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Boerner, S. (2012). Identification and Classification of Long Non-Coding RNA in Zea Mays Using Computational and Bioinformatic Approaches. Retrieved from http://purl.flvc.org/fsu/fd/FSU_migr_etd-4724
Computational analysis of cDNA sequences from multiple organisms suggests that a large portion of transcribed DNA does not code for a functional protein. As studies begin to delve into the possible functions of these noncoding transcripts, the results are revealing an ever more complex genome, where what was once dubbed "junk" is now seemingly necessary. The characterization of several long noncoding (lnc)RNAs in human and mouse has involved the analysis of raw genomic sequence data with a set of rules to computationally predict functional noncoding transcripts; other approaches involve expression datasets from microarray or RNAseq technology to achieve the same end. As these studies increase, the number of functions, classes and names, of noncoding transcripts increase as well. Many examples of lncRNAs appear to have an epigenetic role in humans, including HOTAIR and XIST. While epigenetic gene regulation is clearly an essential mechanism in plants, relatively little is known about the presence or function of lncRNAs in plants. To explore the connection between lncRNA and epigenetic regulation of gene expression in plants, a computational pipeline using the programming language Python that will identify, classify, and localize potential lncRNAs has been developed and applied to maize full length cDNA sequences. This analysis revealed that a large portion of transcribed sequences in maize are not predicted to be coding. In addition, over half of the predicted noncoding transcripts contain small RNA sequences. Also, approximately half of the predicted noncoding transcripts are associated with a gene model. Of these, roughly 20 percent are antisense to their host gene loci. Sequence analysis identified a GA rich motif that is similar to two known motifs in previously charatercterized lncRNAs, roX2 and HOTAIR. Overall these results suggest that lncRNAs may be a component of genome regulation in maize.
long non-coding RNA, maize, non-coding, RNA, Zea mays
Date of Defense
March 29, 2012.
Submitted Note
A Thesis submitted to the Department of Biological Science in partial fulfillment of the requirements for the degree of Master of Science.
Bibliography Note
Includes bibliographical references.
Advisory Committee
Karen McGinnis, Professor Directing Thesis; Hank Bass, Committee Member; Brian Chadwick, Committee Member.
Publisher
Florida State University
Identifier
FSU_migr_etd-4724
Use and Reproduction
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Boerner, S. (2012). Identification and Classification of Long Non-Coding RNA in Zea Mays Using Computational and Bioinformatic Approaches. Retrieved from http://purl.flvc.org/fsu/fd/FSU_migr_etd-4724