CADD v1. 7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions
M Schubach, T Maass, L Nazaretyan… - Nucleic acids …, 2024 - academic.oup.com
Abstract Machine Learning-based scoring and classification of genetic variants aids the
assessment of clinical findings and is employed to prioritize variants in diverse genetic …
assessment of clinical findings and is employed to prioritize variants in diverse genetic …
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 …
DANN: a deep learning approach for annotating the pathogenicity of genetic variants
Annotating genetic variants, especially non-coding variants, for the purpose of identifying
pathogenic variants remains a challenge. Combined annotation-dependent depletion …
pathogenic variants remains a challenge. Combined annotation-dependent depletion …
A variant by any name: quantifying annotation discordance across tools and clinical databases
Background Clinical genomic testing is dependent on the robust identification and reporting
of variant-level information in relation to disease. With the shift to high-throughput …
of variant-level information in relation to disease. With the shift to high-throughput …
[HTML][HTML] SuSPect: enhanced prediction of single amino acid variant (SAV) phenotype using network features
Whole-genome and exome sequencing studies reveal many genetic variants between
individuals, some of which are linked to disease. Many of these variants lead to single amino …
individuals, some of which are linked to disease. Many of these variants lead to single amino …
Updated benchmarking of variant effect predictors using deep mutational scanning
BJ Livesey, JA Marsh - Molecular systems biology, 2023 - embopress.org
The assessment of variant effect predictor (VEP) performance is fraught with biases
introduced by benchmarking against clinical observations. In this study, building on our …
introduced by benchmarking against clinical observations. In this study, building on our …
How good are pathogenicity predictors in detecting benign variants?
Computational tools are widely used for interpreting variants detected in sequencing
projects. The choice of these tools is critical for reliable variant impact interpretation for …
projects. The choice of these tools is critical for reliable variant impact interpretation for …
Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations
BJ Livesey, JA Marsh - Molecular systems biology, 2020 - embopress.org
To deal with the huge number of novel protein‐coding variants identified by genome and
exome sequencing studies, many computational variant effect predictors (VEPs) have been …
exome sequencing studies, many computational variant effect predictors (VEPs) have been …
SNPnexus: assessing the functional relevance of genetic variation to facilitate the promise of precision medicine
Broader functional annotation of genetic variation is a valuable means for prioritising
phenotypically-important variants in further disease studies and large-scale genotyping …
phenotypically-important variants in further disease studies and large-scale genotyping …
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 …