Search

Competing endogenous RNAs, or ceRNAs, are sets of RNAs including mRNAs that can regulate each other by competing for microRNAs binding. ceRNAs form another level of regulation across the transcriptome and thus expand the function of microRNAs. CeRNAs have been shown to play important roles in disease like cancer [1]. Despite the importance, there does not yet exist a systematic tool for identifying ceRNAs. In this work, we propose and develop, TraceRNA, a computational tool and an online application to assist the exploration of ceRNAs. [ABSTRACT FROM PUBLISHER]

MicroRNAs are short regulatory RNAs which play a major role in regulating gene expression. In this study, we employed bioinformatics techniques to predict microRNA target sites for interleukin-17 family. We aimed at identifying the pattern of microRNA regulatory mechanism on interleukin-17 family. This study provides insight into the expression pattern of interleukin-17 family in controlling immunopathologic diseases. [ABSTRACT FROM PUBLISHER]

Gaussian graphical models are often used to infer gene networks based on microarray expression data. Many scientists, however, have begun using high-throughput sequencing technologies to measure gene expression. As the resulting high-dimensional count data consists of counts of sequencing reads for each gene, Gaussian graphical models are not optimal for modeling gene networks based on this discrete data. We develop a novel method for estimating high-dimensional Poisson graphical models, the Log-Linear Graphical Model, allowing us to infer networks based on high-throughput sequencing data. Our model assumes a pair-wise Markov property: conditional on all other variables, each variable is Poisson. We estimate our model locally via neighborhood selection by fitting 1-norm penalized log-linear models. Additionally, we develop a fast parallel algorithm permitting us to fit our graphical model to high-dimensional genomic data sets. We illustrate the effectiveness of our methods for recovering network structure from count data through simulations and a case study on breast cancer microRNA networks. [ABSTRACT FROM PUBLISHER]

MicroRNAs are small non-coding genes that regulate the expression of the protein-coding genes. As these microRNAs play a pivotal role in disease development, it is important for the bioinformaticians and geneticians to know the target genes. This research adopted in silico approach to predict the KRAS-targeting microRNAs. The computational results shed light on the type of microRNAs that gives rise to the development of diseases due to the misregulation of KRAS gene. [ABSTRACT FROM PUBLISHER]