering enzymatic potential in metagenomic reads through DNA language models.Nucleic acids research · 2025Article
- Annotating the microbial dark matter with HiFi-NN.iScience · 2025Article
- Cutting-edge deep-learning based tools for metagenomic research.National science review · 2025Review
- FGeneBERT: function-driven pre-trained gene language model for metagenomics.Briefings in bioinformatics · 2025Article
- Functional profiling of the sequence stockpile: a protein pair-based assessment of in silico prediction tools.Bioinformatics (Oxford, England) · 2025Article
- RFW captures species-level metagenomic functions by integrating genome annotation information.Cell reports methods · 2024Article
- Machine learning classification of archaea and bacteria identifies novel predictive genomic features.BMC genomics · 2024Article
- Article
- A foundational large language model for edible plant genomes.Communications biology · 2024Article
- Unravelling biosynthesis and biodegradation potentials of microbial dark matters in hypersaline lakes.Environmental science and ecotechnology · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.