Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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8.2 Workflows Results and optimise the analysis to make it as stringent or as loose as necessary. The target predictions in GenemiR’s database can be queried through a variety of selectable filters, allowing the user to include or exclude any combination of algorithms, group multiple microRNAs into the same query, and retrieve variable subsets of targeted genes (Fig 8.1). Finally, GenemiR allows the matching of prediction data to any gene expression dataset of interest, and provides a fa- cility for loading external datasets determined by the user. Graphical plotting functions display target expression levels relative to the conditions of the ex- periment, such as tissue-specific transcriptional profiling or time course series. The software itself does not contain internal experimental data, but simply aggregates the output of prediction algorithms in a manner suitable to explo- ration and hypothesis testing, reducing the complexity of this approach by integrating microRNA and gene annotations, genome-wide expression data, and regulatory target predictions in a common analysis framework (Fig 8.1). 8.2 Workflows The GenemiR software allows for the discovery of systemic or tissue-specific patterns that may be hidden in the microRNA targeting prediction data from eight leading algorithms. In order to do this, two flows of information are estab- lished that address reciprocal biological questions: "which genes are targeted by one or more specific microRNAs?" and "which microRNAs are predicted to target a given set of genes?". These issues are intimately related within the context of the same biological system, but are organised as two opposite flows of information in terms of program operation and layout (Fig 8.2). De- pending on the type of query executed against the internal databases, a list of genes predicted to be targeted by a number of microRNAs according to a customer-defined selection of prediction algorithms, is generated (Workflow 1, figure 8.3). In the opposite workflow, a list of microRNAs is generated start- ing from a list of gene symbols or Genbank, RefSeq, EMBL, ENSG or ENST identifiers, according to the customer-defined selection of prediction algorithms (Workflow 2, figure 8.3). Conversion from the identifiers contained originally in the prediction files is constantly active in both workflows to always translate the queries to a human intelligible list of gene symbols. 204
8.3 Databases Results Figure 8.1: Overview of GenemiR. (a) Queries based on various data types (the input section: microRNAs, expression data, genes, and identifiers) are executed against internal or external databases to generate other data types (the output section: genes, microRNAs, graphs and converted identifiers). The shaded boxes distinguish the core primitive functions from the auxiliary ones. (b) Relationships between databases used by GenemiR and the functions that operate on them. Solid lines depict databases of fixed size over the lifetime of the program’s execution, whereas the dashed lines indicate a dataset of variable dimensions according to the files that are inputted by the user. 8.3 Databases All microRNAs and genes that could constitute a query are stored in the form of three internal databases: 1. Identifiers for all known human and mouse microRNAs; 2. Genbank, RefSeq [409], EMBL [252], ENSG, ENST [147] identifiers for all annotated human and mouse genes; 205
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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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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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