RNAGenomicsPeer-reviewed journal article
Splicing conservation signals in plant long non-coding RNAs
José Antonio Corona-GómeziD, Irving Jair García-López, Peter Florian StadleriD, Selene L. Fernández-ValverdeiD
Abstract
Long non-coding RNAs (lncRNAs) have recently emerged as prominent regulators of gene expression in eukaryotes. LncRNAs often drive the modification and maintenance of gene activation or gene silencing states via chromatin conformation rearrangements. In plants, lncRNAs have been shown to participate in gene regulation, and are essential to processes such as vernalization and photomorphogenesis. Despite their prominent functions only over a dozen lncRNAs have been experimentally and functionally characterized. Similar to its animal counterparts, the rates of sequence divergence are much higher in plant lncRNAs than in protein coding mRNAs, making it difficult to identify lncRNA conservation using traditional sequence comparison methods. Beyond this, little is known about the evolutionary patterns of lncRNAs in plants. Here, we characterized the splicing conservation of lncRNAs in Brassicaceae. We generated a whole-genome alignment of 16 Brassica species and used it to identify synthenic lncRNA orthologues. Using a scoring system trained on transcriptomes from A. thaliana and B. oleracea, we identified splice sites across the whole alignment and measured their conservation. Our analysis revealed that 17.9% (112/627) of all intergenic lncRNAs display splicing conservation in at least one exon, an estimate that is substantially higher to previous estimates of lncRNA conservation in this group. Our findings agree with similar studies in vertebrates, demonstrating that splicing conservation can be evidence of stabilizing selection. We provide conclusive evidence for the existence of evolutionary deeply conserved lncRNAs in plants and describe a generally applicable computational workflow to identify functional lncRNAs in plants.
- organism: plant (other)
- assay: long-read sequencing
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 28
- FWCI
- 2.42
- Field percentile
- top 12%
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