ArticleScientific reports2020
SWeeP: representing large biological sequences datasets in compact vectors.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Protease-serpin-microbiome interactions in inflammatory bowel disease: toward integrated biomarkers of gut health.Gut microbes · 2026Review
- GRAMEP: an alignment-free method based on the maximum entropy principle for identifying SNPs.BMC bioinformatics · 2025Article
- Exploring diazotrophic diversity: unveiling Nif core distribution and evolutionary patterns in nitrogen-fixing organisms.BMC genomics · 2025Article
- NifFinder: improved Nif protein prediction using SWeeP vectors and neural networks.Bioinformatics advances · 2025Article
- What are we learning with Yoga? Mapping the scientific literature on Yoga using a vector-text-mining approach.PloS one · 2025Article
- Genomic landscape of the SARS-CoV-2 pandemic in Brazil suggests an external P.1 variant origin.Frontiers in microbiology · 2022Article
- Origin and evolution of nonulosonic acid synthases and their relationship with bacterial pathogenicity revealed by a large-scale phylogenetic analysis.Microbial genomics · 2021Article
- Article
- Prediction and AnalysisFrontiers in microbiology · 2021Article
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Authors and funding
13 authors.
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Abstract
Vectoral and alignment-free approaches to biological sequence representation have been explored in bioinformatics to efficiently handle big data. Even so, most current methods involve sequence comparisons via alignment-based heuristics and fail when applied to the analysis of large data sets. Here, we present "Spaced Words Projection (SWeeP)", a method for representing biological sequences using relatively small vectors while preserving intersequence comparability. SWeeP uses spaced-words by scanning the sequences and generating indices to create a higher-dimensional vector that is later projected onto a smaller randomly oriented orthonormal base. We constructed phylogenetic trees for all organisms with mitochondrial and bacterial protein data in the NCBI database. SWeeP quickly built complete and accurate trees for these organisms with low computational cost. We compared SWeeP to other alignment-free methods and Sweep was 10 to 100 times quicker than the other techniques. A tool to build SWeeP vectors is available at https://sourceforge.net/projects/spacedwordsprojection/.
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