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Arjun Raj

Researcher at University of Pennsylvania

Publications -  142
Citations -  21547

Arjun Raj is an academic researcher from University of Pennsylvania. The author has contributed to research in topics: RNA & Gene. The author has an hindex of 50, co-authored 131 publications receiving 18166 citations. Previous affiliations of Arjun Raj include Rutgers University & University of California, San Diego.

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Many human large intergenic noncoding RNAs associate with chromatin-modifying complexes and affect gene expression

TL;DR: A model in which some lincRNAs guide chromatin-modifying complexes to specific genomic loci to regulate gene expression is proposed, and it is shown that siRNA-mediated depletion of certain linc RNAs associated with PRC2 leads to changes in gene expression.
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Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences

TL;DR: Stochastic gene expression has important consequences for cellular function, being beneficial in some contexts and harmful in others, including the stress response, metabolism, development, the cell cycle, circadian rhythms, and aging.
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Stochastic mRNA Synthesis in Mammalian Cells

TL;DR: The results demonstrate that gene expression in mammalian cells is subject to large, intrinsically random fluctuations and raise questions about how cells are able to function in the face of such noise.
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IMAGING INDIVIDUAL mRNA MOLECULES USING MULTIPLE SINGLY LABELED PROBES

TL;DR: In this article, a method for probing a target sequence of messenger ribonucleic acid molecules (mRNA's) in a fixed, permeabilized cell, including at least 30 non- overlapping probe binding regions of 15-100 nucleotides, was proposed.
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Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance

TL;DR: It is shown that human melanoma cells can display profound transcriptional variability at the single-cell level that predicts which cells will ultimately resist drug treatment, and this work reveals the multistage nature of the acquisition of drug resistance and provides a framework for understanding resistance dynamics in single cells.