Research Article

Comprehensive Cataloging and Analysis of Alternative Splicing in Maize  

X.J. Min
Department of Biological Sciences, Center for Applied Chemical Biology, Youngstown State University, Youngstown, OH 44555, USA
Author    Correspondence author
Computational Molecular Biology, 2017, Vol. 7, No. 1   doi: 10.5376/cmb.2017.07.0001
Received: 20 Jul., 2017    Accepted: 01 Sep., 2017    Published: 04 Sep., 2017
© 2017 BioPublisher Publishing Platform
This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Preferred citation for this article:

X.J.Min, 2017, Comprehensive cataloging and analysis of alternative splicing in maize, Computational Molecular Biology, 7(1): 1-11 (doi: 10.5376/cmb.2017.07.0001)

Abstract

Gene expression is a key step in developmental regulation and responses in changing environments in plants. Alternative splicing (AS) is a process generating multiple RNA isoforms from a single gene pre-mRNA transcript that increases the diversity of functional proteins and RNAs. Identification and analysis of alternatively splicing events are critical for crop improvement and understanding regulatory mechanisms. In maize large numbers of transcripts generated by RNA-seq technology are available, we incorporated these data with data assembled with ESTs and mRNAs to comprehensively catalog all genes having pre-mRNAs undergoing AS. A total of 192 624 AS events were detected and classified, including 103 566 (53.8%) basic events and 89 058 (46.2%) complex events which were formed by combination of various types of basic events. Intron retention was the dominant type of basic AS event, accounting for 24.1%. These AS events were identified from 91 128 transcripts which were generated from 26 669 genomic loci, of which consisted of 20 860 gene models. It was estimated that 55.3% maize genes may be subjected to AS. The transcripts mapping information can be used to improve the predicted gene models in maize. The data can be accessed at Plant Alternative Splicing Database (http://proteomics.ysu.edu/altsplice/). 

Keywords
Alternative splicing; Cereal crops; Gene expression; Maize; mRNA
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