Accurate Identification of Adenosine Deamination

The eukaryotic transcriptome is further diversified by post-transcriptional processing, including alternative splicing and RNA editing. The latter includes the modification of adenosine to inosine (A-to-I) within structured transcripts. Uniquely, inosine has similar base-pairing properties to guanine, which can have downstream consequences to RNA secondary structure or RNA-RNA interactions. While RNA editing events were typically characterized on gene-by-gene basis, advancements in high throughput RNA-sequencing technologies have allowed A-to-I editing to be investigated on a global scale. However, accurate identification of A-to-I edits on a transcriptome-wide scale is compounded by artifacts introduced by reverse transcription, sequencing, and computational alignment, all of which can lead to false positive signals. RNA-seq read alignment for the purposes of RNA editing calls can be affected by the accuracy of spliced, gapped, multi-mapped, and mismatch alignments. To address these challenges, I developed RNAsequel, a software package that runs as a post-processing step in conjunction with an RNA-seq aligner. I benchmarked the accuracy of RNASequel using a combination of human derived simulated and biological datasets and demonstrated a clear improvement in all four of the aforementioned accuracy metrics compared to current RNA-seq alignment tools. Next, I utilized RNASequel to identify clusters of A-to-I hyper-editing in 91 C. elegans samples using a novel algorithm designed to mitigate common sources of false positive calls that are difficult to mitigate during read alignment. This resulted in the most comprehensive map of RNA editing in C. elegans to date with 197,890 sites within 10,941 clusters. I then explored the localization of the clusters to genetic features and heterochromatin. Collectively, these data show the extensive editing events in the worms, while concurrently demonstrating the utility of RNASequel.Ph.D