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Hervé Marie-Nelly

Researcher at University of California, Berkeley

Publications -  17
Citations -  1857

Hervé Marie-Nelly is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Genome & RNA polymerase II. The author has an hindex of 12, co-authored 17 publications receiving 1432 citations. Previous affiliations of Hervé Marie-Nelly include University of Paris & University of Nice Sophia Antipolis.

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RNA polymerase II clustering through carboxy-terminal domain phase separation

TL;DR: It is reported that human and yeast CTDs undergo cooperative liquid phase separation, with the shorter yeast CTD forming less-stable droplets and that CTD phosphorylation liberates Pol II enzymes from hubs for promoter escape and transcription elongation.
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FISH-quant: automatic counting of transcripts in 3D FISH images

TL;DR: FISH-QUANT is developed to close the gap in single-molecule RNA fluorescence in-situ hybridization that cannot reliably quantify the dense mRNA aggregates at transcription sites (TS) in three dimensions (3D), particularly of highly transcribing genes.
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Evidence for DNA-mediated nuclear compartmentalization distinct from phase separation

TL;DR: This work finds that the viral genome remains largely nucleosome-free, and this increase in accessibility allows Pol II and other DNA-binding proteins to repeatedly visit nearby DNA binding sites, which creates local accumulations of protein factors despite their unrestricted diffusion across RC boundaries.
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Normalization of a chromosomal contact map

TL;DR: A simple normalization procedure was developed to process the raw data and allow the generation of a normalized, highly contrasted, chromosomal contact map for S. cerevisiae and was extended to the first human genome contact map.
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Metagenomic chromosome conformation capture (meta3C) unveils the diversity of chromosome organization in microorganisms

TL;DR: Using controlled mixes of bacterial and yeast species, meta3C is developed, a metagenomic chromosome conformation capture approach that allows characterizing individual genomes and their average organization within a mix of organisms.