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PCV: An Alignment Free Method for Finding Homologous Nucleotide Sequences and its Application in Phylogenetic Study
by
Kumar, Rajnish
, Kumar, Nilesh
, Gupta, Rahul
, Lahiri, Tapobrata
, Pal, Manoj Kumar
, Kumar, Gautam
, Mishra, Bharat Kumar
in
Alignment
/ Computer applications
/ Data processing
/ Genes
/ Homology
/ Nucleotide sequence
/ Nucleotides
/ Periodicity
/ Phylogeny
/ Physicochemical properties
/ Representations
/ Stochastic models
2017
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PCV: An Alignment Free Method for Finding Homologous Nucleotide Sequences and its Application in Phylogenetic Study
by
Kumar, Rajnish
, Kumar, Nilesh
, Gupta, Rahul
, Lahiri, Tapobrata
, Pal, Manoj Kumar
, Kumar, Gautam
, Mishra, Bharat Kumar
in
Alignment
/ Computer applications
/ Data processing
/ Genes
/ Homology
/ Nucleotide sequence
/ Nucleotides
/ Periodicity
/ Phylogeny
/ Physicochemical properties
/ Representations
/ Stochastic models
2017
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PCV: An Alignment Free Method for Finding Homologous Nucleotide Sequences and its Application in Phylogenetic Study
by
Kumar, Rajnish
, Kumar, Nilesh
, Gupta, Rahul
, Lahiri, Tapobrata
, Pal, Manoj Kumar
, Kumar, Gautam
, Mishra, Bharat Kumar
in
Alignment
/ Computer applications
/ Data processing
/ Genes
/ Homology
/ Nucleotide sequence
/ Nucleotides
/ Periodicity
/ Phylogeny
/ Physicochemical properties
/ Representations
/ Stochastic models
2017
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PCV: An Alignment Free Method for Finding Homologous Nucleotide Sequences and its Application in Phylogenetic Study
Journal Article
PCV: An Alignment Free Method for Finding Homologous Nucleotide Sequences and its Application in Phylogenetic Study
2017
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Overview
Online retrieval of the homologous nucleotide sequences through existing alignment techniques is a common practice against the given database of sequences. The salient point of these techniques is their dependence on local alignment techniques and scoring matrices the reliability of which is limited by computational complexity and accuracy. Toward this direction, this work offers a novel way for numerical representation of genes which can further help in dividing the data space into smaller partitions helping formation of a search tree. In this context, this paper introduces a 36-dimensional Periodicity Count Value (PCV) which is representative of a particular nucleotide sequence and created through adaptation from the concept of stochastic model of Kolekar et al. (American Institute of Physics 1298:307–312, 2010. doi:10.1063/1.3516320). The PCV construct uses information on physicochemical properties of nucleotides and their positional distribution pattern within a gene. It is observed that PCV representation of gene reduces computational cost in the calculation of distances between a pair of genes while being consistent with the existing methods. The validity of PCV-based method was further tested through their use in molecular phylogeny constructs in comparison with that using existing sequence alignment methods.
Publisher
Springer Nature B.V
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