Chicken

Recent emerging studies suggest that a substantial fraction of microRNA (miRNA) genes is likely to form clusters in terms of evolutionary conservation and biological implications, posing a significant challenge for the research community and shifting the bottleneck of scientific discovery from miRNA singletons to miRNA clusters. In addition, the advance in molecular sequencing technique such as next-generation sequencing (NGS) has facilitated researchers to comprehensively characterize miRNAs with low abundance on genome-wide scale in multiple species.

The double helix is a conformation that genomic DNA usually assumes; under certain conditions, however, guanine-rich DNA sequences can form a four-stranded structure, G-quadruplex, which is found to play a role in regulating gene expression. Indeed, it has been demonstrated that the G-quadruplex formed in the c-MYC promoter suppresses its transcriptional activity. Recent studies suggest that G-quadruplex motifs (GQMs) are enriched in human gene promoters.

In this note, we propose an R function named NqA (Normalization qPCR Array, where qPCR is quantitative real-time polymerase chain reaction) suitable for the identification of a set of microRNAs (miRNAs) to be used for data normalization in view of subsequent validation studies with qPCR data.

High-throughput measurement technologies have triggered a rise in large-scale cancer studies containing multiple levels of molecular data. While there are a number of efficient methods to analyze individual data types, there are far less that enhance data interpretation after analysis. We present the R package Director, a dynamic visualization approach to linking and interrogating multiple levels of molecular data after analysis for clinically meaningful, actionable insights.