Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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8.3 Databases Results opmental stages. Importantly, it also takes into account the number of multiple seeds occurring within the same 3'UTR. The false-positive rate for this algorithm is estimated to be approximately 30% [48]. 2. TargetScanS is the second version of the mammalian microRNA target prediction algorithm developed by Lewis and colleagues following the publication of their original TargetScan approach [272,273]. The earlier program searched for a 7nt-long seed region starting from the second nucleotide of the 5' end miRNA sequence, calculated the free energy of hybridization using RNAFold [57], and computed a score based on the presence of multiple seeds capable of annealing to the same microRNA. The criteria used by TargetScanS are more specific, requiring a 6nt-long seed sequence preceded by an adenosine, positioned in a conserved region that, in turn, is surrounded by areas of lesser conservation. TargetScanS omits the use of free-energy calculations to predict hybridization affinity, and is estimated to have a false-positive rate of between 22 and 31% [48]. 3. DIANA-microT [237,316] version 3.0 recognises only single seed sequences having a central stem-loop secondary structure, instead of select- ing for near-perfect complementarity between the 5' seed region of the microRNA and the 3'UTR of the target message. In addition to search- ing for the classical annealing pattern in the 5' seed region, DIANA- microT also requires complementarity at the 3' end of the microRNA sequence [48]. 4. miRanda [134,213] identifies putative targets throughout the human and Drosophila genomes. It implements a dynamic programming approach to compute local alignments of complementary microRNA and mRNA sequences. The algorithm assigns scores to putative seed sequences using a position matrix, giving a higher weight to those closer to the 5' end of the microRNA sequence. However, it is not constrained by the re- quirement for exact seed matches. The program takes into consideration free-energy annealing calculations (via RNAfold), and seed-sequence conservation. To account for tandem annealing of microRNAs to multiple consensus sequences in the targeted transcript, miRanda assigns a high score to alignment matches to the same as well as different microRNAs. 5. ElMMo [155] uses a general Bayesian method that scores the conservation of microRNA binding sites according to an evolutionary model. 208
8.4 Filters Results Bayesian methods are based on prior probability of an observation and are updated as new data keeps entering. This model assumes a phyloge- netic relationship among several species. 6. miRBase [179] uses the miRanda algorithm to identify potential binding sites of a microRNA. Dynamic programming alignment is used to identify highly complementary sites that also require strict complementarity at the 5' seed region and thermodynamic stability, which is estimated for each target site. For inclusion in the database, conservation of the target site at the exact same position in at least two species is needed. 7. PITA [224] takes into consideration the strength of microRNA repression given target site accessibility and for each target site, an energy-based measure representing the difference between the free energy gained by the binding of the microRNA to the target, and the free energy lost by un-pairing the nucleotides within the target site, is calculated. The energy used to un-pair additional nucleotides flanking the target sites is also taken into account. Table 8.1: microRNA target prediction algorithms used by GenemiR with number of microRNA:3'UTR interactions predicted. The original target identifiers refer to the identifiers used by a prediction algorithm to identify the targeted genes. The final target identifiers refer to the identifiers that are returned by any query of any prediction algorithm database. Prediction algorithm Number microRNAs Number of targets Original target identifiers Final target identifiers PITA 678 22,974 RefSeq HGNC/MGI PicTar 5-way 178 9,334 RefSeq HGNC/MGI PicTar 6-way 130 3,585 RefSeq HGNC/MGI DIANA-microT 555 18,986 ENSG/ENSMUSG HGNC/MGI Elmmo 1206 31,303 RefSeq,EMBL HGNC/MGI Miranda 1100 32,641 EMBL HGNC/MGI Microcosm 694 34,507 ENST/ENSMUST HGNC/MGI TargetscanS 967 17,725 HGNC/MGI HGNC/MGI 8.4 Filters Critical to the successful outcome of user queries is the appropriate use of the built-in filtering functions. The purpose of the filter auxiliary functions (Fig 209
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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 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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