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Predicting RNA splicing from DNA sequence using Pangolin
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Abstract
Recent progress in deep learning has greatly improved the prediction of RNA splicing from DNA sequence. Here, we present Pangolin, a deep learning model to predict splice site strength in multiple tissues. Pangolin outperforms state-of-the-art methods for predicting RNA splicing on a variety of prediction tasks. Pangolin improves prediction of the impact of genetic variants on RNA splicing, including common, rare, and lineage-specific genetic variation. In addition, Pangolin identifies loss-of-function mutations with high accuracy and recall, particularly for mutations that are not missense or nonsense, demonstrating remarkable potential for identifying pathogenic variants.
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![Fig. 1 Overview of Pangolin and evaluation. a Schematic and architecture of Pangolin. b Heatmap summarizing the performance of Pangolin, SpliceAI, HAL, MMSplice, and MaxEntScan with respect to three metrics including top-1 accuracy. c Precision-recall curves representing the precision and recall from multiple methods for the prediction of splice-disrupting variants as identified in Cheung et al. [8] (1050 splice-disrupting variants out of 27,733 total). d Scatter plots showing measured versus predicted effects of single genetic variants (left) or a combination of genetic variants (right) on RNA splicing. Measured effects of single genetic variants and combinations of variants were obtained from Julien et al. [15] and Baeza-Centurion et al. [3] respectively. e In silico mutagenesis of 6416 exons from human chromosomes 7 and 8. Barplots show for each base the percent of mutations (square root) predicted to increase or decrease usage by at least 0.2](/paper-figures/79b62d602d5e48879c2b7d197150d5b5/predicting-rna-splicing-from-dna-sequence-using-pangolin-figure-1.jpg)
![Fig. 2 Application of Pangolin to a variety of prediction tasks. a Cumulative density plot of the log10 sQTL p-value fold difference between the SNP predicted to affect splicing and that of the lead sQTL SNP for the top 500 sQTLs identified in DGN (All predictions), or for the 100 predictions with the largest predicted effects (inset). b Example of a splice site that shows a large inter-species difference in usage. A single-nucleotide difference between chimp (T) and human (C) is predicted to strongly decrease (resp. increase) usage of a chimp (resp. human) splice site (dashed vertical line indicates the human site). The T (resp. C) difference likely disrupts (resp. creates) a 3’ canonical splice site in chimp (resp. human). c Locations and effects of SNVs ±50bp from a splice site predicted to underlie inter-species differences in splice site usage for 71 3’ and 74 5’ sites. A large fraction—but not all—of splice-altering variants are located near the canonical splice sites. d Survival function plots of BRCA1 variants in splice regions as a function of their predicted effects on splicing. The variants are separated by their classification as loss-of-function (LOF, blue), intermediate effect (INT, orange), or functional (FUNC, green). We observe a huge enrichment of LOF variants among variants with large predicted splicing effects. e Precision-recall curves for different variant types representing the precision and recall for distinguishing LOF variants from functional variants. Pangolin achieves a remarkable AUPRC for variants in extended splice regions (note that this excludes canonical splice variants). See Additional file 1: Fig. S8 for variants from additional annotation bins. f Predicted splicing effects of mutations in or flanking 4 BRCA1 exons from Findlay et al. [12]. Mutations identified to be LOF or to have intermediate phenotypes, as well as missense, nonsense, and canonical splice site mutations are annotated. See Additional file 1: Fig. S9 for all 13 exons with predictions. g Precision-recall curves representing the precision and recall for distinguishing variants annotated as pathogenic from variants annotated as benign in ClinVar. The blue (resp. orange) line represents the PRC for variants excluding (resp. including) variants in annotated splice sites. Missense and nonsense variants are excluded](/paper-figures/719817e19cb84ed09f8e103dd87cc85f/predicting-rna-splicing-from-dna-sequence-using-pangolin-figure-2.jpg)
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“Similarly, sequence-based models of post-transcriptional regulation rely on genome annotations and transformed measurements extracted from RNA-seq to isolate each regulatory mechanism (for example, percent spliced-in for splicing)14–23.”
From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [16]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“When comparing Borzoi to Pangolin16 for the task of classifying the causal sQTLs from matched negatives, Pangolin has a slight advantage (Fig. 7d–e and Supplementary Fig. 12c; dAUPRC = 0.01, evaluated on all SNPs within distances of ≤10,000 bp from an annotated splice site).”
From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [16]
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“Identifying splice sites in DNA sequences has formed the basis for a successful approach to interpreting the splicing code and prioritizing pathogenic splicing variants15,16.”
From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [16]
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Cites
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
Decoupled Weight Decay Regularization
Loshchilov, I., & Hutter, F. (2017) · arXiv · doi:10.48550/arxiv.1711.05101 · Cited by 12,522
The GTEx Consortium atlas of genetic regulatory effects across human tissues
The GTEx Consortium, Aguet, F., Anand, S., Ardlie, K. G., Gabriel, S., Getz, G. A., Graubert, A., Hadley, K., Handsaker, R. E., Huang, K. H., et al. (2020) · Science · doi:10.1126/science.aaz1776 · Cited by 5,547
ClinVar: improving access to variant interpretations and supporting evidence
Landrum, M. J., Lee, J. M., Benson, M., Brown, G. R., Chao, C., Chitipiralla, S., Gu, B., Hart, J., Hoffman, D., Jang, W., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1153 · Cited by 4,196
Predicting splicing from primary sequence with deep learning
Jaganathan, K., Kyriazopoulou Panagiotopoulou, S., McRae, J. F., Darbandi, S. F., Knowles, D., Li, Y. I., Kosmicki, J. A., Arbelaez, J., Cui, W., Schwartz, G. B., et al. (2019) · Cell · doi:10.1016/j.cell.2018.12.015 · Cited by 2,853
Self-citationMaximum Entropy Modeling of Short Sequence Motifs with Applications to RNA Splicing Signals
Yeo, G. W.-M., & Burge, C. B. (2004) · Journal of Computational Biology · doi:10.1089/1066527041410418 · Cited by 2,042
Liftoff: accurate mapping of gene annotations
Shumate, A., & Salzberg, S. L. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa1016 · Cited by 991
Accurate classification of BRCA1 variants with saturation genome editing
Findlay, G. M., Daza, R. M., Martin, B., Zhang, M. D., Leith, A. P., Gasperini, M., Janizek, J. D., Huang, X., Starita, L. M., & Shendure, J. (2018) · Nature · doi:10.1038/s41586-018-0461-z · Cited by 843
Gene expression across mammalian organ development
Cardoso-Moreira, M., Halbert, J., Valloton, D., Velten, B., Chen, C., Shao, Y., Liechti, A., Ascenção, K., Rummel, C., Ovchinnikova, S., et al. (2019) · Nature · doi:10.1038/s41586-019-1338-5 · Cited by 838
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AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model
Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2025) · doi:10.1101/2025.06.25.661532 · Cited by 395
Advancing regulatory variant effect prediction with AlphaGenome
Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2026) · Nature · doi:10.1038/s41586-025-10014-0 · Cited by 373
“…outcomes can be modelled at three levels: the probability that any given nucleotide acts as a splice donor or acceptor (splice site prediction) 4,11,16 , competitive selection among potential splice sites (splice site usage prediction) 11,16 and prediction of specific introns (splice junction…”
Regulation of pre-mRNA splicing: roles in physiology and disease, and therapeutic prospects
Rogalska, M. E., Vivori, C., & Valcárcel, J. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00556-8 · Cited by 284
Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation
Linder, J., Srivastava, D., Yuan, H., Agarwal, V., & Kelley, D. R. (2025) · Nature Genetics · doi:10.1038/s41588-024-02053-6 · Cited by 265
SpliceAI-visual: a free online tool to improve SpliceAI splicing variant interpretation
Agathe, J. D. S., Filser, M., Isidor, B., Besnard, T., Guéguen, P., Perrin, A., Goethem, C. V., Verebi, C., Masingue, M., Rendu, J., et al. (2023) · Human Genomics · doi:10.1186/s40246-023-00451-1 · Cited by 201
Metadata
- Authors
- Zeng, T., & Li, Y. I.
- Affiliations
- University of Chicago (US)
- Corresponding author
- Li, Yang I
- ORCID
- Venue
- Genome Biology
- Published
- 2022-04-21
- Volume
- 23
- Issue
- 1
- Pages
- 103-103
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC9022248
- OpenAlex
- W4226065462
- Cited by
- 283
- References
- 32
- Citations by year
- 2026: 100 · 2025: 96 · 2024: 56 · 2023: 27 · 2022: 4
- FWCI
- 14.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Genome biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- national institute of general medical sciences (R01GM130738)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- splice site strength, pangolins, deep learning, genetic variants, variant effect prediction, loss-of-function mutations, pathogenic variant identification, tissue-specific splicing
- MeSH terms
- Pangolins, Animals, Base Sequence, Mutation, RNA Splicing, RNA Splice Sites
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