2021
DOI: 10.1186/s13073-021-00945-4
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X-CNV: genome-wide prediction of the pathogenicity of copy number variations

Abstract: Background Gene copy number variations (CNVs) contribute to genetic diversity and disease prevalence across populations. Substantial efforts have been made to decipher the relationship between CNVs and pathogenesis but with limited success. Results We have developed a novel computational framework X-CNV (www.unimd.org/XCNV), to predict the pathogenicity of CNVs by integrating more than 30 informative features such as allele frequency (AF), CNV leng… Show more

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Cited by 32 publications
(34 citation statements)
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“…The poor performance of transcript consequence from VEP reinforces the known limitations of prioritizing variants with sequence ontology terms in isolation. We also evaluated X-CNV 35 and CADD-SV 36 on this test dataset ( Figure S6 ). The AUC for X-CNV was 0.68, and the AUC for CADD-SV was 0.70.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…The poor performance of transcript consequence from VEP reinforces the known limitations of prioritizing variants with sequence ontology terms in isolation. We also evaluated X-CNV 35 and CADD-SV 36 on this test dataset ( Figure S6 ). The AUC for X-CNV was 0.68, and the AUC for CADD-SV was 0.70.…”
Section: Resultsmentioning
confidence: 99%
“…AnnotSV was run with human annotation and default settings. We retrieved X-CNV 35 on September 27, 2021, and it was run with default settings on variants converted to GRCh37 via the UCSC liftover tool. We retrieved CADD-SV 36 v1.0 on September 13, 2021, and it was run with default settings.…”
Section: Methodsmentioning
confidence: 99%
“…We ran the benchmark on a case basis; we added all variants of the case report (i.e., both SVs in case of compound heterozygous genotype) in turn to one of ten background VCF files, and we recorded the median SV rank from the ten VCF files. We used the benchmarking schema to compare SvAnna with AnnotSV [ 27 ], X-CNV [ 28 ], SvScore [ 29 ], and ClassifyCNV [ 30 ]. The benchmark was performed using the pipeline engine Nextflow [ 31 ].…”
Section: Methodsmentioning
confidence: 99%
“…X-CNV [ 28 ] uses extreme gradient boosted trees to predict deleteriousness of deletions and duplications in the form of meta-voting prediction (MVP) score. The MVP score integrates several dozens of features, including SV characteristics (type, allele frequency based on DGV, dbVar), functional deleteriousness predictions, non-coding features (CTCF, cCREs, pELS, and dELS elements), and genome-wide annotations.…”
Section: Methodsmentioning
confidence: 99%
“…Methods that evaluate the functional consequence of SVs in individual genomes use different strategies. Several approaches include genomic information, such as variant length, haploinsufficiency measures or GC contents, to separate pathogenic from benign SVs ( Hehir-Kwa et al , 2010 ; Kleinert and Kircher, 2021 ; Sharo et al , 2020 ; Zhang et al , 2021 ). Furthermore, the predicted pathogenicity of deleterious single nucleotide variants within an SV can be used to estimate pathogenicity of SVs ( Ganel et al , 2017 ).…”
Section: Introductionmentioning
confidence: 99%