Identification of highly synchronized subnetworks from gene expression data

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• 作者：Shouguo Gao (11) (12)
  Xujing Wang (11) (12)
  
• 刊名：BMC Bioinformatics
• 出版年：2013
• 出版时间：June 2013
• 年：2013
• 卷：14
• 期：9-supp
• 全文大小：795KB
• 参考文献：1. Das J, Mohammed J, Yu H: Genome-scale analysis of interaction dynamics reveals organization of biological networks. / Bioinformatics 2012,28(14):1873鈥?. CrossRef
  2. Han JD, Bertin N, Hao T, Goldberg DS, Berriz GF, Zhang LV, Vidal M: Evidence for dynamically organized modularity in the yeast protein-protein interaction network. / Nature 2004,430(6995):88鈥?3. CrossRef
  3. Qi Y, Ge H: Modularity and dynamics of cellular networks. / PLoS computational biology 2006,2(12):e174. CrossRef
  4. Lichtenberg de, Jensen LJ, Brunak S, Bork P: Dynamic complex formation during the yeast cell cycle. / Science 2005,307(5710):724鈥?27. CrossRef
  5. Tang X, Wang J, Liu B, Li M, Chen G, Pan Y: A comparison of the functional modules identified from time course and static PPI network data. / BMC Bioinformatics 2011, 12:339. CrossRef
  6. Jin R, McCallen S, Liu CC, Xiang Y, Almaas E, Zhou XJ: Identifying dynamic network modules with temporal and spatial constraints." Pacific Symposium on Biocomputing. / Pacific Symposium on Biocomputing 2009, 203鈥?14.
  7. Liu Z, Wang Y, Wen T, Zhang X, Xia W, Chen L: Dynamically dysfunctional protein interactions in the development of Alzheimer's disease. / Proc SMC 2009, 4262鈥?267.
  8. Keskin Ozlem, Ma Buyong, Rogale Kristina, Gunasekaran K, Nussinov Ruth: Protein-protein interactions: organization, cooperativity and mapping in a bottom-up Systems Biology approach. / Physical Biology 2005,2(2):S24鈥?5. CrossRef
  9. Guo Z, Li Y, Gong X, Yao C, Ma W, Wang D, Li Y, Zhu J, Zhang M, Yang D, Wang J: Edge-based scoring and searching method for identifying condition-responsive protein-protein interaction sub-network. / Bioinformatics 2009,25(12):1574. CrossRef
  10. Przytycka TM, Singh M, Slonim DK: Toward the dynamic interactome: it's about time. / Brief Bioinformatics 2010, 11:15鈥?9. CrossRef
  11. Gao S, Hartman J IV, Carter JL, Hessner MJ, Wang X: Global analysis of phase locking in gene expression during cell cycle: the potential in network modelling. / BMC Systems Biology 2010,4(1):167. CrossRef
  12. Gurrin L, Sly P: / Tutorials in Biostatistics. John Wiley & Sons Press; 2005.
  13. Gao S, Wang Xu: TAPPA: topological analysis of pathway phenotype association. / Bioinformatics 2007,23(22):3100. CrossRef
  14. Granovskaia MV, Jensen Lars J, Ritchie Matthew E, Toedling Joern, Ning Ye, Bork P, Huber W, Steinmetz LM: High-resolution transcription atlas of the mitotic cell cycle in budding yeast. / Genome Biology 2010,11(3):R24. CrossRef
  15. Leek JT, Monsen EC, Dabney AR, Storey JD: EDGE: Extraction and analysis of differential gene expression. / Bioinformatics 2006,22(4):507. CrossRef
  16. Gao S, Jia S, Hessner MJ, Wang X: Predicting disease related subnetworks for type 1 diabetes using a new network activity score. / Omics 2012,16(10):566鈥?8.
  17. Rahnenfuhrer A, Lengauer T: Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. / Bioinformatics 2006,22(13):1600. CrossRef
  18. Yu H, Luscombe NM, Qian J, Gerstein M: Genomic analysis of gene expression relationships in transcriptional regulatory networks. / Trends Genet 2004,20(6):227. CrossRef
  19. Bobola N, Jansen RP, Shin TH, Nasmyth K: Asymmetric accumulation of Ash1p in postanaphase nuclei depends on a myosin and restricts yeast mating-type switching to mother cells. / Cell 1996,84(5):699. CrossRef
  20. Euskirchen GM: Nnf1p, Dsn1p, Mtw1p, and Nsl1p: a new group of proteins important for chromosome segregation in Saccharomyces cerevisiae. / Eukaryot Cell 2002,1(2):229. CrossRef
  21. Giacometti R, Kronberg F, Ricardo BiondiM, Susana Passeron: Candida albicans Tpk1p and Tpk2p isoforms differentially regulate pseudohyphal development, biofilm structure, cell aggregation and adhesins expression. / Yeast 2011,28(4):293. CrossRef
  22. Chuwattanakul V, Sugiyama M, Khatun F, Kurata K, Tomita I, Kaneko Y, Harashima S: Increased transcription of NOP15, involved in ribosome biogenesis in Saccharomyces cerevisiae, enhances the production yield of RNA as a source of nucleotide seasoning. / Journal of Bioscience and Bioengineering 2012,114(1):17. CrossRef
  23. Li N, Yuan L, Liu N, Shi D, Li X, Tang Z, Liu J, Venkatesan Sundaresan, Yang W: SLOW WALKER2, a NOC1/MAK21 Homologue, Is Essential for Coordinated Cell Cycle Progression during Female Gametophyte Development in Arabidopsis. / Plant Physiol 2009,151(3):1486. CrossRef
  24. Gavin AC, / et al.: Proteome survey reveals modularity of the yeast cell machinery. / Nature 2006, 440:631鈥?36. CrossRef
  25. Dezs艖 Z, Oltvai ZN, Barab谩si AL: Bioinformatics Analysis of Experimentally Determined Protein Complexes in the Yeast Saccharomyces cerevisiae. / Genome Research 2003, 13:2450. CrossRef
  26. Shannan J, Ho Sui, Fulton DL, Arenillas DJ, Kwon AT, Wasserman WW: oPOSSUM: integrated tools for analysis of regulatory motif over-representation. / Nucleic Acids Research 2007, 35:245. CrossRef
  27. Simon I, / et al.: Serial Regulation of Transcriptional Regulators in the Yeast Cell Cycle. / Cell 2001,106(6):697. CrossRef
  28. Zhu C, / et al.: High-resolution DNA-binding specificity analysis of yeast transcription factors. / Genome Research 2009, 19:556. CrossRef
  
• 作者单位：Shouguo Gao (11) (12)
  Xujing Wang (11) (12)
  
  11. Department of Physics, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
  12. The Comprehensive Diabetes Center, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
  
• ISSN：1471-2105

文摘

Background There has been a growing interest in identifying context-specific active protein-protein interaction (PPI) subnetworks through integration of PPI and time course gene expression data. However the interaction dynamics during the biological process under study has not been sufficiently considered previously. Methods Here we propose a topology-phase locking (TopoPL) based scoring metric for identifying active PPI subnetworks from time series expression data. First the temporal coordination in gene expression changes is evaluated through phase locking analysis; The results are subsequently integrated with PPI to define an activity score for each PPI subnetwork, based on individual member expression, as well topological characteristics of the PPI network and of the expression temporal coordination network; Lastly, the subnetworks with the top scores in the whole PPI network are identified through simulated annealing search. Results Application of TopoPL to simulated data and to the yeast cell cycle data showed that it can more sensitively identify biologically meaningful subnetworks than the method that only utilizes the static PPI topology, or the additive scoring method. Using TopoPL we identified a core subnetwork with 49 genes important to yeast cell cycle. Interestingly, this core contains a protein complex known to be related to arrangement of ribosome subunits that exhibit extremely high gene expression synchronization. Conclusions Inclusion of interaction dynamics is important to the identification of relevant gene networks.