Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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6.16 Tag mapping of TPM1 on UCSC genome browser. . . . . . . . . 149 6.17 Schematisation of the process of finding genes with differentially expressed isoforms. . . . . . . . . . . . . . . . . . . . . . . . . . 150 6.18 Schematisation of the localisation of the microRNA seeds on the mapped isoforms. . . . . . . . . . . . . . . . . . . . . . . . . . . 151 6.19 Isoform detection by multi tag mapping of gene GAPVD1 on UCSC genome browser. . . . . . . . . . . . . . . . . . . . . . . . 154 6.20 Isoform detection by multi tag mapping of gene SMAD1 on UCSC genome browser. . . . . . . . . . . . . . . . . . . . . . . . 156 6.21 Correlated expression of CTSC and a nearby ncRNA. . . . . . . 159 6.22 Histogram of expression levels of HOTAIRM1 and surrounding HOX genes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 7.1 Enrichment plots for the top two pathways revealed through GSEA of the KEGG database of pathways [2]. . . . . . . . . . . 165 7.2 GSEA plots of (a) nominal p-values vs normalised enrichment score and (b) line graph of the enrichment scores across pathways.165 7.3 Correlation of Tag-seq interrogated cell lines with glioblastoma subtype expression signatures. . . . . . . . . . . . . . . . . . . . 170 7.4 Core gene expression changes in GNS lines are mirrored in glioblastoma tumours. . . . . . . . . . . . . . . . . . . . . . . . 172 7.5 Association between GNS signature and other survival predictors.184 7.6 Association between GNS signature score and patient survival. . 185 7.7 The integrated glioblastoma pathway subdivided into sections identifying the gene networks that participate in the pathway. . 188 7.8 Integrated GBM pathway used to overlay the Tag-seq GNS dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 7.9 Affected p53, RB1 and PTEN/PI3K pathways. . . . . . . . . . 192 7.10 Four integrated GBM pathways overlaid with Tag-seq expression level measures for each GNS cell line. . . . . . . . . . . . . 194 7.11 Integrated GBM pathway with the TCGA dataset overlaid. . . . 199 7.12 Integrated GBM pathway with the HGG dataset overlaid. . . . 200 7.13 Three integrated GBM pathways overlaid with the GNS, TCGA and HGG datasets. . . . . . . . . . . . . . . . . . . . . . . . . . 201 8.1 Overview of GenemiR software. . . . . . . . . . . . . . . . . . . 205 8.2 Internal organisation of the target prediction database of Gen- emiR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206
8.3 Workflows at the core of the primitive functions of the GenemiR software. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 8.4 Step by step diagram of the ensemble method adopted to find the score E (=C2/C1) of prediction accuracy for prediction al- gorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214 D.1 Integrated GBM pathway with G144 cell line Tag-seq expression data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282 D.2 Integrated GBM pathway with G144ED cell line Tag-seq expression data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 D.3 Integrated GBM pathway with G166 cell line Tag-seq expression data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284 D.4 Integrated GBM pathway with G179 cell line Tag-seq expression data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285
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Transcriptional Characterization of
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This dissertation is my own work an
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Acknowledgements I would like to th
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5.9 External Dataset Expression Cor
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Introduction 1
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1.1 Glial Cells in the Central Nerv
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1.2 Glioblastoma Multiforme Introdu
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1.3 Primary and Secondary Glioblast
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1.4 Pathways Involved in Glioblasto
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1.5 Pathway Crosstalk Introduction
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Chapter 2 Neurogenesis Contents 2.1
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2.1 Radial Glia Introduction VZ adj
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2.2 Neural Stem Cells Introduction
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Chapter 3 Brain Cancer Stem Cells C
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3.1 The Cancer Stem Cell Hypothesis
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3.1 The Cancer Stem Cell Hypothesis
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3.2 Brain Cancer Stem Cells Introdu
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3.3 Glioma Culture Systems Introduc
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Chapter 4 The Non-Coding RNA World
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4.1 MicroRNA regulation Introductio
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4.1 MicroRNA regulation Introductio
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4.2 Target Prediction and Validatio
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Methods 93
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5.1 Tag-sequencing Data Processing
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5.1 Tag-sequencing Data Processing
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5.2 Array Comparative Genomic Hybri
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5.4 Quantitative Real Time-PCR Vali
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5.5 Literature Mining Methods Figur
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5.5 Literature Mining Methods How m
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5.6 Differential Isoform Expression
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5.9 External Dataset Expression Cor
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5.10 Glioblastoma Pathway Construct
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5.11 MicroRNA Target Prediction Ana
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Results 119
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6.2 Tag mapping Results Table 6.1:
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6.2 Tag mapping Results 123 Figure
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6.3 Copy Number Aberrations Results
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6.3 Copy Number Aberrations Results
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6.4 Core Differentially Expressed G
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6.5 Large-scale qRT-PCR Validation
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6.6 Literature Mining for Different
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6.7 Isoform Differential Expression
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6.8 Long ncRNA Differential Express
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6.8 Long ncRNA Differential Express
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Chapter 7 Dataset Correlation Analy
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7.1 Enrichment Analysis Results Tab
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7.1 Enrichment Analysis Results Fig
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7.1 Enrichment Analysis Results Tab
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7.2 Glioblastoma Expression Signatu
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7.3 Tumour Expression Correlation R
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7.4 Survival Analysis Results Table
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7.4 Survival Analysis Results 867 g
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7.5 Glioblastoma Pathway Analysis R
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8.1 Principles Results say, this is
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8.3 Databases Results Figure 8.1: O
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8.3 Databases Results Figure 8.3: W
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8.4 Filters Results Bayesian method
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8.5 Target Prediction Ensemble Anal
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9.1. Digital Profiling of GNS Cell
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9.2. MicroRNA Target Prediction Ana
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9.3. Concluding Remarks Appendix pr
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A.1 Differential Expression Appendi
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A.2 Classified Differential Express
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A.3 Quantitative RT-PCR Appendix Ta
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Page 277 and 278: End Select End If End Sub Appendix
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Page 295 and 296: E. Exon Array Data Appendix Average
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Page 323 and 324: Bibliography [1] Cell signaling tec
Page 325 and 326: [38] G. Bain, D. Kitchens, M. Yao,
Page 327 and 328: [78] S. Bustin, V. Benes, J. Garson
Page 329 and 330: [112] M. Czystowska, J. Han, M. Szc
Page 331 and 332: [153] T. Fujiwara, M. Bandi, M. Nit
Page 333 and 334: [192] M. Hernandez, M. Nieto, and M
Page 335 and 336: [231] T.-M. Kim, W. Huang, R. Park,
Page 337 and 338: [268] H. Lemjabbar-Alaoui, A. van Z
Page 339 and 340: [305] K. MAEDA, S. MATSUHASHI, K. T
Page 341 and 342: [342] H. Moon, M. Ahn, J. Park, K.
Page 343 and 344: [379] D. Park and J. Rich. Biology
Page 345 and 346: [417] P. Rakic. Guidance of neurons
Page 347 and 348: [456] T. Shima, N. Okumura, T. Taka
Page 349 and 350: [490] P. A. C. t’Hoen, Y. Ariyure
Page 351 and 352: [523] T. Watanabe, A. Takeda, T. Ts
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