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 …
VariPred: Enhancing Pathogenicity Prediction of Missense Variants Using Protein Language Models
Computational approaches for predicting the pathogenicity of genetic variants have
advanced in recent years. These methods enable researchers to determine the possible …
advanced in recent years. These methods enable researchers to determine the possible …
Evaluating the relevance of sequence conservation in the prediction of pathogenic missense variants
E Capriotti, P Fariselli - Human Genetics, 2022 - Springer
Evolutionary information is the primary tool for detecting functional conservation in nucleic
acid and protein. This information has been extensively used to predict structure …
acid and protein. This information has been extensively used to predict structure …
[HTML][HTML] 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 …
Performance comparison of computational methods for the prediction of the function and pathogenicity of non-coding variants
Z Wang, G Zhao, B Li, Z Fang, Q Chen… - Genomics …, 2023 - academic.oup.com
Non-coding variants in the human genome significantly influence human traits and complex
diseases via their regulation and modification effects. Hence, an increasing number of …
diseases via their regulation and modification effects. Hence, an increasing number of …
VPOT: a customizable variant prioritization ordering tool for annotated variants
Next-generation sequencing (NGS) technologies generate thousands to millions of genetic
variants per sample. Identification of potential disease-causal variants is labor intensive as it …
variants per sample. Identification of potential disease-causal variants is labor intensive as it …
A benchmark study of scoring methods for non-coding mutations
Motivation Detailed knowledge of coding sequences has led to different candidate models
for pathogenic variant prioritization. Several deleteriousness scores have been proposed for …
for pathogenic variant prioritization. Several deleteriousness scores have been proposed for …
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 …
[HTML][HTML] 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 …