Predicting RNA splicing from DNA sequence using Pangolin

• Published in: Genome Biology Vol. 23; no. 1; p. 103
• Main Authors: Zeng, Tony, Li, Yang I
• Format: Journal Article
• Language: English
• 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

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...