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Donny D. Licatalosi

Researcher at Case Western Reserve University

Publications -  35
Citations -  5694

Donny D. Licatalosi is an academic researcher from Case Western Reserve University. The author has contributed to research in topics: Alternative splicing & RNA-binding protein. The author has an hindex of 17, co-authored 33 publications receiving 5023 citations. Previous affiliations of Donny D. Licatalosi include Rockefeller University & University of Waterloo.

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FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism

TL;DR: A brain polyribosome-programmed translation system is developed, revealing that FMRP reversibly stalls ribosomes specifically on its target mRNAs and suggests multiple targets for clinical intervention in FXS and ASD.
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HITS-CLIP yields genome-wide insights into brain alternative RNA processing

TL;DR: A genome-wide means of mapping protein–RNA binding sites in vivo, by high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation (HITS-CLIP), which revealed a large number of Nova–RNA interactions in 3′ untranslated regions, leading to the discovery that Nova regulates alternative polyadenylation in the brain.
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RNA processing and its regulation: global insights into biological networks

TL;DR: Advances in technologies for characterizing RNA populations are revealing increasingly complete descriptions of RNA regulation and complexity, yielding transcriptome-wide insights into mechanisms of RNA processing and providing new insights into molecular cell biology and disease.
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Functional interaction of yeast pre-mRNA 3' end processing factors with RNA polymerase II.

TL;DR: The RNA polymerase II CTD is essential for 3' end cleavage of metazoan pre-mRNAs and binds3' end processing factors in vitro and genetic and biochemical interactions between the CTD and the Pcf11 subunit of the yeast cleavage/polyadenylation factor, CFIA are shown.
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Integrative Modeling Defines the Nova Splicing-Regulatory Network and Its Combinatorial Controls

TL;DR: A general approach to understanding mammalian RNA regulation at the systems level is developed, using Bayesian networks to probabilistically model diverse data sets and predict the target networks of specific regulators.