Title page for 952211007


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Student Number 952211007
Author Yi-Sheng Wu(dq)
Author's Email Address No Public.
Statistics This thesis had been viewed 1353 times. Download 231 times.
Department Graduate Institute of Systems Biology and Bioinformatics
Year 2007
Semester 2
Degree Master
Type of Document Master's Thesis
Language English
Title Inferring gene transcriptional regulatory network from gene expression data using RECEC
Date of Defense 2008-07-03
Page Count 37
Keyword
  • gene transcriptional regulation
  • microarray
  • network inference
  • transcriptional regulatory network
  • Abstract Network inference from microarray data has been applied to and eased the task of identifying transcriptional regulatory interactions. However, gene expression is generally controlled by combinatorial interaction of transcription factors (TFs). Its hard to reconstruct the network properly using the relatedness of gene expression between pairs of genes assessing by traditional methods. Here we developed and applied the Relatedness Estimation under Confounding Effect Control (RECEC) algorithm. Our approach enables a more proper estimation of the relatedness with less confounding effect resulted from combinatorial regulation of TFs. We inferred the network from 612 Escherichia coli microarray data and evaluated the inference performance using known 3,124 transcriptional regulatory interactions. Our algorithm demonstrates a better AUC(ROC) 73.74% compared to traditional approach 70.66%. We also conducted EMSA experiments to indentify putative transcriptional regulatory interactions inferred by our algorithm. We found TF LexA binds to the upstream region of nac gene. The relatedness of this interaction is ranked number 1 in our algorithm compared to number 38 in traditional methods when TF is restricted to LexA. Our approach offers the potential to identified novel transcriptional regulatory interactions which are involved in combinatory regulation of transcription.
    Table of Content Chpater 1Introduction
    1.1.Background
    1.2.Motivation
    1.3.Goal
    Chpater 2Relative works
    2.1.Context likelihood of relatedness (CLR) algorithm
    Chpater 3Materials and Methods
    3.1.Materials
    3.1.1.Microarray data
    3.1.2.Transcriptional regulator network
    3.2.Methods
    3.2.1.Correlation coefficient
    3.2.2.Mutual information
    3.2.3.RECEC algorithm
    3.2.4.Evaluating the performance of network inference methods
    3.2.5.Parameter combinations of MI method
    3.2.6.Network Inference
    3.2.7.Regulatory motif identification
    3.2.8.EMSA experiments
    Chpater 4Results
    4.1.ROC curve
    4.2.The known interactions distribution in those networks
    4.3.Performance improvement in combinatory regulation
    4.4.LexA binds to the promoter region of nac gene
    Chpater 5Discussion
    References
    Reference 1. GuhaThakurta D. Computational identification of transcriptional regulatory elements in DNA sequence. Nucleic Acids Res. 2006;34(12):3585.
    2. Harbison CT, Gordon DB, Lee TI, Rinaldi NJ, Macisaac KD, Danford TW, et al. Transcriptional regulatory code of a eukaryotic genome. Nature. 2004;431:99-104.
    3. Lee TI, Rinaldi NJ, Robert F, Odom DT, Bar-Joseph Z, Gerber GK, et al. Transcriptional regulatory networks in saccharomyces cerevisiae. Science. 2002;298(5594):799-804.
    4. D'haeseleer P, Liang S, Somogyi R. Genetic network inference: From co-expression clustering to reverse engineering. Bioinformatics. 2000;16(8):707-26.
    5. Butte AJ, Kohane IS. Unsupervised knowledge discovery in medical databases using relevance networks. Proc AMIA Symp. 1999:711-5.
    6. Butte AJ, Kohane IS. Mutual information relevance networks: Functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput. 2000:418-29.
    7. Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, et al. Large-scale mapping and validation of escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 2007 Jan;5(1):e8.
    8. Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, et al. ARACNE: An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics. 2006;7(Suppl 1):S7.
    9. Schafer J, Strimmer K. An empirical bayes approach to inferring large-scale gene association networks. Bioinformatics. 2005;21(6):754-64.
    10. Soranzo N, Bianconi G, Altafini C. Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks: Synthetic versus real data. Bioinformatics. 2007 Jul 1;23(13):1640-7.
    11. de la Fuente, A., Bing N, Hoeschele I, Mendes P. Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics. 2004;20(18):3565-74.
    12. Hackney JA, Ehrenkaufer GM, Singh U. Identification of putative transcriptional regulatory networks in entamoeba histolytica using bayesian inference. Nucleic Acids Res. 2007;35(7):2141-52.
    13. Gama-Castro S, Jimenez-Jacinto V, Peralta-Gil M, Santos-Zavaleta A, Penaloza-Spinola MI, Contreras-Moreira B, et al. RegulonDB (version 6.0): Gene regulation model of escherichia coli K-12 beyond transcription, active (experimental) annotated promoters and textpresso navigation. Nucleic Acids Res. 2008 Jan;36(Database issue):D120-4.
    14. Faith JJ, Driscoll ME, Fusaro VA, Cosgrove EJ, Hayete B, Juhn FS, et al. Many microbe microarrays database: Uniformly normalized affymetrix compendia with structured experimental metadata. Nucleic Acids Res. 2008 Jan;36(Database issue):D866-70.
    15. Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP. Summaries of affymetrix GeneChip probe level data. Nucleic Acids Res. 2003;31(4):e15.
    16. IRIZARRY RA, HOBBS B, COLLIN F, BEAZER-BARCLAY YD, ANTONELLIS KJ, SCHERF U, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003;4(2):249.
    17. Steuer R, Kurths J, Daub CO, Weise J, Selbig J. The mutual information: Detecting and evaluating dependencies between variables. Bioinformatics. 2002;18(90002):231-40.
    18. Daub CO, Steuer R, Selbig J, Kloska S. Estimating mutual information using B-spline functions--an improved similarity measure for analysing gene expression data. BMC Bioinformatics. 2004 Aug 31;5:118.
    19. Werhli AV, Grzegorczyk M, Husmeier D. Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks. Bioinformatics. 2006;22(20):2523.
    20. Eddy SR. Profile hidden markov models. Bioinformatics. 1998;14(9):755-63.
    21. Fernandez De Henestrosa AR, Ogi T, Aoyagi S, Chafin D, Hayes JJ, Ohmori H, et al. Identification of additional genes belonging to the LexA regulon in escherichia coli. Mol Microbiol. 2000 Mar;35(6):1560-72.
    Advisor
  • Li-Ching Wu(d߫C)
  • Files
  • 952211007.pdf
  • approve in 1 year
    Date of Submission 2008-07-14

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