Dataset Information

ClassifyCNV: a tool for clinical annotation of copy-number variants.

ABSTRACT: Copy-number variants (CNVs) are an important part of human genetic variation. They can be benign or can play a role in human disease by creating dosage imbalances and disrupting genes and regulatory elements. Accurate identification and clinical annotation of CNVs is essential, however, manual evaluation of individual CNVs by clinicians is challenging on a large scale. Here, we present ClassifyCNV, an easy-to-use tool that implements the 2019 ACMG classification guidelines to assess CNV pathogenicity. ClassifyCNV uses genomic coordinates and CNV type as input and reports a clinical classification for each variant, a classification score breakdown, and a list of genes of potential importance for variant interpretation. We validate ClassifyCNV's performance using a set of known clinical CNVs and a set of manually evaluated variants. ClassifyCNV matches the pathogenicity category for 81% of manually evaluated variants with the significance of the remaining pathogenic and benign variants automatically determined as uncertain, requiring a further evaluation by a clinician. ClassifyCNV facilitates the implementation of the latest ACMG guidelines in high-throughput CNV analysis, is suitable for integration into NGS analysis pipelines, and can decrease time to diagnosis. The tool is available at https://github.com/Genotek/ClassifyCNV .

SUBMITTER: Gurbich TA

PROVIDER: S-EPMC7683568 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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ClassifyCNV: a tool for clinical annotation of copy-number variants.

Gurbich Tatiana A TA Ilinsky Valery Vladimirovich VV

Scientific reports 20201123 1

Copy-number variants (CNVs) are an important part of human genetic variation. They can be benign or can play a role in human disease by creating dosage imbalances and disrupting genes and regulatory elements. Accurate identification and clinical annotation of CNVs is essential, however, manual evaluation of individual CNVs by clinicians is challenging on a large scale. Here, we present ClassifyCNV, an easy-to-use tool that implements the 2019 ACMG classification guidelines to assess CNV pathogen ...[more]

PMID: 33230148

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Project description:Carbapenemase production is one of the leading mechanisms of carbapenem resistance in Gram-negative bacteria. An increase in carbapenemase gene (blaCarb) copies is an important mechanism of carbapenem resistance. No currently available bioinformatics tools allow for reliable detection and reporting of carbapenemase gene copy numbers. Here, we describe the carbapenemase-encoding gene copy number estimator (CCNE), a ready-to-use bioinformatics tool that was developed to estimate blaCarb copy numbers from whole-genome sequencing data. Its performance on Klebsiella pneumoniae carbapenemase gene (blaKPC) copy number estimation was evaluated by simulation and quantitative PCR (qPCR), and the results were compared with available algorithms. CCNE has two components, CCNE-acc and CCNE-fast. CCNE-acc detects blaCarb copy number in a comprehensive and high-accuracy way, while CCNE-fast rapidly screens blaCarb copy numbers. CCNE-acc achieved the best accuracy (100%) and the lowest root mean squared error (RMSE; 0.07) in simulated noise data sets, compared to the assembly-based method (23.4% accuracy, 1.697 RMSE) and the OrthologsBased method (78.9% accuracy, 0.395 RMSE). In the qPCR validation, a high consistency was observed between the blaKPC copy number determined by qPCR and that determined with CCNE. Reverse transcription-qPCR transcriptional analysis of 40 isolates showed that blaKPC expression was positively correlated with the blaKPC copy numbers detected by CCNE (P < 0.001). An association study of 357 KPC-producing K. pneumoniae isolates and their antimicrobial susceptibility identified a significant association between the estimated blaKPC copy number and MICs of imipenem (P < 0.001) and ceftazidime-avibactam (P < 0.001). Overall, CCNE is a useful genomic tool for the analysis of antimicrobial resistance genes copy number; it is available at https://github.com/biojiang/ccne. IMPORTANCE Globally disseminated carbapenem-resistant Enterobacterales is an urgent threat to public health. The most common carbapenem resistance mechanism is the production of carbapenemases. Carbapenemase-producing isolates often exhibit a wide range of carbapenem MICs. Higher carbapenem MICs have been associated with treatment failure. The increase of carbapenemase gene (blaCarb) copy numbers contributes to increased carbapenem MICs. However, blaCarb gene copy number detection is not routinely conducted during a genomic analysis, in part due to the lack of optimal bioinformatics tools. In this study, we describe a ready-to-use tool we developed and designated the carbapenemase-encoding gene copy number estimator (CCNE) that can be used to estimate the blaCarb copy number directly from whole-genome sequencing data, and we extended the data to support the analysis of all known blaCarb genes and some other antimicrobial resistance genes. Furthermore, CCNE can be used to interrogate the correlations between genotypes and susceptibility phenotypes and to improve our understanding of antimicrobial resistance mechanisms.

Dataset Information

ClassifyCNV: a tool for clinical annotation of copy-number variants.

Publications

ClassifyCNV: a tool for clinical annotation of copy-number variants.

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