R01 funded!

We propose to develop statistical methods for microRNA sequencing data with support of a $1.97 million R01 research grant from the National Institute of General Medical Sciences.

Alterations in microRNAs have been shown to disrupt entire cellular pathways, substantially contributing to a variety of human diseases such as heart disease and cancer. However, despite their importance, our understanding of the role of microRNAs is hampered by a lack of statistical methods designed specifically to analyze microRNA-sequencing data.

The grant aims to improve the analysis of microRNA-sequencing data by developing statistical methods that directly address the challenges unique to measuring expression levels of microRNAs. Statistical analysis of processed microRNA-seq data is currently performed using methods developed for mRNA-seq data despite the fact that the assumptions of these methods are violated. Specifically, methods for mRNA-seq data assume approximate independence between feature counts; however, the small total number of microRNAs and presence of a small number of very highly expressed microRNAs result in a lack of independence between microRNA counts.

Additionally, normalization methods for mRNA-seq data assume either the overall level of transcription is constant across samples or an equal number of features are over- and under-expressed when comparing any two samples, neither of which hold for microRNA-seq data.

The development of statistical methods that address the challenges of microRNA-seq data represents a critical need for miRNA research. These methods are necessary to fully elucidate the role miRNAs play in many human disease processes.

Matthew N. McCall
Matthew N. McCall
Associate Professor of Biostatistics and Biomedical Genetics