Mutalyzer 2: next generation HGVS nomenclature checker

Lefter, Mihai; Vis, Jonathan K.; Vermaat, Martijn; Dunnen, Johan T. den; Taschner, Peter E.M.; Laros, Jeroen F. J.

doi:10.1093/bioinformatics/btab051

Cited by 69 publications

(48 citation statements)

References 17 publications

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“…Each reported mutation in NDP was tabulated, along with its corresponding amino acid change if the mutation was in the coding region. Consistent nomenclature of coding DNA mutation consequences was ensured through the use of mutalyser ( Lefter et al, 2021 ) using the NCBI reference sequence and the search term “NG_009,832.1(NDP):c.___” followed by the cDNA change. A common variation in nomenclature was for authors to number nucleic acid changes from the beginning of exon one rather than from the beginning of the coding sequence.…”

Section: Methodsmentioning

confidence: 99%

Spectrum of Mutations in NDP Resulting in Ocular Disease; a Systematic Review

et al. 2022

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Aims and Rationale: The inner retina is supplied by three intraretinal capillary plexi whereas the outer retina is supplied by the choroidal circulation: NDP is essential for normal intraretinal vascularisation. Pathogenic variants in NDP (Xp11.3) may result in either a severe retinal phenotype associated with hearing loss (Norrie Disease) or a moderate retinal phenotype (Familial Exudative Vitreoretinopathy, FEVR). However, little is known about whether the nature or location of the NDP variant is predictive of severity. In this systematic review we summarise all reported NDP variants and draw conclusions about whether the nature of the NDP variant is predictive of the severity of the resulting ocular pathology and associated hearing loss and intellectual disability.Findings: 201 different variants in the NDP gene have been reported as disease-causing. The pathological phenotype that may result from a disease-causing NDP variant is quite diverse but generally comprises a consistent cluster of features (retinal hypovascularisation, exudation, persistent foetal vasculature, tractional/exudative retinal detachment, intellectual disability and hearing loss) that vary predictably with severity. Previous reviews have found no clear pattern in the nature of NDP mutations that cause either FEVR or Norrie disease, with the exception that mutations affecting cysteine residues have been associated with Norrie Disease and that visual loss amongst patients with Norrie disease tends to be more severe if the NDP mutation results in an early termination of translation as opposed to a missense related amino acid change. A key limitation of previous reviews has been variability in the case definition of Norrie disease and FEVR amongst authors. We thus reclassified patients into two groups based only on the severity of their retinal disease. Of the reported pathogenic variants that have been described in more than one patient, we found that any given variant caused an equivalent severity of retinopathy each time it was reported with very few exceptions. We therefore conclude that specific NDP mutations generally result in a consistent retinal phenotype each time they arise. Reports by different authors of the same variant causing either FEVR or Norrie disease conflict primarily due to variability in the authors’ respective case definitions rather than true differences in disease severity.

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Section: Methodsmentioning

confidence: 99%

Spectrum of Mutations in NDP Resulting in Ocular Disease; a Systematic Review

et al. 2022

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“…All other criteria were determined based on the clinical information available for each variant. Mutalyzer ( Lefter et al, 2021 ), a web-based tool for mapping variants to reference sequences, was used to validate the unpublished variants found in the IBEM-IS ( Supplementary Table S1 ). ClinVitae (Invitae | Clinvitae, 2019 ) was used as a secondary source of published variants.…”

Section: Methodsmentioning

confidence: 99%

Using Long-Term Follow-Up Data to Classify Genetic Variants in Newborn Screened Conditions

et al. 2022

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With the rapid increase in publicly available sequencing data, healthcare professionals are tasked with understanding how genetic variation informs diagnosis and affects patient health outcomes. Understanding the impact of a genetic variant in disease could be used to predict susceptibility/protection and to help build a personalized medicine profile. In the United States, over 3.8 million newborns are screened for several rare genetic diseases each year, and the follow-up testing of screen-positive newborns often involves sequencing and the identification of variants. This presents the opportunity to use longitudinal health information from these newborns to inform the impact of variants identified in the course of diagnosis. To test this, we performed secondary analysis of a 10-year natural history study of individuals diagnosed with metabolic disorders included in newborn screening (NBS). We found 564 genetic variants with accompanying phenotypic data and identified that 161 of the 564 variants (29%) were not included in ClinVar. We were able to classify 139 of the 161 variants (86%) as pathogenic or likely pathogenic. This work demonstrates that secondary analysis of longitudinal data collected as part of NBS finds unreported genetic variants and the accompanying clinical information can inform the relationship between genotype and phenotype.

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“…Mutalyzer version 2.0.32 (12) was used to convert HGVS c. positions to hg19 genomic coordinates followed by functional annotation using SnpEff version 4.3 (13). Pre-computed scores were downloaded from reference files as cited in papers for MPC (14), VEST version 3.0 (15), FATHMM (16), and REVEL (7) to annotate variants using the vcfanno package (17).…”

Section: Methodsmentioning

confidence: 99%

Evaluating the impact ofin silicopredictors on clinical variant classification

Wilcox

Sarmady

Wulf

et al. 2021

Preprint

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Background: In silico evidence is important to consider when interpreting genetic variants. According to the ACMG/AMP, in silico evidence is applied at the supporting strength level using the PP3 and BP4 criteria, for pathogenic and benign evidence, respectively. While PP3 has been determined to be one of the most commonly applied criteria, less is known about the effect of these two criteria on variant classification outcomes. Methods: In this study, a total of 727 missense variants curated by Clinical Genome Resource (ClinGen) Variant Curation Expert Panels (VCEPs) were analyzed to determine how often PP3 and BP4 were applied and how often they influenced final variant classifications. The current categorical system of variant classification was compared with a point-based system being developed by the ClinGen Sequence Variant Interpretation Working Group. In addition, the performance of four in silico tools (REVEL, VEST, FATHMM, and MPC) was assessed by using a gold set of 237 variants (classified as benign or pathogenic independent of PP3 or BP4) to calculate pathogenicity likelihood ratios. Results: Collectively, the PP3 and BP4 criteria were applied by ClinGen VCEPs to 55% of missense variants in this data set. Removing in silico criteria from variants where they were originally applied caused variants to change classification from pathogenic to likely pathogenic (14%), likely pathogenic to variant of uncertain significance (VUS) (24%), or likely benign to VUS (64%). The proportion of downgrades with the categorical classification system was similar to that of the point-based system, though the latter resolved borderline classifications. REVEL and VEST performed at a level consistent with moderate strength towards either benign or pathogenic evidence, while FATHMM performed at the supporting level. Conclusions: Overall, this study demonstrates that in silico criteria PP3 and BP4 are commonly applied in variant classification and often affect the final classification. Our results suggest that when sufficient thresholds for in silico predictors are established, PP3 and BP4 may be appropriate to use at a moderate strength. However, further calibration with larger datasets is needed to optimize the performance of current in silico tools given the impact they have on clinical variant classification.

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Mutalyzer 2: next generation HGVS nomenclature checker

Cited by 69 publications

References 17 publications

Spectrum of Mutations in NDP Resulting in Ocular Disease; a Systematic Review

Spectrum of Mutations in NDP Resulting in Ocular Disease; a Systematic Review

Using Long-Term Follow-Up Data to Classify Genetic Variants in Newborn Screened Conditions

Evaluating the impact ofin silicopredictors on clinical variant classification

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