Enabling the Efficient, Dependable CloudBased Storage of Human Genomes Efficiently storing large data sets of human genomes is a longterm ambition from both the research and clinical life sciences communities. For instance, biobanks stock thousands to millions of biological physical samples and have been under pressure to store also their resulting digitized genomes. However, these and other life sciences institutions lack the infrastructure and expertise to efficiently store this data. Cloud computing is a natural economic alternative to private infrastructures, but it is not as good an alternative in terms of security and privacy. In this work, we present an endtoend composite pipeline intended to enable the efficient, dependable cloudbased storage of human genomes by integrating three mechanisms we have recently proposed. These mechanisms encompass (1) a privacysensitivity detector for human genomes, (2) a similaritybased deduplication and deltaencoding algorithm for sequencing data, and (3) an auditability scheme to verify who has effectively read data in storage systems that use secure information dispersal. By integrating them with appropriate storage configurations, one can obtain reasonable privacy protection, security, and dependability guarantees at modest costs (e.g., less than 1GenomeYear). Our preliminary analysis indicates that this pipeline costs only 3% more than nonreplicated systems, 48% less than fullyreplicating all data, and 31% less than secure information dispersal schemes.

optimizing the Research of DNA Sequences in a NoSQL Document Database: A Preliminary Study The study of DNA sequences has become indispensable for basic biological research, and in numerous applied fields such as comparative genomics, evolutionary biology, pan genomics, genetics of disease, regulation of gene expression, oncology and many others, all supported by bioinformatics. In the era of Cloud computing, federating the Cloud systems of different genetics research organisations paves the way towards a new era of data sharing and new mashup services and applications. However, due to the huge amount of genomics data (genomics Big Data) that have to be managed, a parallel distributed NoSQL DataBase Management System (DBMS) approach becomes fundamental. Specifically, due to the textual nature of genomics data, a NoSQL DBMS appears to be the most suitable solution. In this paper, by considering the whole human genome, we present a preliminary study comparing this latter using MongoDB with a SQLlike database solution, i.e., MySQL in order to look for DNA sequences. Moreover, in order to optimize the research of genomics codes, we adopt hash functions that allow mapping nucleotides sequences of arbitrary size onto data of a fixed smaller size. Experiments, shows that MongoDB apart simplifying the management of genomics data provides better performances.