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Journal of Integrative Bioinformatics

Editor-in-Chief: Schreiber, Falk / Hofestädt, Ralf

Managing Editor: Sommer, Björn

Ed. by Baumbach, Jan / Chen, Ming / Orlov, Yuriy / Allmer, Jens

Editorial Board: Giorgetti, Alejandro / Harrison, Andrew / Kochetov, Aleksey / Krüger, Jens / Ma, Qi / Matsuno, Hiroshi / Mitra, Chanchal K. / Pauling, Josch K. / Rawlings, Chris / Fdez-Riverola, Florentino / Romano, Paolo / Röttger, Richard / Shoshi, Alban / Soares, Siomar de Castro / Taubert, Jan / Tauch, Andreas / Yousef, Malik / Weise, Stephan / Hassani-Pak, Keywan


CiteScore 2017: 0.77

SCImago Journal Rank (SJR) 2017: 0.336

Open Access
Online
ISSN
1613-4516
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Volume 4, Issue 3

Issues

Monophyletic clustering and characterization of protein families

Jian Zhang
  • Corresponding author
  • Institute of Mathematics, Statistics and Actuarial Science, University of Kent Canterbury, Kent CT2 7NF, United Kingdom of Great Britain and Northern Ireland
  • Email
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/ Zhiyuan Zhao
  • Academy of Mathematics and Systems Science, Chinese Academy of Sciences Beijing 100080, China
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/ Jennifer Evershed
  • GlaxoSmithKline, New Frontiers Science Park South, Third Avenue, Harlow, Essex CM19 5AW, United Kingdom of Great Britain and Northern Ireland
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/ Guoying Li
  • Academy of Mathematics and Systems Science, Chinese Academy of Sciences Beijing 100080, China
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Published Online: 2016-10-18 | DOI: https://doi.org/10.1515/jib-2007-67

Summary

A protein family contains sequences that are evolutionarily related. Generally, this is reflected by sequence similarity. There have been many attempts to organize the set of protein families into evolutionarily homogenous clusters using certain clustering methods. How do we characterize these clusters? How can we cluster protein families using these characterizations? In this work, these questions were addressed by use of a concept called group-wide co-evolution, and was exemplified by some real and simulated protein family data. The results have shown that the trend of a group of monophyletic proteins might be characterized by a normal distribution, while the strength and variability of this trend can be described by the sample mean and variance of the observed correlation coefficients after a suitable transformation. To exploit this property, we have developed a monophyletic clustering method called monophyletic k−medoids clustering. A software package written in R has been made available at http://www.kent.ac.uk/ims/personal/jz .

About the article

Published Online: 2016-10-18

Published in Print: 2007-12-01


Citation Information: Journal of Integrative Bioinformatics, Volume 4, Issue 3, Pages 89–100, ISSN (Online) 1613-4516, DOI: https://doi.org/10.1515/jib-2007-67.

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© 2007 The Author(s). Published by Journal of Integrative Bioinformatics.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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