Abstract
Thanks to the rapid advances in sequencing technologies, genomic data is now being produced at an unprecedented rate. To adapt to this growth, several algorithms and paradigm shifts have been proposed to increase the throughput of the classical DNA workflow, e.g. by relying on the cloud to perform CPU intensive operations. However, the scientific community raised an alarm due to the possible privacy-related attacks that can be executed on genomic data. In this paper we review the state of the art in cloud-based alignment algorithms that have been developed for performance. We then present several privacy-preserving mechanisms that have been, or could be, used to align reads at an incremental performance cost. We finally argue for the use of risk analysis throughout the DNA workflow, to strike a balance between performance and protection of data.
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Notes
- 1.
Ancestry – https://www.ancestry.com.
- 2.
DisGeNet – http://www.disgenet.org.
- 3.
Surname Navigator – http://www.surnamenavigator.org.
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Acknowledgements
This work was supported by the Fonds National de la Recherche Luxembourg (FNR) through PEARL grant FNR/P14/8149128, and by the Fundação para a Ciência e para a Tecnologia (FCT) through funding of the LaSIGE Research Unit, ref. UID/CEC/00408/2013.
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Fernandes, M., Decouchant, J., Couto, F.M., Esteves-Verissimo, P. (2017). Cloud-Assisted Read Alignment and Privacy. In: Fdez-Riverola, F., Mohamad, M., Rocha, M., De Paz, J., Pinto, T. (eds) 11th International Conference on Practical Applications of Computational Biology & Bioinformatics. PACBB 2017. Advances in Intelligent Systems and Computing, vol 616. Springer, Cham. https://doi.org/10.1007/978-3-319-60816-7_27
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DOI: https://doi.org/10.1007/978-3-319-60816-7_27
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