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International audience; Given the growing adoption of AI, cloud data lakes are facing the need to support cost-effective "just-in-case" data archival over long time periods to meet regulatory compliance requirements. Unfortunately, current media technologies suffer from fundamental issues that will soon, if not already, make cost-effective data archival infeasible. In this paper, we present a vision for redesigning the archival tier of cloud data lakes based on a novel, obsolescence-free storage medium-synthetic DNA. In doing so, we make two contributions: (i) we highlight the challenges in using DNA for data archival and list several open research problems, (ii) we outline OligoArchive-DSM (OA-DSM)-an end-to-end DNA storage pipeline that we are developing to demonstrate the feasibility of our vision.

International audience; Gapped alignment of sequenced data to a reference genome has traditionally been a computationally-intensive task due to the use of edit distance for dealing with indels and mismatches introduced by sequencing. In prior work, we developed Accel-Align [1], a Seed-Embed-Extend (SEE) sequence aligner that uses randomized embedding algorithms to quickly identify optimal candidate locations using Hamming distance rather than edit distance. While Accel-Align provides up to an order of magnitude improvement over state-of-the-art aligners, the randomized nature of embedding can lead to alignment errors resulting in lower precision and recall with downstream variant callers. In this work, we propose several techniques for improving the accuracy of randomized embedding-based sequence alignment. We provide an efficient implementation of these techniques in Accel-Align, and use it to present a comparative evaluation that demonstrates that the accuracy improvements can be achieved without sacrificing performance. Code is accessible in github.com/raja-appuswamy/accel-align-release.

The growing adoption of AI and data analytics in various sectors has resulted in digital preservation emerging as a cross-sectoral problem that affects everyone from data-driven enterprises to memory institutions alike. As all contemporary storage media suffer from fundamental density and durability limitations, researchers have started investigating new media that can offer high-density, long-term preservation of digital data. In the European Union-funded Future and Emerging Technologies project OligoArchive, we are exploring one such media, namely, synthetic Deoxyribo Nucleic Acid (DNA). In this paper, we provide an overview of the ongoing collaboration between project OligoArchive and the Danish National Archive in preserving culturally important digital data with synthetic DNA.