SWeeP: representing large biological sequences datasets in compact vectors.
Sci Rep
; 10(1): 91, 2020 01 09.
Article
em En
| MEDLINE
| ID: mdl-31919449
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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1
Base de dados:
MEDLINE
Assunto principal:
Proteínas de Bactérias
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Software
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Biologia Computacional
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Proteoma
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Proteínas Mitocondriais
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Mitocôndrias
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article