ArticleAlgorithms for molecular biology : AMB2021
Disk compression of k-mer sets.
Article in Algorithms for molecular biology : AMB, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Efficient and robust search of microbial genomes via phylogenetic compression.Nature methods · 2025Article
- Seamless, rapid, and accurate analyses of outbreak genomic data using splitGenome research · 2024Article
- Compression algorithm for colored de Bruijn graphs.Algorithms for molecular biology : AMB · 2024Article
- Compression Algorithm for Colored de Bruijn Graphs.Algorithms in bioinformatics : ... International Workshop, WABI ..., proceedings. WABI (Workshop) · 2023Article
- Matchtigs: minimum plain text representation of k-mer sets.Genome biology · 2023Article
- Navigating bottlenecks and trade-offs in genomic data analysis.Nature reviews. Genetics · 2023Review
- SPRISS: approximating frequent k-mers by sampling reads, and applications.Bioinformatics (Oxford, England) · 2022Article
- Genome-wide alignment-free phylogenetic distance estimation under a no strand-bias model.Bioinformatics advances · 2022Article
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3 authors.
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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.
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