ArticleBioinformatics (Oxford, England)2023
MetaProFi: an ultrafast chunked Bloom filter for storing and querying protein and nucleotide sequence data for accurate identification of functionally relevant genetic variants.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Kaminari: a frugal colored index for approximateBioinformatics advances · 2026Article
- The backpack quotient filter: A dynamic and space-efficient data structure for queryingiScience · 2024Article
- A survey of k-mer methods and applications in bioinformatics.Computational and structural biotechnology journal · 2024Review
- Indexing and searching petabase-scale nucleotide resources.Nature methods · 2024Article
- Indexing and real-time user-friendly queries in terabyte-sized complex genomic datasets with kmindex and ORA.Nature computational science · 2024Article
- MetaBIDx: a new computational approach to bacteria identification in microbiomes.Microbiome research reports · 2024Article
- kmtricks: efficient and flexible construction of Bloom filters for large sequencing data collections.Bioinformatics advances · 2022Article
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Authors and funding
5 authors.
Funding
Abstract
motivationBloom filters are a popular data structure that allows rapid searches in large sequence datasets. So far, all tools work with nucleotide sequences; however, protein sequences are conserved over longer evolutionary distances, and only mutations on the protein level may have any functional significance.
resultsWe present MetaProFi, a Bloom filter-based tool that, for the first time, offers the functionality to build indexes of amino acid sequences and query them with both amino acid and nucleotide sequences, thus bringing sequence comparison to the biologically relevant protein level. MetaProFi implements additional efficient engineering solutions, such as a shared memory system, chunked data storage and efficient compression. In addition to its conceptual novelty, MetaProFi demonstrates state-of-the-art performance and excellent memory consumption-to-speed ratio when applied to various large datasets. AVAILABILITY AND IMPLEMENTATION: Source code in Python is available at https://github.com/kalininalab/metaprofi.
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