Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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8.3 Databases Results Figure 8.2: Internal organisation of the target prediction database of GenemiR. From top to bottom, the two conceptual workflows are consecutively displayed. Each of eight algorithms (circles) is represented by a different acronym: Diana-microT=DN-T; miRanda=MR; miRBase=MC;ElMMo=EM; PITA=PA; Pic- Tar=PT5/6; TargetscanS=TSS. MicroRNAs (diamonds) and target genes (squares) are assigned random labels for illustrative purposes. 3. Gene symbols as determined by the HUGO Gene Nomenclature Commit- tee for [203] and the MGI Mouse Genome Informatics database resource for mouse [333]. All expression datasets that the user wants to upload in the software are re- ferred to as "external databases" because they are not part of the internal databases, they are user-defined and variable in size. Gene annotation data are derived from the Ensembl database [147], and a utility script is provided with the program to allow automatic updating of files, via the Ensembl Perl API, to reflect the latest annotation release of the human and mouse genomes. The internal databases of human and mouse microRNAs and gene identifiers for all annotated human and mouse genes are linked in a meaningful way by the algorithms that predict which of the microRNAs regulate which of the gens and, vice versa, which of the genes are regulated by which microRNAs. Thus, these linked databases consists of the union of prediction results gen- erated from the eight most widely used algorithms (considering PicTar 5-way and PicTar 6-way as separate algorithms) (Table 8.1), with the possibility of increasing this number as new algorithms are produced with new sets of pre- dictions, thanks to the modular internal structure with which GenemiR was designed: 1. PicTar [127,247] identifies putative microRNA targets in vertebrates, C. elegans and Drosophila, using the principle of cross-conservation. The two versions, PicTar 5-way and PicTar 6-way, refer to the number of species in the comparison: 5-way includes human, chimp mouse, rat and dog genomes, and 6-way adds the chicken genome. PicTar employs 206
8.3 Databases Results Figure 8.3: Workflows at the core of the primitive functions of the GenemiR software. The first workflow answers the question: "which microRNAs target this list of genes?" and returns a list of microRNAs filtered according to the Minimum or Cumulative filter or both. The second workflow answers the question: "which genes are targeted by this list of microRNAs?" and returns a list of genes filtered according to the settings of the Minimum or Cumulative filter or both. pair-wise alignments to filter seed sequences conserved in 7 species of Drosophila and 8 species of vertebrates. In addition to sequence conservation, PicTar considers secondary evidence, such as co-expression and clustering of microRNAs, as well as target identification based on onto- logical parameters, such as expression in the common cell types or devel- 207
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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.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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