Biology subjects

Kucharik, M.

Publications and source records attributed to Kucharik, M..

5 recordsLinked to original sources

bioRxiv2022

Combination of Expert Guidelines-based and MachineLearning-based Approaches Leads to Superior Accuracy ofAutomated Prediction of Clinical Effect of Copy Number Variations

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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bioRxiv2020

Varlock: privacy preserving storage and dissemination of sequenced genomic data

IntroductionCurrent and future applications of genomic data may raise ethical and privacy concerns. Processing and storing these data introduces a risk of abuse by a potential adversary since a human genome contains sensitive personal information. For this reason, we developed a privacy preserving method, called Varlock, for secure storage of sequenced genomic data. Materials and methodsWe used a public set of population allele frequencies to mask personal alleles detected in genomic reads. Each personal allele described by the public set is masked by a randomly selected population allele with respect to its frequency. Masked alleles are preserved in an encrypted confidential file that can be shared, in whole or in part, using public-key cryptography. ResultsOur method masked personal variants and introduced new variants detected in a personal masked genome. Alternative alleles with lower population frequency were masked and introduced more often. We performed a joint PCA analysis of personal and masked VCFs, showing that the VCFs between the two groups can not be trivially mapped. Moreover, the method is reversible and personal alleles can be unmasked in specific genomic regions on demand. ConclusionOur method masks personal alleles within genomic reads while preserving valuable non-sensitive properties of sequenced DNA fragments for further research. Personal alleles may be restored in desired genomic regions and shared with patients, clinics, and researchers. We suggest that the method can provide an additional layer of security for storing and sharing the raw aligned reads.

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bioRxiv2020

Automated prediction of the clinical impact of structural copy number variations

Introduction: Copy number variants (CNVs) play an important role in many biological processes, including the development of genetic diseases, making them attractive targets for genetic analyses. The interpretation of the effect of structural variants is a challenging problem due to highly variable numbers of gene, regulatory or other genomic elements affected by the CNV. This led to the demand for the interpretation tools that would relieve researchers, laboratory diagnosticians, genetic counselors, and clinical geneticists from the laborious process of annotation and classification of CNVs. Materials and Methods: We designed a classifier method based on the annotations of CNVs from several publicly available databases. The attributes take into account gene elements, regulatory elements affected by the CNV, as well as other CNVs with known clinical significance that overlap the candidate CNV. We also describe the process of model selection and the construction of training, validation, and test set. Results: The presented approach achieved more than 98% prediction accuracy on both copy number loss and copy number gain variants and can be improved by imposing probability thresholds to eliminate low confidence predictions. Discussion: Method has shown considerable performance in predicting the clinical impact of CNVs and therefore has a great potential to guide users to more precise conclusions. The CNV annotation and pathogenicity prediction can be fully automated, relieving users of tedious interpretation processes. Availability and Implementation: The results can be reproduced by following instructions at {{https://github.com/tsladecek/isv}}.

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bioRxiv2019

Non-invasive prenatal testing by low coverage genomic sequencing: Detection limits of screened chromosomal microdeletions

ObjectiveTo study the detection limits of chromosomal microaberrations in non-invasive prenatal testing with aim for five target microdeletion syndromes, including DiGeorge, Prader-Willi/Angelman, 1p36, Cri-Du-Chat, and Wolf-Hirschhorn syndromes.\n\nMethodWe used known cases of pathogenic deletions from ISCA database to specifically define regions critical for the target syndromes. Our approach to detect microdeletions, from whole genome sequencing data, is based on sample normalization and read counting for individual bins. We performed both an in-silico study using artificially created data sets and a laboratory test on mixed DNA samples, with known microdeletions, to assess the sensitivity of prediction for varying fetal fractions, deletion lengths, and sequencing read counts.\n\nResultsThe in-silico study showed sensitivity of 79.3% for 10% fetal fraction with 20M read count, which further increased to 98.4% if we searched only for deletions longer than 3Mb. The test on laboratory-prepared mixed samples was in agreement with in-silico results, while we were able to correctly detect 24 out of 29 control samples.\n\nConclusionOur results suggest that it is possible to incorporate microaberration detection into basic NIPT as part of the offered screening/diagnostics procedure, however, accuracy and reliability depends on several specific factors.\n\nWhats already known about this topic?Microdeletion detection accuracy, similarly to most common trisomies detection, was found to be dependent mostly on technical and biological parameters of the test and tested samples, such as coverage of target region, fetal fraction, size and positions of the deletions.\n\nWhat does this study add?Estimation of relevant regions for five chosen microdeletion syndromes. Confirmation and improvement upon previous methods. Systematic evaluation of sensitivity of microdeletion detection with read counts from 10M to 20M.

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bioRxiv2018

Innovative method for reducing uninformative calls in non-invasive prenatal testing

MotivationNon-invasive prenatal testing or NIPT is currently among the top researched topic in obstetric care. While the performance of the current state-of-the-art NIPT solutions achieve high sensitivity and specificity, they still struggle with a considerable number of samples that cannot be concluded with certainty. Such uninformative results are often subject to repeated blood sampling and re-analysis, usually after two weeks, and this period may cause a stress to the future mothers as well as increase the overall cost of the test.\n\nResultsWe propose a supplementary method to traditional z-scores to reduce the number of such uninformative calls. The method is based on a novel analysis of the length profile of circulating cell free DNA which compares the change in such profiles when random-based and length-based elimination of some fragments is performed. The proposed method is not as accurate as the standard z-score; however, our results suggest that combination of these two independent methods correctly resolves a substantial portion of healthy samples with an uninformative result. Additionally, we discuss how the proposed method can be used to identify maternal aberrations, thus reducing the risk of false positive and false negative calls.\n\nAvailability and ImplementationA particular implementation of the proposed methods is not provided with the manuscript.\n\nContactCorrespondence regarding the manuscript should be directed at Frantisek Duris (fduris@dcs.fmph.uniba.sk).\n\nSupplementary InformationNo additional supplementary information is available.

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