WASHINGTON (GenomeWeb News) – By bringing together matched RNA and DNA sequence data, researchers are getting new clues about the prevalence, position, and predicted consequences of RNA editing events in the human genome, University of Pennsylvania biostatistics researcher Mingyao Li told attendees at the American Society of Human Genetics meeting here yesterday.
Speaking during a plenary session, Li described her team's efforts to uncover examples of RNA-DNA differences resulting from RNA editing processes rather than transcriptional infidelity.
To do this, the researchers compared matched DNA and RNA sequences in B cells from 27 unrelated Caucasian individuals assessed through the HapMap and 1000 Genomes Project, as well as millions of RNA sequence reads generated using the Illumina Genome Analyzer II.
During their subsequent computational analysis, the team focused in on cases in which RNA-DNA differences occurred at sites covered by at least 20 RNA sequencing and four DNA sequencing reads 20 percent of the time or more.
In the process, they found about 102,000 RNA-DNA difference events — an average of nearly 4,000 such differences per individual. These changes affected more than 20,000 sites in the genome and roughly 4,500 known genes, Li noted.
Many of the sites with RNA and DNA differences overlapped from one individual to the next, Li explained, lending credence to the notion that the differences stemmed from authentic RNA editing-related processes.
When they compared their findings with the patterns identified in RNA and DNA sequence data generated by another group, Li said, the researchers found about 89 percent agreement between the two datasets.
Moreover, validation studies using Sanger sequence data — and RNA and DNA sequence data from other cell types — also support the notion that RNA editing is more widespread than previously appreciated, she added.
The team is also looking at the types of RNA changes that occurred and their predicted consequences, along with their prevalence and locations in the genome. For instance, they found that sites that tend to show RNA-DNA differences in a greater number of sequence reads also tend to be those showing RNA-DNA differences in a larger number of individuals.
The mechanisms behind many of the apparent RNA editing examples remain unclear, Li noted. And the team has not yet determined whether messenger RNA that differs from source DNA gets translated or degraded, she explained.
Even so, the team argues that the findings may eventually help to provide a clearer picture of some of the modifications that occur after transcription.
"To our knowledge, our analysis represents the first whole-genome study on RNA editing in humans," Li and her co-authors wrote in the abstract for the talk. "The catalog of RNA editing sites will contribute to our understanding of the mechanisms of post-transcriptional modifications."