ArticleBioinformatics (Oxford, England)2013
Compressive genomics for protein databases.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Efficient and robust search of microbial genomes via phylogenetic compression.Nature methods · 2025Article
- Image-centric compression of protein structures improves space savings.BMC bioinformatics · 2023Article
- Levenshtein Distance, Sequence Comparison and Biological Database Search.IEEE transactions on information theory · 2021Article
- AC2: An Efficient Protein Sequence Compression Tool Using Artificial Neural Networks and Cache-Hash Models.Entropy (Basel, Switzerland) · 2021Article
- Single Cell Genomics Reveals Viruses Consumed by Marine Protists.Frontiers in microbiology · 2020Article
- Mantis: A Fast, Small, and Exact Large-Scale Sequence-Search Index.Cell systems · 2018Article
- HFSP: high speed homology-driven function annotation of proteins.Bioinformatics (Oxford, England) · 2018Article
- Improved Search of Large Transcriptomic Sequencing Databases Using Split Sequence Bloom Trees.Journal of computational biology : a journal of computational molecular cell biology · 2018Article
- Fast batch searching for protein homology based on compression and clustering.BMC bioinformatics · 2017Article
- Harnessing Big Data for Systems Pharmacology.Annual review of pharmacology and toxicology · 2017Review
- Fast genotyping of known SNPs through approximate k-mer matching.Bioinformatics (Oxford, England) · 2016Article
- Computational Biology in the 21st Century: Scaling with Compressive Algorithms.Communications of the ACM · 2016Article
- Fast search of thousands of short-read sequencing experiments.Nature biotechnology · 2016Article
- High speed BLASTN: an accelerated MegaBLAST search tool.Nucleic acids research · 2015Article
- Entropy-scaling search of massive biological data.Cell systems · 2015Article
- Reference-based compression of short-read sequences using path encoding.Bioinformatics (Oxford, England) · 2015Article
- Faster sequence homology searches by clustering subsequences.Bioinformatics (Oxford, England) · 2015Article
- eCAMBer: efficient support for large-scale comparative analysis of multiple bacterial strains.BMC bioinformatics · 2014Article
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Authors and funding
6 authors.
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Abstract
motivationThe exponential growth of protein sequence databases has increasingly made the fundamental question of searching for homologs a computational bottleneck. The amount of unique data, however, is not growing nearly as fast; we can exploit this fact to greatly accelerate homology search. Acceleration of programs in the popular PSI/DELTA-BLAST family of tools will not only speed-up homology search directly but also the huge collection of other current programs that primarily interact with large protein databases via precisely these tools.
resultsWe introduce a suite of homology search tools, powered by compressively accelerated protein BLAST (CaBLASTP), which are significantly faster than and comparably accurate with all known state-of-the-art tools, including HHblits, DELTA-BLAST and PSI-BLAST. Further, our tools are implemented in a manner that allows direct substitution into existing analysis pipelines. The key idea is that we introduce a local similarity-based compression scheme that allows us to operate directly on the compressed data. Importantly, CaBLASTP's runtime scales almost linearly in the amount of unique data, as opposed to current BLASTP variants, which scale linearly in the size of the full protein database being searched. Our compressive algorithms will speed-up many tasks, such as protein structure prediction and orthology mapping, which rely heavily on homology search. AVAILABILITY: CaBLASTP is available under the GNU Public License at http://cablastp.csail.mit.edu/ CONTACT: bab@mit.edu.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.