L'émergence des biotechnologies, telles que les puces ADN, a permis l'acquisition d'énormes quantités de données d'une cellule à un instant donné et sous certaines conditions. Elles sont devenues incontournables lorsqu'il s'agit de comprendre une maladie qui proviendrait d'une anomalie génomique perturbant le développement naturel entre la croissance, la division et la mort des cellules. En utilisant cette biotechnologie, l'objectif est d'identifier les gènes impliqués dans la maladie étudiée. Mais chaque puce donne l'information de plus de 19 000 gènes rendant difficile toute exploitation et analyse des résultats. La fouille de données a longtemps été étudiée pour mettre en évidence des corrélations non triviales à partir de grande base de données. Initialement proposées pour répondre aux interrogations des décideurs lorsqu'il s'agissait de mieux connaître le comportement des clients d'un supermarché, ces méthodes connaissent aujourd'hui un tel succès qu'elles ont été utilisées et adaptées dans divers domaines d'applications allant du marketing jusqu'à la santé. L'étude que nous proposons de mener est de proposer de nouvelles méthodes de fouille de données pour aider les biologistes à déduire de nouvelles connaissances à partir des données obtenues par l'analyse des puces ADN. Plus précisément, nous proposons de mettre en évidence des gènes fréquemment ordonnés selon leurs expressions et nous étudions l'apport de ce type d'information comme nouveau matériel d'étude pour les biologistes. / The emergence of biotechnology, such as DNA chips, has acquired huge amounts of data in a cell at a given moment and under certain conditions. They are used in order to understand a disease whose origin is a genomic abnormality disrupting the natural development between growth, division and cell death. Using this biotechnology, the aim is to identify the genes involved in disease studied. But each chip gives information on more than 19,000 genes then it is difficult to use and to analyse the results. Methods of Data mining are used in order to find interesting correlations from large database. Initially proposed to address questions about the behavior of customers of a supermarket, these methods are now used and adapted in various fields of applications ranging marketing to health. In this study, we propose new methods in order to help biologists to deduce new knowledge from data obtained by DNA microarray analysis. Specifically, we propose to identify genes frequently ordered by their expressions and we study the contribution of such information as the new study material for biologists.

二代cDNA测序技术，又名“RNA-Seq“，为转录组(transcriptome)的研究提供了新的手段。作为革命性的技术方法，RNA-Seq 不仅可以帮助准确测量转录体(transcript)的表达水平，更可以发现新的转录体和揭示转录调控的机理。同时，整合多个不同水平的测序数据，例如基因组(genome)测序，甲基化组(methylome)测序等，可以为深入挖掘生物学意义提供一个强有力的的工具。 / 我的博士研究主要集中在二代测序(next-generation sequencing，NGS)，特别是RNA-Seq数据的分析。它主要包含三部分：分析工具开发，数据分析和机理研究。 / 大量测序数据的分析对于二代测序技术来说是一个重大的挑战。目前，相对于剪接比对工具(splice-aware aligner)，普通比对工具可以极速(ultrafast)的将数以千万记的短序列(Reads)比对到基因组，但是他们很难处理那些跨过剪接位点(splice junction)的短序列(spliced reads)或者匹配多个基因组位置的短序列(multireads)。我们开发了一个利用two-seed策略的全新的序列比对工具-ABMapper。基准测试(Benchmark test) 结果显示ABMapper比其他的同类工具：TopHat和SpliceMap有更高的accuracy和recall。另一方面，spliced reads和multireads在基因组上会有多个匹配的位置，选择最可能的位置也成为一个大问题。在计算基因表达值时，multireads和spliced reads常会被随机的选定其中之一，或者直接被排除。这种处理方式会引入偏差而直接影响下游(downstream)分析的准确性。为了解决multireads和spliced reads位置选择问题，我们提出了一个利用内含子(intron)长度的Geometric-tail (GT) 经验分布的最大似然估计 (maximum likelihood estimation) 的方法。这个概率模型可以适用于剪接位点位于短序列上或者位于成对短序列(Pair-ended, PE) 之间的情况。基于这个模型，我们可以更好的确定那些在基因组上存在多个匹配的成对短序列（pair-ended, PE reads）的最可能位置。 / 测序数据的积累为深入研究生物学意义提供了丰富的资源。利用RNA-Seq数据和甲基化测序数据，我们建立了一个基于DNA甲基化模式 (pattern) 的基因表达水平的预测模型。根据这个模型，我们发现DNA甲基化可以相当准确的预测基因表达水平，准确率达到78%。我们还发现基因主体上的DNA甲基化比启动子 (promoter) 附近的更重要。最后我们还从整合所有甲基化模式和CpG模式的组合数据集中，利用特征筛选(feature selection)选择了一个最优化子集。我们基于最优子集建立了特征重叠作用网络，进一步揭示了DNA甲基化模式对于基因表达的协作调控机理。 / 除了开发RNA-Seq数据分析的工具和数据挖掘，我们还分析斑马鱼(zebrafish)的转录组(transcriptome)。RNA-Seq数据分析结合荧光成像，定量PCR等生物学实验，揭示了Calycosin处理之后的相关作用通路(pathway)和差异表达基因，分析结果还证明了Calycosin在体内的血管生成活性。 / 综上所述，本论文将会详细阐述我在二代测序数据分析，基于数据挖掘的生物学意义的发现和转录组分析方面的工作。 / The recent development of next generation RNA-sequencing, termed ‘RNA-Seq’, has offered an opportunity to explore the RNA transcripts from the whole transcriptome. As a revolutionary method, RNA-Seq not only could precisely measure the abundances of transcripts, but discover the novel transcribed contents and uncover the unknown regulatory mechanisms. Meanwhile, the combination of different levels of next-generation sequencing, such as genome sequencing and methylome sequencing has provided a powerful tool for novel discovery in the biological context. / My PhD study focuses on the analysis of next-generation sequencing data, especially on RNA-Seq data. It mainly includes three parts: pipeline development analysis, data analysis and mechanistic study. / As the next-generation sequencing (NGS) technology, the analysis of massive NGS data is a great challenge. Many existing general aligners (as contrast to splicing-aware alignment tools) are capable of mapping millions of sequencing reads onto a reference genome. However, they are neither designed for reads that span across splice junctions (spliced reads) nor for reads that could match multiple locations along the reference genome (multireads). Hence, we have developed an ab initio mapping method - ABMapper, using two-seed strategy. The benchmark results show that ABMapper can get higher accuracy and recall compared with the same kind of tools: TopHat and SpliceMap. On the other hand, the selection of the most probable location for spliced reads and multireads becomes a big problem. These reads are randomly assigned to one of the possible locations or discarded completely when calculating the expression level, which would bias the downstream analysis, such as the differentiated expression analysis and alternative splicing analysis. To rationally determine the location of spliced reads and multireads, we have proposed a maximum likelihood estimation method based on a geometric-tail (GT) distribution of intron length. This probabilistic model deals with splice junctions between reads, or those encompassed in one or both of a pair-ended (PE) reads. Based on this model, multiple alignments of reads within a PE pair can be properly resolved. / The accumulation of NGS data has provided rich resources for deep discovery of biological significance. We have integrated RNA-Seq data and methylation sequencing data to build a predictive model for the regulation of gene expression based on DNA methylation patterns. We found that DNA methylation could predict gene expression fairly accurately and the accuracy can reach up to 78%. We have also found DNA methylation at gene body is the most important region in these models, even more useful than promoter. Finally, feature overlap network based on an optimum subset of combination of all methylation patterns and CpG patterns has indicated the collaborative regulation of gene expression by DNA methylation patterns. / Not only new algorithms were developed to facilitate the RNA-Seq data analysis, but the transcriptome analysis was performed on zebrafish. The analysis of differentially-expressed genes and pathways involved after calycosin treatment, combined with other experimental evidence such as fluorescence microscopy and quantitative real-time polymerase chain reaction (qPCR), has well demonstrated the proangiogenic effects of calycosin in vivo. / In summary, this thesis detailed my work on NGS data analysis, discovery of biological significance using data-mining algorithms and transcriptome analysis. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Lou, Shaoke. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 135-146). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / 摘要 --- p.iii / Acknowledgement --- p.v / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Bioinformatics --- p.1 / Chapter 1.2 --- Bioinformatics application --- p.1 / Chapter 1.3 --- Motivation --- p.2 / Chapter 1.4 --- Objectives --- p.3 / Chapter 1.5 --- Thesis outline --- p.3 / Chapter Chapter 2 --- Background --- p.4 / Chapter 2.1 --- Biological and biotechnology background --- p.4 / Chapter 2.1.1 --- Central dogma and biology ABC --- p.4 / Chapter 2.1.2 --- Transcription --- p.5 / Chapter 2.1.3 --- Splicing and Alternative Splicing --- p.6 / Chapter 2.1.4 --- Next-generation Sequencing --- p.10 / Chapter 2.1.5 --- RNA-Seq --- p.18 / Chapter 2.2 --- Computational background --- p.20 / Chapter 2.2.1 --- Approximate string matching and read mapping --- p.21 / Chapter 2.2.2 --- Read mapping algorithms and tools --- p.22 / Chapter 2.2.3 --- Spliced alignment tools --- p.27 / Chapter Chapter 3 --- ABMapper: a two-seed based spliced alignment tool --- p.29 / Chapter 3.1 --- Introduction --- p.29 / Chapter 3.2 --- State-of-the-art --- p.30 / Chapter 3.3 --- Problem formulation --- p.31 / Chapter 3.4 --- Methods --- p.33 / Chapter 3.5 --- Results --- p.35 / Chapter 3.5.1 --- Benchmark test --- p.35 / Chapter 3.5.2 --- Complexity analysis --- p.39 / Chapter 3.5.3 --- Comparison with other tools --- p.39 / Chapter 3.6 --- Discussion and conclusion --- p.41 / Chapter Chapter 4 --- Geometric-tail (GT) model for rational selection of RNA-Seq read location --- p.42 / Chapter 4.1 --- Introduction --- p.42 / Chapter 4.2 --- State-of-the-art --- p.44 / Chapter 4.3 --- Problem formulation --- p.44 / Chapter 4.4 --- Algorithms --- p.45 / Chapter 4.5 --- Results --- p.49 / Chapter 4.5.1 --- Workflow of GT MLE method --- p.49 / Chapter 4.5.2 --- GT distribution and insert-size distribution --- p.50 / Chapter 4.5.3 --- Multiread analysis --- p.51 / Chapter 4.5.4 --- Splice-site comparison --- p.52 / Chapter 4.6 --- Discussion and conclusion --- p.55 / Chapter Chapter 5 --- Explore relationship between methylation patterns and gene expression --- p.56 / Chapter 5.1 --- Introduction --- p.56 / Chapter 5.2 --- State-of-the-art --- p.58 / Chapter 5.3 --- Problem formulation --- p.62 / Chapter 5.4 --- Methods --- p.62 / Chapter 5.4.1 --- NGS sequencing and analysis --- p.62 / Chapter 5.4.2 --- Data preparation and transformation --- p.64 / Chapter 5.4.3 --- Random forest (RF) classification and regression --- p.65 / Chapter 5.5 --- Results --- p.68 / Chapter 5.5.1 --- Genome wide profiling of methylation --- p.68 / Chapter 5.5.2. --- Aggregation plot of methylation levels at different regions --- p.72 / Chapter 5.5.3. --- Scatterplot between methylation and gene expression --- p.75 / Chapter 5.5.4 --- Predictive model of gene expression using DNA methylation features --- p.76 / Chapter 5.5.5 --- Comb-model based on the full dataset --- p.87 / Chapter 5.6 --- Discussion and conclusion --- p.98 / Chapter Chapter 6 --- RNA-Seq data analysis and applications --- p.99 / Chapter 6.1 --- Transcriptional Profiling of Angiogenesis Activities of Calycosin in Zebrafish --- p.99 / Chapter 6.1.1 --- Introduction --- p.99 / Chapter 6.1.2 --- Background --- p.100 / Chapter 6.1.3 --- Materials and methods and ethics statement --- p.101 / Chapter 6.1.4 --- Results --- p.104 / Chapter 6.1.5 --- Conclusion --- p.108 / Chapter 6.2 --- An integrated web medicinal materials DNA database: MMDBD (Medicinal Materials DNA Barcode Database). --- p.110 / Chapter 6.2.1 --- Introduction --- p.110 / Chapter 6.2.2 --- Background --- p.110 / Chapter 6.2.3 --- Construction and content --- p.113 / Chapter 6.2.4 --- Utility and discussion --- p.116 / Chapter 6.2.5 --- Conclusion and future development --- p.119 / Chapter Chapter 7 --- Conclusion --- p.121 / Chapter 7.1 --- Conclusion --- p.121 / Chapter 7.2 --- Future work --- p.123 / Appendix --- p.124 / Chapter A1. --- Descriptive analysis of trio data --- p.124 / Chapter A2. --- Whole genome methylation level profiling --- p.125 / Chapter A3. --- Global sliding window correlation between individuals --- p.128 / Chapter A4. --- Features selected after second-run filtering --- p.133 / Bibliography --- p.135 / Chapter A. --- Publications --- p.135 / Reference --- p.135

DNA arrays, capable of detecting specific DNA sequences from a sample have become widely used. They rely on DNA heterogeneous hybridization, which is the binding between a single strand of DNA immobilized on a surface (probe) and its complementary strand present in the bulk (target). In order to improve the hybridization time in DNA arrays, it is crucial to understand the kinetics of DNA hybridization. The study of the Damkohler number that compares the DNA supply by diffusion to the DNA consumption by reaction (hybridization) shows that in many cases we can expect DNA hybridization to be a diffusion limited process. This is verified by a finite element study, where a whole microfluidic chamber (bulk and reacting surface) is simulated. In these cases, the formation of a depletion zone above the sensing zone is observed. The reaction rate is much lower than in the ideal case where the reaction would be reaction rate limited. A better DNA transport could be a solution to overcome the diffusion barrier. Therefore, the influence of convection on DNA hybridization was studied. Finite element simulation shows that even a small DNA velocity (10 ƒ�m/s) can greatly enhance the overall reaction rate and help preventing the formation of a depletion zone. These observations are valid when one kind of probe reacts with one kind of target. In reality, non specific hybridization can happen between a probe and a non complementary target. We show that in some cases, non specific hybridization can slow down the kinetics and reduce the fraction of specifically hybridized probes at equilibrium. The fraction of non specific hybrids can reach a maximum before decreasing and reaching equilibrium, suggesting that a longer hybridization time would lead to a better specificity. The addition of convective transport does not affect the equilibrium, but allows to reach it faster and with a better ratio between specific and non specific hybrids during the process. Therefore, convective transport of DNA appears to be beneficial. Another possibility is to act on the DNA itself to focus it near the sensing zone. Our study of the different electrokinetic forces leads us to derive the expression of the dielectrophoretic force in a field resulting from the combination of a DC field and an AC field. This could be a novel way to act on polarizable particles like DNA.

Toda medida, desde que feita por um instrumento real, tem uma imprecisão associada. Neste trabalho, abordamos a questão das imprecisões em experimentos de microarranjos de cDNA de dois canais, uma tecnologia que tem sido muito explorada nos últimos anos e que ainda é um importante auxiliar nos estudos de expressões gênicas. Dezenas de milhares de representantes de genes são impressos em uma lâmina de vidro e hibridizados simultaneamente com RNA mensageiro de duas amostras diferentes de células. Essas amostras são marcadas com corantes fluorescentes diferentes e a lâmina, após a hibridização, é digitalizada, obtendo-se duas imagens. As imagens são analisadas com programas especiais que segmentam os locais que estavam os genes e extraem estatísticas dos píxeis de cada local. Por exemplo, a média, a mediana e a variância das intensidades do conjunto de píxeis de cada local (o mesmo é feito normalmente para uma área em volta de cada local, chamada de fundo). Estimadores estatísticos como o da variância nos dão uma estimativa de quão precisa é uma certa medida. Uma vez de posse das estimativas das intensidades de cada local, para se obter a efetiva expressão de um gene, algumas transformações são feitas nos dados de forma a eliminar variabilidades sistemáticas. Neste trabalho, mostramos como podem ser feitas as análises a partir de uma medida de expressão gênica com um erro estimado. Mostramos como estimar essa imprecisão e estudamos, em termos de propagação da imprecisão, os efeitos de algumas transformações realizadas nos dados, por exemplo, a remoção do viés estimado pelo método de regressão local robusta, mais conhecido como \\textit{lowess}. Uma vez obtidas as estimativas das imprecisões propagadas, mostramos também como utilizá-las na determinação dos genes diferencialmente expressos entre as amostras estudadas. Por fim, comparamos os resultados com os obtidos por formas clássicas de análise, em que são desconsideradas as imprecisões das medidas. Concluímos que a modelagem das imprecisões das medidas pode favorecer as análises, já que os resultados obtidos em uma aplicação com dados reais de expressões gênicas foram condizentes com os que encontramos na literatura. / Any measurement, since it is made for a real instrument, has an uncertainty associated with it. In the present paper, we address this issue of uncertainty in two-channel cDNA Microarray experiments, a technology that has been widely used in recent years and is still an important tool for gene expression studies. Tens of thousands of gene representatives are printed onto a glass slide and hybridized simultaneously with mRNA from two different cell samples. Different fluorescent dyes are used for labeling both samples. After hybridization, the glass slide is scanned yielding two images. Image processing and analysis programs are used for spot segmentation and pixel statistics computation, for instance, the mean, median and variance of pixel intensities for each spot. The same statistics are computed for the pixel intensities in the background region. Statistical estimators such as the variance gives us an estimate of the accuracy of a measurement. Based on the intensity estimates for each spot, some data transformations are applied in order to eliminate systematic variability so we can obtain the effective gene expression. This paper shows how to analyze gene expression measurements with an estimated error. We presented an estimate of this uncertainty and we studied, in terms of error propagation, the effects of some data transformations. An example of data transformation is the correction of the bias estimated by a robust local regression method, also known as \\textit{lowess}. With the propagated errors obtained, we also showed how to use them for detecting differentially expressed genes between different conditions. Finally, we compared the results with those obtained by classical analysis methods, in which the measurement errors are disregarded. We conclude that modeling the measurements uncertainties can improve the analysis, since the results obtained in a real gene expressions data base were consistent with the literature.

by Shih Sheung Mei. / Thesis submitted in: July 2003. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 206-215). / Abstracts in English and Chinese. / Abstract --- p.ii / Achnoledgements --- p.vi / Abbreviations --- p.viii / List of contents --- p.viv / List of tables --- p.xiii / List of figures --- p.xv / Chapter Chapter One --- Literature Review / Chapter 1.1 --- Introducation of Lentinula edodes --- p.1 / Chapter 1.1.1 --- Life cycle of Basidiomycete --- p.1 / Chapter 1.1.2 --- Differentially Expressed Genes in stages of Lentinula edodes --- p.3 / Chapter 1.2 --- Relationship of Monokaryons and Dikaryons in Basidiomycetes --- p.4 / Chapter 1.2.1 --- Mating Type Gene in Filamentous Fungi --- p.4 / Chapter 1.2.3 --- Dikaryon Formation and Homeodomain Proteins --- p.6 / Chapter 1.2.4 --- Clamp Connection formation in Dikaryon --- p.9 / Chapter 1.3 --- Stuctural Protein of Mushroom --- p.11 / Chapter 1.3.1 --- Hydrophobin --- p.11 / Chapter 1.3.1.1 --- General Introduction --- p.11 / Chapter 1.3.1.2 --- Structure of hydrophobin --- p.11 / Chapter 1.3.1.3 --- Formation of Disulphide bonds and Glycosylation --- p.12 / Chapter 1.3.1.4 --- Functions of Hydrophobins --- p.13 / Chapter 1.4 --- Genomics of filamentous fungi --- p.15 / Chapter 1.5 --- Genetic analysis of filamentous fungi --- p.18 / Chapter 1.6 --- Objectives of the Project --- p.20 / Chapter Chapter Two --- Identification of Differentially Expressed Genes in Dikaryons of Lentinula edodes by Microarray of Primordium Expressed Sequence Tags / Chapter 2.1 --- Introduction --- p.23 / Chapter 2.2 --- Materials and Methods --- p.27 / Chapter 2.2.1 --- Construction of EST database --- p.27 / Chapter 2.2.2 --- Construction of EST Microarray cDNA gene-chip --- p.27 / Chapter 2.2.2.1 --- Amplification of the primordium EST clones --- p.27 / Chapter 2.2.2.2 --- Purification of the amplified EST clones --- p.28 / Chapter 2.2.2.3 --- Spotting of the amplified EST clones onto chips --- p.29 / Chapter 2.2.3 --- Screening of the Differentially Expressed Genes in Dikaryons by Primordium Microarray --- p.31 / Chapter 2.2.3.1 --- Mycelium Cultivation and Preparation of Total RNA --- p.31 / Chapter 2.2.3.2 --- cDNA synthesis and labeling --- p.32 / Chapter 2.2.3.3 --- cDNA purification --- p.33 / Chapter 2.2.3.4 --- Probe Storage Conditions --- p.34 / Chapter 2.2.3.5 --- cDNA analysis --- p.35 / Chapter 2.2.3.6 --- Microarray hybridization --- p.37 / Chapter 2.2.3.7 --- Stringency washes --- p.39 / Chapter 2.2.3.8 --- Detection with TSA --- p.39 / Chapter 2.2.3.9 --- Microarray scanning and data anlysis --- p.41 / Chapter 2.3 --- Results --- p.45 / Chapter 2.3.1 --- Amplification of primordium ESTs --- p.45 / Chapter 2.3.2 --- Purification of PCR products --- p.45 / Chapter 2.3.3 --- Data Analysis of Microarray Data --- p.47 / Chapter 2.3.3.1 --- Generation of Primordium EST Microarray Image for analysis --- p.47 / Chapter 2.3.3.2 --- Normalization of the Data --- p.49 / Chapter 2.3.3.3. --- Transciption Profile of Dikaryon compared with Monokaryon --- p.79 / Chapter 2.3.3.4. --- Differentially Expression of Dikaryon L54 --- p.80 / Chapter 2.4 --- Discussion --- p.85 / Chapter Chapter Three --- Enrichment of Genes with Differentially Expression in Dikaryons by Construction of Full-length Subtractive Library / Chapter 3.1 --- Introduction of Subtraction Cloning --- p.93 / Chapter 3.2 --- Materials and Methods --- p.97 / Chapter 3.2.1 --- Construction of Full-length Dikaryotic Subtractive library --- p.97 / Chapter 3.2.1.1 --- Isolation of PolyA+ mRNA of Dikaryon for Subtraction --- p.97 / Chapter 3.2.1.2 --- Enrichment of Differentially Expressed Genes in Dikaryon L54 by Subtraction with Monokaryons A and B --- p.99 / Chapter 3.2.1.3 --- First-Strand cDNA Synthesis --- p.102 / Chapter 3.2.1.4 --- cDNA Amplification by Long-Distance PCR --- p.102 / Chapter 3.2.1.5 --- Proteinase K Digestion --- p.103 / Chapter 3.2.1.6 --- Sfi Digestion --- p.104 / Chapter 3.2.1.7 --- cDNA size fractionation by CHROMA SPIN-400 --- p.104 / Chapter 3.2.1.8 --- Determination of the Ligation Efficiency --- p.106 / Chapter 3.2.1.9 --- Ligation of cDNA to lamda TriplEx2 Vector --- p.107 / Chapter 3.2.1.10 --- Lamda-phage Packaging Reaction --- p.107 / Chapter 3.2.1.11 --- Titering the Unamplifled Library and Determining the Percentage of Recombinant Clones --- p.108 / Chapter 3.2.1.12 --- Library Amplification --- p.109 / Chapter 3.2.1.13 --- Conversion of λTriplEx2 Recombinant Clones to pTriplEx2 Recombinant Plasmids --- p.111 / Chapter 3.2.2 --- Screening of the Subtractive library --- p.114 / Chapter 3.2.2.1 --- Verification of the enrichment by Plaque Lifting hybridization --- p.114 / Chapter 3.2.2.1.1 --- Lifting the Plaques --- p.114 / Chapter 3.2.2.1.2 --- Synthesis of the Probes for Plaque Lift Hybridization --- p.115 / Chapter 3.2.2.1.3 --- Hybridization to the Membranes --- p.116 / Chapter 3.2.2.2 --- Screening the Subtractive library by Macroarray Hybridization --- p.117 / Chapter 3.2.2.2.1 --- Colony Picking by QPik System --- p.117 / Chapter 3.2.2.2.2 --- Gridding of Macroarray --- p.118 / Chapter 3.2.2.2.3 --- Filter Processing of Gridded Membrane --- p.119 / Chapter 3.2.2.2.4 --- Hybridization to the Macroarray Membrane --- p.120 / Chapter 3.3 --- Results and Discussion --- p.121 / Chapter 3.3.1 --- Enrichment of Differentially Expressed Genes in Dikaryon L54 by Subtraction with Monokaryons A and B --- p.121 / Chapter 3.3.2 --- Construction of the full-length subtractive library --- p.123 / Chapter 3.3.3 --- Conversion of A TriplEx2 Recombinant Clones to pTriplEx2 Recombinant Plamid --- p.124 / Chapter 3.3.4 --- Verification the Enrichment of Subtractive library by Plaque lifting Hybridization --- p.125 / Chapter 3.3.5 --- Screening of the Subtractive library by Macroarray --- p.125 / Chapter 3.4 --- Discussion --- p.126 / Chapter Chapter Four --- Identification of Genes with Differentially Expression in Dikaryons by Subtactive cDNA Library Microarray / Chapter 4.1 --- Introduction --- p.135 / Chapter 4.2 --- Materials and Methods / Chapter 4.2.1 --- Selection and Amplification of clonesin SubtractionLlibrary for Microarray screening --- p.140 / Chapter 4.2.2 --- PCR product Purification --- p.141 / Chapter 4.2.3 --- Generation of Subtractive Dikaryotic Library Microarray Chip --- p.142 / Chapter 4.2.4 --- Screening the Differentially Expressed Genesin Dikaryon L54 by the Subtraction Dikaryotic Library cDNA Microarray Analysis --- p.143 / Chapter 4.2.4.1 --- Preparation of Total RNA --- p.143 / Chapter 4.2.4.2 --- Synthesis and fluorescent labeling of total cDNA --- p.145 / Chapter 4.2.4.3 --- Purification of labeled cDNA --- p.146 / Chapter 4.2.4.4 --- Storage Condition of Probe --- p.147 / Chapter 4.2.4.5 --- Analysis of labeled total cDNA --- p.148 / Chapter 4.2.4.6 --- Microarray hybridization --- p.150 / Chapter 4.2.4.7 --- Stringency washes --- p.152 / Chapter 4.2.4.8 --- Detection with TSA --- p.153 / Chapter 4.2.4.9 --- Image generation and data analysis --- p.155 / Chapter 4.2.5 --- Sequence analysis of clones showing differentially expressed in dikaryons in microarray screening --- p.157 / Chapter 4.2.5.1 --- Single-pass partial sequencing of 3´ة-end of subtractive cDNA clones --- p.157 / Chapter 4.2.5.2 --- Compiling dikaryotic EST database --- p.158 / Chapter 4.2.6 --- Comparison microarray analysis with SAGE analysis of the differentially expressed genes --- p.159 / Chapter 4.3 --- Results --- p.161 / Chapter 4.3.1 --- Preparation of clones for microarray hybridization --- p.161 / Chapter 4.3.2 --- Screening the differentially expressed genesin dikaryon L54 by the subtractive dikaryotic library cDNA microarray analysis --- p.162 / Chapter 4.3.2.1 --- Image capture and microarray data analysis --- p.162 / Chapter 4.3.2.2 --- Comparision of dikaryon L54 with monokaryons A and B --- p.163 / Chapter 4.3.2.3 --- Sequenced and comparison of the differentially expressed genes in dikaryon --- p.166 / Chapter 4.3.3 --- Comparison microarray analysis with SAGE analysis of the differentially expressed genes --- p.169 / Chapter Chapter Five --- Conclusion and Future Perpectives --- p.198 / References --- p.206

Consistency modeling for gene selection is a new topic emerging from recent cancer bioinformatics research. The result of classification or clustering on a training set was often found very different from the same operations on a testing set. Here, the issue is addressed as a consistency problem. In practice, the inconsistency of microarray datasets prevents many typical gene selection methods working properly for cancer diagnosis and prognosis. In an attempt to deal with this problem, a new concept of performance-based consistency is proposed in this thesis.An interesting finding in our previous experiments is that by using a proper set of informative genes, we significantly improved the consistency characteristic of microarray data. Therefore, how to select genes in terms of consistency modelling becomes an interesting topic. Many previously published gene selection methods perform well in the cancer diagnosis domain, but questions are raised because of the irreproducibility of experimental results. Motivated by this, two new gene selection methods based on the proposed performance-based consistency concept, GAGSc (Genetic Algorithm Gene Selection method in terms of consistency) and LOOLSc (Leave-one-out Least-Square bound method with consistency measurement) were developed in this study with the purpose of identifying a set of informative genes for achieving replicable results of microarray data analysis.The proposed consistency concept was investigated on eight benchmark microarray and proteomic datasets. The experimental results show that the different microarray datasets have different consistency characteristics, and that better consistency can lead to an unbiased and reproducible outcome with good disease prediction accuracy.As an implementation of the proposed performance-based consistency, GAGSc and LOOLSc are capable of providing a small set of informative genes. Comparing with those traditional gene selection methods without using consistency measurement, GAGSc and LOOLSc can provide more accurate classification results. More importantly, GAGSc and LOOLSc have demonstrated that gene selection, with the proposed consistency measurement, is able to enhance the reproducibility in microarray diagnosis experiments.

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 118-123).

Thesis (Ph. D.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

Thesis (Ph. D.)--Drexel University, 2007. / Includes abstract and vita. Includes bibliographical references (leaves 103-115).

<p>In this thesis, we have firstly cloned and expressed the domains that harbours the putative catalytic GC domain in these receptor molecules and demonstrate that these molecules can convert GTP to cGMP in vitro. Secondly, we show that exogenous application of both Phytosulfokine and Brassinosteroid increase changes of intracellular cGMP levels in Arabidopsis mesophyll protoplast demonstrating that these molecules have GC activity in vivo and therefore provide a link as second messenger between the hormones and down-stream responses. In order to elucidate a relationship between the kinase and GC domains of the PSK receptor, we have used the AtPSKR1 receptor as a model and show that it has Serine/Threonine kinase activity using the Ser/Thr peptide 1 as a substrate. In addition, we show that the receptor`s ability to phosphorylate a substrate is affected by the product (cGMP) of its co-domain (GC) and that the receptor autophosphorylates on serine residues and this step was also observed to be affected by cGMP. When Arabidopsis plants are treated with a cell permeable analogue of cGMP, we note that this can affect changes in the phosphoproteome in Arabidopsis and conclude therefore that the cGMP plays a role in kinase-dependent downstream signalling. The obtained results suggest that the receptor molecules investigated here belong to a novel class of GCs that contains both a cytosolic kinase and GC domains, and thus have a domain organisation that is not dissimilar to that of atrial natriuretic peptide receptors NPR1 and NPR2. The findings also strongly suggest that cGMP has a role as a second messenger in both Brassinosteroid and Phytosulfokine signalling. We speculate that other proteins with similar domain organisations may also have dual catalytic activities and that a significant number of GCs, both in plants and animals, remain to be discovered and characterised.</p>