Predicting essential proteins based on subcellular localization, orthology and PPI networks
School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, China
SON is a novel essential protein prediction method which integrates the information of subcellular localization, orthologous proteins and protein-protein interaction networks.
Software download: SON
DataSet
The original protein-protein interaction data was downloaded from DIP (http://dip.doe-mbi.ucla.edu/).
The final protein-protein interaction network used in this paper includes 5093 proteins and 24743 interactions
by filtering self-interactions and repeated interactions, as shown in DIP.txt. A list of essential proteins of
S. cerevisiae are collected from the following databases:
MIPS,
SGD,
DEG,
and SGDP. The essential proteins used
in this paper is given as Essential.txt. Subcellular localization dataset (subcellular.txt) includes 5095 yeast
proteins and 206831 subcellular localization records. Orthologous proteins dataset is taken from Version 7 of
InParanoid consisting a set of pairwise comparisons between 100 whole genomes.
SON Publication
Please see the following paper for more information about the algorithm:
Gaoshi Li, Min Li, Jianxin Wang, Jingli Wu，Fang-Xiang Wu and Yi Pan.
Predicting essential proteins based on subcellular localization, orthology and PPI networks. BMC Bioinformatics.