A Cognitive Sensing Algorithm for Coexistence Scenario with LTE

Increasing demand for high data rate wireless communication motivates the wireless engineers to develop advanced technologies to address such needs. LTE and LTE-Advanced are examples of such wireless technologies, which support high data rate and a large number of users. However, higher data rate communication requires more frequency bandwidth. Recent studies have shown that the inefficient utilization of frequency spectrum is one of the main reasons for the scarcity of frequency bandwidth. Cognitive Radio Network is introduced as a promising solution for this problem. It increases the utilization of bandwidth, by intelligently sensing the channel environment and dynamically providing access to the available resources (frequency bands) for a secondary user. In this thesis, we developed an algorithm for dynamically detecting and anticipating the existence of underutilized resources in LTE system. The algorithm should be a real-time operation, i.e. the decision on availability of a detected resource should be made within a time much less than scheduling update period of LTE. This is the only way that rest of the unused resources becomes usable. For each specific channel assignment, the algorithm requires to start sensing as soon as possible. Therefore, we develop the algorithm in three main steps. The first step is to blindly detect and identify the LTE-Downlink signal using cyclostationarity property of OFDM scheme. The second step is the acquisition of the LTE-Downlink sub-frame timing, which is basically performed by detecting the Primary Synchronization Signal. The third step is to detect unused resources, for the duration of their transmission. This step is using a frequency domain energy detector. By performing the first and second steps, the sub-frame timing and scheduling update instances are known. So basically the algorithm does not require any previous knowledge of the LTE signal. We evaluate the performance of the proposed algorithm with respect to the tolerable amount of interference at the primary user side. Using the proposed algorithm, in average up to 81 % of unused resources can be used by the secondary user

Investigating enrichment patterns of cancer genomic aberrations in protein interaction networks

Next generation sequencing and complementary DNA (cDNA) microarrays are ableto identify aberrations in the structure of DNA and levels of RNA respectively. Whilemany bioinformatics tools exist that help researchers find potential candidate aberratedpathways and proteins, we need more strategies that can enable us to integrate these genomicdata at many levels in order to unravel the biological causality and implicationsof these molecular aberrations. Use of protein interaction networks has the advantageof implementing already known protein collaborations in cellular processes. Integratinggenomic aberrations (such as DNA mutations or changes in level of RNA) within thesenetworks allows us to prioritize specific proteins that would otherwise be lost among a listof hundreds of aberrated genes.Here I have developed a method to analyze cancer genomics data in the context of proteininteraction networks and evaluated its performance on genomics datasets of breast andrenal cancers. Breast cancer is one of the most ubiquitous and heterogeneous types ofcancer and has been studied and characterized relatively well. By mapping breast cancergenomic aberrations based on mutations and differential expression analysis of The CancerGenome Atlas (TCGA) breast cancer datasets to human protein interaction networks,I demonstrate the efficacy of hypergeometric modeling of each protein’s network to findhubs with enrichment in aberrations specific to each class of breast cancer patients. Someof the proteins found by this method are already reported in the literature and others havethe potential of being novel breast cancer biomarkers specific to each PAM50 class of patients.Furthermore, I applied this method on a dataset of renal cell carcinoma, which is less characterizedcompared to breast cancer. Based on my findings, I suggest that this pipeline canbe used for analysis of other types of cancer to help biologists discover potential biomarkers. Thus I provided the core functionalities of this pipeline in the form of an R packagecalled AbHAC (Aberration Hub Analysis for Cancer). AbHAC is multi-threaded and assuch it can take advantage of multi core nodes to execute its computation faster. Chapter1 of the manuscript provides a literature review of the field while chapter 2 describes theAbHAC method and algorithm. Chapter 3 and 4 present the results of AbHAC in breastand renal cancer, respectively. Chapter 5 concludes the findings.Le séquençage de nouvelle génération et les puces à ADN complémentaire (ADNc)permettent l’identification de modifications au niveau de la structure de l’ADN et del’expression de l’ARN respectivement. Plusieurs outils bioinformatiques sont actuellementdisponibles pour aider les chercheurs à identifier les voies moléculaires et les protéines quisont affectées. Par contre, l’implémentation de différentes stratégies serait nécessaire pourpermettre l’intégration, à plusieurs niveaux, de ces diverses données génomiques dans lebut de découvrir les causes biologiques et les implications des modifications moléculaires.Les avantages d’utiliser des réseaux d’interactions de protéines inclus l’intégration decollaborations entre protéines connues pour être impliquées dans les processus cellulaires.L’intégration de modifications génomiques (dont les mutations d’ADN ou les changementsau niveau de l’expression de l’ARN) au sein de ces réseaux permet de prioriser certainesprotéines qui ne seraient autrement pas distinguables parmi une liste de centaines de gènesmodifiés.Pour ce projet, j’ai développé une méthode d’analyse de données génomiques de cancersdans un contexte de réseau d’interaction de protéines et j’ai évalué sa performance enutilisant des données génomiques de cancers du sein et des reins. Le cancer du seinest un des types de cancers les plus ubiquitaires et hétérogènes ainsi que l’un des pluscaractérisés. En intégrant des données de mutations et d’analyse de différences d’expressionde gènes du TCGA Breast Cancer à des réseaux d’interactions de protéines humaines, j’aidémontré que l’utilisation d’un modèle hypergéométrique pour chacun des réseaux deprotéines mène à l’identification d’un groupe de protéines enrichies de modifications étantspécifiques à différents groupes de patients atteints. Certaines des protéines identifiéesselon cette méthode ont déjà été rapportées dans la littérature. Les autres pourraient êtrede nouveaux biomarqueurs de cancer du sein spécifiques à différentes classes de patients(selon PAM50).J’ai appliqué cette méthode à un cancer moins bien caractérisé que le cancer du sein, soitle cancer du rein. Selon les résultats obtenus, la méthode développée peut être appliquée àdifférents types de cancer afin d’identifier de nouveaux biomarqueurs associés à ceux-ci.Par conséquent, les principales fonctions de cette méthode sont disponibles sous formed’un package R nommé AbHAC (Aberration Hub Analysis for Cancer). AbHAC est uneapplication multithread et ainsi elle peut prendre avantage de noeuds de calcul à plusieurscoeurs pour accélérer son traitement des données. Le chapitre 1 du manuscrit fait part dela revue de la littérature du champ d’expertise. Le chapitre 2 décrit la méthode AbHAC etl’algorithme y étant lié. Les chapitres 3 et 4 présentent les résultats de la méthode AbHACdans le cancer du sein et le cancer rénal respectivement. Finalement, le chapitre 5 est uneconclusion des résultats obtenus

The Epigenome through the Lens of the Transcriptome and How it Regulates Tumour Development in Cancer

Battling complex human disease requires deep understanding of the disease etiology at the molecular level. Recent international collaborations provide a multitude of datasets assessing changes in DNA at the single-nucleotide resolution, chemical modifications such as cytosine methylation, protein binding to DNA such as transcription factors (TFs), and gene expression for a variety of patient samples and cell lines. Getting the most out of these datasets, however, requires methods which address technical difficulties and provide valuable biological insights. Assessing cytosine methylation through sequencing or arrays, to date, remains the most widely used assay for investigating the epigenome. The experimental procedure, however, changes the nature of the DNA assembly that the sequencing reads could map to. In this dissertation, I will propose a solution and provide publicly available datasets to allow researchers use DNA methylation assessments properly. Identifying TF binding sites remains an important challenge in epigenomics. The available assays, such as ChIP-seq, however, require a large amount of cells not always available from patient tissues. For decades, researchers have relied on the sequence preference of TFs instead of directly assessing TF binding. In this dissertation, I will show that relying solely on TF sequence preference will bring many false negative findings as well as previously reported false positive findings (futility theorem). I propose a solution, Virtual ChIP-seq. To predict TF binding, Virtual ChIP-seq uses as input the data of two assays which require only minimal tissue: RNA-seq and ATAC-seq. These assays assess the transcriptome and the accessible chromatin, respectively. For certain TFs, Virtual ChIP-seq predicts cell-type–specific DNA binding with high accuracy. I provide predictions of Virtual ChIP-seq for binding of 36 TFs in 31 human tissue types as a public resource. In the last part of this dissertation, I investigate the transcriptome and epigenome in a specific group of tumours: squamous cell carcinomas infected with human papillomavirus (HPV). I found that HPV integration into the host genome modifies the local host epigenome and transcriptome, possibly because of a conserved CTCF binding site within its 8 kbp genome. This dissertation introduces publicly available tools that allow the research community better assess the epigenome and it also enhances our understanding of how HPV contributes to tumourigenesis.Ph.D