Predicting RNA splicing from DNA sequence using Pangolin

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 pred...

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Bibliographic Details
Published inGenome Biology Vol. 23; no. 1; p. 103
Main Authors Zeng, Tony, Li, Yang I
Format Journal Article
LanguageEnglish
Published England BioMed Central 21.04.2022
BMC
Subjects
Accuracy
Animals
Base Sequence
Chromosomes
Deep learning
Deoxyribonucleic acid
DNA
Genes
Genetic diversity
genetic variation
genome
loss-of-function mutation
Method
Mutation
Neural networks
Nucleotide sequence
nucleotide sequences
Pangolins
prediction
Predictions
Ribonucleic acid
RNA
RNA Splice Sites
RNA Splicing
Splicing
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Summary: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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ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-022-02664-4