title: RNA editing in the human ENCODE RNA-seq data.
abstract: RNA-seq data can be mined for sequence differences relative to the reference genome to identify both genomic SNPs and RNA editing events. We analyzed the long, polyA-selected, unstranded, deeply sequenced RNA-seq data from the ENCODE Project across 14 human cell lines for candidate RNA editing events. On average, 43% of the RNA sequencing variants that are not in dbSNP and are within gene boundaries are A-to-G(I) RNA editing candidates. The vast majority of A-to-G(I) edits are located in introns and 3' UTRs, with only 123 located in protein-coding sequence. In contrast, the majority of non-A-to-G variants (60%-80%) map near exon boundaries and have the characteristics of splice-mapping artifacts. After filtering out all candidates with evidence of private genomic variation using genome resequencing or ChIP-seq data, we find that up to 85% of the high-confidence RNA variants are A-to-G(I) editing candidates. Genes with A-to-G(I) edits are enriched in Gene Ontology terms involving cell division, viral defense, and translation. The distribution and character of the remaining non-A-to-G variants closely resemble known SNPs. We find no reproducible A-to-G(I) edits that result in nonsynonymous substitutions in all three lymphoblastoid cell lines in our study, unlike RNA editing in the brain. Given that only a fraction of sites are reproducibly edited in multiple cell lines and that we find a stronger association of editing and specific genes suggests that the editing of the transcript is more important than the editing of any individual site.
authors.affiliation: Department of Developmental and Cell Biology, University of California Irvine, Irvine, California 92697, USA.
authors.forename: Eddie
authors.initials: E
authors.lastname: Park
authors.forename: Brian
authors.initials: B
authors.lastname: Williams
authors.forename: Barbara J
authors.initials: BJ
authors.lastname: Wold
authors.forename: Ali
authors.initials: A
authors.lastname: Mortazavi
mesh_terms: D002460:Cell Line; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D023281:Genomics / Q000379:methods; D006801:Humans; D016366:Open Reading Frames; D020641:Polymorphism, Single Nucleotide; D012313:RNA / Q000737:chemistry / Q000378:metabolism*; D017393:RNA Editing; D012326:RNA Splicing; D012333:RNA, Messenger / Q000378:metabolism; D017423:Sequence Analysis, RNA
chemical_list: D012333:RNA, Messenger; D012313:RNA