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

Abstract

Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their regulatory molecules, usually transcription factors, and a descriptive model of such connections are known as gene regulatory networks (GRNs). Elucidating GRNs is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer gene regulatory networks. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here, we propose new MapReduce algorithms for inferring gene regulatory networks on a Hadoop cluster in a cloud environment. These algorithms employ an information-theoretic approach to infer GRNs using time-series microarray data. Experimental results show that our MapReduce program is much faster than an existing tool while achieving slightly better prediction accuracy than the existing tool.

Inference of Large-scale Time-delayed Gene Regulatory Network with Parallel MapReduce Cloud Platform

“…In this part, the dataset is from the Gene Expression Omnibus (GEO) at http://www.ncbi.nlm.nih.gov/geo/ (GEO accession: GSE30052) 34,50 . This dataset contains 5,744 probe sets, 10,928 genes and 49 time points.…”

“…Abduallah et al . proposed a new MapReduce algorithm based on information-theoretic approach to infer GRN in a cloud environment 34 . You et al .…”

MapReduce Solutions Classification by Their Implementation

“…For example, MapReduce has been used in finance to identify fraudulent activity and improve risk management [10]; in healthcare, it has been used to analyze electronic health records to improve patient outcomes [11]; in social media, it has been used to analyze user behavior and sentiment [12]. The MapReduce model has also been applied in bioinformatics for gene expression analysis [13] and genome assembly [14], [15] in teaching quality assessment [16].…”

Comparing MapReduce and Spark in Computing the PCC Matrix in Gene Co-expression Networks

“…These libraries are in continuous evolution, so MPIGeneNet will benefit from future library updates without requiring any modification in its code. In [31], an algorithm for constructing the GCN using MapReduce in a cloud environment has been developed. In that algorithm, an approach from the information theory, ARACNE [21], has been employed.…”

“…In this research, a comparison is held between the MapReduce, and Spark to compute the PCC matrix in real data of time series microarrays of Hepatocellular Carcinoma, HCC, containing a massive number of genes. The comparison has been done in a cloud environment which is more inexpensive, and flexible than the on-premises computing resources [31]. Cloud computing model has achieved an incredible performance for many applications in bioinformatics [35], [36].…”