Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/6666
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dc.contributorDepartment of Industrial and Systems Engineering-
dc.creatorChen, B-
dc.creatorJi, P-
dc.date.accessioned2014-12-11T08:26:55Z-
dc.date.available2014-12-11T08:26:55Z-
dc.identifier.issn0305-1048-
dc.identifier.urihttp://hdl.handle.net/10397/6666-
dc.language.isoenen_US
dc.publisherOxford University Pressen_US
dc.rights© The Author(s) 2010. Published by Oxford University Press.en_US
dc.rightsThe article is available at http://dx.doi.org/10.1093/nar/gkq891en_US
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.subjectEukaryotic gene predictionen_US
dc.subjectHidden-Markov-Modelen_US
dc.subjectDNA-sequencesen_US
dc.subjectNucleotide-sequencesen_US
dc.subjectTriplet periodicityen_US
dc.subjectGenomic sequencesen_US
dc.subjectZ curveen_US
dc.subjectIdentificationen_US
dc.subjectRecognitionen_US
dc.subjectFourieren_US
dc.titleVisualization of the protein-coding regions with a self adaptive spectral rotation approachen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1-
dc.identifier.epage14-
dc.identifier.volume39-
dc.identifier.issue1-
dc.identifier.doi10.1093/nar/gkq891-
dcterms.abstractIdentifying protein-coding regions in DNA sequences is an active issue in computational biology. In this study, we present a self adaptive spectral rotation (SASR) approach, which visualizes coding regions in DNA sequences, based on investigation of the Triplet Periodicity property, without any preceding training process. It is proposed to help with the rough coding regions prediction when there is no extra information for the training required by other outstanding methods. In this approach, at each position in the DNA sequence, a Fourier spectrum is calculated from the posterior subsequence. Following the spectrums, a random walk in complex plane is generated as the SASR's graphic output. Applications of the SASR on real DNA data show that patterns in the graphic output reveal locations of the coding regions and the frame shifts between them: arcs indicate coding regions, stable points indicate non-coding regions and corners’ shapes reveal frame shifts. Tests on genomic data set from Saccharomyces Cerevisiae reveal that the graphic patterns for coding and non-coding regions differ to a great extent, so that the coding regions can be visually distinguished. Meanwhile, a time cost test shows that the SASR can be easily implemented with the computational complexity of O(N).-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationNucleic acids research, Jan. 2011, v. 39, no. 1, e3, p. 1-14-
dcterms.isPartOfNucleic acids research-
dcterms.issued2011-01-
dc.identifier.isiWOS:000286008500003-
dc.identifier.scopus2-s2.0-78651307018-
dc.identifier.pmid20947567-
dc.identifier.eissn1362-4962-
dc.identifier.rosgroupidr53894-
dc.description.ros2010-2011 > Academic research: refereed > Publication in refereed journal-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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