Genome interpretation using in silico predictors of variant impact
Estimating the effects of variants found in disease driver genes opens the door to
personalized therapeutic opportunities. Clinical associations and laboratory experiments …
personalized therapeutic opportunities. Clinical associations and laboratory experiments …
Primary Coenzyme Q deficiencies: A literature review and online platform of clinical features to uncover genotype-phenotype correlations
M Alcázar-Fabra, F Rodríguez-Sánchez… - Free Radical Biology …, 2021 - Elsevier
Abstract Primary Coenzyme Q (CoQ) deficiencies are clinically heterogeneous conditions
and lack clear genotype-phenotype correlations, complicating diagnosis and prognostic …
and lack clear genotype-phenotype correlations, complicating diagnosis and prognostic …
Expanding the boundaries of RNA sequencing as a diagnostic tool for rare Mendelian disease
HD Gonorazky, S Naumenko, AK Ramani… - The American Journal of …, 2019 - cell.com
Gene-panel and whole-exome analyses are now standard methodologies for mutation
detection in Mendelian disease. However, the diagnostic yield achieved is at best 50 …
detection in Mendelian disease. However, the diagnostic yield achieved is at best 50 …
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 …
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 …
In silico methods for predicting functional synonymous variants
Single nucleotide variants (SNVs) contribute to human genomic diversity. Synonymous
SNVs are previously considered to be “silent,” but mounting evidence has revealed that …
SNVs are previously considered to be “silent,” but mounting evidence has revealed that …
CI-SpliceAI—improving machine learning predictions of disease causing splicing variants using curated alternative splice sites
Background It is estimated that up to 50% of all disease causing variants disrupt splicing.
Due to its complexity, our ability to predict which variants disrupt splicing is limited, meaning …
Due to its complexity, our ability to predict which variants disrupt splicing is limited, meaning …
Performance evaluation of differential splicing analysis methods and splicing analytics platform construction
K Li, T Luo, Y Zhu, Y Huang, A Wang… - Nucleic Acids …, 2022 - academic.oup.com
A proportion of previously defined benign variants or variants of uncertain significance in
humans, which are challenging to identify, may induce an abnormal splicing process. An …
humans, which are challenging to identify, may induce an abnormal splicing process. An …
Introme accurately predicts the impact of coding and noncoding variants on gene splicing, with clinical applications
Predicting the impact of coding and noncoding variants on splicing is challenging,
particularly in non-canonical splice sites, leading to missed diagnoses in patients. Existing …
particularly in non-canonical splice sites, leading to missed diagnoses in patients. Existing …
Learning the regulatory code of gene expression
Data-driven machine learning is the method of choice for predicting molecular phenotypes
from nucleotide sequence, modeling gene expression events including protein-DNA …
from nucleotide sequence, modeling gene expression events including protein-DNA …