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.
