Bioinformatics Advance Access originally published online on March 29, 2005
Bioinformatics 2005 21(11):2730-2738; doi:10.1093/bioinformatics/bti398
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Correlation between gene expression profiles and protein–protein interactions within and across genomes
Bioinformatics Program, Department of Bioengineering, University of Illinois at Chicago Chicago, IL 60607, USA
*To whom correspondence should be addressed.
Motivation: Function annotation of an unclassified protein on the basis of its interaction partners is well documented in the literature. Reliable predictions of interactions from other data sources such as gene expression measurements would provide a useful route to function annotation. We investigate the global relationship of protein–protein interactions with gene expression. This relationship is studied in four evolutionarily diverse species, for which substantial information regarding their interactions and expression is available: human, mouse, yeast and Escherichia coli.
Results: In E.coli the expression of interacting pairs is highly correlated in comparison to random pairs, while in the other three species, the correlation of expression of interacting pairs is only slightly stronger than that of random pairs. To strengthen the correlation, we developed a protocol to integrate ortholog information into the interaction and expression datasets. In all four genomes, the likelihood of predicting protein interactions from highly correlated expression data is increased using our protocol. In yeast, for example, the likelihood of predicting a true interaction, when the correlation is >0.9, increases from 1.4 to 9.4. The improvement demonstrates that protein interactions are reflected in gene expression and the correlation between the two is strengthened by evolution information. The results establish that co-expression of interacting protein pairs is more conserved than that of random ones.
Availability: Complete lists of metagenes across the genomes, microarray and protein interaction dataset used in this study are available on our webpage: http://proteomics.bioengr.uic.edu/inter_expr/index.htm
Contact: huilu{at}uic.edu
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  C.-Y. Yang, C.-H. Chang, Y.-L. Yu, T.-C. E. Lin, S.-A. Lee, C.-C. Yen, J.-M. Yang, J.-M. Lai, Y.-R. Hong, T.-L. Tseng, et al. PhosphoPOINT: a comprehensive human kinase interactome and phospho-protein database Bioinformatics, August 15, 2008; 24(16): i14 - i20. [Abstract] [PDF]
|
|
|
|
 |
|
 S. A. Small Retromer Sorting: A Pathogenic Pathway in Late-Onset Alzheimer Disease Arch Neurol, March 1, 2008; 65(3): 323 - 328. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. J. Lercher and C. Pal Integration of Horizontally Transferred Genes into Regulatory Interaction Networks Takes Many Million Years Mol. Biol. Evol., March 1, 2008; 25(3): 559 - 567. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 T. Obayashi, S. Hayashi, M. Shibaoka, M. Saeki, H. Ohta, and K. Kinoshita COXPRESdb: a database of coexpressed gene networks in mammals Nucleic Acids Res., January 11, 2008; 36(suppl_1): D77 - D82. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. A. Langston, A. D. Perkins, A. M. Saxton, J. A. Scharff, and B. H. Voy Innovative Computational Methods for Transcriptomic Data Analysis: A Case Study in the Use of FPT for Practical Algorithm Design and Implementation The Computer Journal, January 1, 2008; 51(1): 26 - 38. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Gao and X. Wang TAPPA: topological analysis of pathway phenotype association Bioinformatics, November 15, 2007; 23(22): 3100 - 3102. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. Geisler-Lee, N. O'Toole, R. Ammar, N. J. Provart, A. H. Millar, and M. Geisler A Predicted Interactome for Arabidopsis Plant Physiology, October 1, 2007; 145(2): 317 - 329. [Abstract] [Full Text] [PDF]
|
|