Chapter 2 - University of British Columbia

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2.3.2 Description of application scope and functionality SIGMA 2 is built to handle a variety of analysis techniques typically used in the high-throughput study of cancer, allowing the combinatorial integration of multiple 'omics disciplines. The hierarchy, which underlies the program, groups data into genome, epigenome, and transcriptome is shown in Figure 2.2a and the overall functionality map is given in Figure 2.2b and listed in Table 2.2. With each 'omics dimension, data sets may be imported representing any of the major types of biological measurements being assayed, for example, (i) examining both DNA copy number and LOH assays within the genomic bundle, (ii) examining both DNA methylation and histone modification status within the epigenomics bundle, and (iii) examining both gene expression profiles and microRNAs expression assays within the transcriptomic bundle. Each assay may branch into data sources from a multitude of technology platforms. 2.3.3 Approach to integration between array platforms and assays SIGMA 2 treats all data in the context of genome position based on the relevant human genome build using the UCSC genome assemblies. An interval-based approach is used to sample across different array platforms and assays and data from each interval are merged together. Briefly, this is done by querying data at fixed genomic intervals for each platform and subsequently taking an average of the measurements within each interval. The algorithm is listed in Figure 2.3. 2.3.4 Format requirements of input data Standard tab-delimited text files are used for the input of data for all of the assay types. For genomic data, specifically array CGH, normalization is recommended using external algorithms such as CGH-Norm and MANOR [19, 20]. Segmentation analysis can be performed within SIGMA 2 , but results from external analysis can be imported and used in the consensus calling feature. The algorithms which can be called within SIGMA 2 currently include DNACopy and GLAD [21, 22]. Multiple sample batch importing is available to facilitate efficient loading of 27
datasets. To utilize this, the user must create an information file which describes each sample in the dataset. Formatting requirements of the information file are specified in the manual. Alternatively, for Affymetrix SNP array analysis, data should also be pre-processed and normalized using the appropriate software, such as CNAG before importing into SIGMA 2 [23]. Genotyping calls should be made prior to importing, using the "AA", "AB" and "BB" convention. If the genotype call does not exist, "NC" must be specified. For epigenomic data, data from affinity based-approaches (MeDIP [6] and ChIP [24]) should contain a value representing the level of enrichment and the genomic coordinates for each spot. Similarly, for bisulphite-based approaches [25], a percent of converted CpGs should be provided along with the genomic coordinates for each spot. Finally, for transcriptome data, gene expression data from Affymetrix experiments can be directly imported and processed as CEL files and are normalized using the MAS 5.0 algorithm implemented in the "affy" package of R. For any assay type, custom data can be imported whereby the user provides a map of the platform based on the given genome build, and the unique identifier for the map must be used for the data generated from those experiments. 2.3.5 Description of user interface The main user interface in SIGMA 2 utilizes a tabbed window-pane which allows the user to open multiple visualizations simultaneously (Figure 2.4). The left part of the window manages the analyses and projects which belong to the current user and button shortcuts for the main functionality are spread along the top of the window. Using an example of an array CGH profile from the Agilent 244K platform, we demonstrate the step-wise interrogation of a region of interest [26]. Briefly, using the highlighting toolbar button, the user can select a region of interest and subsequently, by clicking the right mouse button, the user can search for annotated genes within the specified genomic coordinates. 28
Page 1 and 2: DEVELOPMENT AND APPLICATION OF AN I
Page 3 and 4: Table of Contents Abstract ........
Page 5 and 6: 3.3.2 Multi-dimensional analysis (M
Page 7 and 8: List of Tables Table 2.1. Features
Page 9 and 10: Figure 5.3. Overlay of chromosomal
Page 11 and 12: RB1 Retinoblastoma 1 RNA Ribonuclei
Page 13 and 14: Dedication To my family. xiii
Page 15 and 16: Chapter 5: Chari R, Thu KL, Wilson
Page 17 and 18: 1.1 Lung cancer Lung cancer has the
Page 19 and 20: expression changes are reactive to
Page 21 and 22: number of different cancer types. M
Page 23 and 24: large number of tumors, that a give
Page 25 and 26: (B) By using an integrative approac
Page 27 and 28: platforms, with one of the latest s
Page 29 and 30: 1.12.1 Development of tools for gen
Page 31 and 32: quantitative PCR experiments as wel
Page 33 and 34: 1.13 References 1. Jemal A, Siegel
Page 35 and 36: 33. Garber ME, Troyanskaya OG, Schl
Page 37 and 38: 71. Shivapurkar N, Gazdar AF: DNA M
Page 39 and 40: Chapter 2: SIGMA 2 : A system for t
Page 41: Here, we present SIGMA 2 , a novel
Page 45 and 46: 2.3.7 Analysis of data from multipl
Page 47 and 48: expression (Figure 2.8). ERBB2 has
Page 49 and 50: Figure 2.1 R -Segmentation -Statist
Page 51 and 52: Figure 2.3 numSamples
Page 53 and 54: Figure 2.5. Consensus calling and h
Page 55 and 56: Figure 2.6 HCC2218 HCC2218 HCC2218B
Page 57 and 58: Figure 2.8. Multi-dimensional persp
Page 59 and 60: Table 2.1. Features required for in
Page 61 and 62: 2.6 References 1. Garnis C, Buys TP
Page 63 and 64: Chapter 3: An integrative multi-dim
Page 65 and 66: observed gene expression deregulati
Page 67 and 68: Raw gene expression profiles from a
Page 69 and 70: screening approach to gene amplific
Page 71 and 72: many regions of frequent LOH/AI do
Page 73 and 74: approach to examining the whole gen
Page 75 and 76: PTEN expression [44]. Using this pa
Page 77 and 78: mechanisms at the DNA and RNA level
Page 79 and 80: In this study, three DNA dimensions
Page 81 and 82: Figure 3.2. Quantitative and qualit
Page 83 and 84: Figure 3.3 a Proportion of genes in
Page 85 and 86: 70 Figure 3.5 -log(pvalue) 5.0 4.0
Page 87 and 88: 72 Figure 3.7 Sample: HCC1008 Copy
Page 89 and 90: Figure 3.8 a b Proportion of random
Page 91 and 92: 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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University of British Columbia - Br
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Chapter 2 - University of British Columbia

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