Next generation networks will provide customers with a service mix placing variable demands for resources on the underlying infrastructure, motivating automated telecommunications services management approaches. This thesis proposes the Autonomic Service Architecture (ASA) for automated service delivery over next generation networks. First, we propose an architectural blueprint for ASA. We describe our view of the next generation network infrastructure, which will be application oriented. We elaborate on the layered design of ASA, the virtualization of resources, and the separation between manual and autonomic functions in the service delivery lifecycle. The autonomic functions are delivered by the interaction between Autonomic Resource Brokers (ARBs). The architecture of an ARB is then detailed, with a description of its different components and the message exchanges needed. Next, we discuss a Peer-to-Peer (P2P) naming and mobility management approach for next generation networks using ASA. This P2P approach will help ensure the scalability, robustness, and flexibility that ASA needs to ensure service delivery over next generation networks. The proposed P2P naming and mobility management infrastructure is then detailed, and its performance is evaluated. Finally, we suggest several autonomic resource management algorithms for ASA. The first algorithm is based on the Transportation Model, commonly used in the Operations Research community for cost minimization in delivering a commodity from sources to destinations, adapted to perform allocation of virtual resources. The second algorithm is based on the Assignment Model, commonly used in the Operations Research community for cost minimization in assigning several jobs to several workers, adapted to perform autonomic assignment of dedicated virtual resources. The third algorithm is based on Inventory Control, commonly used in the Operations Research community to analyze inventory systems, placing and receiving orders when needed for a given product, adapted to predict the demand on virtual resources. The fourth algorithm is based on Reinforcement Learning, commonly used in the Machine Learning community by agents to find a control policy that will maximize the observed rewards over their lifetime, adapted to adjust the prices of virtual resources.

currently DNA and RNA sequencing are performed as standard parts of many scientific experiments. While the majority of the reads produced in these experiments do map to the genome of the organism of interest there are a significant fraction that do not. These reads have often been viewed as uninteresting and thus discarded, sometimes explained as errors created in the sequencing process. However, there may be a real possibility that these reads actually contain genomic sequences belonging to, but not currently in the genome ofthe organism investigated, as well as information about other organisms which live and thrivein the sample material. Considering this, it is of great interest to investigate these reads to see if they contain any usable information. In this project the unmapped reads from SOLiD sequencing of blood and saliva from a twin pair were assembled. The assembled parts were thencompared to different blast databases to investigate if similar genomic regions are reported inother species. We can conclude that indeed a large fraction of the contigs found in this assemblyhave homology to bacterial genes while other contigs share similarity to genomic regions foundin apes and other species closely related to us. All in all the results show that there is more to the unmapped reads than just sequencing errors.

Next generation networks will provide customers with a service mix placing variable demands for resources on the underlying infrastructure, motivating automated telecommunications services management approaches. This thesis proposes the Autonomic Service Architecture (ASA) for automated service delivery over next generation networks. First, we propose an architectural blueprint for ASA. We describe our view of the next generation network infrastructure, which will be application oriented. We elaborate on the layered design of ASA, the virtualization of resources, and the separation between manual and autonomic functions in the service delivery lifecycle. The autonomic functions are delivered by the interaction between Autonomic Resource Brokers (ARBs). The architecture of an ARB is then detailed, with a description of its different components and the message exchanges needed. Next, we discuss a Peer-to-Peer (P2P) naming and mobility management approach for next generation networks using ASA. This P2P approach will help ensure the scalability, robustness, and flexibility that ASA needs to ensure service delivery over next generation networks. The proposed P2P naming and mobility management infrastructure is then detailed, and its performance is evaluated. Finally, we suggest several autonomic resource management algorithms for ASA. The first algorithm is based on the Transportation Model, commonly used in the Operations Research community for cost minimization in delivering a commodity from sources to destinations, adapted to perform allocation of virtual resources. The second algorithm is based on the Assignment Model, commonly used in the Operations Research community for cost minimization in assigning several jobs to several workers, adapted to perform autonomic assignment of dedicated virtual resources. The third algorithm is based on Inventory Control, commonly used in the Operations Research community to analyze inventory systems, placing and receiving orders when needed for a given product, adapted to predict the demand on virtual resources. The fourth algorithm is based on Reinforcement Learning, commonly used in the Machine Learning community by agents to find a control policy that will maximize the observed rewards over their lifetime, adapted to adjust the prices of virtual resources.

Salmonella is one of the most important pathogens of mankind and animals alike, causing several billion pounds worth of damage worldwide each year. We have sequenced, annotated and published 4 genomes of Salmonella of well-defined virulence in farm animals. This provides valuable measures of intraserovar diversity and opportunities to formally link genotypes to phenotypes in target animals. Specifically, we have examined pathway detrition and mutagenesis and linked this to host specificity of the serovars. With the advent of next generation sequencing there has been a boom in genomic sequence submission, and an onslaught of -omics data has ensued. Integrating these different data types is complex and there is little available to visualise this data in the context of its genome. We present GeneBook, a web-based tool that synchronously integrates disparate datasets, displaying a fully annotated genome, enriched with publicly available data and the user's private experiments. It is accessed through a user-friendly interface that allows scientists to interrogate genomic features across multiple, heterogeneous, experiments.

Background: Obesity is a metabolic disease caused by environmental and genetic factors. However, the epigenetic mechanisms of obesity are incompletely understood. The aim of our study was to investigate the role of skeletal muscle DNA methylation in combination with transcriptomic changes in obesity. Results: Muscle biopsies were obtained basally from lean (n = 12; BMI = 23.4 +/- 0.7 kg/m(2)) and obese (n = 10; BMI = 32.9 +/- 0.7 kg/m(2)) participants in combination with euglycemic-hyperinsulinemic clamps to assess insulin sensitivity. We performed reduced representation bisulfite sequencing (RRBS) next-generation methylation and microarray analyses on DNA and RNA isolated from vastus lateralis muscle biopsies. There were 13,130 differentially methylated cytosines (DMC; uncorrected P < 0.05) that were altered in the promoter and untranslated (5' and 3'UTR) regions in the obese versus lean analysis. Microarray analysis revealed 99 probes that were significantly (corrected P < 0.05) altered. Of these, 12 genes (encompassing 22 methylation sites) demonstrated a negative relationship between gene expression and DNA methylation. Specifically, sorbin and SH3 domain containing 3 (SORBS3) which codes for the adapter protein vinexin was significantly decreased in gene expression (fold change -1.9) and had nine DMCs that were significantly increased in methylation in obesity (methylation differences ranged from 5.0 to 24.4 %). Moreover, differentially methylated region (DMR) analysis identified a region in the 5' UTR (Chr. 8: 22,423,530-22,423,569) of SORBS3 that was increased in methylation by 11.2 % in the obese group. The negative relationship observed between DNA methylation and gene expression for SORBS3 was validated by a site-specific sequencing approach, pyrosequencing, and qRT-PCR. Additionally, we performed transcription factor binding analysis and identified a number of transcription factors whose binding to the differentially methylated sites or region may contribute to obesity. Conclusions: These results demonstrate that obesity alters the epigenome through DNA methylation and highlights novel transcriptomic changes in SORBS3 in skeletal muscle.

Thesis advisor: Gabor T. Marth / Partitioning an individual's phenotype into genetic and environmental components has been a major goal of genetics since the early 20th century. Formally, the proportion of phenotypic variance attributable to genetic variation in the population is known as heritability. Genome wide association studies have explained a modest percentage of variability of complex traits by genotyping common variants. Currently, there is great interest in what role rare variants play in explaining the missing heritability of complex traits. Advances of next generation sequencing and genomic enrichment technologies over the past several years have made it feasible to re-sequence large numbers of individuals, enabling the discovery of the full spectrum of genetic variation segregating in the human population, including rare variants. The four projects that comprise my dissertation all revolve around the discovery of rare variants from next generation sequencing datasets. In my first project, I analyzed data from the exon sequencing pilot of the 1000 Genomes Project, where I discovered variants from exome capture sequencing experiments in a worldwide sample of nearly 700 individuals. My results show that the allele frequency spectrum of the dataset has an excess of rare variants. My next project demonstrated the applicability of using whole-genome amplified DNA (WGA) in capture sequencing. WGA is a method that amplifies DNA from nanogram starting amounts of template. In two separate capture experiments I compared the concordance of call sets, both at the site and genotype level, of variant calls derived from WGA and genomic DNA. WGA derived calls have excellent concordance metrics, both at the site and genotypic level, suggesting that WGA DNA can be used in lieu of genomic DNA. The results of this study have ramifications for medical sequencing experiments, where DNA stocks are a finite quantity and re-collecting samples maybe too expensive or not possible. My third project kept its focus on capture sequencing, but in a different context. Here, I analyzed sequencing data from Mendelian exome study of non-sensorineural hearing loss (NSHL). A subset of 6 individuals (5 affected, 1 unaffected) from a family of European descent were whole exome sequenced in an attempt to uncover the causative mutation responsible for the loss of hearing phenotype in the family. Previous linkage analysis uncovered a linkage region on chr12, but no mutations in previous candidate genes were found, suggesting a novel mutation segregates in the family. Using a discrete filtering approach with a minor allele frequency cutoff, I uncovered a putative causative non-synonymous mutation in a gene that encodes a transmembrane protein. The variant perfectly segregates with the phenotype in the family and is enriched in frequency in an unrelated cohort of individuals. Finally, for my last project I implemented a variant calling method for family sequencing datasets, named Pgmsnp, which incorporates Mendelian relationships of family members using a Bayesian network inference algorithm. My method has similar detection sensitivities compared to other pedigree aware callers, and increases power of detection for non-founder individuals. / Thesis (PhD) — Boston College, 2013. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Biology.

The earthworm has long been of interest to biologists, most notably Charles Darwin, who was the first to reveal their true role as eco-engineers of the soil. However, to fully understand an animal one needs to combine observational data with the fundamental building blocks of life, DNA. For many years, sequencing a genome was an incredibly costly and time-consuming process. Recent advances in sequencing technology have led to high quality, high throughput data being available at low cost. Although this provides large amounts of sequence data, the bioinformatics knowledge required to assemble and annotate these new data are still in their infancy. This bottleneck is slowly opening up, and with it come the first glimpses into the new and exciting biology of many new species. This thesis provides the first high quality draft genome assembly and annotation of an earthworm, Lumbricus rubellus. The assembly process and resulting data highlight the complexity of assembling a eukaryotic genome using short read data. To improve assembly, a novel approach was created utilising transcripts to scaffold the genome (https://github.com/elswob/SCUBAT). The annotation of the assembly provides the draft of the complete proteome, which is also supported by the first RNA-Seq generated transcriptome. These annotations have enabled detailed analysis of the protein coding genes including comparative analysis with two other annelids (a leech and a polychaete worm) and a symbiont (Verminephrobacter). This analysis identified four key areas which appear to be either highly enhanced or unique to L. rubellus. Three of these may be related to the unique environment from which the sequenced worms originated and add to the mounting evidence for the use of earthworms as bioindicators of soil quality. All data is stored in relational databases and available to search and browse via a website at www.earthworms.org. It is hoped that this genome will provide a springboard for many future investigations into the earthworm and continue research into this wonderful animal.

The Orthogonal Frequency Division Multiplexing is a promising technology for the Next Generation Networks. This technique was selected because of the flexibility for the various parameters, high spectral efficiency, and immunity to ISI. The OFDM technique suffers from significant digital signal processing, especially inside the Inverse/ Fast Fourier Transform IFFT/FFT. This part is used to perform the orthogonality/De-orthogonality between the subcarriers which the important part of the OFDM system. Therefore, it is important to understand the parameter effects on the increase or to decrease the FPGA power consumption for the IFFT/FFT. This thesis is focusing on the FPGA power consumption of the IFFT/FFT uses in the OFDM system. This research finds a various parameters effect on FPGA power of the IFFT/FFT. In addition, investigate the computer software used to measure and analyse the FPGA power consumption of OFDM transceivers, and selects the target hardware used in the computer software. The researched parameters include the number of bits used in calculating the phase factor precision; Cyclic Prefix length effected on IP core IFFT, Subcarrier modulation type, word length width, Real and Complex Value IFFT, IFFT length, and subcarriers sampling frequency. The real value IFFT is proposed in 1987 and implemented in this thesis. These parameters above are discussed by comparing the result between the Real and Complex value IFFT used inside the OFDM system.

Begonia is one of the most diverse genera on the planet, with a species count approaching 2000 and a distribution across tropics in South America, Africa and South East Asia. The genus has occupied a vast range of niches; many highly variable growth forms can be found across the distribution, and species exhibit very diverse morphologies, even in closely related species. A recent study has revealed a putative whole genome duplication (WGD) event in the evolutionary history of Begonia, which has prompted an interest in investigating the impact gene and genome duplication has had on the diversification of Begonia. To answer questions about phenotypic and ecological diversification in Begonia, two species from South America, B. conchifolia and B. plebeja were chosen as study species based on their close phylogenetic relationship and divergent ecology and phenotype. RNA-seq data for six tissues from B. conchifolia and B. plebeja was generated using the Illumina sequencing platform, and normalised relative expression data was obtained by mapping reads to transcripts predicted from the B. conchifolia draft genome. A bioinformatics pipeline was devised to compare expression profiles across 6 different tissues between duplicated gene pairs shared between B. conchifolia and B. plebeja. Gene duplicate pairs were selected as candidates if they showed divergent expression in one species but not in another. Such duplicate pairs are suggestive of neofunctionalization in one species, providing evidence of a potential basis for phenotypic divergence and diversification between B. conchifolia and B. plebeja. Two duplicate pairs were identified as showing such divergent expression patterns as well as being functionally ecologically relevant, Chalcone Synthase and 3-Ketoacyl-CoA synthase, involved in anthocyanin biosynthesis and wax biosynthesis respectively. Investigation of expression and duplication patterns in both gene families showed the candidate gene families to be strikingly different. While 3-Ketoacyl-CoA synthase showed deeper duplications shared with outgroup taxa, Chalcone Synthase appeared to be expanded very recently, with a burst of duplications specific to the genus. 3-Ketoacyl-CoA synthase showed examples of partitioned expression by tissue for different gene family members, with at least five members of the gene family being highly expressed in one or two tissues only. Chalcone Synthase, however, showed dominance of one basal gene family member. Other Chalcone Synthase members, though expressed at lower levels, showed some evidence of reciprocal silencing in B. plebeja, though this pattern was not observed in B. conchifolia. Further investigation of the Chalcone Synthase gene family revealed lineage specific duplication in B. plebeja, and more extensive differential duplication patterns were found across other South American Begonias. Additionally, signals of positive selection were found in two branches on the Chalcone Synthase phylogeny.

Next Generation 9-1-1 (NG 9-1-1) will revolutionize how the public accesses emergency services and will alter the technological landscape within which existing public safety agencies operate. A lack of systematic methodologies exists for quality control of the required geospatial data layers for NG 9-1-1 systems. The primary objective of this study was to develop and systematize a highly accurate NG 9-1-1 GIS database for Counties of Southern Illinois (CSI). The project goals included mapping relevant geospatial data layers required by and based on NENA standard data formats; conducting data quality control and standardization; and providing standardized spatial datasets for NG 9-1-1 to relevant stakeholders. The approach was developed using a conceptual model for error and uncertainty analysis of the GIS-based NG 9-1-1 system. This included the identification of various sources of input uncertainties often associated with spatial data layers; modeling the accumulation and propagation of errors; analyzing their impact on the quality of the spatial data layers; and correcting the errors. Modeling uncertainty propagation focused on positional errors and was conducted through a simulation procedure. The results showed that the original spatial datasets possessed a large account of uncertainties, especially location errors of railroads and roads. The errors had different sources, including input map errors, the use of different map projection and coordinate systems, a lack of topological structures, etc. In addition, they varied from county to county. From the error propagation simulation, it was also found that the location errors measured as root mean square error (RMSE) fluctuated when the perturbed distance of the ground control points (GCP) was less than 15 m. After that, the RMSE increased as the perturbed distance of GCPs increased. This relationship was significantly linear. In addition, the location errors from railroads were larger than those from roads.