CNV-ClinViewer

The CNV-ClinViewer is a user-friendly web-application for the interactive visualization, genomic exploration and standardized clinical significance interpretation of large copy-number variants (CNVs)









Upload your CNVs





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The content on this website is based on version Novvember 19, 2022


Classification of CNVs

Download score sheet

Please click on CNV of interest in table above to see specifics below.

Report of selected CNV

Viewer

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Microdeletion and microduplication syndromes from DECIPHER

CNV allele frequency from 482,734 individuals in the UK-Biobank (filtered for CNVs > 50kb)

CNV allele frequency from 14,891 genomes (gnomAD; filtered for CNVs > 50kb)

Gene set enrichment analysis

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About


Copy-number variants (CNVs) and their emerging role in complex and rare diseases are an active field of research. Clinical CNV pathogenicity classification and genotype-phenotype analyses are challenging and time-consuming tasks that require the integration and analysis of information from many sources. Here, we introduce the CNV-ClinViewer, an open-source web-application for the clinical evaluation and visual exploration of CNVs. We combined data of >250,000 CNVs from patients and the general population with publicly available genomic, bioinformatic and clinical annotations, using R studio’s Shiny framework. We integrated an existing CNV classification tool in CNV-ClinViewer and provide access to enrichment analyses in >180 gene-set libraries. To ensure effectiveness and usability without bioinformatical expertise, we put great emphasis on an interactive workflow with real-time results, visualizations and a user-friendly interface design.

After uploading CNV data, the CNV-ClinViewer enables:
a) semi-automated CNV clinical significance classification based on the 2019 ACMG/ClinGen Technical Standards for CNVs,
b) comparative and interactive visual inspection of uploaded CNVs along with other pathogenic and general population CNV datasets,
c) evaluation and prioritization of the genomic content by various gene dosage sensitivity scores, clinical annotations, and gene set enrichment analyses, and
d) generation of comprehensive individual CNV reports including clinical significance classification and observed annotations details.

The tool aids in identifying possible driver genes in a given CNV, which in turn is important for genetic counselling and possibly disease prognosis. Overall, this resource will facilitate biomedical CNV interpretation and re-evaluations of large sets of CNVs identified in rare disease cases and, in combination with clinical judgment, enable clinicians and researchers to formulate novel hypotheses and guide their decision-making process.

The CNV-ClinViewer is an open-source project, and its code will continue to grow and improve through version control in the GitHub repository (https://github.com/LalResearchGroup/CNV-clinviewer).

Marie Macnee (1), Eduardo Pérez-Palma (2), Tobias Brünger (1), Chiara Klöckner (3), Konrad Platzer (3), Arthur Stefanski (4-5), Ludovica Montanucci (4), Allan Bayat (6-7), Maximilian Radtke (3), Ryan Collins (8-9), Michael Talkowski (8-9), Daniel Blankenberg (4), Rikke S Møller (6-7), Johannes Lemke (3), Michael Nothnagel (1,10), Patrick May (11), Dennis Lal (1,4-5,8)

1 Cologne Center for Genomics (CCG), University of Cologne, Germany.
2 Universidad del Desarrollo, Centro de Genética y Genómica, Facultad de Medicina Clínica Alemana, Santiago, Chile.
3 Institute of Human Genetics, Leipzig Medical Center, Leipzig, Germany.
4 Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
5 Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA.
6 Department of Epilepsy Genetics and Personalized Medicine, Member of ERN Epicare, Danish Epilepsy Centre, Dianalund, Denmark.
7 Department of Regional Health Research, Faculty of Health Sciences, University of Southern Denmark, Denmark.
8 Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.
9 Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
10 University Hospital Cologne, Cologne, Germany.
11 Luxembourg Centre for Systems Biomedicine, University Luxembourg, Esch-sur-Alzette, Luxembourg.

The CNV-ClinViewer relies on your feedback. Please send an E-mail if you wish to make a request, a comment, or report a bug.

Step 1: Data upload


You can upload CNVs in a modified bed or tab-delimited text file, or in an excel file with the following columns:

Required columns:
1. CHR: chromosome, for example 'chr1'
2. START: genomic start coordiante of CNV
3. END: genomic end coordiante of CNV
4. TYPE: type of CNV ('DEL' or 'DUP')

Optional columns:
5. ID: sample ID/ identifier (CNVs with the same sample ID will be visualized in the same row; if you do not provide sample IDs each CNV will be given a unique ID)
6. POINTS: The CNVs are automatically classified based on the 2019 ACMG/ClinGen Technical Standards for CNVs. Evaluated evidence categories for copy-number losses are: 1A/B, 2A-H, 3A-C, 4O, and for copy-number gains: 1A/B, 2A-H, 2J-L, 3A-C, 4O. If you have further information e.g. about the family history or the 'de novo' status you can give the total score of the non-evaluated evidence categories in the 'POINTS' column as numeric values (eg. -1 or 0.9).
7. FILTER_'name' : additional binary variables (with values 1 (='yes') and 0 (='no')) for filtering. The name of the variable should be provided after 'FILTER_'.


Example data table:


Download example (.bed)
Download example (.xlsx)

Step 2: Classification


After submitting your CNVs you will get directed to the Analysis page. On top of the page you will find your CNVs in a table with annotated classification. The uploaded CNVs are classified based on the 2019 ACMG/ClinGen Technical Standards for CNVs using the ClassifyCNV tool. The final classification results from the score from ClassifyCNV and the additional score from manually evaluated evidence categories by the user (if given as column in the input file).
A description of the scoring algorithm can be found here and details about the automatically evaluated evidence categories can be found in the help section of the classification track on the Analysis page.
Scoring: Pathogenic: 0.99 or more points, Likely Pathogenic: 0.90 to 0.98 points, Variant of Uncertain Significance: 0.89 to −0.89 points, Likely Benign: −0.90 to −0.98 points, Benign: −0.99 or fewer points.

Automatically evaluated evidence categories by ClassifyCNV:
- for copy-number losses: 1A/B, 2A-H, 3A-C, 4O
- for copy-number gains: 1A/B, 2A-H, 2J-L, 3A-C, 4O

Step 3: Report of selected CNV of interest


After selecting a CNV of interest in the table above a report in html format can be downloaded. The report contains summary information of the region, the classification of the CNV as well as the specific scores given in the evaluated evidence categories from the 2019 ACMG/ClinGen Technical Standards for CNVs.


Step 4: Exploration and visualization of uploaded CNVs and genomic region


Ideogram
The ideogram shows the whole chromosome of the selected CNV with its chromosome bands.
To change the selected region you can select a region by click-and-drag on the plot.


Gene track
In the gene track all protein-coding genes are shown. When hovering over the genes you get more information about the gene. Genes that are dosage-sensitive, i.e. genes that are likely to cause a phenotypic effect, are shown in orange.
There are several dosage-sensitivity scores to choose from: loeuf, pLI, pHI, pTS, %HI, HI/TS Score ClinGen.



Uploaded CNVs
In the 'Uploaded CNV' track all uploaded CNVs that intersect the selected region are shown. Deletions are visualized with red bars and duplications with blue bars while each row represents one sample/ID. When hovering over the start or end of the CNV a tooltip with information about the CNV is shown.
The uploaded CNVs can be filtered/ selected based on the Classification and their ID. In case binary phenotype variables were uploaded the CNVs can also be filtered based on those.
To change the selected region you can select a region by click-and-drag on the plot or use use the move/ zoom-in/ zoom-out buttons.


ClinVar CNVs
ClinVar summary tab:
In the ClinVar summary track the number of interescting pathogenic/ likely pathogenic deletions and duplications from ClinVar in 200 kb regions are shown. Numbers were calculated every 100 kb for the region 100 kb up- and downstream.
When hovering over the plot the number of deletions and duplications are shown.
To change the selected region you can select a region by click-and-drag on the plot or use use the move/ zoom-in/ zoom-out buttons.

ClinVar plot and table tab:
In the linVar plot and table tab track the individual CNVs from ClinVar are shown in a plot and in a table.
The plot is splitted in three subpolots, one visualizes the pathogenic/likely pathogenic CNVs, one visualizes CNVs of uncertain significance and one plot visualizes benign/likely benign CNVs. More information about the CNVs can be found in the table or by hovering over the CNVs in the plot. The CNVs can be filtered by their clinical significance and type.


UK Biobank CNVs
In 'UK Biobank' track the combined allele frequency of interescting deletions and duplications from the UK Biobank in 200 kb regions are shown. Numbers were calculated every 100 kb for the region 100 kb up- and downstream.
To remove small CNVs/ structural variants, all CNVs < 50kb were removed prior to the calculation.
When hovering over the plot the allele frequency of deletions and duplications are shown.
To change the selected region you can select a region by click-and-drag on the plot or use the move/ zoom-in/ zoom-out buttons.


GnomAD CNVs
In 'GnomAD' track the combined allele frequency of interescting deletions and duplications from GnomAD in 200 kb regions are shown. Numbers were calculated every 100 kb for the region 100 kb up- and downstream.
To remove small CNVs/ structural variants, all CNVs < 50kb were removed prior to the calculation.
When hovering over the plot the allele frequency of deletions and duplications are shown.
To change the selected region you can select a region by click-and-drag on the plot or use use the move/ zoom-in/ zoom-out buttons.

Gene table
The gene table contains all genes, their genomic coordinates, OMIM links, and gene dosage sensitivity scores.
Clingen gene disease table
The ClinGen Gene Curation working group has developed a framework to standardize the approach to determine the clinical validity for a gene-disease pair.
The ClinGen Gene-Disease Clinical Validity curation process involves evaluating the strength of evidence supporting or refuting a claim that variation in a particular gene causes a particular disease.
Classifications derived with this framework are reviewed and confirmed or adjusted based on clinical expertise of appropriate disease experts.


Possible classifications are:
Definitive > Strong > Moderate > Limited > No known Disease Relationship > Disputed Evidence > Refuted Evidence.


More information can be found here or here.
Clingen regions table
The ClinGen Dosage Sensitivity curation process collects evidence supporting/refuting the haploinsufficiency and triplosensitivity of genomic regions.
Classifications derived with this framework are reviewed and confirmed or adjusted based on clinical expertise of appropriate disease experts.


Possible scores for the Haploinsuficiency score (HI Score) and Triplosensitivity score (TS Score) are:
0 (No Evidence), 1 (Little Evidence), 2 (Emerging Evidence), 3 (Sufficient Evidence), 40 (Dosage Sensitivity Unlikely)


More information can be found here.
CNV syndromes
The table shows expert-curated microdeletion and microduplication syndromes involved in developmental disorders from DECIPHER that intersect the selected region.
All syndromes and more information can be found at DECIPHER.

Enrichment analysis
Gene set enrichment analysis is a computational method for inferring knowledge about an input gene set by comparing it to annotated gene sets representing prior biological knowledge.
Here, all genes from the selected region are used as input to analyze whether the genes significantly overlap with an annotated gene set from the libraries. Libraries can be changed using the dropdown menu.
More information about enrichment analyses can be found here and descriptions of the available libraries can be found here.

Data


We object to any commercial use and disclosure of data. Copyright and use: The authors grants you the right of use to make a private copy for personal purposes. However, you are not entitled to change and/or pass on the materials or publish them yourself. No personal or uploaded data are saved. No data will be passed on to third parties without your consent. Upon request, you will receive free information about the personal data stored about you. To do so, please contact the administrator. No liability: The contents of this webtool have been carefully checked and created to the best of our knowledge. But for the information presented here is no claim on completeness, timeliness, quality and accuracy. No responsibility can be accepted for any damage caused by reliance on or use of the contents of this website.