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A Rapid Method for Characterization of Protein Relatedness Using Feature Vectors

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Title: A Rapid Method for Characterization of Protein Relatedness Using Feature Vectors
Author(s): Carr, Kareem; Murray, Eleanor; Armah, Ebenezer; He, Rong L.; Yau, Stephen S.-T.
Subject(s): biological sequences vector
Abstract: We propose a feature vector approach to characterize the variation in large data sets of biological sequences. Each candidate sequence produces a single feature vector constructed with the number and location of amino acids or nucleic acids in the sequence. The feature vector characterizes the distance between the actual sequence and a model of a theoretical sequence based on the binomial and uniform distributions. This method is distinctive in that it does not rely on sequence alignment for determining protein relatedness, allowing the user to visualize the relationships within a set of proteins without making a priori assumptions about those proteins. We apply our method to two large families of proteins: protein kinase C, and globins, including hemoglobins and myoglobins. We interpret the high-dimensional feature vectors using principal components analysis and agglomerative hierarchical clustering. We find that the feature vector retains much of the information about the original sequence. By using principal component analysis to extract information from collections of feature vectors, we are able to quickly identify the nature of variation in a collection of proteins. Where collections are phylogenetically or functionally related, this is easily detected. Hierarchical agglomerative clustering provides a means of constructing cladograms from the feature vector output.
Issue Date: 2010-03-05
Publisher: Public Library of Science
Citation Info: Carr, K., Murray, E., Armah, E., He, R. L., & Yau, S. S. 2010. A Rapid Method for Characterization of Protein Relatedness Using Feature Vectors. PLoS One, 5(3): e9550. DOI: 10.1371/journal.pone.0009550
Type: Article
Description: © 2010 Carr 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. The original version is available through the Public Library of Science at DOI: 10.1371/journal.pone.0009550.
ISSN: 1932-6203
Date Available in INDIGO: 2011-05-27

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