ArticleJournal of computational biology : a journal of computational molecular cell biology2017
Improving Bloom Filter Performance on Sequence Data Using k-mer Bloom Filters.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- KeBaB:International Symposium on String Processing and Information Retrieval : SPIRE ... : proceedings. SPIRE (Symposium) · 2026Article
- Creating and Using Minimizer Sketches in Computational Genomics.Journal of computational biology : a journal of computational molecular cell biology · 2023Review
- Representing bacteria with unique genomic signatures.Frontiers in big data · 2022Article
- Data Structures to Represent a Set ofACM computing surveys · 2022Article
- Sequence-specific minimizers via polar sets.Bioinformatics (Oxford, England) · 2021Article
- Mismatch-tolerant, alignment-free sequence classification using multiple spaced seeds and multiindex Bloom filters.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- To Petabytes and beyond: recent advances in probabilistic and signal processing algorithms and their application to metagenomics.Nucleic acids research · 2020Article
- deBGR: an efficient and near-exact representation of the weighted de Bruijn graph.Bioinformatics (Oxford, England) · 2017Article
- An efficient strategy usingHeliyon · 2017Article
- Improving Bloom Filter Performance on Sequence Data Using k-mer Bloom Filters.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
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3 authors.
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Abstract
Using a sequence's k-mer content rather than the full sequence directly has enabled significant performance improvements in several sequencing applications, such as metagenomic species identification, estimation of transcript abundances, and alignment-free comparison of sequencing data. As k-mer sets often reach hundreds of millions of elements, traditional data structures are often impractical for k-mer set storage, and Bloom filters (BFs) and their variants are used instead. BFs reduce the memory footprint required to store millions of k-mers while allowing for fast set containment queries, at the cost of a low false positive rate (FPR). We show that, because k-mers are derived from sequencing reads, the information about k-mer overlap in the original sequence can be used to reduce the FPR up to 30 × with little or no additional memory and with set containment queries that are only 1.3 - 1.6 times slower. Alternatively, we can leverage k-mer overlap information to store k-mer sets in about half the space while maintaining the original FPR. We consider several variants of such k-mer Bloom filters (kBFs), derive theoretical upper bounds for their FPR, and discuss their range of applications and limitations.
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