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Protein Function Prediction for Omics Era by Meghana Chitale, Daisuke Kihara (auth.), Daisuke Kihara

By Meghana Chitale, Daisuke Kihara (auth.), Daisuke Kihara (eds.)

Gene functionality annotation has been a crucial query in molecular biology. the significance of computational functionality prediction is expanding simply because an increasing number of huge scale organic information, together with genome sequences, protein buildings, protein-protein interplay information, microarray expression info, and mass spectrometry info, are expecting organic interpretation. regularly whilst a genome is sequenced, functionality annotation of genes is completed by way of homology seek tools, resembling BLAST or FASTA. despite the fact that, when you consider that those tools are built sooner than the genomics period, traditional use of them isn't inevitably most fitted for interpreting a wide scale info. for that reason we realize rising improvement of computational gene functionality prediction tools, that are particular to research huge scale facts, and in addition these which use such omics info as extra resource of functionality prediction. during this booklet, we review this rising interesting box. The authors were chosen from 1) those that boost novel only computational tools 2) those that advance functionality prediction tools which use omics facts three) those that continue and replace facts base of functionality annotation of specific version organisms (E. coli), that are usually referred

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It is known that protein-protein interaction (PPI) networks follow the power-law [36]. 80. Thus, in general both PPI and the functional similarity networks follow the power-law. Next examined was the clustering coefficient of the networks. The clustering coefficient of a node indicates how well nodes neighboring to the central nodes are connected to each other. It is defined in the following way: C= n k(k − 1) 2 (12) k is the number of neighboring nodes connected to the central node and n is the number of pairs of the neighboring nodes that are directly connected.

The network hierarchy was first observed in metabolic pathways [37]. It is an interesting observation that hierarchy of the network arises for the funSim score that integrates single GO-scores, which do not show hierarchy individually. This might imply that the funSim score somewhat captures properties of metabolic pathway networks. In summary, we studied the landscape of the functional space of genes as the functional similarity networks. Analysis of topological properties of these networks revealed different network properties as compared with the PPI networks.

In Silico Biol. 1(1): 55–67 (1998). 48. J. Moonlighting proteins – an update. Mol. Biosyst. 5(4): 345–350 (2009). 49. E. Errors in genome annotation. Trends Genet. 15(4): 132–133 (1999). 50. , Valencia, A. Intrinsic errors in genome annotation. Trends Genet. 17(8): 429–431 (2001). 51. , et al. Annotation error in public databases: misannotation of molecular function in enzyme superfamilies. PLoS Comput. Biol. 5(12): e1000605 (2009). 52. , et al. Modeling the percolation of annotation errors in a database of protein sequences.

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