CNV-PG: a machine-learning framework for accurate copy number variation predicting and genotyping

T, Wang; J, Sun; Zhang, X; Wang, W; Zhou, Qing

doi:10.1101/2020.04.13.039016

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…We also included in the comparison an alternative Python implementation of the CNVnator algorithm called CNVpytor (v1.2.1), which provides speed improvements and additional features when compared to its predecessor [ 17 ]. Another popular tool, Manta v1.6.0 [ 18 ], which is considered one of the top-performing CNV detection algorithms overall in terms of recall, accuracy and precision on both simulated and real data [ 9 , 11 ], was also added to the comparison. In contrast to ConanVarvar, Control-FREEC and CNVnator/CNVpytor, Manta uses read-pair and split-read information instead of read depth.…”

Section: Methodsmentioning

confidence: 99%

ConanVarvar: a versatile tool for the detection of large syndromic copy number variation from whole-genome sequencing data

et al. 2023

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Background A wide range of tools are available for the detection of copy number variants (CNVs) from whole-genome sequencing (WGS) data. However, none of them focus on clinically-relevant CNVs, such as those that are associated with known genetic syndromes. Such variants are often large in size, typically 1–5 Mb, but currently available CNV callers have been developed and benchmarked for the discovery of smaller variants. Thus, the ability of these programs to detect tens of real syndromic CNVs remains largely unknown. Results Here we present ConanVarvar, a tool which implements a complete workflow for the targeted analysis of large germline CNVs from WGS data. ConanVarvar comes with an intuitive R Shiny graphical user interface and annotates identified variants with information about 56 associated syndromic conditions. We benchmarked ConanVarvar and four other programs on a dataset containing real and simulated syndromic CNVs larger than 1 Mb. In comparison to other tools, ConanVarvar reports 10–30 times less false-positive variants without compromising sensitivity and is quicker to run, especially on large batches of samples. Conclusions ConanVarvar is a useful instrument for primary analysis in disease sequencing studies, where large CNVs could be the cause of disease.

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Section: Methodsmentioning

confidence: 99%

ConanVarvar: a versatile tool for the detection of large syndromic copy number variation from whole-genome sequencing data

et al. 2023

View full text Add to dashboard Cite

show abstract

CNV-PG: a machine-learning framework for accurate copy number variation predicting and genotyping

Abstract: 10Motivation: Copy-number variants (CNVs) are one of the major causes of genetic disorders. 11However, current methods for CNV calling have high false-positive rates and low concordance, and 12 a few of them can accurately genotype CNVs.

Cited by 1 publication

References 11 publications

ConanVarvar: a versatile tool for the detection of large syndromic copy number variation from whole-genome sequencing data

ConanVarvar: a versatile tool for the detection of large syndromic copy number variation from whole-genome sequencing data

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