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Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping
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DOI：10.1038/s41467-020-17222-4.png)
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Abstract
En 中文
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. Current methods to generate sequence-function data at large scale are either technically complex or limited to specific applications. Here the authors introduce DNA-based phenotypic recording to overcome these limitations and enable deep learning for accurate prediction of function from sequence.
Keywords:
RIBOSOME BINDING-SITES
GENE-REGULATORY LOGIC
TRANSLATION INITIATION
ESCHERICHIA-COLI
EXPRESSION
DESIGN
TRANSCRIPTION
PREDICTION
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