Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0167430
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dc.titleAn alignment-free algorithm in comparing the similarity of protein sequences based on Pseudo-Markov transition probabilities among amino acids
dc.contributor.authorLi Y.
dc.contributor.authorSong T.
dc.contributor.authorYang J.
dc.contributor.authorZhang Y.
dc.contributor.authorYang J.
dc.date.accessioned2019-11-06T07:42:15Z
dc.date.available2019-11-06T07:42:15Z
dc.date.issued2016
dc.identifier.citationLi Y., Song T., Yang J., Zhang Y., Yang J. (2016). An alignment-free algorithm in comparing the similarity of protein sequences based on Pseudo-Markov transition probabilities among amino acids. PLoS ONE 11 (12) : e0167430. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0167430
dc.identifier.issn19326203
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/161540
dc.description.abstractIn this paper, we have proposed a novel alignment-free method for comparing the similarity of protein sequences. We first encode a protein sequence into a 440 dimensional feature vector consisting of a 400 dimensional Pseudo-Markov transition probability vector among the 20 amino acids, a 20 dimensional content ratio vector, and a 20 dimensional position ratio vector of the amino acids in the sequence. By evaluating the Euclidean distances among the representing vectors, we compare the similarity of protein sequences. We then apply this method into the ND5 dataset consisting of the ND5 protein sequences of 9 species, and the F10 and G11 datasets representing two of the xylanases containing glycoside hydrolase families, i.e., families 10 and 11. As a result, our method achieves a correlation coefficient of 0.962 with the canonical protein sequence aligner ClustalW in the ND5 dataset, much higher than those of other 5 popular alignment-free methods. In addition, we successfully separate the xylanases sequences in the F10 family and the G11 family and illustrate that the F10 family is more heat stable than the G11 family, consistent with a few previous studies. Moreover, we prove mathematically an identity equation involving the Pseudo-Markov transition probability vector and the amino acids content ratio vector. © 2016 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.sourceUnpaywall 20191101
dc.subjectamino acid
dc.subjectglycosidase
dc.subjectxylan endo 1,3 beta xylosidase
dc.subjectamino acid
dc.subjectprotein
dc.subjectaccuracy
dc.subjectalgorithm
dc.subjectalignment free algorithm
dc.subjectamino acid sequence
dc.subjectArticle
dc.subjectcorrelation coefficient
dc.subjecthuman
dc.subjectnonhuman
dc.subjectphylogenetic tree
dc.subjectprobability
dc.subjectthermostability
dc.subjectalgorithm
dc.subjectamino acid sequence
dc.subjectchemistry
dc.subjectprocedures
dc.subjectsequence alignment
dc.subjectsequence analysis
dc.subjectAlgorithms
dc.subjectAmino Acid Sequence
dc.subjectAmino Acids
dc.subjectGlycoside Hydrolases
dc.subjectProbability
dc.subjectProteins
dc.subjectSequence Alignment
dc.subjectSequence Analysis, Protein
dc.typeArticle
dc.contributor.departmentCENTRE FOR MARITIME STUDIES
dc.description.doi10.1371/journal.pone.0167430
dc.description.sourcetitlePLoS ONE
dc.description.volume11
dc.description.issue12
dc.description.pagee0167430
dc.published.statePublished
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