Archive Number: 4.288
Status of Thesis: Open
Due to the availability of large volumes of genomic data, increasing numbers of genomics researchers are turning to the cloud to overcome the deficits in storage and processing resources of traditional IT. As a result, various platforms that allow for performing genomic analyses in cloud computing environments have emerged during the past few years (i.e., Galaxy Cloud, CloudDOE, DNAnexus, etc.). For researchers, the amount of available options to store and process genomic data in the cloud and the inherent sensitivity of genomic data make it difficult to find and select an appropriate cloud service that fits their needs and is best suited for their research endeavor.
The objective of this thesis is to develop a framework for the selection of appropriate genomic cloud services according to individual researchers needs. For this the student working on this thesis will be provided extant research results (in form of a genomics data set taxonomy as well as an overview of cloud security requirements and measures). The student will be tasked to analyze extant genomic cloud services with the help of this data, develop the framework and implement a prototype frontend that maps the developed framework.
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Chung, W.-C., Chen, C.-C., Ho, J.-M., Lin, C.-Y., Hsu, W.-L., Wang, Y.-C., Lee, D. T., Lai, F., Huang, C.-W. and Chang, Y.-J. (2014). “CloudDOE: A User-Friendly Tool for Deploying Hadoop Clouds and Analyzing High-Throughput Sequencing Data with MapReduce.” PLoS ONE (9:6), p. e98146.
Subashini, S.; Kavitha, V. (2011). A survey on security issues in service delivery models of cloud computing. Journal of Network and Computer Applications, 34(1), 1–11.
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Thiebes, S.; Kleiber, G.; Sunyaev, A. (2017a): Cancer Genomics Research in the Cloud: A Taxonomy of Genome Data Sets. In: GenoPri’17, Orlando, Florida, Oct.