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Michaela Fellner

Researcher at Research Institute of Molecular Pathology

Publications -  21
Citations -  3619

Michaela Fellner is an academic researcher from Research Institute of Molecular Pathology. The author has contributed to research in topics: Gene & Biology. The author has an hindex of 8, co-authored 12 publications receiving 3124 citations. Previous affiliations of Michaela Fellner include Austrian Academy of Sciences.

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A genome-wide transgenic RNAi library for conditional gene inactivation in Drosophila

TL;DR: The generation and validation of a genome-wide library of Drosophila melanogaster RNAi transgenes, enabling the conditional inactivation of gene function in specific tissues of the intact organism and opening up the prospect of systematically analysing gene functions in any tissue and at any stage of the Drosophile lifespan.
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Systematic genetic analysis of muscle morphogenesis and function in Drosophila

TL;DR: A role in muscle is identified for 2,785 genes, many of which are phylogenetically conserved, including genes implicated in mammalian sarcomere organization and human muscle diseases.
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The histone chaperone CAF-1 safeguards somatic cell identity

TL;DR: The findings reveal the histone chaperone CAF-1 to be a novel regulator of somatic cell identity during transcription-factor-induced cell-fate transitions and provide a potential strategy to modulate cellular plasticity in a regenerative setting.
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Rational discovery of molecular glue degraders via scalable chemical profiling

TL;DR: A scalable strategy toward glue degrader discovery is described that is based on chemical screening in hyponeddylated cells coupled to a multi-omics target deconvolution campaign to identify compounds that induce ubiquitination and degradation of cyclin K by prompting an interaction of CDK12–cyclin K with a CRL4B ligase complex.
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Multilayered VBC score predicts sgRNAs that efficiently generate loss-of-function alleles

TL;DR: Using optimized screening workflows, the Vienna Bioactivity CRISPR (VBC) scoring system provides an improved selection for sgRNAs that generate loss-of-function alleles, and outperforms previous prediction tools and enables the selection of sg RNAs that effectively produce loss- of- function alleles.