RNABioinformaticsPeer-reviewed journal article
A Common Class of Transcripts with 5′-Intron Depletion, Distinct Early Coding Sequence Features, and N1-Methyladenosine Modification
Can CenikiD, Hon Nian Chua, Guramrit Singh, Abdalla Akef, Michael P Snyder, Alexander F Palazzo, Melissa J Moore, Frederick P Roth
Abstract
Introns are found in 5 untranslated regions (5UTRs) for 35% of all human transcripts. These 5UTR introns are not randomly distributed: genes that encode secreted, membrane-bound and mitochondrial proteins are less likely to have them. Curiously, transcripts lacking 5UTR introns tend to harbor specific RNA sequence elements in their early coding regions. To model and understand the connection between coding-region sequence and 5UTR intron status, we developed a classifier that can predict 5UTR intron status with >80% accuracy using only sequence features in the early coding region. Thus, the classifier identifies transcripts with 5 proximal-intron-minus-like-coding regions (\"5IM\" transcripts). Unexpectedly, we found that the early coding sequence features defining 5IM transcripts are widespread, appearing in 21% of all human RefSeq transcripts. The 5IM class of transcripts is enriched for non-AUG start codons, more extensive secondary structure both preceding the start codon and near the 5 cap, greater dependence on eIF4E for translation, and association with ER-proximal ribosomes. 5IM transcripts are bound by the Exon Junction Complex (EJC) at non-canonical 5 proximal positions. Finally, N1-methyladenosines are specifically enriched in the early coding regions of 5IM transcripts. Taken together, our analyses point to the existence of a distinct 5IM class comprising [~]20% of human transcripts. This class is defined by depletion of 5 proximal introns, presence of specific RNA sequence features associated with low translation efficiency, N1-methyladenosines in the early coding region, and enrichment for non-canonical binding by the Exon Junction Complex.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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