Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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Chapter 8 MicroRNA Target Prediction Ensemble Software Contents 8.1 Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 8.2 Workflows . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 8.3 Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 8.4 Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 8.5 Target Prediction Ensemble Analysis . . . . . . . . . . . . 211 8.1 Principles Hundreds of microRNA sequences are now annotated throughout the human genome and are predicted to modulate the expression levels of thousands of mRNAs. Families of microRNA sequences can be identified through evolution- ary conservation of sequence patterns in related species, and reciprocally, genes targeted by microRNAs are under selection pressure to retain the recognition sites needed for the annealing of microRNA to mRNA duplexes. Numerous regulatory target prediction algorithms have been developed to exploit these properties, but they vary widely in terms of criteria, accuracy and prediction coverage. Most prediction methods search for complementary sequences between microRNAs and putative gene targets, while some consider physical and statistical hybridization properties, cross-conservation of regulatory RNAs between related species, etc. As a result, there is very little overlap between the microRNA:mRNA annealing predicted by different algorithms. It is of- ten the case that researchers have asked themselves "which target prediction algorithm will predict the highest number of genes in my list?". Needless to 202
8.1 Principles Results say, this is a flawed approach triggered by the absence of a unifying common theme in the prediction algorithms that would make them more accurate in their predictions. Rationally, if several algorithms can be developed that yield vastly different results, then we are missing an important variable in our equation. In this perspective, the existing prediction algorithms are not capable of simulating accurately what is happening in Nature time after time in the same reproducible way. Having said this, we were interested in finding out if a single prediction algorithm or a superset of several prediction algorithms was best at predicting microRNA to mRNA annealing. During this analysis we stumbled upon an observation that might be a hint towards the identity of the missing variable in the prediction algorithm equation. In order to determine which set of prediction algorithms were best at predicting microRNA:mRNA annealing, we needed experimentally validated data to help us screen the positives from the false positives, which contribute greatly to the pool of predictions. We used exon array data and microRNA array data that was available to us from our GNS cell lines (Appendix E and F) to validate our findings. Since a large number of prediction algorithms are com- monly used in research and they each predict a vast number of interactions, we built a tool called "GenemiR" to allow molecular biologists to generally access and manage microRNA predictions on a large scale and across different algorithms that would also help us address our research question. The GenemiR software exists as a command line binary executable for Unix-based systems or as a compiled local installation for Windows. Due to its length, the code of the binary executable could not be added to the appendix of this thesis, but it is available for anyone to view or download at www.ebi.ac. uk/~diva/GenemiR/genemir-code.txt. The executable for Windows can be downloaded at www.ebi.ac.uk/~diva/GenemiR/GenemiR.zip. In order to address the need for unified search and presentation of microRNA to mRNA target interactions predicted across multiple algorithms, GenemiR relates human and mouse microRNAs with their predicted target genes as reported by eight leading predictors. At the core of the program are two primitives that allow it to be extremely fast in extrapolating lists of gene targets given a microRNA and, performing the reciprocal analysis, extrapolating lists of microRNAs predicted to repress one or more genes of interest (Fig 8.1). Many other auxiliary functions and filters allow the expert user to parametrize 203
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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.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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