“GenoDedup: Similarity-Based Deduplication and Delta-Encoding for Genome Sequencing Data”
IEEE Transactions on Computers, vol. 70, no. 5, pp. 669–681, May 2021.DOI: 10.1109/TC.2020.2994774.
Abstract: The vast datasets produced in human genomics must be efficiently stored, transferred, and processed while prioritizing storage space and restore performance. Balancing these two properties becomes challenging when resorting to traditional data compression techniques. In fact, specialized algorithms for compressing sequencing data favor the former, while large genome repositories widely resort to generic compressors (e.g., GZIP) to benefit from the latter. Notably, human beings have approximately 99.9% of DNA sequence similarity, vouching for an excellent opportunity for deduplication and its assets: leveraging inter-file similarity and achieving higher read performance. However, identity-based deduplication fails to provide a satisfactory reduction in the storage requirements of genomes. In this work, we balance space savings and restore performance by proposing GenoDedup, the first method that integrates efficient similarity-based deduplication and specialized delta-encoding for genome sequencing data. Our solution currently achieves 67.8% of the reduction gains of SPRING (i.e., the best specialized tool in this metric) and restores data 1.62x faster than SeqDB (i.e., the fastest competitor). Additionally, GenoDedup restores data 9.96x faster than SPRING and compresses files 2.05x more than SeqDB.
Research line(s): Fault and Intrusion Tolerance in Open Distributed Systems (FIT)