Effective identification of essential proteins based on priori knowledge, network topology and gene expressions

 

Min Li,Ruiqing Zheng, Hanhui Zhang, Jianxin Wang, Yi Pan

School of Information Science and Engineering, Central South University, China

 

CPPK predicts new essential proteins based on network topology.
CEPPK predicts new essential proteins by integrating network topology and gene expressions.

 

Software download:  CPPK&CEPPK

 

DataSet

The original protein-protein interaction data was downloaded from DIP (http://dip.doe-mbi.ucla.edu/). The final protein interaction networks used in this paper is given as DIP.txt. The details of DIP network is shown in table1. A list of essential proteins of S. cerevisiae were collected from the following databases: MIPS, SGD, DEG, and SGDP. The essential proteins are given as Essential.txt.The gene expression data of Saccharomyces cerevisiae was retrieved from Tu et al., 2005, containing 6,777 gene products and 36 samples in total, with 4,858 genes involved in the yeast protein interaction network. The gene expression data are given as genedata.txt.

Table 1 The yeast protein interaction network obtained from database DIP.

Databases Proteins Interactions The average degree Essential proteins non-essential proteins

Unknown proteins

DIP 5093 24743 9.72 1167 3591 335

 

 

 

Collaboration
This software is a jar file. If you want to modify the source code and would like the modification to be included in a subsequent release, we would love to hear from you. Please send me an e-mail (limin@mail.csu.edu.cn).
If you have questions about CPPK and CEPPK software, please send me an e-mail.

 

Citation
This manuscript has been submitted to Methods (Revision):

Min Li, Ruiqing Zheng, Hanhui Zhang, Jianxin Wang, Yi Pan.
Effective identification of essential proteins based on priori knowledge, network topology and gene expressions