SwitchFinder – a novel method and query facility for discovering dynamic gene expression patterns

BMC Bioinformatics

Research Article

Svetlana Bulashevska¹  Armin B. Cremers²  Daniel Speicher²  Jörg Zimmermann²  Frank Westermann³  Colin Priest⁴ 

[1] B-IT Bonn-Aachen International Center for Information Technology, University of Bonn, Dahlmannstr. 2, 53113, Bonn, Germany;B-IT Bonn-Aachen International Center for Information Technology, University of Bonn, Dahlmannstr. 2, 53113, Bonn, Germany;Institute of Computer Science, University of Bonn, Roemerstr. 164, 53117, Bonn, Germany;Neuroblastoma Genomics Group, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany;Sigma Plus Consulting Pty Ltd, 2065, Crows Nest, NSW, Australia;

关键词: Time-series analysis;    Dynamic patterns of gene expression;    Change-point problem;    Change-point modeling;    Bayesian modeling;    MCMC;    Gibbs sampling;    Neuroblastoma;    ATRA-induced differentiation;   

DOI  :  10.1186/s12859-016-1391-0

 received in 2016-06-11, accepted in 2016-11-29,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundBiological systems and processes are highly dynamic. To gain insights into their functioning time-resolved measurements are necessary. Time-resolved gene expression data captures temporal behaviour of the genes genome-wide under various biological conditions: in response to stimuli, during cell cycle, differentiation or developmental programs. Dissecting dynamic gene expression patterns from this data may shed light on the functioning of the gene regulatory system. The present approach facilitates this discovery. The fundamental idea behind it is the following: there are change-points (switches) in the gene behaviour separating intervals of increasing and decreasing activity, whereas the intervals may have different durations. Elucidating the switch-points is important for the identification of biologically meanigfull features and patterns of the gene dynamics.ResultsWe developed a statistical method, called SwitchFinder, for the analysis of time-series data, in particular gene expression data, based on a change-point model. Fitting the model to the gene expression time-courses indicates switch-points between increasing and decreasing activities of each gene. Two types of the model - based on linear and on generalized logistic function - were used to capture the data between the switch-points. Model inference was facilitated with the Bayesian methodology using Markov chain Monte Carlo (MCMC) technique Gibbs sampling. Further on, we introduced features of the switch-points: growth, decay, spike and cleft, which reflect important dynamic aspects. With this, the gene expression profiles are represented in a qualitative manner - as sets of the dynamic features at their onset-times. We developed a Web application of the approach, enabling to put queries to the gene expression time-courses and to deduce groups of genes with common dynamic patterns.SwitchFinder was applied to our original data - the gene expression time-series measured in neuroblastoma cell line upon treatment with all-trans retinoic acid (ATRA). The analysis revealed eight patterns of the gene expression responses to ATRA, indicating the induction of the BMP, WNT, Notch, FGF and NTRK-receptor signaling pathways involved in cell differentiation, as well as the repression of the cell-cycle related genes.ConclusionsSwitchFinder is a novel approach to the analysis of biological time-series data, supporting inference and interactive exploration of its inherent dynamic patterns, hence facilitating biological discovery process. SwitchFinder is freely available at https://newbioinformatics.eu/switchfinder.

【 授权许可】

CC BY   
© The Author(s) 2016

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