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Section: Application Domains


Genomics acquiring programs, such as full genomes sequencing projects, are producing larger and larger amounts of data. The analysis of these raw biological data require very large computing resources. In some cases, due to the lack of sufficient computing and storage resources, skilled staff or technical abilities, laboratories cannot afford such huge analyses. Grid computing may be a viable solution to the needs of the genomics research field: it can provide scientists with a transparent access to large computational and data management resources.

In this application domain, we are currently addressing two different problems. In the first one we tackle the clusterization into domain protein families of the sequences contained in international databanks. Our aim is to ensure, through the use of grids, the capacity over time to automatically build databases such as ProDom, when such a database is built from exponentially-fast growing protein databases.

In the second problem, we consider protein functional sites. Functional sites and signatures of proteins are very useful for analyzing raw biological data or for correlating different kinds of existing biological data. These methods are applied, for example, to the identification and characterization of the potential functions of new sequenced proteins. The sites and signatures of proteins can be expressed by using the syntax defined by the PROSITE databank, and written as a “protein regular expression”. Searching one such site in a sequence can be done with the criterion of the identity between the searched and the found patterns. Most of the time, this kind of analysis is quite fast. However, in order to identify non perfectly matching but biologically relevant sites, the user can accept a certain level of error between the searched and the matching patterns. Such an analysis can be very resource consuming.


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