In the course of large-scale proteomics projects, millions of peptide ion collision spectra (MS/MS) may be generated, which must be matched to theoretical spectrum models inferred from known peptide sequences in order to identify proteins. A number of database search engines using different scoring systems have been and are being developed to this end.
The research group lead by Dr. Juan Pablo Albar from CNB has developed a generalized meta-search process that, by integrating partial evidence from any number and type of such database search engines into a single consensus reconstruction, remarkably increases the number of identified proteins. The process may be extended by integration of additional sources of information besides primary search engine results. Its main drawbacks are its reliance on complex data modeling techniques for which no fully automated procedures have been developed, and a significantly increased computational cost compared with conventional methods.
Main innovations and advantages:
- It is extremely flexible and remarkably increases the number of identified proteins.
- High level of accuracy in terms of error rate control.
- Research Group: Juan Pablo Albar
- Application Number and priority date: P200930402, 01/07/2009
- International PCT Application: PCT/ES10/070445
- Countries selected in National Phase: EU, US.
- Present situation: Patent granted in Euripe and under examination in US.