90. Chen W, Erdogan F, Ropers HH, Lenzner S, Ullmann R: CGHPRO -- a comprehensive data analysis tool for array CGH. BMC Bioinformatics 2005, 6:85. 91. Kim SY, Nam SW, Lee SH, Park WS, Yoo NJ, Lee JY, Chung YJ: ArrayCyGHt: a web application for analysis and visualization <strong>of</strong> array-CGH data. Bioinformatics 2005, 21(10):2554-2555. 92. Autio R, Hautaniemi S, Kauraniemi P, Yli-Harja O, Astola J, Wolf M, Kallioniemi A: CGH- Plotter: MATLAB toolbox for CGH-data analysis. Bioinformatics 2003, 19(13):1714- 1715. 93. Lingjaerde OC, Baumbusch LO, Liestol K, Glad IK, Borresen-Dale AL: CGH-Explorer: a program for analysis <strong>of</strong> array-CGH data. Bioinformatics 2005, 21(6):821-822. 94. Chi B, DeLeeuw RJ, Coe BP, MacAulay C, Lam WL: SeeGH--a s<strong>of</strong>tware tool for visualization <strong>of</strong> whole genome array comparative genomic hybridization data. BMC Bioinformatics 2004, 5:13. 95. Chari R, Lockwood WW, Coe BP, Chu A, Macey D, Thomson A, Davies JJ, MacAulay C, Lam WL: SIGMA: a system for integrative genomic microarray analysis <strong>of</strong> cancer genomes. BMC Genomics 2006, 7:324. 96. Chari R, Lockwood WW, Lam WL: Computational methods for the analysis <strong>of</strong> array comparative genomic hybridization. Cancer Inform 2007, 2:48-58. 97. Olshen AB, Venkatraman ES, Lucito R, Wigler M: Circular binary segmentation for the analysis <strong>of</strong> array-based DNA copy number data. Biostatistics 2004, 5(4):557-572. 98. Venkatraman ES, Olshen AB: A faster circular binary segmentation algorithm for the analysis <strong>of</strong> array CGH data. Bioinformatics 2007, 23(6):657-663. 99. Coe BP, Ylstra B, Carvalho B, Meijer GA, Macaulay C, Lam WL: Resolving the resolution <strong>of</strong> array CGH. Genomics 2007, 89(5):647-653. 100. Coe BP, Chari R, MacAulay C, Lam WL: FACADE: A fast and sensitive algorithm for the segmentation and calling <strong>of</strong> high resolution array CGH data. Nucleic Acids Res 2010, Revision. 101. Lonergan KM, Chari R, Coe BP, Wilson IM, Tsao MS, Ng RT, MacAulay C, Lam S, Lam WL: Transcriptome pr<strong>of</strong>iles <strong>of</strong> carcinoma-in-situ and invasive non-small cell lung cancer as revealed by SAGE. PLoS One 2010, Accepted. 102. Lee EHL, Chari R, Lam A, Ng RT, Yee J, English J, Evans KG, MacAulay C, Lam S, Lam WL: Disruption <strong>of</strong> the non-canonical WNT pathway in lung squamous cell carcinoma. Clinical Medicine: Oncology 2008, 2:169-179. 103. Chari R, Lonergan KM, Pikor LA, Coe BP, Zhu CQ, Chan THW, MacAulay C, Tsao MS, Lam S, Ng RT et al: A sequence-based approach to identify reference genes for gene expression analysis. BMC Medical Genomics 2010, Submitted. 104. Lockwood WW, Chari R, Coe BP, Thu KL, Garnis C, Mall<strong>of</strong>f CA, Campbell J, Williams AC, Hwang D, Zhu CQ et al: BRF2 – A Novel Lineage Specific Oncogene in Lung Squamous Cell Carcinoma. PLoS Med 2010, Revisions. 23
<strong>Chapter</strong> 2: SIGMA 2 : A system for the integrative genomic multi-dimensional analysis <strong>of</strong> cancer genomes, epigenomes, and transcriptomes 1 1 A version <strong>of</strong> this chapter has been published. Chari R, Coe BP, Wedselt<strong>of</strong>t C, Benetti M, Wilson IM, Vucic EA, MacAulay C, Ng RT, Lam WL. (2008) SIGMA2: A system for the integrative genomic multi-dimensional analysis <strong>of</strong> cancer genomes, epigenomes, and transcriptomes. BMC Bioinformatics 9:422. doi:10.1186/1471-2105-9-422. Please see the published version <strong>of</strong> this chapter for all supplementary materials. 24
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Figure 3.8 a b Proportion of random
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16. Chari R, Lockwood WW, Coe BP, C
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50. Giovane A, Pintzas A, Maira SM,
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4.1 Introduction Genetic alteration
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4.2.3 Determining frequent regions
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etween loss and UPD (Figure 4.3). S
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obtained, profiled and used as the
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Figure 4.1. Detection of UPD using
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Figure 4.2. Comparison of frequent
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Figure 4.3 Gain Loss 642 441 7 Figu
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94 Figure 4.4 1 2 3 4 5 6 7 8 9 10
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Figure 4.6 a b c Percent of cases A
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Figure 4.7 a b 6p25.3 Log2 fold cha
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Chr BPStart BPEnd # of Chr BPStart
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Table 4.3. Overlap of oncogenes in
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Table 4.5. Cell lines and oncogene
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Table 4.7. RefSeq genes in focal re
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4.6 References 1. Bell DW: Our chan
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33. Garnis C, Coe BP, Lam SL, MacAu
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5.1 Introduction In the past decade
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polymorphism (SNP) arrays with over
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substitutions) and UV exposure (C t
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developed antibodies and methylated
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Histone modification states. While
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metastasis [226]. High throughput a
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Implications on sample size require
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analyzed using the "minet" package
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expression. The next challenge is t
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Figure 5.2 # of copies Total copy n
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Figure 5.4. Integration of copy num
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Figure 5.5. Integration of copy num
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Figure 5.6 SHC1 GRB2 SOS2 RRAS ER R
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Figure 5.8 a Log2 Fold Change (T vs
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Figure 5.10 (a) (b) Figure 5.10. Au
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Table 5.2. List of genomic resource
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5.5 References 1. Pinkel D, Segrave
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37. Oliphant A, Barker DL, Stuelpna
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75. Selzer RR, Richmond TA, Pofahl
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111. Melcher R, Al-Taie O, Kudlich
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145. Takai D, Yagi Y, Wakazono K, O
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179. Kondo M, Suzuki H, Ueda R, Osa
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alterations in human prostate cance
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248. Xi L, Feber A, Gupta V, Wu M,
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281. Fernandez-Ranvier GG, Weng J,
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Chapter 6: Conclusions 162
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can identify nearly three times as
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was identified in a small set of sa
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will be done at the pathway level a
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previously described genetic and ep
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16. Zhang K, Li JB, Gao Y, Egli D,
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APPENDIX I: List of publications Th
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This publication is described in se
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This chapter details the technologi
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epigenetic alteration. This led to
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This manuscript describes the curre
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APPENDIX III: Sources of data Sampl
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APPENDIX V: Kaplan-Meier and Oncomi
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APPENDIX VI: Summary of Kaplan-Meie
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