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Michal Levo

Researcher at Weizmann Institute of Science

Publications -  17
Citations -  1829

Michal Levo is an academic researcher from Weizmann Institute of Science. The author has contributed to research in topics: Gene & Transcription factor. The author has an hindex of 10, co-authored 12 publications receiving 1496 citations.

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Journal ArticleDOI

Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters

TL;DR: A method for obtaining parallel, highly accurate gene expression measurements from thousands of designed promoters is devised and applied to measure the effect of systematic changes in the location, number, orientation, affinity and organization of transcription-factor binding sites and nucleosome-disfavoring sequences.
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Manipulating nucleosome disfavoring sequences allows fine-tune regulation of gene expression in yeast

TL;DR: The transcriptional effects of different elements, nucleosome-disfavoring sequences and, specifically, poly(dA:dT) tracts that are highly prevalent in eukaryotic promoters are explored and a potential mechanism by which promoters yielding prespecified expression patterns can be designed is suggested.
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In pursuit of design principles of regulatory sequences

TL;DR: It is discussed how understanding different aspects of the sequence-encoded regulation can help to unravel the genotype–phenotype relationship, which would lead to a more accurate and mechanistic interpretation of personal genome sequences.
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Transient transcriptional responses to stress are generated by opposing effects of mRNA production and degradation.

TL;DR: Yeast subjected to two stresses showed genome‐wide interplay between mRNA production and degradation, and that alternative modes of such interplay determine the kinetics of the transcriptome in response to stress.
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Unraveling determinants of transcription factor binding outside the core binding site

TL;DR: A novel experimental assay termed BunDLE-seq that provides quantitative measurements of TF binding to thousands of fully designed sequences of 200 bp in length within a single experiment and shows that TF-specific models based on the sequence or DNA shape of the regions flanking the core binding site are highly predictive of the measured differential TF binding.