SWeeP: representing large biological sequences datasets in compact vectors

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/.

Comparative Genomics Provides Insights into the Taxonomy of Azoarcus and Reveals Separate Origins of Nif Genes in the Proposed Azoarcus and Aromatoleum Genera

“…Phylogenetic analyses were conducted using SWeeP [ 47 ] for protein set representations (available at: ). The purpose of this method is to transform any set of amino acid sequences into a single vector that can, for example, represent all proteins in an organism.…”

“…The purpose of this method is to transform any set of amino acid sequences into a single vector that can, for example, represent all proteins in an organism. In practice, the input file to generate the SWeeP vectors consists of a multiFASTA file containing amino acid sequences, in which the protein sequences in a particular organism are concatenated, each protein flanked by delimiters, to get a single sequence [ 47 ].…”

rSWeeP: A R/Bioconductor package deal with SWeeP sequences representation

“…A tutorial for running rSWeeP and the trees are available at https://github.com/DanrleyRF/Suplementar. For all analysis, we applied the same protocol adopted in the study by De Pierri and colleagues (2020) [5] .…”

“…In the era of Big Data, vector and alignment-free approaches to compare and represent biological sequences stand out for being more efficient than most alignment-based heuristic methods [1] . Studies show that vector representation of biological data presents an effective solution for the Bioinformatics field [2][3][4][5] .…”

“…Recently, SWeeP model presented expressive results in the analysis of complete proteomes. SWeeP is a model implemented in MatLab, which allows the representation of biological information through the projection of DNA sequences in vectors with reduced dimensionality [5] . Here, we present rSWeeP, an implementation of SWeeP model using R/Bioconductor programming language.…”