Computational analysis of causality between miRNAs and antisense transcription

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DESCRIPTION (provided by applicant): Antisense transcription is a widespread and pervasive phenomenon affecting about 25% of eukaryotic genes, but its origins and functions are unknown. Small RNAs such as microRNAs (miRNAs) and trans- acting small-interfering RNAs (ta-siRNAs) are also pervasive in metazoans and plants and play important roles in gene regulation, development, and disease, but to date the predictive power of computational methods for plant and animal small RNA discovery is limited. Based on computational analysis of four independent Arabidopsis transcriptome databases, it is hypothesized that miRNAs and ta-siRNAs are triggers for antisense transcription in Arabidopsis. This hypothesis will be rigorously tested using computational and statistical methods on the entire Arabidopsis and rice genomes. Using known and hypothesized miRNA and ta-siRNA target gene antisense transcription as a learning set, a new algorithm will be developed based on antisense transcription combined with established miRNA prediction methods to discover new candidate miRNAs, ta-siRNAs and their hypothetical target genes. The identified putative small RNA target genes would be inherently "interesting" as potential key regulators of growth and development. The predicted novel small RNAs and their targets will be experimentally validated with a facile transient assay system, RNA blots and rapid amplification of cDNA ends (RACE) to prove novel small RNA- directed cleavage of targets. Transcriptome profiling on whole genome tiling arrays of known mutants that affect small RNAs and antisense transcription will determine the extent of causality of antisense and small RNAs. A novel algorithm for small RNA prediction based on a phenomenon shared between plants and animals may have general utility for small RNA discovery in any organism for which deep genomic resources exist, for genome annotation, and for better understanding of human disease etiologies. Such a predictive tool would also have utility in the fields of functional genomics and evolutionary biology. A better understanding of small RNAs and their roles in gene regulation may shed light on "the RNA World" and the origins of life.