Unraveling gene regulatory networks from time-resolved gene expression data -- a measures comparison study

BMC Bioinformatics

Research Article

Aneta Koseska¹  Sabrina Hempel²  Jürgen Kurths³  Zoran Nikoloski⁴ 

[1] Interdisciplinary Center for Dynamics of Complex Systems, University of Potsdam, Campus Golm, Karl-Liebknecht-Str. 24, D-14476, Potsdam, Germany;Interdisciplinary Center for Dynamics of Complex Systems, University of Potsdam, Campus Golm, Karl-Liebknecht-Str. 24, D-14476, Potsdam, Germany;Potsdam Institute for Climate Impact Research (PIK), Telegraphenberg A 31, D-14473, Potsdam, Germany;Department of Physics, Humboldt University of Berlin, Campus Adlershof, Newtonstr. 15, D-12489, Berlin, Germany;Potsdam Institute for Climate Impact Research (PIK), Telegraphenberg A 31, D-14473, Potsdam, Germany;Department of Physics, Humboldt University of Berlin, Campus Adlershof, Newtonstr. 15, D-12489, Berlin, Germany;Institute for Complex Systems and Mathematical Biology, University of Aberdeen, AB243UE, Aberdeen, UK;Systems Biology and Mathematical Modeling Group, Max Planck Institute for Molecular Plant Physiology, Am Mühlenberg 1, D-14476, Potsdam, Germany;Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 25, D-14476, Potsdam, Germany;

关键词: Mutual Information;    Receiver Operating Characteristic Curve;    Granger Causality;    Dynamic Time Warping;    Symbolic Dynamic;   

DOI  :  10.1186/1471-2105-12-292

 received in 2011-03-11, accepted in 2011-07-19,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundInferring regulatory interactions between genes from transcriptomics time-resolved data, yielding reverse engineered gene regulatory networks, is of paramount importance to systems biology and bioinformatics studies. Accurate methods to address this problem can ultimately provide a deeper insight into the complexity, behavior, and functions of the underlying biological systems. However, the large number of interacting genes coupled with short and often noisy time-resolved read-outs of the system renders the reverse engineering a challenging task. Therefore, the development and assessment of methods which are computationally efficient, robust against noise, applicable to short time series data, and preferably capable of reconstructing the directionality of the regulatory interactions remains a pressing research problem with valuable applications.ResultsHere we perform the largest systematic analysis of a set of similarity measures and scoring schemes within the scope of the relevance network approach which are commonly used for gene regulatory network reconstruction from time series data. In addition, we define and analyze several novel measures and schemes which are particularly suitable for short transcriptomics time series. We also compare the considered 21 measures and 6 scoring schemes according to their ability to correctly reconstruct such networks from short time series data by calculating summary statistics based on the corresponding specificity and sensitivity. Our results demonstrate that rank and symbol based measures have the highest performance in inferring regulatory interactions. In addition, the proposed scoring scheme by asymmetric weighting has shown to be valuable in reducing the number of false positive interactions. On the other hand, Granger causality as well as information-theoretic measures, frequently used in inference of regulatory networks, show low performance on the short time series analyzed in this study.ConclusionsOur study is intended to serve as a guide for choosing a particular combination of similarity measures and scoring schemes suitable for reconstruction of gene regulatory networks from short time series data. We show that further improvement of algorithms for reverse engineering can be obtained if one considers measures that are rooted in the study of symbolic dynamics or ranks, in contrast to the application of common similarity measures which do not consider the temporal character of the employed data. Moreover, we establish that the asymmetric weighting scoring scheme together with symbol based measures (for low noise level) and rank based measures (for high noise level) are the most suitable choices.

【 授权许可】

CC BY   
© Hempel et al; licensee BioMed Central Ltd. 2011