臺灣博碩士論文加值系統

21世紀的生物學界不再只研究生物體的部分組成，而是想要直接研究整個生物體系統。這種研究生物學的策略形成了一個新學門"系統生物學"。系統生物學的一個主要目標就是想要有系統地研究基因調控網路：基因之間怎麼動態地互相作用來調控彼此的表現以形成一個有高度協同性的生理系統使生物體能夠順利發育及應付各種內外在刺激所引起的生理變化。

基因晶片及免疫沈析晶片是兩種新發展出來的高通量生物技術。這兩種技術非常適合用來研究基因調控網路。基因晶片可以測量出整個基因體裡面每個基因在某個時間點的基因表現。免疫沈析晶片可以測量出每個轉錄因子在整個基因體裡面的所有可結合的基因或是整個基因體裡面每個基因上所有可能結合的轉錄因子。

在本論文�堙A我們結合基因晶片及免疫沈析晶片的資料來研究酵母菌的基因調控網路。首先，我們發展出了一個名為”時間關連性偵測(TRIA)”的計算方法。TRIA主要的原理是：由免疫沈析晶片的資料，我們可以得知每個轉錄因子可能結合的基因。然後再根據基因晶片的資料來輔助我們從這些可能結合的基因之中，分辨出真正會被轉錄因子調控的基因。也就是我們想利用基因晶片的資料來剔除那些會被轉錄因子結合但是不會被它調控的基因。找出轉錄因子真正會調控的目標基因對於瞭解基因調控機制非常有幫助。TRIA非常適合用來達到這個目的。

另外我們發展出了一個名為”模組偵測(MOFA)”的計算方法。主要的原理是：結合基因晶片及免疫沈析晶片的資料來重建有關酵母菌細胞週期的轉錄調控模組。所謂的轉錄調控模組指的是由共同的轉錄因子們所調控的基因群。生物體利用把它的基因體分成很多轉錄調控模組的方式來使得參與共同生理反應的基因群能夠同時被調控，以產生所需的蛋白質來執行生理功能。因此，找出這些轉錄調控模組對於瞭解生物體如何在各種內外在環境的刺激下，維持正常的生理系統非常有幫助。MOFA非常適合用來達到這個目的。

我們相信發展能結合各種不同生物技術資料的計算方法對於研究複雜的生物系統非常有幫助。特別是有越來越多高通量的生物技術被發展出來測量基因體、轉錄體、蛋白體、作用體等，更使得我們這種研究策略非常具有未來性。

The turn of the 21st century has been marked by a resurgence of interest in achieving a systems-level understanding of biology. One of the long-term goals of a newly emerged research field, called systems biology, aims at a comprehensive understanding of the genetic regulatory networks: how the genes dynamically interact and regulate each other to form highly coherent and coordinated physiological systems during the organism's development and in response to homeostatic challenges. DNA microarray and ChIP-chip are two high-throughput biotechnologies that provide complementary information of genetic regulatory networks. DNA microarray can simultaneously measure the mRNA level of each gene in a genome at a specific time point of a biological process being studied. ChIP-chip can simultaneously determine what are the target genes that a transcription factor (TF) may bind and what are the TFs that may bind to a gene.

In this thesis, we integrate DNA microarray and ChIP-chip data to study the genetic regulatory networks of the yeast. First, we develop a computational method, called Temporal Relationship Identification Algorithm (TRIA), which uses DNA microarray data to identify a TF’s regulatory targets from its binding targets inferred from ChIP-chip data. TRIA can be thought of as a “refinement” process of ChIP-chip data to filter out the binding but non-regulatory targets of a TF. Finding the regulatory targets of TFs is important for understanding gene regulation. TRIA is helpful for achieving this purpose. Second, we develop a computational method, called MOdule Finding Algorithm (MOFA), which integrates DNA microarray and the “refined” ChIP-chip data (derived from applying TRIA to the noisy ChIP-chip data) to reconstruct the transcriptional regulatory modules (TRMs) of the yeast cell cycle process. A TRM is a set of genes that is regulated by a common set of TFs. By organizing the genome into TRMs, a living cell can coordinate the activities of many genes that are needed for a cellular process and carry out complex functions. Therefore, identifying TRMs is useful for understanding cellular responses to internal and external signals. MOFA is helpful for achieving this purpose.

We believe that computational analysis of multiple types of data will be a powerful approach to studying complex biological systems when more and more genomic resources such as genome-wide protein activity data and protein-protein interaction data become available.

Abstract ......................................................................................................................... i
Acknowledgements ..................................................................................................... iii
Contents ....................................................................................................................... iv
List of figures ............................................................................................................ viii
List of tables ................................................................................................................ ix
1 Introduction .......................................................................................................... 1
1.1 Central dogma of molecular biology .............................................................. 1
1.2 Regulation of gene expression ....................................................................... 3
1.3 Systems biology ............................................................................................. 6
1.4 Experimental readout of genetic regulatory networks ................................... 8
1.5 Two goals of this thesis .................................................................................. 9
2 Biological background ....................................................................................... 12
2.1 Saccharomyces cerevisiae as a model organism .......................................... 12
2.2 Yeast cell cycle ............................................................................................ 14
2.3 Why study yeast cell cycle? ......................................................................... 15
2.4 DNA microarray ........................................................................................... 16
2.4.1 A typical dual-color microarray experiment ...................................... 16
2.5 ChIP-chip ..................................................................................................... 18
2.5.1 Workflow of a ChIP-chip experiment ................................................ 18
3 Identifying regulatory targets of cell cycle transcription factors using
gene expression and ChIP-chip data ................................................................ 21
3.1 Introduction .................................................................................................. 21
3.2 Methods ........................................................................................................ 22
3.2.1 Data sets ............................................................................................. 22
3.2.2 Temporal Relationship Identification Algorithm (TRIA) .................. 23
3.3 Results .......................................................................................................... 25
3.3.1 Identification of the plausible regulatory targets of a TF ................... 25
3.3.2 Only a subset of the binding targets are plausible regulatory targets
of a TF ................................................................................................ 26
3.3.3 Enrichment for specific functional categories .................................... 27
3.3.4 Enrichment for cell cycle genes ......................................................... 28
3.3.5 Identifying highly co-expressed genes among the plausible
regulatory targets of a TF ................................................................... 29
3.3.6 Performance comparison with existing methods ................................ 32
3.4 Discussion .................................................................................................... 34
4 Computational reconstruction of transcriptional regulatory modules of
yeast cell cycle ..................................................................................................... 40
4.1 Introduction .................................................................................................. 40
4.2 Methods ........................................................................................................ 44
4.2.1 Data sets ............................................................................................. 44
4.2.2 Identifying temporal relationships of TF-gene pairs .......................... 44
4.2.3 MOdule Finding Algorithm (MOFA) ................................................. 45
4.3 Results ............................................................................................................ 48
4.3.1 Validation of the identified modules .................................................. 50
4.3.2 Identification of important cell cycle TFs and their combinations .... 51
4.3.2.1 The M/G1 phase ................................................................. 55
4.3.2.2 The G1 phase ...................................................................... 57
4.3.2.3 The S phase ......................................................................... 59
4.3.2.4 The S/G2 and G2/M phases ................................................ 60
4.4 Discussion .................................................................................................... 61
4.4.1 Relationships between two TFs of a module ...................................... 61
4.4.2 Advantages of MOFA ......................................................................... 63
4.4.3 Parameter settings of MOFA .............................................................. 69
4.4.4 Refining clusters from Spellman et al. ............................................... 71
5 Conclusions ......................................................................................................... 73
Appendix ................................................................................................................... 76
Bibliography ............................................................................................................. 83
Glossary .................................................................................................................... 96

1. Naveh T: Discriminative learning of composite transcriptional regulatory modules. Master thesis. Hebrew University, School of Computer Science and Engineering; 2004.
2. Ehrenberg M, Elf J, Aurell E, Sandberg R, Tegner J: Systems biology is taking off. Genome Res 2003, 13:2377–2380.
3. Ideker T: Systems biology 101--what you need to know. Nat Biotechnol 2004, 22:473–475.
4. Ideker T, Galitski T, Hood L: A new approach to decoding life: systems biology. Annu Rev Genomics Hum Genet 2001, 2:343–372.
5. Kitano H: Computational systems biology. Nature 2002, 420:206–210.
6. Kitano H: Systems biology: a brief overview. Science 2002, 295:1662–1664.
7. Hood L, Heath JR, Phelps ME, Lin B: Systems biology and new technologies enable predictive and preventative medicine. Science 2004, 306:640–643.
8. Lockhart DJ, Winzeler EA: Genomics, gene expression and DNA arrays. Nature 2000, 405:827–836.
9. Lander ES, Weinberg RA: Genomics: journey to the center of biology. Science 2000, 287:1777–1782.
10. Elkon R: Development of bioinformatics methods for analysis of functional genomics data and their application to the study of DNA damage responses. PhD thesis. Tel Aviv University, Molecular Genetics and Biochemistry Department; 2006.
11. Schena M, Shalon D, Davis RW, Brown PO: Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995, 270:467–470.
12. DeRisi JL, Iyer VR, Brown PO: Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 1997, 278:680–686.
13. Ren B, Robert F, Wyrick JJ, Aparicio O, Jennings EG, Simon I, Zeitlinger J, Schreiber J, Hannett N, Kanin E, Volkert TL, Wilson CJ, Bell SP, Young RA: Genome-wide location and function of DNA binding proteins. Science 2000, 290:2306–2309.
14. Iyer VR, Horak CE, Scafe CS, Botstein D, Snyder M, Brown PO: Genomic binding sites of the yeast cell-cycle transcription factors SBF and MBF. Nature 2001, 409:533–538.
15. Wikipedia [http://en.wikipedia.org/wiki/Model_organism]
16. Wikipedia [http://en.wikipedia.org/wiki/Saccharomyces_cerevisiae]
17. Ostergaard S, Olsson L, Nielsen J: Metabolic Engineering of Saccharomyces cerevisiae. Microbiol Mol Biol Rev 2000, 64:34–50.
18. Yeong F: Severing all ties between mother and daughter: cell separation in budding yeast. Mol Microbiol 2005, 55:1325–1331.
19. Wikipedia [http://en.wikipedia.org/wiki/Cancer]
20. Wikipedia [http://en.wikipedia.org/wiki/Dna_microarray]
21. Wikipedia [http://en.wikipedia.org/wiki/ChIP-chip]
22. Liao JC, Boscolo R, Yang YL, Tran LM, Sabatti C, Roychowdhury VP: Network component analysis: reconstruction of regulatory signals in biological system. Proc Natl Acad Sci USA 2003, 100:15522–15527.
23. Gao F, Foat BC, Bussemaker HJ: Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data. BMC Bioinformatics 2004, 5:31.
24. Yu T, Li KC: Inference of transcriptional regulatory network by two-stage constrained space factor analysis. Bioinformatics 2005, 21:4033–4038.
25. Harbison CT, Gordon DB, Lee TI, Rinaldi NJ, Macisaac KD, Danford TW, Hannett NM, Tagne JB, Reynolds DB, Yoo J, Jennings EG, Zeitlinger J, Pokholok DK, Kellis M, Rolfe PA, Takusagawa KT, Lander ES, Gifford DK, Fraenkel E, Young RA: Transcriptional regulatory code of a eukaryotic genome. Nature 2004, 431:99–104.
26. Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, Eisen MB, Brown PO, Botstein D, Futcher B: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 1998, 9:3273–3297.
27. Schumaker L: Spline functions: basic theory. New York: Wiley; 1981.
28. Chen HC, Lee HC, Lin TY, Li WH, Chen BS: Quantitative characterization of the transcriptional regulatory network in the yeast cell cycle. Bioinformatics 2004, 20:1914–1927.
29. Chen KC, Wang TY, Tseng HH, Huang CY, Kao CY: A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae. Bioinformatics 2005, 21:2883–2890.
30. Chang WC, Li CW, Chen BS: Quantitative inference of dynamic pathways via microarray data. BMC Bioinformatics 2005, 6:44.
31. Kato M, Tsunoda T, Takagi T: Lag analysis of genetic networks in the cell cycle of budding yeast. Genome Inform 2001, 12:266–267.
32. Arkin A, Shen PD, Ross J: A test case of correlation metric construction of a reaction pathway from measurements. Science 1997, 277:1275–1279.
33. Lee TI, Rinaldi NJ, Robert F, Odom DT, Bar-Joseph Z, Gerber GK, Hannett NM, Harbison CT, Thompson CM, Simon I, Zeitlinger J, Jennings EG, Murray HL, Gordon DB, Ren B, Wyrick JJ, Tagne JB, Volkert TL, Fraenkel E, Gifford DK, Young RA: Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 2002, 298:799–804.
34. Mewes HW, Frishman D, Guldener U, Mannhaupt G, Mayer K, Mokrejs M, Morgenstern B, Munsterkotter M, Rudd S, Weil B: MIPS: a database for genomes and protein sequences. Nucleic Acids Res 2002, 30:31–34.
35. Zhou XJ, Kao MC, Huang H, Wong A, Nunez-Iglesias J, Primig M, Aparicio OM, Finch CE, Morgan TE, Wong WHZ: Functional annotation and network reconstruction through cross-platform integration of microarray data. Nat Biotechnol 2005, 23:238-243.
36. Banerjee N, Zhang MQ: Identifying cooperativity among transcription factors controlling the cell cycle in yeast. Nucleic Acids Res 2003, 31:7024–7031.
37. Simon I, Barnett J, Hannett N, Harbison CT, Rinaldi NJ, Volkert TL, Wyrick JJ, Zeitlinger J, Gifford DK, Jaakkola TS, Young RA: Serial regulation of transcriptional regulators in the yeast cell cycle. Cell 2001, 106:697–708.
38. Bar-Joseph Z, Gerber GK, Lee TI, Rinaldi NJ, Yoo JY, Robert F, Gordon DB, Fraenkel E, Jaakkola TS, Young RA, Gifford DK: Computational discovery of gene modules and regulatory networks. Nat Biotechnol 2003, 21:1337–1342.
39. Kato M, Hata N, Banerjee N, Futcher B, Zhang MQ: Identifying combinatorial regulation of transcription factors and binding motifs. Genome Biology 2004, 5:R56.
40. Tsai HK, Hunag TW, Chou MY, Lu HS, Li WH: Method for identifying transcription factor binding sites in yeast. Bioinformatics 2006, 22:1675–1681.
41. Wu WS, Li WH, Chen BS: Computational reconstruction of transcriptional regulatory modules of the yeast cell cycle. BMC Bioinformatics 2006, 7:421.
42. Gasch AP, Spellman PT, Kao CM, Carmel-Harel O, Eisen MB, Storz G, Botstein D, Brown PO: Genomic expression programs in the response of yeast cells to environmental changes. Mol Biol Cell 2000, 11:4241–4257.
43. Bussemaker HJ, Li H, Siggia ED: Regulatory element detection using correlation with expression. Nat Genet 2001, 27:167–171.
44. Yu H, Luscombe NM, Qian J, Gerstein M: Genomic analysis of gene expression relationships in transcriptional regulatory networks. Trends Genet 2003, 19:422–427.
45. Zhu Z, Pilpel Y, Church GM: Computational identification of transcription factor binding sites via a transcription-factor-centric clustering (TFCC) algorithm. J Mol Biol 2002, 318:71–81.
46. Lin LH, Lee HC, Li WH, Chen BS: Dynamic modeling and gene expression prediction for cis regulatory circuit by cross gene identification scheme. BMC Bioinformatics 2005, 6:258.
47. Reis BY, Butte AJ, Kohane IS: Approaching causality: discovering time-lag correlations in genetic expression data with static and dynamic relevance networks. RECOMB 2000, p5.
48. Schmitt WAJr, Raab RM, Stephanopoulos G: Elucidation of gene interaction networks through time-lagged correlation analysis of transcriptional data. Genome Res 2004, 14:1654–1663.
49. Liping J, Tan KL: Identifying time-lagged gene clusters using gene expression data. Bioiformatics 2005, 21:509–516.
50. Qian J, Dolled-Filhart M, Lin J, Yu H, Gerstein M: Beyond synexpression relationships: local clustering of time-shifted and inverted gene expression profiles identifies new, biologically relevant interactions. J Mol Biol 2001, 314:1053–1066.
51. Qian J, Lin J, Luscombe NM, Yu H, Gerstein M: Prediction of regulatory networks: genome-wide identification of transcription factor targets from gene expression data. Bioinformatics 2003, 19:1917–1926.
52. Cho RJ, Campbell MJ, Winzeler EA, Steinmetz L, Conway A, Wodicka L, Wolfsberg TG, Gabrielian AE, Landsman D, Lockhart DJ, Davis RW: A genome-wide transcriptional analysis of the mitotic cell cycle. Mol Cell 1998, 2:65–73.
53. Chu S, DeRisi J, Eisen M, Mulholland J, Botstein D, Brown PO, Herskowitz I: The transcriptional program of sporulation in budding yeast. Science 1998, 282:699–705.
54. Causton HC, Ren B, Koh SS, Harbison CT, Kanin E, Jennings EG, Lee T, True HL, Lander ES, Young RA: Remodeling of Yeast Genome Expression in Response to Environmental Changes. Mol Biol Cell 2001, 12:323–337.
55. Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 1998, 95:14863–14868.
56. Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM: Systematic determination of genetic network architecture. Nat Genet 1999, 22:281–285.
57. Ihmels J, Friedlander G, Bergmann S, Sarig O, Ziv Y, Barkai N: Revealing modular organization in the yeast transcriptional network. Nat Genet 2002, 31:370–377.
58. Pilpel Y, Sudarsanam P, Church GM: Identifying regulatory networks by combinatorial analysis of promoter elements. Nat Genet 2001, 29:153–159.
59. Liang S, Fuhrman S, Somogyi R: REVEAL, a general reverse engineering algorithm for inference of genetic network architectures. Pac Symp Biocomput 1998, 3:18–29.
60. Friedman N, Linial M, Nachman I, Pe'er D: Using Bayesian networks to analyze expression data. J Comput Biol 2000, 7:601–620.
61. Segal E, Shapira M, Regev A, Pe'er D, Botstein D, Koller D, Friedman N: Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet 2003, 34:166–176.
62. Tsai HK, Lu HS, Li WH: Statistical methods for identifying yeast cell cycle transcription factors. Proc Natl Acad Sci USA 2005, 102:13532–13537.
63. Yang YL, Suen J, Brynildsen MP, Galbraith SJ, Liao JC: Inferring yeast cell cycle regulators and interactions using transcription factor activities. BMC Genomics 2005, 6:90.
64. Olson KA, Nelson C, Tai G, Hung W, Yong C, Astell C, Sadowski I: Two regulators of Ste12p inhibit pheromone-responsive transcription by separate mechanisms. Mol Cell Biol 2000, 20:4199–4209.
65. Fung H, Bennett RAO, Demple B: Key role of a downstream specificity protein 1 site in cell cycle-regulated transcription of the AP endonuclease gene APE1/APEX in NIH3T3 cells. J Biol Chem 2001, 276:42011–42017.
66. McBride HJ, Yu Y, Stillman DJ: Distinct regions of the Swi5 and Ace2 transcription factors are required for specific gene activation. J Biol Chem 1999, 274:21029–21036.
67. Aerne BL, Johnson AL, Toyn JH, Johnston LH: Swi5 controls a novel wave of cyclin synthesis in late mitosis. Mol Biol Cell 1998, 9:945–956.
68. Pramila T, Miles S, GuhaThakurta D, Jemiolo D, Breeden LL: Conserved homeodomain proteins interact with MADS box protein Mcm1 to restrict ECB-dependent transcription to the M/G1 phase of the cell cycle. Genes Dev 2002, 16:3034–3045.
69. McInerny CJ, Partridge JF, Mikesell GE, Creemer DP, Breeden LL: A novel Mcm1-dependent element in the SWI4, CLN3, CDC6, and CDC47 promoters activates M/G1-specific transcription. Genes Dev 1997, 11:1277–1288.
70. Koch C, Nasmyth K: Cell cycle regulated transcription in yeast. Curr Opin Cell Biol 1994, 6:451–459.
71. Ho Y, Costanzo M, Moore L, Kobayashi R, Andrews BJ: Regulation of transcription at the Saccharomyces cerevisiae start transition by Stb1, a Swi6-binding protein. Mol Cell Biol 1999, 19:5267–5278.
72. Futcher B: Transcriptional regulatory networks and the yeast cell cycle. Curr Opin Cell Biol 2002, 14:676–683.
73. Dimova D, Nackerdien Z, Furgeson S, Eguchi S, Osley MA: A role for transcriptional repressors in targeting the yeast Swi/Snf complex. Mol Cell 1999, 4:75–83.
74. Koranda M, Schleiffer A, Endler L, Ammerer G: Forkhead-like transcription factors recruit Ndd1 to the chromatin of G2/M-specific promoters. Nature 2000, 406:94–98.
75. Kumar R, Reynolds DM, Shevchenko A, Shevchenko A, Goldstone SD, Dalton S: Forkhead transcription factors, Fkh1p and Fkh2p, collaborate with Mcm1p to control transcription required for M-phase. Curr Biol 2000, 10:896–906.
76. Barral Y, Parra M, Bidlingmaier S, Snyder M: Nim1-related kinases coordinate cell cycle progression with the organization of the peripheral cytoskeleton in yeast. Genes Dev 1999, 13:176–187.
77. Miyake T, Reese J, Loch CM, Auble DT, Li R: Genome-wide analysis of ARS (autonomously replicating sequence) binding factor 1 (Abf1p)-mediated transcriptional regulation in Saccharomyces cerevisiae. J Biol Chem 2004, 279:34865–34872.
78. Loo S, Laurenson P, Foss M, Dillin A, Rine J: Roles of ABF1, NPL3, and YCL54 in silencing in Saccharomyces cerevisiae. Genetics 1995, 141:889–902.
79. Hollenhorst PC, Bose ME, Mielke MR, Muller U, Fox CA: Forkhead genes in transcriptional silencing, cell morphology and the cell cycle. Overlapping and distinct functions for FKH1 and FKH2 in Saccharomyces cerevisiae. Genetics 2000, 154:1533–1548.
80. Packham EA, Graham IR, Chambers A: The multifunctional transcription factors Abf1p, Rap1p and Reb1p are required for full transcriptional activation of the chromosomal PGK gene in Saccharomyces cerevisiae. Mol Gen Genet 1996, 250:348–356.
81. Morrow BE, Johnson SP, Warner JR: Proteins that bind to the yeast rDNA enhancer. J Biol Chem 1989, 264:9061–9068.
82. Carmen AA, Holland MJ: The upstream repression sequence from the yeast enolase gene ENO1 is a complex regulatory element that binds multiple trans-acting factors including REB1. J Biol Chem 1994, 269:9790–9797.
83. Wang KL, Warner JR: Positive and negative autoregulation of REB1 transcription in Saccharomyces cerevisiae. Mol Cell Biol 1998, 18:4368–4376.
84. Doolin MT, Johnson AL, Johnston LH, Butler G: Overlapping and distinct roles of the duplicated yeast transcription factors Ace2p and Swi5p. Mol Microbiol 2001, 40:422–432.
85. Loy CJ, Lydall D, Surana U: NDD1, a high-dosage suppressor of cdc28-1N, is essential for expression of a subset of late-S-phase-specific genes in Saccharomyces cerevisiae. Mol Cell Biol 1999, 19:3312–3327.
86. Darieva Z, Pic-Taylor A, Boros J, Spanos A, Geymonat M, Reece RJ, Sedgwick SG, Sharrocks AD, Morgan BA: Cell cycle-regulated transcription through the FHA domain of Fkh2p and the coactivator Ndd1p. Curr Biol 2003, 13:740–745.
87. Costanzo M, Schub O, Andrews B: G1 transcription factors are differentially regulated in Saccharomyces cerevisiae by the Swi6-binding protein Stb1. Mol Cell Biol 2003, 23:5064–5077.
88. Partridge JF, Mikesell GE, Breeden LL: Cell cycle-dependent transcription of CLN1 involves swi4 binding to MCB-like elements. J Biol Chem 1997, 272: 9071–9077.
89. Reardon BJ, Winters RS, Gordon D, Winter E: A peptide motif that recognizes A.T tracts in DNA. Proc Natl Acad Sci USA 1993, 90:11327–11331.
90. Young Lab [http://jura.wi.mit.edu/young_public/research/HumanCirc.html]
91. Efron B, Tibshirani RJ: An introduction to the Bootstrap. Boca Raton: Chapman & Hall/CRC; 1993.
92. Wikipedia [http://en.wikipedia.org/wiki/Bonferroni_correction]
93. Wikipedia [http://en.wikipedia.org/wiki/False_discovery_rate]