P
Paramjeet Singh Bagga
Researcher at Ramapo College
Publications - 10
Citations - 1171
Paramjeet Singh Bagga is an academic researcher from Ramapo College. The author has contributed to research in topics: Gene & RNA. The author has an hindex of 8, co-authored 10 publications receiving 999 citations.
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QGRS Mapper: a web-based server for predicting G-quadruplexes in nucleotide sequences
TL;DR: A web-based server that predicts quadruplex forming G-rich sequences (QGRS) in nucleotide sequences and features interactive graphic representation of the data is developed, very useful for investigating the functional relevance of G-quadruplex structure, in particular its role in regulating the gene expression by alternative processing.
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GRSDB2 and GRS_UTRdb: databases of quadruplex forming G-rich sequences in pre-mRNAs and mRNAs.
TL;DR: The goal of these experiments has been to build freely accessible resources for exploring the role of G-quadruplex structure in regulation of gene expression at post-transcriptional level.
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Downstream sequence elements with different affinities for the hnRNP H/H′ protein influence the processing efficiency of mammalian polyadenylation signals
TL;DR: Using in vitro reconstitution assays, it is demonstrated that hnRNP H/H' can stimulate processing of two additional model polyadenylation signals by binding at similar relative downstream locations but with significantly different affinities.
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GRSDB: a database of quadruplex forming G-rich sequences in alternatively processed mammalian pre-mRNA sequences
Rumen Kostadinov,Nishtha Malhotra,Manuel Viotti,Robert J. Shine,Lawrence D'Antonio,Paramjeet Singh Bagga +5 more
TL;DR: The computational approach is applied to map putative Quadruplex forming GRSs within the transcribed regions of a large number of alternatively processed human and mouse gene sequences that were obtained as fully annotated entries from GenBank and RefSeq to build the GRSDB database, which provides a unique avenue for studying G-quadruplexes in the context of RNA processing sites.
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QGRS-Conserve: a computational method for discovering evolutionarily conserved G-quadruplex motifs
TL;DR: The QGRS-Conserve method quantitatively evaluates conservation between quadruplexes found in homologous nucleotide sequences based on several motif structural characteristics and efficiently manages overlapping G-quadruplex sequences such that the resulting datasets can be analyzed effectively.