This project is part of an international competition (CAFA). The input dataset consists of 130,000 unknown protein sequences. The goal is to predict the functional characteristics of each of these sequences as accurately as possible.
It is well established that proteins with similar amino acid sequences share molecular structure and therefore biological function. The strategy employed involves searching for sequences similar to those that are unknown in databases and subsequently transferring annotation. This is done using software for local alignment optimized for rapid searching in databases, and functional annotation is transferred based on sequence similarity.
The CloudVeneto platform has allowed for the optimization of this search process by parallelizing the workload across available resources.
