In silico genome-wide computational profiling of non-coding RNA in oil palm Elaeis guineensis and its pathogen Ganoderma boninense

Abstract

Oil palm plantation was first established in Malaysia in 1917. Since then, the oil palm industry in Malaysia flourished especially following the shifting of Deli Dura palm to Tenera palm in the 1960s, which contributed to a 30% increase in yield. However, the outbreak of basal stem rot disease caused by Ganoderma boninense has caused substantial yield losses. With no known cure to date, extensive molecular studies were conducted to better understand the underlying mechanism of G. boninense infection and the role of protein-coding genes as regulators in oil palms against G. boninense. The studies have demonstrated the importance of non-coding RNAs (ncRNAs) in the interaction between oil palm and G. boninense. However, there is still limited genome-scale identification for ncRNAs in oil palm (Elaeis guineensis jacq.) and its pathogen, G. boninense. In this study, we focused on the identification of small and medium-sized non-coding RNA using a computational approach and managed to predict 2,233 ncRNAs and 369 ncRNAs in the E. guineensis and G. boninense genomes, respectively. The identified ncRNAs include transfer RNA (tRNA), ribosomal RNA (rRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), and microRNA (miRNA). Although the number may be far fewer than the real number, the predicted ncRNAs here represent an almost complete dataset of small and medium-sized ncRNA in both the E. guineensis and G. boninense genomes. The information obtained may be useful to tackle the issue of G. boninense attack on oil palm plantations.