Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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6.7 Isoform Differential Expression Results Figure 6.17: This figure summarises the process of finding genes with differentially expressed isoforms (common to both the parametric and logarithmic method) that are predicted to harbour the same microRNA targeting sites. One way to assess this is to find which genes are predicted to be regulated by the same microRNAs. In blue we find the numbers of microRNAs that are predicted to regulate the 2,682 genes (green set) and the 2,040 genes (orange set). In black we find the numbers that refer to genes. The 2,358 microRNAs common to both sets are predicted to regulate 765 genes that are in common to the parametric and the logarithmic methods. The prediction database used comes from the union of five prediction algorithms: PicTar [247], PITA [224], Targetscan [272], miRanda [213] and DIANA-microT [315,316]. In order to find in more detail which of the genes with differentially expressed isoforms that were also predicted to harbour the same microRNA targeting sites, actually had one or more microRNA seed sequences in their 3'UTRs, we used the Ensembl gene coordinates and the union of all microRNA seed coordinates given by the same five prediction algorithms used earlier (Targetscan, miRanda, PITA and DIANA-microT) to validate which genes harboured which microRNA seed sequences. We found that, of the 765 genes predicted to target the same microRNA targeting sites, 226 of the 2,682 genes with differentially expressed isoforms (identified with the parametric method) and 340 of the 2,040 genes with differentially expressed isoforms (identified with the logarithmic method) hosted a microRNA seed sequence between at least two tags (Fig 6.18). These amounts clearly show that microRNA is a widely adopted mech- anism of isoform expression modulation in GNS cell lines. Since microRNA array data was available for four GNS cell lines (G7, G26, G144, G166) and four NS cell lines (CB660, CB130, CB152, CB171), we used it to cross verify 150
6.7 Isoform Differential Expression Results Table 6.7: Summary of predicted microRNAs targeting differentially expressed isoforms of the genes identified with the parametric and logarithmic method. The predictions from each of the five algorithms (PicTar [247], PITA [224], Targetscan [272], miRanda [213] and DIANA-microT [315,316]) are shown in distinct rows. Prediction algorithms Nonparametric microRNAs Parametric microRNAs Common microRNAs Unique to nonparametric Unique to parametric PicTar4 164 164 164 - - PITA 674 673 672 miR-658 miR-937 miR-886-3p TargetscanS 148 148 147 miR-615-3p miR-450a miRanda 677 677 677 - - DIANA 510 508 503 miR-151-1p miR-146b-3p micro-T miR-324-5p miR-199b-5p miR-369-5p miR-566 miR-423-3p miR-744 miR-602 miR-877 miR-658 miR-941 Figure 6.18: Schematisation of the localisation of the microRNA seeds given by the five prediction algorithms within the 3'UTRs of pairs of differentially expressed isoforms identified by two tags that map onto each one. Of the 765 genes with differentially expressed isoforms identified by both the parametric and logarithmic methods, 226/2,682 genes and 340/2,040 genes hosted at least one microRNA seed sequence between at least two tags. that the microRNA predictions found for the 765 genes with differentially expressed isoforms were reflected in experimental data (Appendix F). Of the 11 microRNAs that were predicted to regulate the 765 genes with differentially expressed isoforms that were also available in the microRNA array dataset, we found seven to be implicated in regulatory pathways in GBM (Table 6.8): 151
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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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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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A.3 Quantitative RT-PCR Appendix We
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A.3 Quantitative RT-PCR Appendix Ta
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A.3 Quantitative RT-PCR Appendix We
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A.4 Tag-seq vs qRT-PCR Correlation
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Appendix "BEX5", "DIAPH2", "GBP3",
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End Select End If End Sub Appendix
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If NameStr.ToLower().Equals("query"
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Appendix C Long ncRNAs We detected
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C. Long ncRNAs Appendix Tag Accessi
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D.1 Pathway Interactions Appendix F
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D.2 Pathway Images Appendix Figure
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D.2 Pathway Images Appendix Figure
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E. Exon Array Data Appendix Average
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F. MicroRNA Array Data Appendix Tab
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F. MicroRNA Array Data Appendix GNS
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F. MicroRNA Array Data Appendix GNS
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Ln Natural logarithm LOH Loss of He
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3.3 Schematisation of cell cycle ph
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8.3 Workflows at the core of the pr
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7.1 Selected Gene Ontology terms an
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Bibliography [1] Cell signaling tec
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[38] G. Bain, D. Kitchens, M. Yao,
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[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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