Literature DB >> 30059661

Convex hull analysis of evolutionary and phylogenetic relationships between biological groups.

Kun Tian1, Xin Zhao1, Stephen S-T Yau2.   

Abstract

Comparing DNA and protein sequence groups plays an important role in biological evolutionary relationship research. Despite many methods available for sequence comparison, only a few can be used for group comparison. In this study, we propose a novel approach using convex hulls. We use statistical information contained within the sequences to represent each sequence as a point in high dimensional space. We find that the points belonging to one biological group are located in a different region of space than points belonging to other biological groups. To be more precise, the convex hull of the points from one group are disjoint from the convex hulls of points from other groups. This finding allows us to do phylogenetic analysis for groups in an efficient way. Five different theorems are presented for checking whether two convex hulls intersect or are disjoint. Test results for datasets related to HRV, HPV, Ebolavirus, PKC and protein phosphatase domains demonstrate that our method performs well and provides a new tool for studying group phylogeny. More significantly, the convex analysis presents a new way to search for sequences belonging to a biological group by examining points within the group's convex hull.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Center point; Convex hull; Disjoint; Group comparison; Phylogenetic analysis

Year:  2018        PMID: 30059661     DOI: 10.1016/j.jtbi.2018.07.035

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  4 in total

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Journal:  PeerJ       Date:  2020-08-03       Impact factor: 2.984

2.  A new efficient method for analyzing fungi species using correlations between nucleotides.

Authors:  Xin Zhao; Kun Tian; Stephen S-T Yau
Journal:  BMC Evol Biol       Date:  2018-12-27       Impact factor: 3.260

3.  A protein structural study based on the centrality analysis of protein sequence feature networks.

Authors:  Xiaogeng Wan; Xinying Tan
Journal:  PLoS One       Date:  2021-03-29       Impact factor: 3.240

4.  A study on separation of the protein structural types in amino acid sequence feature spaces.

Authors:  Xiaogeng Wan; Xinying Tan
Journal:  PLoS One       Date:  2019-12-23       Impact factor: 3.240

  4 in total

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