Bioinformatic analysis of degradome data from African oil palm (Elaeis guineensis Jacq.) inflorescence / Sri Devi a/p Ramarao

Ramarao, Sri Devi (2012) Bioinformatic analysis of degradome data from African oil palm (Elaeis guineensis Jacq.) inflorescence / Sri Devi a/p Ramarao. Masters thesis, University of Malaya.

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Abstract

African Oil Palm (Elaeis guineensis Jacq.) is an economically important plant since it is the highest yielding oil-producing crop in the world. Long reproductive cycle and slow seed maturation result in lengthy breeding cycles and a slow rate of improvement through selection. Demand to increase oil palm yield can be supported by analysis of oil palm sequence data towards a functional genomic approach to support marker assisted selection. In this study degradome data from inflorescence of the African Oil Palm was analysed to identify miRNA/siRNA cleavage products and SNPs in miRNA targets. Degradome analysis tools CleaveLand, SeqTar and PARESnip were used to analyse degradome data towards the objective of identifying miRNA/target pairs that may be important in oil palm flowering and hence yield of this major crop. Total miRNA targets predicted by CleaveLand, SeqTar, PARESnip were 1304, 663318 and 12532 respectively. SeqTar is suitable to be used to predict a wide range of targets without missing any possibilities of the predicted targets in oil palm degradome data. SeqTar is also suitable to predict precursor novel miRNA targets in oil palm degradome data. CleaveLand can only be used to predict known miRNA targets and it is not suitable to predict novel miRNA targets in oil palm degradome data. Of the three currently available tools for degradome analysis, PARESnip was found to be the fastest and most convenient tool. PARESnip is user friendly and user configurable tool. PARESnip also shows the highest number of prediction of highly significant miRNA targets compared to CleaveLand and SeqTar. Thus, PARESnip can be considered as a more reliable tool to analyse degradome data from African Oil Palm inflorescence. Computational prediction methods have been successfully employed to find candidate miRNA targets in African Oil Palm inflorescence data.

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