RNA Interference Innovation Database
Dolcera’s aim is to build a one-stop database of patented and non-patented literature (scientific literature) that figure RNAi agents against target genes to cure cancer.
- RNAi agents (siRNA and miRNA) have been carefully extracted and uploaded to the database by screening nearly 5,000 patents granted or published worldwide on target genes such as bcl2, myc (c-myc, n-myc), ras (k-ras, h-ras, n-ras).
- Similarly, scientific literature was screened to extract the RNAi agent information against the mentioned target genes.
Applications and Target audience
- For scientific community: RNAi agent sequences aligned to target genes, extensive experimental data and comparison matrix of data from different patents with same focus.
- For patent attorneys: Competitor watch, assignee vs. number of publications graphs, timeline based on application and publication dates.
- For venture capitalists/ Investors: Patent ranking based on disclosed information, data useful for decision making.
The Dolcera Advantage
Dolcera will provide the client with 4 different packages integrated to one place.
- Interactive maps of taxonomy: 4 levels of in depth classification of patents and scientific literature allow both “bird’s-eye view” and “in-depth views” of technologies.
- Patent and non patent dashboard: Grouping specific elements into more general categories is conceptually easier and cleaner than entertaining hundreds of specific elements separately. Dolcera dashboard has a friendly user interface. It groups the patents according to the taxonomy, patent numbers, publications and application dates at the click of a button.
- Sequence Dashboard: The tool not only aligns all patented/ non patented siRNA/ miRNA sequences to the target gene but allow user to see the sequence related information on a simple click of the mouse.
- Experimental matrix: Experimental data be it quantitative (eg. % inhibition of target mRNA expression) or qualitative (eg. gel blots) are compared amongst patent document with similar focuses. This allows users to compare literature with same focus. The ranking of documents can also be done based on scoring matrices, which allows user to read the most relevant patent first.