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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Sequence-based models of post-transcriptional regulation rely on genome annotations and transformed measurements extracted from RNA-seq to isolate each regulatory mechanism.
“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]
The Methods section ('Generating training and test sets'; page unavailable) says Pangolin mapped RNA-seq reads using genome annotations and derived splice-site usage labels from those reads. Its training measurement was a Splice-Site Strength Estimate, not percent spliced-in; the paper discusses percent spliced-in for a separate evaluation using minigene assays. The source supports the splicing example in broad terms, but not the specific measurement example or the claim about post-transcriptional models generally. (LLM, conf=0.86)
Experimental — accuracy not yet measuredPangolin has a slight advantage over Borzoi for classifying causal sQTLs from matched negatives (dAUPRC = 0.01, evaluated on all SNPs within distances of ≤10,000 bp from an annotated splice site).
“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]
Identifying splice sites in DNA sequences has formed the basis for a successful approach to interpreting the splicing code and prioritizing pathogenic splicing variants.
“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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- 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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