ArticleNature communications2020
Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
35 citing papers in PubMed, 67 citations in OpenAlex.
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- Data-driven protease engineering by DNA-recording and epistasis-aware machine learning.Nature communications · 2025Article
- The knowledge driven DBTL cycle provides mechanistic insights while optimising dopamine production in Escherichia coli.Microbial cell factories · 2025Article
- Generative and predictive neural networks for the design of functional RNA molecules.Nature communications · 2025Article
- Microbial Transcription Factor-Based Biosensors: Innovations from Design to Applications in Synthetic Biology.Biosensors · 2025Review
- Improving the generalization of protein expression models with mechanistic sequence information.Nucleic acids research · 2025Article
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
- Optimization of regulatory DNA with active learning.Computational and structural biotechnology journal · 2025Article
- AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors.Molecules (Basel, Switzerland) · 2024Review
- Transfer learning for cross-context prediction of protein expression from 5'UTR sequence.Nucleic acids research · 2024Article
- Genotype sampling for deep-learning assisted experimental mapping of a combinatorially complete fitness landscape.Bioinformatics (Oxford, England) · 2024Article
- Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges.ACS omega · 2024Review
- Applications of artificial intelligence and machine learning in dynamic pathway engineering.Biochemical Society transactions · 2023Review
- BioAutoMATED: An end-to-end automated machine learning tool for explanation and design of biological sequences.Cell systems · 2023Article
- Applications and Tuning Strategies for Transcription Factor-Based Metabolite Biosensors.Biosensors · 2023Review
- Ultradeep characterisation of translational sequence determinants refutes rare-codon hypothesis and unveils quadruplet base pairing of initiator tRNA and transcript.Nucleic acids research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting effects of gene regulatory elements (GREs) is a longstanding challenge in biology. Machine learning may address this, but requires large datasets linking GREs to their quantitative function. However, experimental methods to generate such datasets are either application-specific or technically complex and error-prone. Here, we introduce DNA-based phenotypic recording as a widely applicable, practicable approach to generate large-scale sequence-function datasets. We use a site-specific recombinase to directly record a GRE's effect in DNA, enabling readout of both sequence and quantitative function for extremely large GRE-sets via next-generation sequencing. We record translation kinetics of over 300,000 bacterial ribosome binding sites (RBSs) in >2.7 million sequence-function pairs in a single experiment. Further, we introduce a deep learning approach employing ensembling and uncertainty modelling that predicts RBS function with high accuracy, outperforming state-of-the-art methods. DNA-based phenotypic recording combined with deep learning represents a major advance in our ability to predict function from genetic sequence.
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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.