Computational Challenges and Solutions to the Analysis of Micro RNA Profiles in Virally-Infected Cells Derived by Massively Parallel Sequencing

Abstract

In this paper we report an ongoing project for identifying human cytomegalovirus (HCMV) micro RNAs (miRNA) expressed in infected human cells using the new massive parallel sequencing technology with the Solexa Sequencer. We developed a data processing pipeline for analyzing such data including mapping segments to genomes, detecting highly expressed sequences and their loci, comparing sequences to existing databases and selecting candidate miRNAs for experimental validation. We identified 114 putative virally-derived miRNAs with high expression levels that included 9 out of 10 known HCMV miRNAs, partially validating our methods. This observation also suggested that other identified sequences with high level of expression are potential miRNAs and this method is an effective way of discovering new small regulatory RNAs. Validation of putative novel viral miRNAs are underway, as are efforts to identify primary transcripts or introns from which they are derived. Future directions include designing the most statistically robust selection criteria, designing methods to measure viral-induced changes in the human miRNA expression profile, and identifying the targets of the miRNAs in the viral and human genomes.