Due to the challenges of analyzing and storing massive volumes of genomic and metagenomic sequence data, significant efforts have been made to accelerate (meta)genomic analyses and store sequence data compressed. Despite the benefits of these techniques, we identify two major outstanding problems in accessing stored sequence data and supplying it to the analysis units: (i) the data movement bottleneck due to moving large amounts of low-reuse data from storage and the unnecessary burden on the rest of the system, and (ii) the data preparation bottleneck, where compressed sequence data needs to be first decompressed and formatted before analysis. We present customized storage-centric systems, which efficiently (i) analyze (meta)genomic data inside storage, and (ii) enable highly-compressed storage and high-performance access of large-scale sequence data, thereby alleviating the overheads of data movement, computation, and data preparation. First, we introduce GenStore, an in-storage processing system that filters genomic data not requiring expensive computation directly inside storage. Second, we propose MegIS, an in-storage processing system that significantly reduces the data movement overhead of metagenomic analysis. Third, we introduce GRAINS, a storage-centric system for analysis on large-scale (meta)genomic graphs in storage. Fourth, we propose SAGe, an algorithm-architecture co-design for highly-compressed storage and high-performance access of sequence data. We demonstrate that the proposed systems significantly (e.g., by one to two orders of magnitude) improve performance, energy efficiency, and cost-efficiency, all at the same time. We hope these systems facilitate broader adoption of (meta)genomics and inspire research on other data-intensive domains in health and life sciences.
