Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping
| العنوان: | Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping |
|---|---|
| المؤلفون: | Anja C Gumpinger, Karsten M. Borgwardt, Christian Beisel, Katrin Fischer, Laetitia Papaxanthos, Markus Jeschek, Yaakov Benenson, Simon Hoellerer |
| المصدر: | Nature Communications, Vol 11, Iss 1, Pp 1-15 (2020) Nature Communications Nature Communications, 11 (1) |
| بيانات النشر: | Nature Portfolio, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Computer science, Science, Datasets as Topic, Computational biology, Regulatory Sequences, Nucleic Acid, Article, 03 medical and health sciences, chemistry.chemical_compound, Gene Knockout Techniques, 0302 clinical medicine, Deep Learning, Gene expression analysis, Gene expression, Recombinase, Escherichia coli, A-DNA, Binding site, lcsh:Science, Gene, Synthetic biology, 030304 developmental biology, 0303 health sciences, Sequence, Binding Sites, business.industry, Bacterial ribosome, Deep learning, Substrate (chemistry), Computational Biology, High-Throughput Nucleotide Sequencing, Molecular Sequence Annotation, Sequence Analysis, DNA, Phenotype, Expression (mathematics), chemistry, Computer modelling, lcsh:Q, Artificial intelligence, business, Ribosomes, 030217 neurology & neurosurgery, DNA, Genome, Bacterial |
| الوصف: | 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. Nature Communications, 11 (1) ISSN:2041-1723 |
| وصف الملف: | application/application/pdf |
| اللغة: | English |
| تدمد: | 2041-1723 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::924dd0ac979d603ba9dd553fdd6f2237 https://doaj.org/article/5cdab11544394bd78e7fc3880bbaa8e2 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....924dd0ac979d603ba9dd553fdd6f2237 |
| قاعدة البيانات: | OpenAIRE |
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