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/.

Details

Title
SWeeP: representing large biological sequences datasets in compact vectors
Author
De Pierri Camilla Reginatto 1   VIAFID ORCID Logo  ; Voyceik Ricardo 2 ; Santos de Mattos Letícia Graziela Costa 3 ; Kulik, Mariane Gonçalves 3 ; Camargo, Josué Oliveira 1 ; Repula de Oliveira Aryel Marlus 4 ; de Lima Nichio Bruno Thiago 1 ; Nunes, Marchaukoski Jeroniza 3 ; da Silva Filho Antonio Camilo 5 ; Dieval, Guizelini 3 ; Miguel, Ortega J 2 ; Pedrosa, Fabio O 1 ; Raittz, Roberto Tadeu 6 

1  Federal University of Paraná - SEPT, Graduate Program in Bioinformatics, Curitiba, Brazil (GRID:grid.20736.30) (ISNI:0000 0001 1941 472X) ; Federal University of Paraná, Department of Biochemistry and Molecular Biology, Curitiba, Brazil (GRID:grid.20736.30) (ISNI:0000 0001 1941 472X) 
2  Federal University of Minas Gerais, Institute of Biological Sciences (ICB), Belo Horizonte, Brazil (GRID:grid.8430.f) (ISNI:0000 0001 2181 4888) 
3  Federal University of Paraná - SEPT, Graduate Program in Bioinformatics, Curitiba, Brazil (GRID:grid.20736.30) (ISNI:0000 0001 1941 472X) 
4  Federal University of Paraná - SEPT, Graduate Program in Bioinformatics, Curitiba, Brazil (GRID:grid.20736.30) (ISNI:0000 0001 1941 472X) ; Federal University of Paraná, Department of Genetics, Curitiba, Brazil (GRID:grid.20736.30) (ISNI:0000 0001 1941 472X) 
5  Federal University of Paraná - SEPT, Graduate Program in Bioinformatics, Curitiba, Brazil (GRID:grid.20736.30) (ISNI:0000 0001 1941 472X) ; Federal University of Paraná, Department of Pharmaceutical Sciences, Curitiba, Brazil (GRID:grid.20736.30) (ISNI:0000 0001 1941 472X) 
6  Federal University of Paraná - SEPT, Graduate Program in Bioinformatics, Curitiba, Brazil (GRID:grid.20736.30) (ISNI:0000 0001 1941 472X) ; Federal University of Minas Gerais, Institute of Biological Sciences (ICB), Belo Horizonte, Brazil (GRID:grid.8430.f) (ISNI:0000 0001 2181 4888) ; Federal University of Paraná, Department of Genetics, Curitiba, Brazil (GRID:grid.20736.30) (ISNI:0000 0001 1941 472X) 
Publication year
2020
Publication date
2020
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41598-019-55627-4
ProQuest document ID
2342982117
Copyright
This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.