Maize

MicroRNAs (miRNAs) impact various biological processes within animals and plants. They complementarily bind target mRNAs, effecting a post-transcriptional negative regulation on mRNA level. The investigation of miRNA target interactions (MTIs) by high throughput screenings is challenging, as frequently used in silico target prediction tools are prone to emit false positives. This issue is aggravated for niche model organisms, where validated miRNAs and MTIs both have to be transferred from well described model organisms.

Competition for microRNA (miRNA) binding between RNA molecules has emerged as a novel mechanism for the regulation of eukaryotic gene expression. Competing endogenous RNA (ceRNA) can act as decoys for miRNA binding, thereby forming a ceRNA network by regulating the abundance of other RNA transcripts which share the same or similar microRNA response elements. Although this type of RNA cross talk was first described in Arabidopsis, and was subsequently shown to be active in animal models, there is no database collecting potential ceRNA data for plants.

High-throughput small RNA (sRNA) sequencing technology enables an entirely new perspective for plant microRNA (miRNA) research and has immense potential to unravel regulatory networks. Novel insights gained through data mining in publically available rich resource of sRNA data will help in designing biotechnology-based approaches for crop improvement to enhance plant yield and nutritional value. Bioinformatics resources enabling meta-analysis of miRNA expression across multiple plant species are still evolving.

The explosion of next-generation sequencing data has spawned the design of new algorithms and software tools to provide efficient mapping for different read lengths and sequencing technologies. In particular, ABI's sequencer (SOLiD system) poses a big computational challenge with its capacity to produce very large amounts of data, and its unique strategy of encoding sequence data into color signals.