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Open Access Highly Accessed Methodology article

A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data

Min Li12*, Hanhui Zhang1, Jian-xin Wang1* and Yi Pan12*

Author Affiliations

1 School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China

2 Department of Computer Science, Georgia State University, Atlanta, GA 30302-4110, USA

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BMC Systems Biology 2012, 6:15  doi:10.1186/1752-0509-6-15

Published: 10 March 2012

Additional files

Additional file 1:

Information of the yeast protein-protein interaction network obtained from the DIP database. This file shows the number of proteins, essential proteins, non-essential proteins, and interactions of the yeast protein-protein interaction network obtained from the DIP database. (DOC 28 kb).

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Additional file 2:

PeC is compared with fifteen recent centrality measures (DC, DMNC, BC, SC, BN, CC, EC, IC, LAC, SoECC, RL, LI, LR, NC, and MC) by a jackknife methodology. This file includes five figures: (a) PeC is compared with DC and DMNC; (b) PeC is compared with BC, SC and BN; (c) PeC is compared with CC, EC and IC; (d) PeC is compared with LAC and SoECC; (e) PeC is compared with RL, LI, LR, NC, and MC. To compare with the results of random sorting, ten random assortments are also plotted in each figure. The X-axis represents the ranked proteins in the yeast protein-protein interaction network, ranked from left to right as the highest to the lowest values of centrality measures. The Y-axis is the cumulative count of essential proteins with respect to the ranked proteins moving left to right. (DOC 7744 kb).

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Additional file 3:

The top 100 proteins identified by PeC and other ten centrality measures. This file is composed by 11 groups of data corresponding to PeC and other ten centrality measures: Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality(SC), Eigenvector Centrality(EC), Information Centrality(IC), Bottle Neck (BN), Density of Maximum Neighborhood Component (DMNC), Local Average Connectivity-based method (LAC), Sum of ECC (SoECC). (XLS 36 kb).

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Additional file 4:

A list of 41 proteins predicted by PeC which are ignored by the ten centrality measures: DC, DMNC, BC, SC, BN, CC, EC, IC, LAC, SoECC when predicting the top 100 proteins. There are some proteins which are ignored by the ten centrality measures: DC, BC, CC, SC, EC, IC, BN, DMNC, LAC, and SoECC, but identified by PeC. This file provides the list of 41 proteins predicted by PeC which are ignored by all the ten centrality measures when predicting the top 100 proteins. (DOC 68 kb).

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Additional file 5:

A list of 25 non-essential proteins with a low value of PeC predicted by DC. The non-essential proteins predicted by DC which have a low value of PeC are shown in this file. For each non-essential protein, its values of SoECC, SoPCC, average of ECC, and average of PCC are also shown in this file. (XLS 17 kb).

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Additional file 6:

Examples of non-essential proteins which have high degree but with low PeC. Two examples of non-essential proteins YGR254W and YDL059C are shown. YGR254W and YDL059C both have a high degree of 67, but their PeC values are very low. The PeC value of YGR254W is 0.007 and that of YDL059C is -0.241. (DOC 246 kb).

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Additional file 7:

Examples of non-essential proteins which have high degree and high SoECC but with low PeC. Two examples of non-essential proteins YML048W and YHR140W are shown. YML048W and YHR140W not only have a high degree but also have a high value of SoECC. However, their PeC values are very low. The PeC of YML048W is -0.241 and that of YHR140W is -2.447. (DOC 518 kb).

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Additional file 8:

A list of 17 non-essential proteins with a low value of PeC predicted by SoECC. The non-essential proteins predicted by SoECC which have a low value of PeC are shown in this file. For each non-essential protein, its values of SoECC, SoPCC, average of ECC, and average of PCC are also shown in this file. (XLS 24 kb).

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