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Valeriya Malysheva

Researcher at Imperial College London

Publications -  15
Citations -  279

Valeriya Malysheva is an academic researcher from Imperial College London. The author has contributed to research in topics: Gene regulatory network & Enhancer. The author has an hindex of 7, co-authored 13 publications receiving 139 citations. Previous affiliations of Valeriya Malysheva include Control Group & Babraham Institute.

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Cohesin-Dependent and -Independent Mechanisms Mediate Chromosomal Contacts between Promoters and Enhancers

TL;DR: This work uses high-resolution Capture Hi-C to interrogate the dynamics of chromosomal contacts of all annotated human gene promoters upon degradation of cohesin and CTCF, and shows that a majority of promoter-anchored contacts are lost in these conditions, but many contacts with distinct properties are maintained, and some new ones are gained.
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TOP1 inhibition therapy protects against SARS-CoV-2-induced lethal inflammation.

TL;DR: In this paper, using multidimensional epigenetic, transcriptional, in-vitro, and invivo analyses, the authors reported that topoisomerase 1 (TOP1) inhibition suppresses lethal inflammation induced by SARS-CoV-2.
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Reconstructed cell fate-regulatory programs in stem cells reveal hierarchies and key factors of neurogenesis

TL;DR: This study demonstrates that a systems view of cell fate specification combined with computational signal transduction models provides the necessary insight in cellular plasticity for cell fate engineering and can be used to monitor the in vitro capacity of cells/tissues to establish cell lineages for regenerative medicine.
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Reconstruction of gene regulatory networks reveals chromatin remodelers and key transcription factors in tumorigenesis

TL;DR: This is the first comprehensive view of the gene regulatory network that is altered during the process of stepwise human cellular tumorigenesis in a virtually isogenic system and suggests a direct implication of CRMs in oncogene-induced tumorsigenesis and identifies new CRMs involved in this process.
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Detecting chromosomal interactions in Capture Hi-C data with CHiCAGO and companion tools.

TL;DR: The Capture Hi-C Analysis of Genomic Organization (CHiCAGO) as mentioned in this paper is a computational pipeline developed specifically for Capture HiC analysis, which implements a statistical model accounting for biological and technical background components, as well as bespoke normalization and multiple testing procedures.