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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| Published in | Genome Biology Vol. 23; no. 1; p. 103 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
England
BioMed Central
21.04.2022
BMC |
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| Online Access | Get full text |
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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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| AbstractList | 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.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. 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. 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. |
| ArticleNumber | 103 |
| Author | Li, Yang I Zeng, Tony |
| Author_xml | – sequence: 1 givenname: Tony surname: Zeng fullname: Zeng, Tony – sequence: 2 givenname: Yang I orcidid: 0000-0002-0736-251X surname: Li fullname: Li, Yang I |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35449021$$D View this record in MEDLINE/PubMed |
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| Snippet | 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... Abstract Recent progress in deep learning has greatly improved the prediction of RNA splicing from DNA sequence. Here, we present Pangolin, a deep learning... |
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| SubjectTerms | 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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| Title | Predicting RNA splicing from DNA sequence using Pangolin |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/35449021 https://www.proquest.com/docview/2666609123 https://www.proquest.com/docview/2654288229 https://www.proquest.com/docview/2660980819 https://pubmed.ncbi.nlm.nih.gov/PMC9022248 https://doaj.org/article/04bc34207daa4848812b5fd4ce7a9f97 |
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