MOTIVATION: DNA sequences are formed by patches or domains of different nucleotide composition. In a few simple sequences, domains can simply be identified by eye; however, most DNA sequences show a complex compositional heterogeneity (fractal structure), which cannot be properly detected by current methods. Recently, a computationally efficient segmentation method to analyse such nonstationary sequence structures, based on the Jensen-Shannon entropic divergence, has been described. Specific algorithms implementing this method are now needed. RESULTS: Here we describe a heuristic segmentation algorithm for DNA sequences, which was implemented on a Windows program (SEGMENT). The program divides a DNA sequence into compositionally homogeneous domains by iterating a local optimization procedure at a given statistical significance. Once a sequence is partitioned into domains, a global measure of sequence compositional complexity (SCC), accounting for both the sizes and compositional biases of all the domains in the sequence, is derived. SEGMENT computes SCC as a function of the significance level, which provides a multiscale view of sequence complexity.
MOTIVATION: DNA sequences are formed by patches or domains of different nucleotide composition. In a few simple sequences, domains can simply be identified by eye; however, most DNA sequences show a complex compositional heterogeneity (fractal structure), which cannot be properly detected by current methods. Recently, a computationally efficient segmentation method to analyse such nonstationary sequence structures, based on the Jensen-Shannon entropic divergence, has been described. Specific algorithms implementing this method are now needed. RESULTS: Here we describe a heuristic segmentation algorithm for DNA sequences, which was implemented on a Windows program (SEGMENT). The program divides a DNA sequence into compositionally homogeneous domains by iterating a local optimization procedure at a given statistical significance. Once a sequence is partitioned into domains, a global measure of sequence compositional complexity (SCC), accounting for both the sizes and compositional biases of all the domains in the sequence, is derived. SEGMENT computes SCC as a function of the significance level, which provides a multiscale view of sequence complexity.
Authors: Pierre Nicolas; Laurent Bize; Florence Muri; Mark Hoebeke; François Rodolphe; S Dusko Ehrlich; Bernard Prum; Philippe Bessières Journal: Nucleic Acids Res Date: 2002-03-15 Impact factor: 16.971
Authors: Ricardo Lebrón; Cristina Gómez-Martín; Pedro Carpena; Pedro Bernaola-Galván; Guillermo Barturen; Michael Hackenberg; José L Oliver Journal: Nucleic Acids Res Date: 2016-10-27 Impact factor: 16.971
Authors: Andrés Moya; José L Oliver; Miguel Verdú; Luis Delaye; Vicente Arnau; Pedro Bernaola-Galván; Rebeca de la Fuente; Wladimiro Díaz; Cristina Gómez-Martín; Francisco M González; Amparo Latorre; Ricardo Lebrón; Ramón Román-Roldán Journal: Sci Rep Date: 2020-11-04 Impact factor: 4.379