Brown University is awarded a grant from the Faculty Early Career Development program (CAREER) to develop statistical and computational methods for RNA sequencing (RNA-seq) data. Recent advances in sequencing technology have made this an increasingly popular platform for measuring transcription and studying the functional elements of the genome. The RNA-seq technology provides several advantages over other high throughput platforms, including the ability to detect de novo transcripts, lower background noise and increase dynamic range. RNA-seq has been expected to revolutionize transcriptional analysis. As RNA-seq data accumulate, new challenges to data analysis emerge. These include, but are not limited to, detecting bias due to the transcript length, sequence composition and variation in amplification efficiency. The key contribution from this study is a statistical framework that reflects the biases and variations due to sample processing and quantification. Building on the framework, they will develop novel statistical methodology for 1) accurate and sensitive identification of differential expression, isoform expression and functional relationships among genes 2) data consolidation between RNA-seq and DNA microarrays. Collaboration with biologists who use RNA-seq on a variety of organisms will ensure the validity and generalizability of the statistical methodologies.
The investigator will also work to prepare the next generation of computational biologists with the ability to adapt to emerging technologies. The research discoveries will be integrated into course development by expanding an existing graduate course in Statistical Bioinformatics as well as designing stand alone modules for students with diverse background to prepare for training in bioinformatics. Brown University will host a bioinformatics working group open for students in Biostatistics, Computation Molecular Biology and Molecular Biology programs to promote interdisciplinary education and research collaboration.
