Automated prediction of the clinical impact of structural copy number variations

Gažiová, Michaela; Sládeček, Tomáš; Pös, Ondrej; Števko, M; Krampl, Werner; Pös, Zuzana; Hekel, Rastislav; Hlavačka, M; Kucharík, Marcel; Radvánszky, Ján; Budiš, Jaroslav; Szemes, Tomáš

doi:10.1038/s41598-021-04505-z

Cited by 6 publications

(13 citation statements)

References 42 publications

(68 reference statements)

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“…In the process of interpretation of small DNA sequence variants 1 several in silico tools are widely used. Similar fully automated tools, such as ISV [6][7][8] , that use prediction methods based on machine learning algorithms to evaluate the clinical impact of CNVs have also been developed.…”

Section: Discussionmentioning

confidence: 99%

“…ISV (Interpretation of Structural Variation) is a machine learning-based approach in which the pathogenicity of a candidate CNV is assessed by observing the counts of overlapped subcategories of genes and regulatory elements 8 . ISV is based on boosted trees and it classifies most variants reliably and with a high accuracy (~98%) among the compared tools 8 .…”

Section: Isvmentioning

confidence: 99%

“…These professional standards tend to encourage consistency and transparency of CNV evaluation across clinical laboratories. In another approach, CNV classification is based on the fully automated machine learning algorithms that can go beyond the established scheme, thus having a great potential to further improve the prediction accuracy [6][7][8] . The CNVs' pathogenicity effect can be also evaluated according to aggregation of per-base single nucleotide polymorphism (SNP) pathogenicity scores within the CNV intervals 9 .…”

Section: Introductionmentioning

confidence: 99%

“…Another available method for pathogenicity prediction is X-CNV 7 , the model trained using the XGBoost classifier. Recently, we have developed a tool called ISV (Interpretation of Structural Variants) 8 which predicts the probability of pathogenicity of a CNV and its likely clinical impact based on specified CNV coordinates (see the ISV subsection in Methods for details).…”

Section: Introductionmentioning

confidence: 99%

“…Several in silico CNV pathogenicity prediction tools have been reported recently differing in the specific machine learning algorithms they implement, in the usage of specific genomic elements as features for model training, and in the parameters applied for model training. One of the tools is the ISV (Interpretation of Structural Variants) 8 which predicts the probability of pathogenicity of a CNV and its likely clinical impact based on specified CNV coordinates (see the ISV subsection in Methods for details). One of the tools is StrVCTVRE 6 focusing on SVs ’ overlapping exons.…”

Section: Introductionmentioning

confidence: 99%

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Combination of Expert Guidelines-based and Machine Learning-based Approaches Leads to Superior Accuracy of Automated Prediction of Clinical Effect of Copy Number Variations

Sládeček

Gažiová

Kucharík

et al. 2022

Preprint

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Clinical interpretation of copy number variants (CNVs) is a complex process that requires skilled clinical professionals. General recommendations have been recently released to guide the CNV interpretation based on predefined criteria to uniform the decision process. Several semiautomatic computational methods have been proposed to recommend appropriate choices, relieving the clinicians from the tedious search in vast genomic databases. We have developed and evaluated such a tool called MarCNV and tested it on CNV records collected from the ClinVar database. Alternatively, the emerging machine learning-based tools, such as the recently published ISV (Interpretation of Structural Variants), showed promising ways of even fully automated predictions using wider characterization of affected genomic elements. Such tools utilize features that are additional to ACMG criteria, thus, they have the potential to significantly improve and/or provide supportive evidence for accurate CNV classification. Since both approaches contribute to evaluation of CNVs clinical impact, we propose a combined solution in the form of a decision support tool based on automated ACMG guidelines (MarCNV) supplemented by a machine learning-based pathogenicity prediction (ISV) for classification of CNVs. We provide evidence that such a combined approach is able to reduce the number of uncertain classifications and reveal potentially incorrect classifications using automated guidelines. CNV interpretation using MarCNV, ISV, and combined approach is available for non-commercial use at https://predict.genovisio.com/.

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Section: Discussionmentioning

confidence: 99%

Section: Isvmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combination of Expert Guidelines-based and Machine Learning-based Approaches Leads to Superior Accuracy of Automated Prediction of Clinical Effect of Copy Number Variations

Sládeček

Gažiová

Kucharík

et al. 2022

Preprint

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Combination of expert guidelines-based and machine learning-based approaches leads to superior accuracy of automated prediction of clinical effect of copy number variations

Sládeček

Gažiová

Kucharík

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Clinical interpretation of copy number variants (CNVs) is a complex process that requires skilled clinical professionals. General recommendations have been recently released to guide the CNV interpretation based on predefined criteria to uniform the decision process. Several semiautomatic computational methods have been proposed to recommend appropriate choices, relieving clinicians of tedious searching in vast genomic databases. We have developed and evaluated such a tool called MarCNV and tested it on CNV records collected from the ClinVar database. Alternatively, the emerging machine learning-based tools, such as the recently published ISV (Interpretation of Structural Variants), showed promising ways of even fully automated predictions using broader characterization of affected genomic elements. Such tools utilize features additional to ACMG criteria, thus providing supporting evidence and the potential to improve CNV classification. Since both approaches contribute to evaluation of CNVs clinical impact, we propose a combined solution in the form of a decision support tool based on automated ACMG guidelines (MarCNV) supplemented by a machine learning-based pathogenicity prediction (ISV) for the classification of CNVs. We provide evidence that such a combined approach is able to reduce the number of uncertain classifications and reveal potentially incorrect classifications using automated guidelines. CNV interpretation using MarCNV, ISV, and combined approach is available for non-commercial use at https://predict.genovisio.com/.

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dbCNV: deleteriousness-based model to predict pathogenicity of copy number variations

Chen

Xiong

et al. 2023

BMC Genomics

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Background Copy number variation (CNV) is a type of structural variation, which is a gain or loss event with abnormal changes in copy number. Methods to predict the pathogenicity of CNVs are required to realize the relationship between these variants and clinical phenotypes. ClassifyCNV, X-CNV, StrVCTVRE, etc. have been trained to predict the pathogenicity of CNVs, but few studies have been reported based on the deleterious significance of features. Results From single nucleotide polymorphism (SNP), gene and region dimensions, we collected 79 informative features that quantitatively describe the characteristics of CNV, such as CNV length, the number of protein genes, the number of three prime untranslated region. Then, according to the deleterious significance, we formulated quantitative methods for features, which fall into two categories: the first is variable type, including maximum, minimum and mean; the second is attribute type, which is measured by numerical sum. We used Gradient Boosted Trees (GBT) algorithm to construct dbCNV, which can be used to predict pathogenicity for five-tier classification and binary classification of CNVs. We demonstrated that the distribution of most feature values was consistent with the deleterious significance. The five-tier classification model accuracy for 0.85 and 0.79 in loss and gain CNVs, which proved that it has high discrimination power in predicting the pathogenicity of five-tier classification CNVs. The binary model achieved area under curve (AUC) values of 0.96 and 0.81 in the validation set, respectively, in gain and loss CNVs. Conclusion The performance of the dbCNV suggest that functional deleteriousness-based model of CNV is a promising approach to support the classification prediction and to further understand the pathogenic mechanism.

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Automated prediction of the clinical impact of structural copy number variations

Cited by 6 publications

References 42 publications

Combination of Expert Guidelines-based and Machine Learning-based Approaches Leads to Superior Accuracy of Automated Prediction of Clinical Effect of Copy Number Variations

Combination of Expert Guidelines-based and Machine Learning-based Approaches Leads to Superior Accuracy of Automated Prediction of Clinical Effect of Copy Number Variations

Combination of expert guidelines-based and machine learning-based approaches leads to superior accuracy of automated prediction of clinical effect of copy number variations

dbCNV: deleteriousness-based model to predict pathogenicity of copy number variations

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