Functional inference of hypothetical proteins (HPs) is a significant task in the post-genomic era. We described here a network-based protocol for functional inference of HPs using experimental transcriptomic, proteomic, and protein-protein interaction (PPI) datasets. The protocol includes two steps: i) co-expression networks were constructed using large proteomic or transcriptomic datasets of Synechocystis sp. PCC 6803 under various stress conditions, and then combined with a Synechocystis PPI network to generate bi-colored networks that include both annotated proteins and HPs; ii) a global algorithm was adapted to the bi-colored networks for functional inference of HPs. The algorithm ranked the associations between genes/proteins with known GO functional categories, and assumed that the top one ranked HP for each GO functional category might have a function related to the GO functional category. We applied the protocol to all HPs of the model cyanobacterium Synechocystis, and were able to assign putative functions to 122 HPs that have never been functionally characterized previously. Finally, the functional inference was validated by the known biological information of operon, and results showed that more than 70% HPs could be correctly validated. The study provided a new protocol to integrate different types of OMICS datasets for functional inference of HPs, and could be useful in achieving new insights into the Synechocystis metabolism.
Keywords: Global algorithm; Hypothetical proteins; Network; Operon; Synechocystis.
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