Functional Annotation

Next generation sequencing is a key technique in small RNA biology research that has led to the discovery of functionally different classes of small non-coding RNAs in the past years. However, reliable annotation of the extensive amounts of small non-coding RNA data produced by high-throughput sequencing is time-consuming and requires robust bioinformatics expertise. Moreover, existing tools have a number of shortcomings including a lack of sensitivity under certain conditions, limited number of supported species or detectable sub-classes of small RNAs.

Salvia miltiorrhiza has been comprehensively studied as a medicinal model plant. However, research progress on this species is significantly hindered by its unavailable genome sequences and limited number of expressed sequence tags in the National Center for Biotechnology Information database. Thus, a transcript database must be developed to assist researchers to browse, search, and align sequences for gene cloning and functional analysis in S. miltiorrhiza.

A large number of new genomic features are being discovered using high throughput techniques. The next challenge is to automatically map them to the reference genome for further analysis and functional annotation. We have developed a tool that can be used to map important genomic features to the latest version of the human genome and also to annotate new features. These genomic features could be of many different source types, including miRNAs, microarray primers or probes, Chip-on-Chip data, CpG islands and SNPs to name a few.

The use of swine in biomedical research has increased dramatically in the last decade. Diverse genomic- and proteomic databases have been developed to facilitate research using human and rodent models. Current porcine gene databases, however, lack the robust annotation to study pig models that are relevant to human studies and for comparative evaluation with rodent models. Furthermore, they contain a significant number of errors due to their primary reliance on machine-based annotation.