Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping.

Nature Communications
Simon HöllererMarkus Jeschek

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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Citations

Oct 9, 2020·Nature Communications·Nicolaas M Angenent-MariJames J Collins
Oct 4, 2020·Molecular Cell·Thijs NieuwkoopNico J Claassens
May 21, 2021·Scientific Reports·Zhenhua ZhangK Joeri van der Velde
Jun 26, 2021·Microbial Biotechnology·Lisa Tietze, Rahmi Lale

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Methods Mentioned

BETA
PCR
biosensors
electrophoresis
Illumina sequencing

Software Mentioned

Keras
SAPIENs
NGS
Tensorflow
uASPIre
fastq
bcl2fastq
Illumina RTA
Ridge
Random Forest

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