Biological Network Graph Structure Dynamic Analysis Model

In the last two decades, our understanding of the molecular mechanisms within the cell has witnessed a great leap forward. For the most part this is due to the fast innovation of the genomic measurements technologies and wide spread usage of computational methods which enables knowledge extraction from the massive datasets produced by these measurements. A notable example of a field that has substantially benefitted from this progress is cancer patient outcome prediction, in which the aim is to predict patient prognosis from common clinical variables such as tumor size, age or histological parameters. With the application of machine learning methods to gene expression profiles of the tumor a major improvement of the prediction accuracy could be realized. These models are later succeeded by Network based Outcome Predictors (NOP) that consider the cellular wiring diagram of cell in the model to identify stable and relevant markers that can accurately estimate outcome of patients. Problematically, after a decade of research in this area, NOPs did not find extensive application compared to the classical models due to contradicting reports regarding their performance, stability and relevance of markers in the literature. In this thesis, we introduce a new NOP - called FERAL - that alleviates several fundamental issues in state-of-the-art NOPs which prevented these models to reach the optimal prediction performance, stability and marker relevance. We furthermore demonstrate that generic biological networks do not contain sufficiently informative interactions to truly aid NOP. We therefore infer a phenotype-specific network called SyNet which connects pairs of genes that together achieve patient outcome prediction performance beyond what is attainable by individually genes. We show that a NOP that use identical gene expression datasets, yields superior performance merely by considering groups of genes suggested by SyNet. We, moreover, show that model performance is severely reduced if nodes in SyNet are shuffled, which confirms that also the links in SyNet are relevant to outcome prediction. An important limitation of current biological networks is that they are restricted to pairwise interactions. We show that higher order interactions between functional elements in the cell are relevant in outcome prediction. We later introduce a novel genomics method called Multi-Contact 4C (MC-4C) to measure and investigate multi-way interactions between functional elements. In contrast to existing methods, MC-4C exploits long-read 3rd generation sequencing technologies and detects higher order interactions that occur in a region of interest at the level of a single allele. We further devise a well-founded statistical model that is required for significance estimation of observed interactions. UsingMC-4C, we experimentally confirm a 26 years old hypothesis regarding the looping and co-localization of enhancers in the O -globin region in the mouse genome. Additionally, we provide the first experimental explanation for the “vermicelli” phenomenon that was observed through microscopic inspection of cells depleted of WAPL (the element responsible for unwinding of loops in mammalian cells). Therefore, targeted multi-way conformation analysis methods like MC-4C promise to uncover how the multitude of regulatory sequences and genes coordinate their activity in the spatial context of the genome.

Chromosome 21 is the smallest human chromosome and represents a model for physical mapping of the entire human genome. Three copies of this autosome cause Down syndrome, the most frequent genetic disorder associated with significant mental retardation. Five years ago, in the framework of the international human genome project, and as an integrated part of the chromosome 21 community, a consortium, of academic groups from Japan and Germany was formed to map and sequence this chromosome. Several whole-genome and chromosome-specific DNA libraries were constructed for mapping purposes. Using a combination of nested-deletion [1] and shotgun sequencing approaches, our center determined and analyzed over 17 million bases of high-quality data. In total, over 33.5 million basepairs of DNA, distributed over four contigs, were sequenced with very high accuracy [2]. The largest of these contigs is nearly 25.5Mb long. Only three small clone gaps remain, which together comprise approximately 100kb. Thus, we achieved a coverage of 99.7% of 21q. In addition, 211,116 bp from the short arm were also sequenced. Analysis of the chromosome revealed far fewer genes than previously estimated. Instead of 800-1000 expected genes, we found only 225 genes, of which 127 are known genes and 98 are predicted genes. In addition, 59 pseudogenes were identified. The completion of the 21q sequence provides a unique resource for understanding the molecular pathophysiology of Down syndrome, as well as all other monogenic and complex disorders that map to this chromosome, including Alzheimer’s disease, leukemia, autoimmune disease, epilepsy and manic-depressive psychosis. It also stands as a structural framework from which the complete molecular architecture of the chromosome can be determined. With the announcement of the first “working draft” of the human genome by the international human genome sequencing consortium this past June, there has been a great interest in the total number of genes in the human genome[3]. Some of the current estimates range from as low as 34,000 genes to as high as 140,000 genes. The low gene-density of chromosome 21 was quite unexpected. Most striking is a 7-Mb region near the region near the centromere that contains only one gene. This region is much larger than the whole genome of some species, such as Escherichia coli, yet evolutionary processes permitted the existence of such a gene-poor SNA segment. This finding leads us to propose that there are additional large gene-less regions in other human/mammalian chromosomes. By combining the gene numbers of chromosomes 21 and 22 [4] (770genes; 2-3% of human genome) and assuming that together they represent the average gene content of the human genome, we estimate that the total number of human genes may be close to 40,000. As part of the international human genome sequencing project, we are also sequencing parts of chromosomes 11 and 18. Our high-throughput sequence production line generates about 10 Mb per day. In order to handle such a high volume of output, we have developed a system for automated data assembly, annotation and release. All the data is released through our website and through the DNA Database of Japan immediately according to the policies established by the international consortium. In addition to chromosomes 11, 18 and 21, we are also interested in the structure of the entire human genome. In collaboration with the University of Tokyo, we have developed a database called HGREP [5] (Human Genome Reconstruction Project) that contains working draft and finished sequences which cover more than 85% of the human genomic sequences. Sequence entries are aligned along the chromosomes based on sequence similarities to STS markers, BAC-end and other entry sequences. Further, biological features, such as genes, gene functions, repeats and CpG islands, are fully annotated on these entries. To identify evolutionally conserved and biologically important information in the genome, we are taking the strategy of comparative genomic sequence analysis between human and other related genomes such as those of rodents and primates. We are currently sequencing several regions of the mouse, including some regions that are counterpart to human chromosome 21, and are preparing genomic resources for sequencing of the chimpanzee. Through comparative methods, we are also trying to understand telomeric regions, structures at the end of chromosomes that play an important role in several biological functions and have been associated with human diseases like an oncogenesis and a cellular aging. The structure and function of all genes and their regulatory regions can only be fully understood by looking at how they interact under different circumstances and in several different backgrounds. Determining which genes and elements are human-specific will lead to a deeper understanding of who we are and will advance medical sciences drastically.

Contributors

During my Ph.D., I used the next generation sequencing technology to investigate patterns of recombination and the genetic consequences of different reproductive modes of Daphnia magna. More precisely, I have used Restriction site Associated (RAD) sequencing to construct a high-density genetic map that can be coupled with the draft genome assembly of D. magna, thus, providing an essential tool for genome investigations in this widely used model organism (Chapter I). Such a map has enabled characterization of variation in the meiotic recombination rates across the genome of D. magna for the first time. Since recombination rates are an important parameter in almost any type of genetic research, this newly gained insight into the recombination landscape of D. magna offers a fundamental information for future studies of genome evolution, identification of genes underlying phenotypic traits and population genetic analyses. 
\nIn addition to sexual reproduction, D. magna can also reproduce asexually to generate clutches of clonal offspring (ameiotic parthenogenesis). This feature of Daphnia biology is extremely useful for scientific experimentation where the genetic variation among tested individuals has to be minimized. However, over the last decade, reports of genome homogenization (loss of heterozygosity - LOH) in asexual lineages of D. pulex have indicated that asexual genomes are not static as it was previously assumed and that some levels of ameiotic recombination, in addition to mutation, may induce genetic variation among putative clones. However, comparing parthenogenetic offspring with their mothers at several thousand genetic markers generated by RAD-sequencing, I was not able to detect any LOH events in D. magna (Chapter II). I cannot exclude the possibility that ameiotic recombination indeed occurs in D. magna, however, my results indicate that such phenomenon is extremely rare or restricted to the very short genomic regions that I was unable to investigate, despite a high-density of markers used in this study. 
\nNevertheless, the implementation of RAD-sequencing protocol for the genome studies of D. magna still enables interrogation of the transmission of genetic information from parents to offspring at unprecedented resolution. For an example, a RAD-sequencing based analysis of reduction in parental heterozygosity among rare ephippial hatchlings (typically produced by sexual reproduction) found in non-male producing populations of D. magna, has enabled differentiation between self-fertilization and automixis (meiotic parthenogenesis), by uncovering the subtle differences in genetic consequences of these reproductive strategies (Chapter III). Harnessing the ability of high-resolution genetic analysis it was demonstrated that, in the absence of males, D. magna can produce diapause eggs by automixis, and an additional type of asexual reproduction that was not previously reported for this species.
\nFinally, RAD-sequencing European populations of D. magna revealed an association of genetic variation with the geographic location of individual samples (Chapter IV), a task which was not previously amenable using mitochondrial or microsatellite markers. This study provided a better insight into population genetic structure of D. magna and suggested that genetic differentiation is mainly driven by geographic distance. These results set a foundation for forthcoming studies aiming to disentangle past and future evolutionary processes shaping populations of this intriguing model organism.
\nTaken together, research presented in my thesis illustrates the practicality of reduced representation genome sequencing for tackling diverse topics in evolutionary biology. By increasing awareness of non-randomness of meiotic recombination across the genome of D. magna, the diversity of reproductive mechanisms it can employ, and its large-scale population structure, I hope this work will contribute to further understanding of the remarkable adaptive capacity this crustacean is famous for.
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Since the discovery of the DNA double helix, major advances in biology have been; the development of recombinant DNA technology in the 1970s, methods to amplify DNA and gene targeting technology in the late 1980s. In organisms such as yeast and mice, the ability to accurately add or delete genetic information transformed biology, allowing an unmatched level of precision in studies of gene function. But, the ability to easily and specifically edit the genetic material of other cells and organisms remained impossible until recently for molecular biologists. The recent advent of programmable nucleases has dramatically changed the efficiency and speed of genome manipulation in several model organisms including cultured cells, as well as whole animals and plants. These tools opened up a powerful technique for biology research now called “genome editing” or “genome engineering” (Carroll, 2011; Hsu et al., 2014; Kim and Kim, 2014). In the first half of my doctoral studies, I developed genome-editing strategies to discover drug targets for a rare genetic disease called Friedreich’s Ataxia. Friedreich’s Ataxia (FRDA) is a neurodegenerative disease caused by deficiency of the mitochondrial protein frataxin (FXN) (Campuzano et al., 1997). This deficiency results from an expansion of a trinucleotide GAA repeat in the first intron of the FXN gene (Campuzano et al., 1996; Durr et al., 1996). Therapeutics that reactivate FXN gene expression are expected to be beneficial to FRDA patients (Gottesfeld, 2007). However, high-throughput screening (HTS) for FXN activators has so far met with limited success because current cellular models do not accurately assess endogenous FXN gene regulation. Here I used genome-editing technologies to generate a cellular model in which a luciferase reporter is introduced into the endogenous FXN locus. Using this system in a high-throughput genomic screen, we discovered novel inhibitors of FXN-luciferase expression. I confirmed that reducing expression of one of these inhibitors, PRKD1, led to an increase in FXN expression in FRDA patient fibroblasts (Villasenor et al., 2015). We then used reprogramming technologies to create a disease-relevant situation and test small molecules that specifically modulate PRKD1. We found that WA-21-JO19, a chemical inhibitor of PRKD1, increases FXN expression levels in iPSC-derived FRDA patient neurons. This approach, developed at the interface between academic and pharmaceutical research, demonstrates how a combination of genome editing, cellular reprogramming, and high-throughput biology can generate an effective novel drug discovery platform. In the second part of my doctoral work, we developed an interface between genome editing and proteomics to isolate native protein complexes produced from their natural genomic contexts. In many biological processes, proteins act as members of protein complexes. Understanding the molecular composition of protein complexes is a key task towards explaining their function in the cell. Conventional affinity purification followed by mass spectrometry analysis is a broadly applicable method to decipher molecular interaction networks and infer protein function. However, traditional affinity purification methods are limited by a number of factors such as antibody specificity and are sensitive to perturbations induced by overexpressed target proteins. Here, we combined genome editing with tandem affinity purification to circumvent current limitations. I uncovered subunits and interactions among well-characterized complexes and report the isolation of novel Mettl3-binding partners. The multi-protein complex composed of two active methyltransferases Mettl3 and Mettl14 mediates methylation of adenosines at position N6 on RNA molecules (Bokar et al., 1994; Bokar et al., 1997; Liu et al., 2014). N6-methyladenosine is the most abundant internal modification in eukaryotic mRNA and is often found on introns, which implies that methylation occurs co-transcriptionally (Fu et al., 2014). My work identified a set of nuclear RNA binding proteins, which specifically interact with the Mettl3-Mettl14 complex. We are currently testing the ability of these factors to function as “recruiters” of the Mettl3-Mettl14 complex to nascent mRNAs in the cell nucleus. In summary, our approach solidly establishes how a combination of genome editing and proteomics can simplify explorations of protein complexes as well as the study of post-translational modifications. In addition, this approach opens up new opportunities to study native protein complexes in a wide variety of cells and model organisms and will likely enable the systematic investigation of mammalian proteome function.

The main focus of this thesis is the use of high-throughput sequencing technologies in functional genomics (in particular in the form of ChIP-seq, chromatin immunoprecipitation coupled with sequencing, and RNA-seq) and the study of the structure and regulation of transcriptomes. Some parts of it are of a more methodological nature while others describe the application of these functional genomic tools to address various biological problems. A significant part of the research presented here was conducted as part of the ENCODE (ENCyclopedia Of DNA Elements) Project. The first part of the thesis focuses on the structure and diversity of the human transcriptome. Chapter 1 contains an analysis of the diversity of the human polyadenylated transcriptome based on RNA-seq data generated for the ENCODE Project. Chapter 2 presents a simulation-based examination of the performance of some of the most popular computational tools used to assemble and quantify transcriptomes. Chapter 3 includes a study of variation in gene expression, alternative splicing and allelic expression bias on the single-cell level and on a genome-wide scale in human lymphoblastoid cells; it also brings forward a number of critical to the practice of single-cell RNA-seq measurements methodological considerations. The second part presents several studies applying functional genomic tools to the study of the regulatory biology of organellar genomes, primarily in mammals but also in plants. Chapter 5 contains an analysis of the occupancy of the human mitochondrial genome by TFAM, an important structural and regulatory protein in mitochondria, using ChIP-seq. In Chapter 6, the mitochondrial DNA occupancy of the TFB2M transcriptional regulator, the MTERF termination factor, and the mitochondrial RNA and DNA polymerases is characterized. Chapter 7 consists of an investigation into the curious phenomenon of the physical association of nuclear transcription factors with mitochondrial DNA, based on the diverse collections of transcription factor ChIP-seq datasets generated by the ENCODE, mouseENCODE and modENCODE consortia. In Chapter 8 this line of research is further extended to existing publicly available ChIP-seq datasets in plants and their mitochondrial and plastid genomes. The third part is dedicated to the analytical and experimental practice of ChIP-seq. As part of the ENCODE Project, a set of metrics for assessing the quality of ChIP-seq experiments was developed, and the results of this activity are presented in Chapter 9. These metrics were later used to carry out a global analysis of ChIP-seq quality in the published literature (Chapter 10). In Chapter 11, the development and initial application of an automated robotic ChIP-seq (in which these metrics also played a major role) is presented. The fourth part presents the results of some additional projects the author has been involved in, including the study of the role of the Piwi protein in the transcriptional regulation of transposon expression in Drosophila (Chapter 12), and the use of single-cell RNA-seq to characterize the heterogeneity of gene expression during cellular reprogramming (Chapter 13). The last part of the thesis provides a review of the results of the ENCODE Project and the interpretation of the complexity of the biochemical activity exhibited by mammalian genomes that they have revealed (Chapters 15 and 16), an overview of the expected in the near future technical developments and their impact on the field of functional genomics (Chapter 14), and a discussion of some so far insufficiently explored research areas, the future study of which will, in the opinion of the author, provide deep insights into many fundamental but not yet completely answered questions about the transcriptional biology of eukaryotes and its regulation.

Attaining a detailed understanding of the various biological networks in an organism lies at the core of the emerging discipline of systems biology. A precise description of the relationships formed between genes, mRNA molecules, and proteins is a necessary step toward a complete description of the dynamic behavior of an organism at the cellular level, and toward intelligent, efficient, and directed modification of an organism. The importance of understanding such regulatory, signaling, and interaction networks has fueled the development of numerous in silico inference algorithms, as well as new experimental techniques and a growing collection of public databases. The Software Environment for BIological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment, evaluation, and improvement of algorithms used to reconstruct the structure of biological regulatory and interaction networks. SEBINI can be used to analyze high-throughput gene expression, protein abundance, or protein activation data via a suite of state-of-the-art network inference algorithms. It also allows algorithm developers to compare and train network inference methods on artificial networks and simulated gene expression perturbation data. SEBINI can therefore be used by software developers wishing to evaluate, refine, or combine inference techniques, as well as by bioinformaticians analyzing experimental data. Networks inferred from the SEBINI software platform can be further analyzed using the Collective Analysis of Biological Interaction Networks (CABIN) tool, which is an exploratory data analysis software that enables integration and analysis of protein-protein interaction and gene-to-gene regulatory evidence obtained from multiple sources. The collection of edges in a public database, along with the confidence held in each edge (if available), can be fed into CABIN as one "evidence network," using the Cytoscape SIF file format. Using CABIN, one may increase the confidence in individual edges in a network inferred by an algorithm in SEBINI, as well as extend such a network by combining it with species-specific or generic information, e.g., known protein-protein interactions or target genes identified for known transcription factors. Thus, the combined SEBINI-CABIN toolkit aids in the more accurate reconstruction of biological networks, with less effort, in less time.A demonstration web site for SEBINI can be accessed from https://www.emsl.pnl.gov/SEBINI/RootServlet . Source code and PostgreSQL database schema are available under open source license. ronald.taylor@pnl.gov. For commercial use, some algorithms included in SEBINI require licensing from the original developers. CABIN can be downloaded from http://www.sysbio.org/dataresources/cabin.stm . mudita.singhal@pnl.gov.

Protein-ligand interactions lie at the heart of most biological and medical processes. To lay the groundwork for a mechanistic understanding of complex biological networks, a detailed description of the associated proteins, their roles, and their interaction partners is essential. Furthermore, a detailed description of protein-ligand interactions is of pharmaceutical interest, as small molecules targeting proteins constitute the basis for the treatment of numerous medical conditions. In this thesis, we used nuclear magnetic resonance (NMR) spectroscopy to probe the conformational properties of biologically and pharmaceutically essential proteins, either alone or complexed with small molecules, to reveal the mechanistic grounds of these interactions. The first chapter entails the structural investigation of Tumor necrosis factor-alpha (TNFα) by NMR spectroscopy. TNFα is a homotrimeric protein that enjoys tremendous scientific and pharmaceutical interest due to its pivotal role in the human immune response. However, its overexpression leads to numerous chronic inflammatory diseases. Studying the interaction between TNFα and small molecular inhibitors is thus of utmost importance if better cures for these diseases are to be found. Here, we investigated active and inhibited forms of TNFα in solution by NMR spectroscopy and showed that TNFα exhibits significant conformational exchange, especially in the receptor binding site and the trimer interface, which is crucial for its biological activity. Inhibition of TNFα by perturbing the trimer interface, either by mutagenesis or by targeting it with small molecules, does not lead to disruption of the postulated trimer in solution. Instead, it induces a significant increase in the conformational dynamics of TNFα propagating from the trimer interface to the receptor-binding site. Therefore, we propose that an allosteric dynamic coupling between the trimer interface and the active site mediates inhibition. This provides novel insights into the mechanisms of TNFα inhibition, which in turn may lead to the development of better-suited drug assays against this highly important molecule. The second chapter presents the study of alpha acid glycoprotein-1 (AGP1) by NMR. The heavily glycosylated AGP1 is one of the two most abundant human serum proteins and binds various metabolites as well as, perhaps most interestingly from a pharmaceutical perspective, many administered drugs. Understanding the interaction between AGP1 and small molecules on a molecular level in addition to revealing the yet unclear impact of the appended glycans in this process is thus of great importance - as drug binding to plasma proteins modulates their pharmacokinetics and pharmacodynamics. In this thesis, efficient protocols were developed which enabled us to shed light on the influence of glycosylation on the structure of AGP1 and to characterize its ligand binding at a level of detail which had not yet been achieved. We showed that glycosylation affects the protein conformation in the vicinity of the glycosylation site, and provide evidence for a conformational rearrangement upon binding of small molecules. By giving these novel insights, our results contribute to the functional understanding of AGP1 and more broadly to the relatively unexplored class of glycoproteins. The third chapter discusses the combination of our measured NMR data (presented in chapters one and two) with DNA-encoded chemical libraries (DECL) in drug discovery. DECL allows for the efficient identification of potential small molecule binders, while our concomitant NMR data provide the framework for assisting both the validation of ‘hits’ and the optimization process. We could confirm interactions between TNFα or AGP1 and binders identified by DECL. Here, the first application of this approach for drug discovery for TNFα and AGP1 is presented. In summary, our study contributes to a better understanding of two highly relevant proteins: AGP1 and TNFα. The data presented provides original insights into the inhibition mechanisms of small molecules and revealed the importance of conformational dynamics in these processes. Finally, our results will add a new aspect to the complex process of rational drug development, which will ultimately benefit many patients affected by diverse medical conditions.

The core service of most research portal sites is providing relevant research papers to various researchers that match their research interests. This kind of service may only be effective and easy to use when a user can provide correct and concrete information about a paper such as the title, authors, and keywords. However, unfortunately, most users of this service are not acquainted with concrete bibliographic information. It implies that most users inevitably experience repeated trial and error attempts of keyword-based search. Especially, retrieving a relevant research paper is more difficult when a user is novice in the research domain and does not know appropriate keywords. In this case, a user should perform iterative searches as follows : i) perform an initial search with an arbitrary keyword, ii) acquire related keywords from the retrieved papers, and iii) perform another search again with the acquired keywords. This usage pattern implies that the level of service quality and user satisfaction of a portal site are strongly affected by the level of keyword management and searching mechanism. To overcome this kind of inefficiency, some leading research portal sites adopt the association rule mining-based keyword recommendation service that is similar to the product recommendation of online shopping malls. However, keyword recommendation only based on association analysis has limitation that it can show only a simple and direct relationship between two keywords. In other words, the association analysis itself is unable to present the complex relationships among many keywords in some adjacent research areas. To overcome this limitation, we propose the hybrid approach for establishing association network among keywords used in research papers. The keyword association network can be established by the following phases : i) a set of keywords specified in a certain paper are regarded as co-purchased items, ii) perform association analysis for the keywords and extract frequent patterns of keywords that satisfy predefined thresholds of confidence, support, and lift, and iii) schematize the frequent keyword patterns as a network to show the core keywords of each research area and connecting keywords among two or more research areas. To estimate the practical application of our approach, we performed a simple experiment with 600 keywords. The keywords are extracted from 131 research papers published in five prominent Korean journals in 2009. In the experiment, we used the SAS Enterprise Miner for association analysis and the R software for social network analysis. As the final outcome, we presented a network diagram and a cluster dendrogram for the keyword association network. We summarized the results in Section 4 of this paper. The main contribution of our proposed approach can be found in the following aspects : i) the keyword network can provide an initial roadmap of a research area to researchers who are novice in the domain, ii) a researcher can grasp the distribution of many keywords neighboring to a certain keyword, and iii) researchers can get some idea for converging different research areas by observing connecting keywords in the keyword association network. Further studies should include the following. First, the current version of our approach does not implement a standard meta-dictionary. For practical use, homonyms, synonyms, and multilingual problems should be resolved with a standard meta-dictionary. Additionally, more clear guidelines for clustering research areas and defining core and connecting keywords should be provided. Finally, intensive experiments not only on Korean research papers but also on international papers should be performed in further studies.

With the requirement to find methods to analysis the rapidly increasing data, as a branch of computer science, data mining technique has been widely apply in different domain .One of the most enticing application areas of data mining in these emerging technologies is in finance. To complete the financial tasks, data mining technique is used refer to Stock market forecasting, Bankruptcy Prediction, Credit Risk Estimation, Corporate Performance Prediction and Management Fraud. This paper focuses on new data mining applications in finance. The fast development of Compute technology (such as Cloud Computing) and database together gives data mining a new challenge and chance in data mining of finance. How to apply the new computer and database technique to the data mining, and integrate the previous framework and the new technology together to build a more efficiency and safer framework is a long way to go in the future . Meanwhile, after accumulate enough empirical regularity and experiences, deep field-specific theories will be set up. This means, in the future, it will have more standardized and accurate model and method to apply in different domain of finance by generating more empirical regularities and combining them with domain knowledge.

Data integration and analysis of biological networks

Actually, the diagnostic procedures for the molecular diagnosis and monitoring of a T-cell neoplasia on tissue sections or DNA extracted from cutaneous lesions include: a. the demonstration of clonality (a clone is a group of cells expanded from a single cell), of the lymphoid tissue infiltrates, by using molecular probes specific for the T-cell receptors (TCR); TCR-Gamma, TCR-Beta and, more rarely TCR-Delta subunits. This analysis, in the past done by using Southern-Blot method, may be performed by using polymerase chain reaction (PCR) non-radioactive very sensitive methods, such as heteroduplex analysis of TCR1 on DNA extracted from lesional skin and may be indicative of malignancy, because reactive cutaneous lymphoid infiltrates are usually polyclonal. b. the detection of chromosomal translocations by Southern-Blot, PCR on the extracted DNA or on tissue sections/cytological smears by the new fluorescence in situ hybridization (FISH) techniques : i.e. the t (2;5) translocation detected in the systemic CD30+ anaplastic large cell (ALC) lymphomas and not in primary cutaneous CD30+ LC lymphomas or lymphomatoid papulosis (LYP).2 c. the demonstration of oncogenes or oncosuppressor genes involvement, by PCR method or FISH, mainly useful to the understanding of the pathogenesis and the progress ion/trans­form ation processes characteristics of these diseases: such as p16, p53, lyt-10, tal-1.3 d. the involvement of specific viruses (by quantitative PCR or FISH), such as HTLV1 and EBV, but also HHV6, HHV7, HHV8. e. the demonstration of chromosomal imbalances on DNA extracted from the cutaneous lesion by using the array-CGH (comparative genomic hybridization): this is a technique for the comparative hybridization of the genome based on array; platforms in which genomic clones containing specific sequences of the human genome (genic and extragenic sequences) are located, the clones are distributed at different distances one from the other, depending from the type of array used. Traditional cytogenetic has a resolution of 10Mb, high resolution HR-CGH on metaphases has a resolution of 5 Mb, whereas array-GH has a resolution of 100Kb and now of 6Kb.4 f. the determination of the gene expression profile (GEP) of the infiltrate neoplastic cells on the tissue extracted RNA and cDNA, to look for marker genes (always low or highly expressed) to use for diagnosis, could be in imbalanced areas of the genome. Recently a group of 27 genes had been defined for the diagnosis of mycosis fungoides. In our study we analyzed by a very sensitive oligo-array. CGH methods the DNA extracted from the cutaneous lesions of a group of rare CTCL classified by the recent WHO/EORTC classification.5

Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g. point-dimension, observation-variable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: CASE: Exploiting the programming CASE construct; SPJ: Based on standard relational algebra operators (SPJ queries); PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, where as the SPJ method does not. In a relational database, especially with normalized tables, a significant effort is required to prepare a summary data set that can be used as input for a data mining or statistical algorithm. Most algorithms require as input a data set with a horizontal layout, with several Records and one variable or dimension per column. That is the case with models like clustering, classification, regression and PCA; consult. Each research discipline uses different terminology to describe the data set. In data mining the common terms are point-dimension. Statistics literature generally uses observation-variable. Machine learning research uses instance-feature. This article introduces a new class of aggregate functions that can be used to build data sets in a horizontal layout (denormalized with aggregations), automating SQL query writing and extending SQL capabilities. We show evaluating horizontal aggregations is a challenging and interesting problem and we introduced alternative methods and optimizations for their efficient evaluation. II. MOTIVATION As mentioned above, building a suitable data set for data mining purposes is a time- consuming task. This task generally requires writing long SQL statements or customizing SQL Code if it is automatically generated by some tool. There are two main ingredients in such SQL code: joins and aggregations; we focus on the second one. The most widely- known aggregation is the sum of a column over groups of rows. Some other aggregations return the average, maximum, minimum or row count over groups of rows. There exist many aggregations functions and operators in SQL. Unfortunately, all these aggregations have limitations to build data sets for data mining purposes. The main reason is that, in general, data sets that are stored in a relational database (or a data warehouse) come

Constraint-based modeling is a well established modelling methodology used to analyze and study biological networks on both a medium and genome scale. Due to their large size, genome scale models are typically analysed using constraint-based optimization techniques. One widely used method is Flux Balance Analysis (FBA) which, for example, requires a modelling description to include: the definition of a stoichiometric matrix, an objective function and bounds on the values that fluxes can obtain at steady state. The Flux Balance Constraints (FBC) Package extends SBML Level 3 and provides a standardized format for the encoding, exchange and annotation of constraint-based models. It includes support for modelling concepts such as objective functions, flux bounds and model component annotation that facilitates reaction balancing. The FBC package establishes a base level for the unambiguous exchange of genome-scale, constraint-based models, that can be built upon by the community to meet future needs (e. g. by extending it to cover dynamic FBC models).

African lily (Agapanthus africanus L.), native in South-Africa, is currently a popular bulbous floral crop in Taiwan and many other countries. An African lily exhibiting foliar chlorotic strip and mottle symptom was collected from Mai-Fung nursery of Taiwan University in 2005. A virus isolate, designated as AL-1, was obtained from this plant by establishing a single lesion isolate in Chenopodium quinoa Willd. Back inoculation of AL-1 onto seedlings of African lilies confirmed that AL-1 was able to induce chlorotic mottle symptom which was similar to what was observed in the field. Virions of AL-1 were successfully purified from the inoculated tissue of C. quinoa by differential and equilibrium centrifugation in cesium sulfate. Elongated virus particles estimated at 508 nm in length were observed from the purified sample under an electron microscope. By SDS-PAGE, molecular weight of AL-1's coat protein was determined as 25 kDa. An antiserum against purified AL-1 virions was prepared in rabbit and shown to react strongly and specifically with its homologous antigens prepared from diseased samples of field-collected or inoculated ones in enzyme-linked immunosorbent assay (ELISA), SDS-immunodiffusion, and western blotting tests. Full-length genome of AL-1 was revealed by RT-PCR amplification with random primer and several primer pairs designed in this study according to those closely related virus sequences in the GenBank. Genome of AL-1 was found to compose 6577 nucleotides, excluding the poly (A) tails, in which five open reading frames (ORF) encoding presumably replicase (ORF 1), triple gene blocks (ORF 2-4) and coat protein (ORF 5) were located. This genome structure was equivalent to that of Potexvirus and most resembled to Nerine virus X (NVX) (Acc. No. AB219105) in the GenBank. The percent identities of nucleotide and amino acid sequences of the full-length genomes between AL-1 and NVX were 98 and 93%, respectively, indicating they were isolates of the same virus. A primer pair for specific detection of AL-1 was designed and shown to be useful in routine RT-PCR test. Our results revealed that AL-1 was an isolate of NVX based on the biological and serological characterizations and the molecular analysis of the full-length genomic sequences. To our knowledgement, it's the first report of NVX occurrence in Taiwan.