ArticlebioRxiv : the preprint server for biology2026
Predicting human mRNA isoform levels from site-specific splicing kinetics
Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Splicing of pre-mRNA can result in multiple possible mRNA isoforms per gene due to alternative splicing. The frequency at which individual isoforms occur depends on the intrinsic splicing kinetics of the pre-mRNA as well as intracellular chemical conditions. Computational modeling can potentially provide a platform to rapidly assess how variations in intracellular and environmental conditions, for example differential levels of regulatory splicing proteins, affect kinetics and resulting mRNA isoforms. Overcoming the vast combinatoric possibilities of splicing, however, has remained a significant challenge in modeling its kinetics. Here we report the development of a stochastic kinetic model of splicing that is extensible to most protein-coding genes in the human genome. Our model allows for variations in site-specific reaction rates as well as the ability to introduce additional splicing factors. We experimentally validate the predictive capability of our computational model by exploring the spliced isoform ratio of a target gene (
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.