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Microarray Meta-Analysis of RNA-Binding Protein Functions in Alternative Polyadenylation

Overview of attention for article published in PLOS ONE, March 2014
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Title
Microarray Meta-Analysis of RNA-Binding Protein Functions in Alternative Polyadenylation
Published in
PLOS ONE, March 2014
DOI 10.1371/journal.pone.0090774
Pubmed ID
Authors

Wenchao Hu, Yuting Liu, Jun Yan

Abstract

Alternative polyadenylation (APA) is a post-transcriptional mechanism to generate diverse mRNA transcripts with different 3'UTRs from the same gene. In this study, we systematically searched for the APA events with differential expression in public mouse microarray data. Hundreds of genes with over-represented differential APA events and the corresponding experiments were identified. We further revealed that global APA differential expression occurred prevalently in tissues such as brain comparing to peripheral tissues, and biological processes such as development, differentiation and immune responses. Interestingly, we also observed widespread differential APA events in RNA-binding protein (RBP) genes such as Rbm3, Eif4e2 and Elavl1. Given the fact that RBPs are considered as the main regulators of differential APA expression, we constructed a co-expression network between APAs and RBPs using the microarray data. Further incorporation of CLIP-seq data of selected RBPs showed that Nova2 represses and Mbnl1 promotes the polyadenylation of closest poly(A) sites respectively. Altogether, our study is the first microarray meta-analysis in a mammal on the regulation of APA by RBPs that integrated massive mRNA expression data under a wide-range of biological conditions. Finally, we present our results as a comprehensive resource in an online website for the research community.

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The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
China 1 2%
Brazil 1 2%
Unknown 54 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 33%
Researcher 14 24%
Student > Bachelor 7 12%
Student > Master 5 9%
Student > Doctoral Student 3 5%
Other 5 9%
Unknown 5 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 38%
Biochemistry, Genetics and Molecular Biology 14 24%
Computer Science 4 7%
Neuroscience 3 5%
Medicine and Dentistry 3 5%
Other 6 10%
Unknown 6 10%