Open AccessOn DNA numerical representations for genomic similarity computation
Author(s) -
Gerardo Mendizabal-Ruiz,
Israel Román-Godínez,
Sulema Torres-Ramos,
Ricardo A. Salido-Ruiz,
J. Alejandro Morales
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0173288
Subject(s) - computer science , representation (politics) , genomic dna , computational biology , signal processing , similarity (geometry) , computation , transformation (genetics) , genomics , signal (programming language) , dna , pattern recognition (psychology) , theoretical computer science , data mining , bioinformatics , genetics , algorithm , artificial intelligence , genome , biology , digital signal processing , gene , politics , political science , computer hardware , law , image (mathematics) , programming language
Genomic signal processing (GSP) refers to the use of signal processing for the analysis of genomic data. GSP methods require the transformation or mapping of the genomic data to a numeric representation. To date, several DNA numeric representations (DNR) have been proposed; however, it is not clear what the properties of each DNR are and how the selection of one will affect the results when using a signal processing technique to analyze them. In this paper, we present an experimental study of the characteristics of nine of the most frequently-used DNR. The objective of this paper is to evaluate the behavior of each representation when used to measure the similarity of a given pair of DNA sequences.