Finding Borders between Coding and Noncoding DNA Regions by an Entropic Segmentation Method

Bernaola‐Galván, Pedro; Große, Ivo; Carpena, Pedro; Oliver, José Luis Tejera; Román-Roldán, Ramón; Stanley, H. Eugene

doi:10.1103/physrevlett.85.1342

Cited by 119 publications

(111 citation statements)

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“…The number of parameters in the two models are K2 = 7 (the partition point i is also a free parameter) and K1 = 3. So 2NDJS under the null hypothesis should obey the χ 2 df =4 distribution (the same conclusion was reached before, see [6] and (I Grosse, et al in preparation), only the df used there is 3, instead 4).…”

Section: The Divide-and-conquer Segmentation As a Likelihood Ratio Testmentioning

confidence: 73%

DNA segmentation as a model selection process

2001

Proceedings of the Fifth Annual International Conference on Computational Biology

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Previous divide-and-conquer segmentation analyses of DNA sequences do not provide a satisfactory stopping criterion for the recursion. This paper proposes that segmentation be considered as a model selection process. Using the tools in model selection, a limit for the stopping criterion on the relaxed end can be determined. The Bayesian information criterion, in particular, provides a much more stringent stopping criterion than what is currently used. Such a stringent criterion can be used to delineate larger DNA domains. A relationship between the stopping criterion and the average domain size is empirically determined, which may aid in the determination of isochore borders.

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Section: The Divide-and-conquer Segmentation As a Likelihood Ratio Testmentioning

confidence: 73%

DNA segmentation as a model selection process

2001

Proceedings of the Fifth Annual International Conference on Computational Biology

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“…The most commonly used procedure [4,5,6] is based on maximization of the Jensen-Shannon (J-S) divergence through which a given DNA string is recursively separated into compostionallly homogeneous segments called domains (or patches). This results in a coarse-grained description of the DNA string as a sequence of distinct domains.…”

Section: Introductionmentioning

confidence: 99%

“…A domain set may thus be interpreted as a larger homogeneous sequence, parts of which are scattered nonuniformly in a genomic sequence. The number of domain sets constructed thus is found to be much fewer than the domains obtained upon segmentation [4,5,6,7]. We propose here an optimal procedure, starting from the domains found from one of the above segmentation methods, and building up a domain set by adding together all its components.…”

Section: Introductionmentioning

confidence: 99%

Simplifying the mosaic description of DNA sequences

Azad

Rao

et al. 2002

Phys. Rev. E

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By using the Jensen-Shannon divergence, genomic DNA can be divided into compositionally distinct domains through a standard recursive segmentation procedure. Each domain, while significantly different from its neighbours, may however share compositional similarity with one or more distant (nonneighbouring) domains. We thus obtain a coarse-grained description of the given DNA string in terms of a smaller set of distinct domain labels. This yields a minimal domain description of a given DNA sequence, significantly reducing its organizational complexity. This procedure gives a new means of evaluating genomic complexity as one examines organisms ranging from bacteria to human. The mosaic organization of DNA sequences could have originated from the insertion of fragments of one genome (the parasite) inside another (the host), and we present numerical experiments that are suggestive of this scenario.

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“…This type of finite size effects is of major relevance for statistical analysis of DNA and other biosequences, e.g. [4,5,6,7]. In this article we want to calculate the expected frequency distribution which one finds in dependence on the sample size M .…”

Section: Introductionmentioning

confidence: 99%