An Edge-based Protein Complex Identification Algorithm With Gene Co-expression Data (PCIA-GeCo)

Recent studies have shown that protein complex is composed of core proteins and attachment proteins, and proteins inside the core are highly co-expressed. Based on this new concept, we reconstruct weighted PPI network by using gene expression data, and develop a novel protein complex identification algorithm from the angle of edge (PCIA-GeCo). First, we select the edge with high co-expressed coefficient as seed to form the preliminary cores. Then, the preliminary cores are filtered according to the weighted density of complex core to obtain the unique core. Finally, the protein complexes are generated by identifying attachment proteins for each core. A comprehensive comparison in term of F-measure, Coverage rate, P-value between our method and three other existing algorithms HUNTER, COACH and CORE has been made by comparing the predicted complexes against benchmark complexes. The evaluation results show our method PCIA-GeCo is effective; it can identify protein complexes more accurately.

[1]  Giulio Superti-Furga,et al.  Protein complexes and proteome organization from yeast to man. , 2003, Current opinion in chemical biology.

[2]  P. Bork,et al.  Functional organization of the yeast proteome by systematic analysis of protein complexes , 2002, Nature.

[3]  Siu-Ming Yiu,et al.  Predicting Protein Complexes from PPI Data: A Core-Attachment Approach , 2009, J. Comput. Biol..

[4]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[5]  P. Bork,et al.  Structure-Based Assembly of Protein Complexes in Yeast , 2004, Science.

[6]  Lin Gao,et al.  Predicting protein complexes in protein interaction networks using a core-attachment algorithm based on graph communicability , 2012, Inf. Sci..

[7]  Gary D Bader,et al.  Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry , 2002, Nature.

[8]  Chung-Yen Lin,et al.  A hub-attachment based method to detect functional modules from confidence-scored protein interactions and expression profiles , 2010, BMC Bioinformatics.

[9]  Hon Wai Leong,et al.  MCL-CAw: a refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure , 2010, BMC Bioinformatics.

[10]  Jacques van Helden,et al.  Evaluation of clustering algorithms for protein-protein interaction networks , 2006, BMC Bioinformatics.

[11]  Igor Jurisica,et al.  Protein complex prediction via cost-based clustering , 2004, Bioinform..

[12]  D. Bu,et al.  Topological structure analysis of the protein-protein interaction network in budding yeast. , 2003, Nucleic acids research.

[13]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[14]  L. Mirny,et al.  Protein complexes and functional modules in molecular networks , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[15]  M. Gerstein,et al.  Global Analysis of Protein Activities Using Proteome Chips , 2001, Science.

[16]  Lin Gao,et al.  Detecting protein complexes in PPI networks: The roles of interactions , 2011, 2011 IEEE International Conference on Systems Biology (ISB).

[17]  A. Barabasi,et al.  Bioinformatics analysis of experimentally determined protein complexes in the yeast Saccharomyces cerevisiae. , 2003, Genome research.

[18]  Kok-Leong Ong,et al.  Dynamical Systems for Discovering Protein Complexes and Functional Modules from Biological Networks , 2007, IEEE ACM Trans. Comput. Biol. Bioinform..

[19]  Ron Shamir,et al.  A clustering algorithm based on graph connectivity , 2000, Inf. Process. Lett..

[20]  Dong Xu,et al.  Global protein function annotation through mining genome-scale data in yeast Saccharomyces cerevisiae. , 2004, Nucleic acids research.

[21]  R. Ozawa,et al.  A comprehensive two-hybrid analysis to explore the yeast protein interactome , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Dmitrij Frishman,et al.  MIPS: analysis and annotation of proteins from whole genomes in 2005 , 2006, Nucleic Acids Res..

[23]  Guimei Liu,et al.  Assessing and predicting protein interactions using both local and global network topological metrics. , 2008 .

[24]  Xiaohua Hu,et al.  Data Mining and Predictive Modeling of Biomolecular Network from Biomedical Literature Databases , 2007, TCBB.

[25]  P. Bork,et al.  Proteome survey reveals modularity of the yeast cell machinery , 2006, Nature.

[26]  Illés J. Farkas,et al.  CFinder: locating cliques and overlapping modules in biological networks , 2006, Bioinform..

[27]  Anton J. Enright,et al.  Detection of functional modules from protein interaction networks , 2003, Proteins.

[28]  Zhang Wang,et al.  Antioxidant properties of wheat germ protein hydrolysates evaluated in vitro , 2006 .

[29]  Gary D. Bader,et al.  An automated method for finding molecular complexes in large protein interaction networks , 2003, BMC Bioinformatics.