Browsing by Author "Wan, Ping"

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    Computational Analysis of Drought Stress-Associated miRNAs and miRNA Co-Regulation Network in Physcomitrella patens.
    (Elsevier, 2011-04) Wan, Ping; Wu, Jun; Zhou, Yuan; Xiao, Junshu; Feng, Jie; Zhao, Weizhong; Xiang, Shen; Jiang, Guanglong; Chen, Jake Yue; Department of Biohealth Informatics, IU School of Informatics and Computing
    miRNAs are non-coding small RNAs that involve diverse biological processes. Until now, little is known about their roles in plant drought resistance. Physcomitrella patens is highly tolerant to drought; however, it is not clear about the basic biology of the traits that contribute P. patens this important character. In this work, we discovered 16 drought stress-associated miRNA (DsAmR) families in P. patens through computational analysis. Due to the possible discrepancy of expression periods and tissue distributions between potential DsAmRs and their targeting genes, and the existence of false positive results in computational identification, the prediction results should be examined with further experimental validation. We also constructed an miRNA co-regulation network, and identified two network hubs, miR902a-5p and miR414, which may play important roles in regulating drought-resistance traits. We distributed our results through an online database named ppt-miRBase, which can be accessed at http://bioinfor.cnu.edu.cn/ppt_miRBase/index.php. Our methods in finding DsAmR and miRNA co-regulation network showed a new direction for identifying miRNA functions.
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    SLDR: a computational technique to identify novel genetic regulatory relationships
    (Springer (Biomed Central Ltd.), 2014) Yue, Zongliang; Wan, Ping; Huang, Hui; Xie, Zhan; Chen, Jake Yue; Department of BioHealth Informatics, School of Informatics and Computing
    We developed a new computational technique called Step-Level Differential Response (SLDR) to identify genetic regulatory relationships. Our technique takes advantages of functional genomics data for the same species under different perturbation conditions, therefore complementary to current popular computational techniques. It can particularly identify "rare" activation/inhibition relationship events that can be difficult to find in experimental results. In SLDR, we model each candidate target gene as being controlled by N binary-state regulators that lead to ≤2N observable states ("step-levels") for the target. We applied SLDR to the study of the GEO microarray data set GSE25644, which consists of 158 different mutant S. cerevisiae gene expressional profiles. For each target gene t, we first clustered ordered samples into various clusters, each approximating an observable step-level of t to screen out the "de-centric" target. Then, we ordered each gene x as a candidate regulator and aligned t to x for the purpose of examining the step-level correlations between low expression set of x (Ro) and high expression set of x (Rh) from the regulator x to t, by finding max f(t, x): |Ro-Rh| over all candidate × in the genome for each t. We therefore obtained activation and inhibitions events from different combinations of Ro and Rh. Furthermore, we developed criteria for filtering out less-confident regulators, estimated the number of regulators for each target t, and evaluated identified top-ranking regulator-target relationship. Our results can be cross-validated with the Yeast Fitness database. SLDR is also computationally efficient with o(N²) complexity. In summary, we believe SLDR can be applied to the mining of functional genomics big data for future network biology and network medicine applications.