Kevin Byron

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

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.

A computational approach to finding RNA tertiary motifs in genomic sequences: a case study.

Motif finding in DNA, RNA and proteins plays an important role in life science research. Recent patents concerning motif finding in biomolecular data are recorded in the DNA Patent Database which serves as a resource for policy makers and members of the general public interested in fields like genomics, genetics and biotechnology. In this paper, we present a computational approach to mining for RNA tertiary motifs in genomic sequences. Specifically, we describe a method, named CSminer, and show, as a case study, the application of CSminer to genome-wide search for coaxial helical stackings in RNA 3-way junctions. A coaxial helical stacking occurs in an RNA 3-way junction where two separate helical elements form a pseudocontiguous helix and provide thermodynamic stability to the RNA molecule as a whole. Experimental results demonstrate the effectiveness of our approach.

Genome-Wide Prediction of Coaxial Helical Stacking Using Random Forests and Covariance Models

Developing effective artificial intelligence tools to find motifs in DNA, RNA and proteins poses a challenging yet important problem in life science research. In this paper, we present a computational approach for finding RNA tertiary motifs in genomic sequences. Specifically, we predict genomic coordinate locations for coaxial helical stackings in 3-way RNA junctions. These predictions are provided by our tertiary motif search package, named CSminer, which utilizes two versatile methodologies: random forests and covariance models. A coaxial helical stacking tertiary motif occurs in a 3-way RNA junction where two separate helical elements form a pseudocontiguous helix and provide thermodynamic stability to the RNA molecule as a whole. Our CSminer tool first uses a genome-wide search method based on covariance models to find a genomic region that may potentially contain a coaxial helical stacking tertiary motif. CSminer then uses a random forests classifier to predict whether the genomic region indeed contains the tertiary motif. Experimental results demonstrate the effectiveness of our approach.

Genome-wide search for coaxial helical stacking motifs

Motif finding in DNA, RNA and proteins plays an important role in life science research. In this paper, we present a computational approach to searching for RNA tertiary motifs in genomic sequences. Specifically, we describe a method, named CSminer, and show, as a case study, the application of CSminer to genome-wide search for coaxial helical stackings in RNA 3-way junctions. A coaxial helical stacking motif occurs in an RNA 3-way junction where two separate helical elements form a pseudocontiguous helix and provide thermodynamic stability to the RNA molecule as a whole. Experimental results demonstrate the effectiveness of our approach.