scholarly journals Comparative RNA-Seq analysis reveals pervasive tissue-specific alternative polyadenylation in Caenorhabditis elegans intestine and muscles

BMC Biology ◽  
2015 ◽  
Vol 13 (1) ◽  
Author(s):  
Stephen M Blazie ◽  
Cody Babb ◽  
Henry Wilky ◽  
Alan Rawls ◽  
Jin G Park ◽  
...  
2018 ◽  
Vol 34 (12) ◽  
pp. 2123-2125 ◽  
Author(s):  
Guoli Ji ◽  
Moliang Chen ◽  
Wenbin Ye ◽  
Sheng Zhu ◽  
Congting Ye ◽  
...  

Cell Reports ◽  
2013 ◽  
Vol 3 (3) ◽  
pp. 969 ◽  
Author(s):  
Peter Smibert ◽  
Pedro Miura ◽  
Jakub O. Westholm ◽  
Sol Shenker ◽  
Gemma May ◽  
...  

Cell Reports ◽  
2012 ◽  
Vol 1 (3) ◽  
pp. 277-289 ◽  
Author(s):  
Peter Smibert ◽  
Pedro Miura ◽  
Jakub O. Westholm ◽  
Sol Shenker ◽  
Gemma May ◽  
...  

2017 ◽  
Author(s):  
Zhuyi Xue ◽  
René L Warren ◽  
Ewan A Gibb ◽  
Daniel MacMillan ◽  
Johnathan Wong ◽  
...  

AbstractAlternative polyadenylation (APA) of 3’ untranslated regions (3’ UTRs) has been implicated in cancer development. Earlier reports on APA in cancer primarily focused on 3’ UTR length modifications, and the conventional wisdom is that tumor cells preferentially express transcripts with shorter 3’ UTRs. Here, we analyzed the APA patterns of 114 genes, a select list of oncogenes and tumor suppressors, in 9,939 tumor and 729 normal tissue samples across 33 cancer types using RNA-Seq data from The Cancer Genome Atlas, and we found that the APA regulation machinery is much more complicated than what was previously thought. We report 77 cases (gene-cancer type pairs) of differential 3’ UTR cleavage patterns between normal and tumor tissues, involving 33 genes in 13 cancer types. For 15 genes, the tumor-specific cleavage patterns are recurrent across multiple cancer types. While the cleavage patterns in certain genes indicate apparent trends of 3’ UTR shortening in tumor samples, over half of the 77 cases imply 3’ UTR length change trends in cancer that are more complex than simple shortening or lengthening. This work extends the current understanding of APA regulation in cancer, and demonstrates how large volumes of RNA-seq data generated for characterizing cancer cohorts can be mined to investigate this process.


2011 ◽  
Vol 40 (4) ◽  
pp. 1523-1535 ◽  
Author(s):  
Julia L. MacIsaac ◽  
Aaron B. Bogutz ◽  
A. Sorana Morrissy ◽  
Louis Lefebvre

2013 ◽  
Vol 29 (13) ◽  
pp. i108-i116 ◽  
Author(s):  
D. Hafez ◽  
T. Ni ◽  
S. Mukherjee ◽  
J. Zhu ◽  
U. Ohler

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