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Bioinformatics. 2018 Nov 1;34(21):3616-3623. doi: 10.1093/bioinformatics/bty324.

Predicting gene structure changes resulting from genetic variants via exon definition features.

Author information

Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, USA.
Center for Genomic and Computational Biology, Duke University Medical School, Durham, NC, USA.
Department of Human Genetics, Eccles Institute of Human Genetics, University of Utah and School of Medicine, Salt Lake City, UT, USA.
USTAR Center for Genetic Discovery, University of Utah, Salt Lake City, UT, USA.
Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
USDA ARS NEA Robert W. Holley Center for Agriculture and Health, Cornell University, Ithaca, NY, USA.
Department of Biostatistics and Bioinformatics, Duke University Medical School, Durham, NC, USA.



Genetic variation that disrupts gene function by altering gene splicing between individuals can substantially influence traits and disease. In those cases, accurately predicting the effects of genetic variation on splicing can be highly valuable for investigating the mechanisms underlying those traits and diseases. While methods have been developed to generate high quality computational predictions of gene structures in reference genomes, the same methods perform poorly when used to predict the potentially deleterious effects of genetic changes that alter gene splicing between individuals. Underlying that discrepancy in predictive ability are the common assumptions by reference gene finding algorithms that genes are conserved, well-formed and produce functional proteins.


We describe a probabilistic approach for predicting recent changes to gene structure that may or may not conserve function. The model is applicable to both coding and non-coding genes, and can be trained on existing gene annotations without requiring curated examples of aberrant splicing. We apply this model to the problem of predicting altered splicing patterns in the genomes of individual humans, and we demonstrate that performing gene-structure prediction without relying on conserved coding features is feasible. The model predicts an unexpected abundance of variants that create de novo splice sites, an observation supported by both simulations and empirical data from RNA-seq experiments. While these de novo splice variants are commonly misinterpreted by other tools as coding or non-coding variants of little or no effect, we find that in some cases they can have large effects on splicing activity and protein products and we propose that they may commonly act as cryptic factors in disease.

Availability and implementation:

The software is available from

Supplementary information:

Supplementary information is available at Bioinformatics online.

[Available on 2019-11-01]

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