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Assessment of variant load in an idiopathic autoinflammatory index patientNordin, Jessika January 2014 (has links)
No description available.
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Data-Intensive Biocomputing in the CloudMeeramohideen Mohamed, Nabeel 25 September 2013 (has links)
Next-generation sequencing (NGS) technologies have made it possible to rapidly sequence the human genome, heralding a new era of health-care innovations based on personalized genetic information. However, these NGS technologies generate data at a rate that far outstrips Moore\'s Law. As a consequence, analyzing this exponentially increasing data deluge requires enormous computational and storage resources, resources that many life science institutions do not have access to. As such, cloud computing has emerged as an obvious, but still nascent, solution.
This thesis intends to investigate and design an efficient framework for running and managing large-scale data-intensive scientific applications in the cloud. Based on the learning from our parallel implementation of a genome analysis pipeline in the cloud, we aim to provide a framework for users to run such data-intensive scientific workflows using a hybrid setup of client and cloud resources. We first present SeqInCloud, our highly scalable parallel implementation of a popular genetic variant pipeline called genome analysis toolkit (GATK), on the Windows Azure HDInsight cloud platform. Together with a parallel implementation of GATK on Hadoop, we evaluate the potential of using cloud computing for large-scale DNA analysis and present a detailed study on efficiently utilizing cloud resources for running data-intensive, life-science applications. Based on our experience from running SeqInCloud on Azure, we present CloudFlow, a feature rich workflow manager for running MapReduce-based bioinformatic pipelines utilizing both client and cloud resources. CloudFlow, built on the top of an existing MapReduce-based workflow manager called Cloudgene, provides unique features that are not offered by existing MapReduce-based workflow managers, such as enabling simultaneous use of client and cloud resources, automatic data-dependency handling between client and cloud resources, and the flexibility of implementing user-defined plugins for data transformations. In-general, we believe that our work attempts to increase the adoption of cloud resources for running data-intensive scientific workloads. / Master of Science
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Human Genome and Transcriptome Analysis with Next-Generation SequencingKhuder, Basil January 2017 (has links)
No description available.
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Séquençage d’exomes d’une cohorte de familles caucasiennes simplex dont les patients sont atteints du syndrome d’interruption de la tige hypophysaireJean-Louis, Martineau 04 1900 (has links)
No description available.
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