A computational algorithm for functional clustering of proteome dynamics during development

Yaqun Wang, Ningtao Wang, Han Hao, Yunqian Guo, Yan Zhen, Jisen Shi, Rongling Wu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)237-243
Number of pages7
JournalCurrent Genomics
Issue number3
StatePublished - Sep 1 2014

All Science Journal Classification (ASJC) codes

  • Genetics
  • Genetics(clinical)


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