Prediction and feature analysis of intron retention events in plant genome. (June 2017)

• Record Type: Journal Article
• Authors: Cui, Ying; Zhang, Chao; Cai, Meng
• Is Part Of: Computational biology and chemistry. Volume 68(2017)
• Page Start: 219
• Page End: 223
• Publication Date: 2017-06
• Subjects: Alternative splicing -- Intron retention -- Feature selection -- Random forest -- Plant; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2017.04.004
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Abstract: Alternative splicing (AS) is a major contributor to increase the potential informational content of eukaryotic genomes by creating multiple mRNA species and proteins from a single gene. In plants, up to 60% genes are alternatively spliced and the most common type of AS is intron retention (IR). Genomic analyses of IR have illuminated its crucial role in shaping the evolution of genomes, in the control of developmental processes, and in the dynamic regulation of the transcriptome to influence phenotype. To explore the relationship between the sequence feature and the formation mechanism of IR, we statistically analyzed the retained introns and proposed an improved random forest-based hybrid method to predict intron retention events in plant genome. The results indicate that IR has significant relationship with individual introns which have weaker 5' splice sites, lower GC content and less termination codon occurrence. By the method we proposed, 93.48% retained introns can be correctly distinguished from constitutive introns. Strikingly, our study will facilitate a better understanding of underlying mechanisms involved in intron retention.