Beyond gene length: Exon-intron architecture and isoform potential in the evolution of eukaryotic complexity
Lu, S.; Bao, Y.; Sheynkman, G. M.; Korkin, D.
Show abstract
Alternative splicing is a major source of human transcriptomic and phenotypic variation, yet its evolutionary contribution to genomic complexity remains unresolved. It has been shown that a mean gene length can be the most basic but remarkably efficient proxy for multicellular genome complexity, whereas mean protein length is not, as it plateaus abruptly early in eukaryotic evolution. Here, we show that, across 2,683 genomes, exon count continues to increase beyond this transition and then rapidly saturates at [~]10 exons per gene, supporting its role as an additional dimension of genomic complexity linked to exon-intron architecture. A simple stochastic exon-splitting model reproduces the observed biphasic exon-number growth pattern and identifies minimal exon length as a key determinant.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- BANDITS: Bayesian differential splicing accounting for sample-to-sample variability and mapping uncertainty 94%
- Revisiting the Central Dogma: the distinct roles of genome, methylation, transcription, and translation on protein expression in Arabidopsis thaliana 93%
- GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership 93%
Similar papers in this journal
- An optimal growth law for RNA composition and its partial implementation through ribosomal and tRNA gene locations in bacterial genomes 94%
- Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults 93%
- Probabilistic classification of gene-by-treatment interactions on molecular count phenotypes 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.