---
title: Getting started with CNVCapture
description: Discover SeqOne's workset CNVCapture, how to set it up, and how to interpret the results for germline copy number variant detection from your NGS fastq data.
---
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# Getting started with CNVCapture
## Welcome! This guide is designed to help you understand and use the SeqOne CNVCapture pipeline. We'll walk through what it does, how it works, how to set up an analysis, and how to interpret the results when looking for germline Copy Number Variations (CNVs) in your gene panel data.
### Introducing CNVCapture: What Does It Do?
CNVCapture is a specialized tool within the SeqOne platform, focused on identifying germline CNVs specifically from data generated by sequencing captured gene panels.
### Methodology and Best Practices
Understanding how CNVCapture works can help you get the most reliable results. The core idea is to compare the amount of sequencing data (coverage depth) for specific regions in your sample against the coverage seen in a set of 'normal' control samples from the same analysis batch.
- **Finding the Right Controls**: Selecting appropriate control samples is crucial. CNVCapture does this by calculating a correlation score between potential controls and your sample. This calculation uses coverage data from all the regions defined in your experiment's manifest file, but it excludes the sex chromosomes (X and Y) at this stage to avoid bias. Samples from the same analysis group (cohort) with high correlation values are chosen as controls.
- **Tips for Reliable Results**: Because the analysis relies on comparing coverage, consistency across your samples is vital. To maximize the performance and accuracy of CNVCapture, it's highly recommended to follow these guidelines:
- Try to keep sample types consistent within a single analysis; for instance, avoid mixing blood samples with FFPE (formalin-fixed paraffin-embedded) samples.
- Ensure your batch doesn't contain too many samples expected to have the exact same CNV – ideally, keep this below 25% of the total samples.
- For similar reasons, avoid including technical replicates of the same sample within one analysis run.
- Batch size matters! You need a sufficient number of samples for the statistics to work reliably. A minimum of 8 samples is required to launch a CNVCapture analysis. We recommend using at least 16 is possible.
- Whenever possible, use samples that were processed together in the same capture and sequencing run, as this minimizes technical variation.
- **Mandatory Requirements for Analysis**: for a region to be called by CNVCapture, certain conditions must be met:
- For selecting controls in a germline analysis, the minimum correlation threshold used is 0.90.
- The pipeline must be able to identify at least 6 suitable control samples for each sample being analyzed. Therefore, as mentioned, the analysis absolutely requires a minimum batch size of 8 samples.
- - It's essential to provide the biological sex for each sample before starting the analysis. This information is critical for the pipeline to accurately detect CNVs on the X and Y chromosomes (if the information is absent, the X and Y chromosomes will be ignored).
- Any given genomic region needs to have at least 50 sequencing reads covering it to be included in the CNV calculations.
- **A Note on Exon Overlap**: Pay attention to how your manifest file defines the target regions. CNVCapture will only analyze exons where there is at least an 80% overlap between the exon's actual genomic coordinates and the corresponding interval listed in the manifest. If a gene has no exons that meet this 80% overlap criterion, that entire gene will be skipped during the CNV analysis.
### Getting Started: Launching Your CNVCapture Analysis
Initiating a CNVCapture run is straightforward within the SeqOne platform:
1. Begin within your relevant project environment.
2. Select the option to start a "New analysis".
3. From the list of available pipelines (worksets), choose CNVCapture. Make sure to select the latest version if multiple are available.
4. Select all the samples you wish to include in this batch – remembering the minimum requirement of 8 samples.
5. *Need More Samples?* If your current project doesn't reach the 8-sample minimum, you have the option to pull in additional samples from a different project. However, this is only permissible if the other project has *exactly matching* metadata, covering the Assay type, Gene panel used, Capture method, and the identical Manifest file.
### Visualizing CNVCapture Results
Once the CNVCapture pipeline finishes, you'll access the results *within the associated GermlineVar or GermlineFamily analysis*. The CNVCapture analysis contains mainly the region processing logs (you’ll find the excluded regions and samples, with reason for exclusion) and output files.
#### Finding the CNV Tab
Navigate to the completed GermlineVar or GermlineFamily analysis for your sample. Click on the dedicated CNVs tab to access the copy number variation results.
If the tab is greyed out, it means that no CNV analysis was run for this particular sample.
#### Initial Overview
The CNVs tab provides a helpful graphical summary, highlighting the genes where potential CNVs have been detected.
Filters and sorting options help you focus your investigation:
- You can restrict the view to only show results for specific regions or genes by applying a filter based on a BED file (this BED file needs to have been previously imported into the platform using the ‘Manifest’ feature).
- A simple 'Search gene' function lets you jump directly to results for a gene of interest.
- You can sort the detected CNVs based on their type (the default sorts Gains first, then Losses) or by their genomic position.
Clic on an event (Gene name) to display the details of this gene’s results.
#### Decoding the 'Per-region details' Table
Once you select a gene in the top part of the interface, you’ll access all of the details for this gene. On the left side of the page is a table with all of the region details.
- #: An identification number for the region.
- Region type: Classifies the region (e.g., exon-CDS, intron, upstream, downstream).
- Chr, Start, Stop: The chromosome and genomic coordinates for the region.
- Size: The length of the analyzed region in base pairs.
- Status: This is the call for the specific region: 'Gain' indicates a duplication, 'Lost' indicates a deletion, 'Normal' means no CNV was detected here, and 'Not called' signifies the region was excluded from the analysis (you can investigate the reason by checking the main CNVCapture analysis logs).
- Copy-ratio: This value represents the calculated number of copies relative to the expected normal (where a ratio of 1 corresponds to a normal diploid copy number of 2).
- Z-score: A statistical measure indicating how many standard deviations the observed copy-ratio for this region deviates from the average of the control samples.
- CV (Coefficient of Variation): This reflects the amount of variability or dispersion in coverage seen across the control samples for this particular region.
- Warning flag: Sometimes, a flag might appear with a message indicating potential issues with the call for that specific region. These can include:
- Low zscore: The Z-score's absolute value is low, suggesting the evidence for a CNV is not strong.
- High variation: There was a lot of coverage variability among the control samples for this region, making the comparison less reliable.
- Low coverage: Your sample had low sequencing coverage in this specific region.
- Number of controls: A low number of suitable control samples were available for comparison for this region.
- Percentage of variation: The coverage variation across all samples was deemed too high for this region.
- Read depth: The average coverage was low, and the copy ratio was also low, potentially indicating poor data quality for this region.
#### Visualizing with the Copy-ratio Graph
Next to the table, you'll find a graph. This graph plots the copy-ratio calculated for each region in your sample. It also shows the distribution of copy-ratios for those same regions across the entire cohort of samples analyzed together (represented by grayed points, which you can hover with your mouse to check the sample ID), providing valuable visual context for evaluating potential CNVs in your sample.
#### Why Might a Gene Be 'Not Called'?
Even if a gene seems eligible initially, the entire gene might be marked as 'Not called' in the final results under certain circumstances:
- If the gene is located on the X or Y chromosome, but the sample's sex information was missing.
- If 20% or more of the individual regions within that gene failed the quality checks (e.g., due to insufficient controls or high variation).
#### Note on large insertions / Alu:
Be mindful that large insertions within a targeted region (like big indels or mobile element insertions such as Alu) can sometimes trick the algorithm. Because they disrupt sequencing coverage, they might be misinterpreted as a reduction in coverage, potentially leading CNVCapture to incorrectly call a deletion ('Lost') if the insertion's size is substantial compared to the region being analyzed.
### Downloading the Details
If you need the raw data, you can easily export the detailed CNV calls. Click the download icon found in the top right corner, to get a TSV (tab-separated values) file containing either the global CNV data or information for specific genes.
### More Information
Details of the algorithm are available in [this article](https://support.seqone.com/knowledge-base/en/knowledge-base/technical-doc/cnv-capture?hsLang=en).
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