Journal Article

Impact of U2-type introns on splice site prediction in A. thaliana species using deep learning

De Neve, Wesley, Depuydt, Stephen, Van Messem, Arnout, Kabanga, Espoir, Jee, Seonil, Yun, Soeun

2025

Overview

Background Splice site prediction in plant genomes poses substantial challenges that can be addressed using deep learning models. U2-type introns are especially useful for such studies given their ubiquity in plant genomes and the availability of rich datasets. We formulated two hypotheses: one proposing that short introns may enhance prediction effectiveness due to reduced spatial complexity, and another suggesting that sequences with multiple introns provide a richer context for splicing events. Results Our findings demonstrate that (1) models trained on datasets containing shorter introns achieve improved effectiveness for acceptor splice sites, but not for donor splice sites, indicating a more nuanced relationship between intron length and splice site prediction than initially hypothesized, and (2) models trained on datasets with multiple introns per sequence show higher effectiveness compared to those trained on datasets with a single intron per sequence. Notably, among the 402 bp sequences analyzed, 72% contained single introns while 28% contained multiple introns for donor sites (36,399 versus 13,987 sequences), with similar proportions observed for acceptor sites (37,236 versus 14,112 sequences). These computational insights align with biological observations, particularly regarding the conserved spatial relationship between branch points and acceptor splice sites, as well as the synergistic effects of multiple introns on splicing efficiency. Conclusions The obtained results contribute to a deeper understanding of how intronic features influence splice site prediction and suggest that future prediction models should consider factors such as intron length, multiplicity, and the spatial arrangement of splice-related signals.

• Publisher: BioMed Central,BioMed Central Ltd,BMC
• Subject: Acceptor sites / Algorithms / Applied Mathematics / Arabidopsis / Arabidopsis - genetics / Arabidopsis thaliana / Biochemistry / Biochemistry, biophysics & molecular biology / Biochimie, biophysique & biologie moléculaire / Bioinformatics / Biological observations / Biomedical and Life Sciences / Biotechnologie / Biotechnology / Botanical research / CNN / Computational Biology / Computational Biology - methods / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Computer science / Computer Science Applications / Deep Learning / Engineering, computing & technology / Genetic aspects / Genome, Plant / Genomes / Ingénierie, informatique & technologie / Introns / Learning models / Life Sciences / Microarrays / Molecular Biology / Neural networks / Plant genomes / RNA Splice Sites / RNA Splicing / Sciences du vivant / Sciences informatiques / Spatial complexity / Splice site / Splice site prediction / Structural Biology / Thaliana / U2-type intron / U2-type introns