CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores
Background Splicing of genomic exons into mRNAs is a critical prerequisite for the accurate
synthesis of human proteins. Genetic variants impacting splicing underlie a substantial …
synthesis of human proteins. Genetic variants impacting splicing underlie a substantial …
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 …
DNA sequence. Here, we present Pangolin, a deep learning model to predict splice site …
SPiP: Splicing Prediction Pipeline, a machine learning tool for massive detection of exonic and intronic variant effects on mRNA splicing
Modeling splicing is essential for tackling the challenge of variant interpretation as each
nucleotide variation can be pathogenic by affecting pre‐mRNA splicing via …
nucleotide variation can be pathogenic by affecting pre‐mRNA splicing via …
Genome-wide prediction of disease variant effects with a deep protein language model
Predicting the effects of coding variants is a major challenge. While recent deep-learning
models have improved variant effect prediction accuracy, they cannot analyze all coding …
models have improved variant effect prediction accuracy, they cannot analyze all coding …
Aberrant splicing prediction across human tissues
Aberrant splicing is a major cause of genetic disorders but its direct detection in
transcriptomes is limited to clinically accessible tissues such as skin or body fluids. While …
transcriptomes is limited to clinically accessible tissues such as skin or body fluids. While …
Machine learning approaches for the prioritization of genomic variants impacting pre-mRNA splicing
CF Rowlands, D Baralle, JM Ellingford - Cells, 2019 - mdpi.com
Defects in pre-mRNA splicing are frequently a cause of Mendelian disease. Despite the
advent of next-generation sequencing, allowing a deeper insight into a patient's variant …
advent of next-generation sequencing, allowing a deeper insight into a patient's variant …
A plugin for the Ensembl Variant Effect Predictor that uses MaxEntScan to predict variant spliceogenicity
J Shamsani, SH Kazakoff, IM Armean… - …, 2019 - academic.oup.com
Assessing the pathogenicity of genetic variants can be a complex and challenging task.
Spliceogenic variants, which alter mRNA splicing, may yield mature transcripts that encode …
Spliceogenic variants, which alter mRNA splicing, may yield mature transcripts that encode …
MMSplice: modular modeling improves the predictions of genetic variant effects on splicing
Predicting the effects of genetic variants on splicing is highly relevant for human genetics.
We describe the framework MMSplice (modular modeling of splicing) with which we built the …
We describe the framework MMSplice (modular modeling of splicing) with which we built the …
SpliceAI-visual: a free online tool to improve SpliceAI splicing variant interpretation
JM de Sainte Agathe, M Filser, B Isidor, T Besnard… - Human Genomics, 2023 - Springer
SpliceAI is an open-source deep learning splicing prediction algorithm that has
demonstrated in the past few years its high ability to predict splicing defects caused by DNA …
demonstrated in the past few years its high ability to predict splicing defects caused by DNA …
Benchmarking deep learning splice prediction tools using functional splice assays
Hereditary disorders are frequently caused by genetic variants that affect pre‐messenger
RNA splicing. Though genetic variants in the canonical splice motifs are almost always …
RNA splicing. Though genetic variants in the canonical splice motifs are almost always …