MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach

摘要

Gene regulation is a series of processes that control gene expression and itsextent. The connections among genes and their regulatory molecules, usuallytranscription factors, and a descriptive model of such connections, are knownas gene regulatory networks (GRNs). Elucidating GRNs is crucial to understandthe inner workings of the cell and the complexity of gene interactions. Todate, numerous algorithms have been developed to infer gene regulatorynetworks. However, as the number of identified genes increases and thecomplexity of their interactions is uncovered, networks and their regulatorymechanisms become cumbersome to test. Furthermore, prodding throughexperimental results requires an enormous amount of computation, resulting inslow data processing. Therefore, new approaches are needed to expeditiouslyanalyze copious amounts of experimental data resulting from cellular GRNs. Tomeet this need, cloud computing is promising as reported in the literature.Here we propose new MapReduce algorithms for inferring gene regulatory networkson a Hadoop cluster in a cloud environment. These algorithms employ aninformation-theoretic approach to infer GRNs using time-series microarray data.Experimental results show that our MapReduce program is much faster than anexisting tool while achieving slightly better prediction accuracy than theexisting tool.