Author

Simon Höllerer

ETH ZurichORCID 0000-0002-1328-9427Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:DNA methylation· 2 Organism:bacteria (other)· 1 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Organism:E. coli· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Markus JeschekiD· 2 shared Anja GumpingeriD· 1 shared Christian BeiseliD· 1 shared Karsten Michael BorgwardtiD· 1 shared Katrin FischeriD· 1 shared Laetitia Meng-PapaxanthosiD· 1 shared Yaakov BenensoniD· 1 shared

Activity by Year

2020-2023 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 21 mutual co-author links

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2 shared papersSynthetic Biology

Markus JeschekORCID

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2 papers

Recent·Most cited

Synthetic BiologyNucleic Acids ResearchPeer-reviewedJan 14, 2023

Ultradeep characterisation of translational sequence determinants refutes rare-codon hypothesis and unveils quadruplet base pairing of initiator tRNA and transcript

Simon Höllerer, Markus Jeschek

The paper uses ultradeep sequence-function mapping to analyze 1.2 million 5-UTR-CDS pairs in E. coli, refuting the rare-codon hypothesis and revealing base-pairing between initiator tRNA and mRNA.

21 citations

E. coliDNA methylation

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Synthetic BiologyNature CommunicationsPeer-reviewedJul 15, 2020

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

Simon Höllerer, Laetitia Meng-Papaxanthos, Anja Gumpinger…·Markus Jeschek

The paper introduces DNA-based phenotypic recording to generate large-scale sequence-function datasets and uses deep learning to predict gene regulatory element function with high accuracy.

68 citations

bacteria (other)DNA methylation

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