High-throughput RNA sequencing (RNA-seq)  is becoming a technology of choice for transcriptome analyses. It  allows us to reduce the sequencing cost and significantly increase data throughput, but it is computationally challenging to use such data for reconstructing full-length transcripts and accurately estimating their abundances across all cell types. The common applications of RNA-seq are gene and transcript expression levels estimation, transcriptome discovery, and reconstruction. We present a novel general framework  that includes the “genome-guided” and “annotation-guided” transcriptome reconstruction methods as well as methods for transcript and gene expression level estimation. Empirical experiments on both synthetic and real RNA-seq datasets show that the proposed methods improve transcriptome quantification and reconstruction accuracy compared to previous methods.

Committee
Dr. Alex Zelikovsky (chair)
Dr. Robert Harrison
Dr. Ion Mandoiu
Dr. Yi Pan