Statistical Detection of Intrinsically Multivariate Predictive Genes

Quantifying the notions of canalizing and master genes in a gene regulatory network-a Boolean network modeling perspective

MOTIVATION

Canalizing genes enforce broad corrective actions on cellular processes for the purpose of biological robustness maintaining a constant phenotype to remain unchanged in spite of genetic mutations or environmental perturbations. Despite their central role in biological systems, the observation/detection of canalizing genes is often impeded because the behavior of affected genes is highly varied relative to the inactive canalizer. Therefore, the activity of canalizing genes is difficult to predict to any significant degree by their subject genes under normal cell conditions.

RESULTS

We investigate this question and present a quantitative framework that allows for the estimation of the power of canalizing genes in the context of Boolean Networks (BNs) with perturbation. This framework borrows tools from the Pattern Recognition theory and uses the coefficient of determination (CoD) to capture the capacity of the canalizing genes. The canalizing power (CP) of a gene is quantitatively characterized by two terms: regulation power (RP) and incapacitating power (IP). We base this assumption on the idea that canalizing power of a gene should be quantified by the extent of its regulation on the overall network and the extent of control that the gene takes over from other master genes when it is activated, which is equivalent to reduction of the control of other master genes upon its activation. Following this, the CP concept is illustrated with examples in which the goal is to provide preliminary evidence that CP can be used to characterize the ability of canalizing genes.

AVAILABILITY AND IMPLEMENTATION

A library of functions written in MATLAB for computing CP is available at http://github.com/eunjikim-angie/CanalizingPower.

GeNICE: A Novel Framework for Gene Network Inference by Clustering, Exhaustive Search, and Multivariate Analysis

Gene network (GN) inference from temporal gene expression data is a crucial and challenging problem in systems biology. Expression data sets usually consist of dozens of temporal samples, while networks consist of thousands of genes, thus rendering many inference methods unfeasible in practice. To improve the scalability of GN inference methods, we propose a novel framework called GeNICE, based on probabilistic GNs; the main novelty is the introduction of a clustering procedure to group genes with related expression profiles and to provide an approximate solution with reduced computational complexity. We use the defined clusters to perform an exhaustive search to retrieve the best predictor gene subsets for each target gene, according to multivariate criterion functions. GeNICE greatly reduces the search space because predictor candidates are restricted to one gene per cluster. Finally, a multivariate analysis is performed for each defined predictor subset to retrieve minimal subsets and to simplify the network. In our experiments with in silico generated data sets, GeNICE achieved substantial computational time reduction when compared to solutions without the clustering step, while preserving the gene expression prediction accuracy even when the number of clusters is small (about 50) relative to the number of genes (order of thousands). For a Plasmodium falciparum microarray data set, the prediction accuracy achieved by GeNICE was roughly 97%, while the respective topologies involving glycolytic and apicoplast seed genes had a very large intramodularity, very small interconnection between modules, and some module hub genes, reflecting small-world and scale-free topological properties, as expected.

Bayesian estimation of the discrete coefficient of determination

The discrete coefficient of determination (CoD) measures the nonlinear interaction between discrete predictor and target variables and has had far-reaching applications in Genomic Signal Processing. Previous work has addressed the inference of the discrete CoD using classical parametric and nonparametric approaches. In this paper, we introduce a Bayesian framework for the inference of the discrete CoD. We derive analytically the optimal minimum mean-square error (MMSE) CoD estimator, as well as a CoD estimator based on the Optimal Bayesian Predictor (OBP). For the latter estimator, exact expressions for its bias, variance, and root-mean-square (RMS) are given. The accuracy of both Bayesian CoD estimators with non-informative and informative priors, under fixed or random parameters, is studied via analytical and numerical approaches. We also demonstrate the application of the proposed Bayesian approach in the inference of gene regulatory networks, using gene-expression data from a previously published study on metastatic melanoma.

Heterogeneous Cloud Framework for Big Data Genome Sequencing

The next generation genome sequencing problem with short (long) reads is an emerging field in numerous scientific and big data research domains. However, data sizes and ease of access for scientific researchers are growing and most current methodologies rely on one acceleration approach and so cannot meet the requirements imposed by explosive data scales and complexities. In this paper, we propose a novel FPGA-based acceleration solution with MapReduce framework on multiple hardware accelerators. The combination of hardware acceleration and MapReduce execution flow could greatly accelerate the task of aligning short length reads to a known reference genome. To evaluate the performance and other metrics, we conducted a theoretical speedup analysis on a MapReduce programming platform, which demonstrates that our proposed architecture have efficient potential to improve the speedup for large scale genome sequencing applications. Also, as a practical study, we have built a hardware prototype on the real Xilinx FPGA chip. Significant metrics on speedup, sensitivity, mapping quality, error rate, and hardware cost are evaluated, respectively. Experimental results demonstrate that the proposed platform could efficiently accelerate the next generation sequencing problem with satisfactory accuracy and acceptable hardware cost.