SEGMENT: identifying compositional domains in DNA sequences

Abstract

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.

Low-Complexity Regions in Plasmodium falciparum Proteins

“…Other DNA segmentation algorithms-for example, into compositionally homogeneous DNA domains (Oliver et al 1999) or regions with similar combinatorial features (Chrochemore and Vérin 1998)-have been proposed. The topic is reviewed in Braun and Mueller (1998).…”

Analysis of Information Content for Biological Sequences

“…For a single Dl\A sequence, our method is equivalent to a generalization of the Jensen-Shannon divergence based segmentation method (Oliver et aL, 1999) except that we take into account the possible overdispersion effect on the choice of the significance leveL Here the overdispersion means that in reality there is greater variability among the columns in (1.1) than would be expected from a statistical model (e.g., product multinomial model), because we can not expect each domain to be completely homogeneous. There are many different segmentation methods in literature.…”

Changes in primary DNA sequence complexity influence the phenotypic consequences of mutations in human gene regulatory regions

“…Oliver et al 1999;Wan and Wootton 1998). Such measures are sensitive to the presence/absence of certain sub-sequences or consensus patterns in a DNA sequence but fail to take the linguistic structure of the sequence into account.…”