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Discussion of "A Bayesian approach to DNA sequence segmentation"

Booth, Hilary; Burden, Conrad; Maindonald, John; Santoso, Lucia; Wakefield, Matthew; Wilson, Susan

Description

This article discusses the results in Boys and Henderson (2004, Biometrics 60, 573 581) in which the authors propose a new approach to the classification of genomic DNA into a number of hidden Markov states with a variable order of dependency, potentially allowing for the high-throughput detection of structure within genomic DNA. This article is likely to be an important point of departure for further modeling of this type. We question whether the genome of the bacteriophage lambda is the most...[Show more]

dc.contributor.authorBooth, Hilary
dc.contributor.authorBurden, Conrad
dc.contributor.authorMaindonald, John
dc.contributor.authorSantoso, Lucia
dc.contributor.authorWakefield, Matthew
dc.contributor.authorWilson, Susan
dc.date.accessioned2015-12-13T22:45:33Z
dc.identifier.issn0006-341X
dc.identifier.urihttp://hdl.handle.net/1885/79845
dc.description.abstractThis article discusses the results in Boys and Henderson (2004, Biometrics 60, 573 581) in which the authors propose a new approach to the classification of genomic DNA into a number of hidden Markov states with a variable order of dependency, potentially allowing for the high-throughput detection of structure within genomic DNA. This article is likely to be an important point of departure for further modeling of this type. We question whether the genome of the bacteriophage lambda is the most appropriate example with which to demonstrate the method's effectiveness, whether it can be expected that the method will carry over to genomes where there is only one direction of transcription and no operon structure, and suggest, a graphical display that seems to offer insight into the results. It would be interesting to see an analysis that uses the codon alphabet.
dc.publisherInternational Biometrics Society
dc.sourceBiometrics
dc.subjectKeywords: genomic DNA; Bayesian analysis; DNA; genome; bacteriophage lambda; Bayes theorem; bioinformatics; codon; DNA sequence; genetic transcription; genome; nonhuman; operon; probability; review; statistical model; Algorithms; Bacteriophage lambda; Bayes Theorem (Hidden) Markov model; Bacteriophage lambda; Bioinformatics; Gene finding; Sequence analysis; Sequence composition
dc.titleDiscussion of "A Bayesian approach to DNA sequence segmentation"
dc.typeJournal article
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.citationvolume61
dc.date.issued2005
local.identifier.absfor010202 - Biological Mathematics
local.identifier.ariespublicationMigratedxPub8217
local.type.statusPublished Version
local.contributor.affiliationBooth, Hilary, College of Physical and Mathematical Sciences, ANU
local.contributor.affiliationBurden, Conrad, College of Physical and Mathematical Sciences, ANU
local.contributor.affiliationMaindonald, John, College of Physical and Mathematical Sciences, ANU
local.contributor.affiliationSantoso, Lucia, College of Physical and Mathematical Sciences, ANU
local.contributor.affiliationWakefield, Matthew, College of Medicine, Biology and Environment, ANU
local.contributor.affiliationWilson, Susan, College of Physical and Mathematical Sciences, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.issue2
local.bibliographicCitation.startpage635
local.bibliographicCitation.lastpage639
local.identifier.doi10.1111/j.0006-341X.2005.040701_1.x
dc.date.updated2015-12-11T10:23:47Z
local.identifier.scopusID2-s2.0-20744441080
CollectionsANU Research Publications

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