Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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5.2 Array Comparative Genomic Hybridization Methods which transcript it was extracted. If the cut site was less than 17nt from the end of the transcript, we extended the sequence with adenine stretches, rep- resenting the poly-adenine (poly-A) tail. In these cases, additional upstream tags were also extracted until one tag fully contained in the Ensembl cDNA sequence was obtained. We disregarded Ensembl transcripts not belonging to a gene of biotype "protein_coding" or "processed_transcript". Ensembl annotates genes of several other biotypes, e.g. pseudogenes and small RNAs, but those annotations are not based on full-length transcript sequences, so we would not expect to find valid virtual tags in those transcripts. For the majority of this study, we used a conservative subset of the virtual tags from SAGE Genie and Ensembl comprising 25,593 unique tags assigned to 15,103 genes (Table 5.2). Specifically, we used SAGE Genie tags extracted from to the 3’-most cut site in RefSeq or MGC cDNAs having a poly-A tail or a polyadeny- lation signal, and Ensembl tags from transcripts of type "protein_coding" or "non_coding". Any virtual tags that mapped to multiple loci by these criteria were excluded. For certain analyses, we made use of more comprehensive virtual tag sets. In addition, we determined unique, perfect matches for tags to the genome using Bowtie as described above. We calculated a single expression value for each gene in each cell line by summing the counts of tags assigned to the gene. 5.2 Array Comparative Genomic Hybridization We re-analysed the array comparative genomic hybridization (CGH) data described by Pollard et al [404] CGH was performed with Human Genome CGH Microarray 4x44K arrays (Agilent), using genomic DNA from each cell line hybridised in duplicate (dye swap) and normal human female DNA as reference (Promega). Log2 ratios were computed from processed Cy3 and Cy5 intensities reported by the software CGH Analytics (Agilent). We corrected for effects related to GC content and restriction fragment size using a modi- fied version of the waves array CGH correction algorithm [271]. Log2 ratios were adjusted by sequential loess normalization on three factors: fragment GC content, fragment size, and probe GC content. These were selected after in- vestigating dependence of log ratio on multiple factors, including GC content in windows of up to 500 kilobases centred around each probe. The Biocon- ductor package CGHnormaliter [506] was then used to correct for intensity dependence and log2 ratios scaled to be comparable between arrays using the 98
5.2 Array Comparative Genomic Hybridization Methods Table 5.2: Classification of sequenced tags in each cell line. G144ED G144 G166 G179 CB541 CB660 Sequenced tags 6,383,175 7,133,520 13,415,402 11,610,415 12,103,066 10,043,561 Filtered tags 751,698 11.78% 912,971 12.80% 378,009 2.82% 347,537 2.99% 327,597 2.71% 382,819 3.81% Adapter 594,513 9.31% 765,484 10.73% 90,407 0.67% 45,318 0.39% 39,992 0.33% 248,799 2.48% Mitochondrial 156,534 2.45% 147,245 2.06% 285,881 2.13% 302,148 2.60% 287,493 2.38% 133,935 1.33% Ribosomal RNA 651 0.01% 242 0.00% 1,721 0.01% 71 0.00% 112 0.00% 85 0.00% Tags assigned to a single locus 2,812,750 44.07% 2,640,558 37.02% 6,271,471 46.75% 5,603,364 48.26% 5,984,114 49.44% 3,708,853 36.93% Reference tags, unique 1,420,009 22.25% 1,344,879 18.85% 2,970,554 22.14% 2,805,423 24.16% 2,894,539 23.92% 1,712,143 17.05% Reference tags, best 628,707 9.85% 577,418 8.09% 1,783,685 13.30% 1,267,594 10.92% 1,485,211 12.27% 982,190 9.78% cDNA tags, unique 146,442 2.29% 142,369 2.00% 295,764 2.20% 284,990 2.45% 261,411 2.16% 171,970 1.71% 99 cDNA tags, best 34,999 0.55% 34,472 0.48% 103,632 0.77% 105,787 0.91% 81,547 0.67% 49,374 0.49% Other SAGE Genie tags, unique 345,759 5.42% 322,034 4.51% 652,449 4.86% 684,718 5.90% 749,653 6.19% 455,045 4.53% Other SAGE Genie tags, best 72,658 1.14% 60,725 0.85% 158,601 1.18% 107,955 0.93% 148,854 1.23% 84,542 0.84% Tags not mapping to known transcrip- 164,176 2.57% 158,661 2.22% 306,786 2.29% 346,897 2.99% 362,899 3.00% 253,589 2.52% tome but uniquely to genome Ambiguously mapping tags 205,626 3.22% 185,115 2.60% 608,978 4.54% 418,267 3.60% 437,887 3.62% 388,085 3.86% Reference tags 165,895 2.60% 148,512 2.08% 516,134 3.85% 337,710 2.91% 366,115 3.02% 288,253 2.87% cDNA tags 5,996 0.09% 4,464 0.06% 10,566 0.08% 6,455 0.06% 6,708 0.06% 40,908 0.41% Other SAGE Genie tags 8,581 0.13% 6,717 0.09% 25,914 0.19% 14,668 0.13% 13,553 0.11% 12,508 0.12% Tags not mapping to known transcrip- 25,154 0.39% 25,422 0.36% 56,364 0.42% 59,434 0.51% 51,511 0.43% 46,416 0.46% tome but to multiple genomic locations Unclassified tags 2,613,101 40.94% 3,394,874 47.59% 6,156,944 45.89% 5,241,247 45.14% 5,353,468 44.23% 5,563,803 55.40%
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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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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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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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