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• W3175899557 abstract "Abstract K-mer based methods have become prevalent in many areas of bioinformatics. In applications such as database search, they often work with large multi-terabyte-sized datasets. Storing such large datasets is a detriment to tool developers, tool users, and reproducibility efforts. General purpose compressors like gzip, or those designed for read data, are sub-optimal because they do not take into account the specific redundancy pattern in k-mer sets. In our earlier work (Rahman and Medvedev, RECOMB 2020), we presented an algorithm UST-Compress that uses a spectrum-preserving string set representation to compress a set of k-mers to disk. In this paper, we present two improved methods for disk compression of k-mer sets, called ESS-Compress and ESS-Tip-Compress. They use a more relaxed notion of string set representation to further remove redundancy from the representation of UST-Compress. We explore their behavior both theoretically and on real data. We show that they improve the compression sizes achieved by UST-Compress by up to 27 percent, across a breadth of datasets. We also derive lower bounds on how well this type of compression strategy can hope to do." @default.
• W3175899557 created "2021-07-05" @default.
• W3175899557 creator A5055479093 @default.
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• W3175899557 date "2021-06-21" @default.
• W3175899557 modified "2023-10-14" @default.
• W3175899557 title "Disk compression of k-mer sets" @default.
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• W3175899557 doi "https://doi.org/10.1186/s13015-021-00192-7" @default.
• W3175899557 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8218509" @default.
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• W3175899557 hasPublicationYear "2021" @default.
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