An integrative framework for clinical diagnosis and knowledge discovery from exome sequencing data

Non-silent single nucleotide genetic variants, like nonsense changes and insertion-deletion variants, that affect protein function and length substantially are prevalent and are frequently misclassified. The low sensitivity and specificity of existing variant effect predictors for nonsense and indel...

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Bibliographic Details
Published in:Computers in biology and medicine 2024-02, Vol.169, p.107810-107810, Article 107810
Main Authors: Shojaei, Mona, Mohammadvand, Navid, Doğan, Tunca, Alkan, Can, Çetin Atalay, Rengül, Acar, Aybar C.
Format: Article
Language:English
Subjects:
Algorithms
Amino acids
Annotations
Computational Biology - methods
Datasets
Elongation
Exome
Exome Sequencing
Gene deletion
Gene ontology
Genetic diversity
Genetic variance
Genomes
Humans
Insertion-deletion variants
Knowledge Discovery
Machine learning
Medical diagnosis
Methods
Mutation
Nucleotides
Ontology
Pathogenicity
Pathogens
Pearson correlation
Protein sequence
Proteins
Sensitivity
Support vector machines
Variant pathogenicity prediction
Web services
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Summary:Non-silent single nucleotide genetic variants, like nonsense changes and insertion-deletion variants, that affect protein function and length substantially are prevalent and are frequently misclassified. The low sensitivity and specificity of existing variant effect predictors for nonsense and indel variations restrict their use in clinical applications. We propose the Pathogenic Mutation Prediction (PMPred) method to predict the pathogenicity of single nucleotide variations, which impair protein function by prematurely terminating a protein's elongation during its synthesis. The prediction starts by monitoring functional effects (Gene Ontology annotation changes) of the change in sequence, using an existing ensemble machine learning model (UniGOPred). This, in turn, reveals the mutations that significantly deviate functionally from the wild-type sequence. We have identified novel harmful mutations in patient data and present them as motivating case studies. We also show that our method has increased sensitivity and specificity compared to state-of-the-art, especially in single nucleotide variations that produce large functional changes in the final protein. As further validation, we have done a comparative docking study on such a variation that is misclassified by existing methods and, using the altered binding affinities, show how PMPred can correctly predict the pathogenicity when other tools miss it. PMPred is freely accessible as a web service at https://pmpred.kansil.org/, and the related code is available at https://github.com/kansil/PMPred. •A method is proposed for predicting harmful nonsense and indel mutations, specifically, using GO enrichment analysis.•Differential functional annotation and conserved domain prediction based on mutation location were employed.•The prediction method considers the effect of protein function on variant pathogenicity.•Compared to state-of-the-art, the sensitivity is improved for nonsense and indel mutations, with no loss in specificity.
ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2023.107810