Abstract
Phenotypic traits, such as seed development, are a consequence of complex biochemical interactions among genes, proteins and metabolites, but the underlying mechanisms that operate in a coordinated and sequential manner remain elusive. Here, we address this issue by developing a computational algorithm to monitor proteome changes during the course of trait development. The algorithm is built within the mixture-model framework in which each mixture component is modeled by a specific group of proteins that display a similar temporal pattern of expression in trait development. A nonparametric approach based on Legendre orthogonal polynomials was used to fit dynamic changes of protein expression, increasing the power and flexibility of protein clustering. By analyzing a dataset of proteomic dynamics during early embryogenesis of the Chinese fir, the algorithm has successfully identified several distinct types of proteins that coordinate with each other to determine seed development in this forest tree commercially and environmentally important to China. The algorithm will find its immediate applications for the characterization of mechanistic underpinnings for any other biological processes in which protein abundance plays a key role.
Keywords: Functional clustering, Unsupervised analysis, Dynamic proteomics, Seed development, Forest tree.
Current Genomics
Title:A Computational Algorithm for Functional Clustering of Proteome Dynamics During Development
Volume: 15 Issue: 3
Author(s): Yaqun Wang, Ningtao Wang, Han Hao, Yunqian Guo, Yan Zhen, Jisen Shi and Rongling Wu
Affiliation:
Keywords: Functional clustering, Unsupervised analysis, Dynamic proteomics, Seed development, Forest tree.
Abstract: Phenotypic traits, such as seed development, are a consequence of complex biochemical interactions among genes, proteins and metabolites, but the underlying mechanisms that operate in a coordinated and sequential manner remain elusive. Here, we address this issue by developing a computational algorithm to monitor proteome changes during the course of trait development. The algorithm is built within the mixture-model framework in which each mixture component is modeled by a specific group of proteins that display a similar temporal pattern of expression in trait development. A nonparametric approach based on Legendre orthogonal polynomials was used to fit dynamic changes of protein expression, increasing the power and flexibility of protein clustering. By analyzing a dataset of proteomic dynamics during early embryogenesis of the Chinese fir, the algorithm has successfully identified several distinct types of proteins that coordinate with each other to determine seed development in this forest tree commercially and environmentally important to China. The algorithm will find its immediate applications for the characterization of mechanistic underpinnings for any other biological processes in which protein abundance plays a key role.
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Cite this article as:
Wang Yaqun, Wang Ningtao, Hao Han, Guo Yunqian, Zhen Yan, Shi Jisen and Wu Rongling, A Computational Algorithm for Functional Clustering of Proteome Dynamics During Development, Current Genomics 2014; 15 (3) . https://dx.doi.org/10.2174/1389202915666140407212147
DOI https://dx.doi.org/10.2174/1389202915666140407212147 |
Print ISSN 1389-2029 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5488 |
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