Lab tab

The Lab tab displays sample quality in the following sections:

Summary dashboard

highlights the key sample quality indicators, with more details provided in the subsequent sections.

Sequencing lab information

reports sequencing run technicalities as indicated during case creation:

• Lab

• Instrument

• Reagents

• Kit type

• Expected coverage

• Protocol

Case quality (34.0+)

provides a broad overview of the case quality:

Chromosome validation

Ensures each chromosome has a minimum of one variant with high quality.

GnomAD validation

Ensures each chromosome has a minimum of one variant annotated with GnomAD.

1. ClinVar validation

Ensures each chromosome has a minimum of one variant annotated with ClinVar.

Auto analysis validation

Ensures at least one variant has been tagged by the AI Shortlist.

mtDNA reference validation

Ensures that the mtDNA reference used was rCRS.

Sample quality

highlights metrics for each sample:

Quality

Overall sample quality indicator based on the average depth of coverage for the indicated kit (or RefSeq coding regions if no kit is provided), percentage of bases covered >20x, error rate, percentage of reads mapped to the reference sequence, and presence of contamination.

Sex validation results

Sex validation (called "gender validation" in versions before 33.0) is performed by comparing the observed homozygous/heterozygous genotype ratio on the X chromosome with the expected ratios for females (<2) and males (>2). Only high-quality Single Nucleotide Variants (SNVs) in the targeted regions specified by the kit (or RefSeq coding regions if no kit is provided) are considered. It is crucial to note that a minimum of 50 variants is required for accurate sex validation. Importantly, if we lack 50 high-quality SNVs, sex validation would not be performed, resulting in an "empty" return. For sample where sex was designated as "unknown" during case creation, the sex validation will present the "predicted" sex.

Ploidy estimation results (32.0+)

The DRAGEN Ploidy Estimator detects aneuploidies and determines the sex karyotype in whole genome samples. When the customer hovers their mouse over 'Failed,' they can view the problematic Chromosomes. It's worth mentioning that the 'Failed' notification appears when any of the autosomal median scores are below 0.9 or above 1.1. Learn more about the algorithm in the DRAGEN™ Bio-IT Platform documentation.

Specific quality parameters

Average coverage, bases with coverage >20x, error rate, % mapped reads, etc. Blue bars represent each of these parameters per sample, while a vertical line represents a general metric across all the samples across all the cases in the account.

Detailed QC metrics can be downloaded upon clicking on the download icon next to the section title.

Pedigree

Displays the results of the relatedness check by Peddy.

For each possible pair of samples in a pedigree, the declared family relation is compared with the observed relatedness coefficient. The relatedness coefficient is calculated from the percentage of shared alleles and the size of the Identity By Descent blocks. IBS0 indicates the number of sites lacking shared alleles. This metric can help differentiate between sibling-sibling and parent-child relationships when both are expected to have ~50% relatedness.

When the relatedness coefficient for a parent-child pair or a full sibling pair falls outside the range of 40 to 60%, the relatedness check is considered failed.

If the relatedness coefficient indicates a very low chance of shared ancestry, it is classified as 'Shared Ancestry' (0.2%-4%). For relatedness coefficients that suggest closer genetic ties, it results in 'Consanguinity' (4%-15%). When the genetic similarity reaches a high threshold (>15%), it not only results in 'Consanguinity' but also triggers a warning/ 'Failed' quality.

Genes with insufficient coverage

Here, you can directly check if your genes of interest have been entirely covered. Consider using Sanger sequencing to cover gaps in the genes of interest.

To examine if the gene contains regions with insufficient coverage:

1. Enter a gene symbol in the search box and select it from the dropdown.

2. From the Coverage dropdown menu, choose between ≤0x, ≤5x, ≤10x, ≤20x, and ≤All.

3. You can Download insufficient regions or explore them in the table. Pressing the More details button will open a pop-up window specifying the genomic coordinates of the poorly covered regions.

You may look up the coverage for multiple genes that are saved as a Gene list:

Simply click the Add Gene List button and select any of your pre-loaded gene lists.

You may further filter regions

by the maximal depth of coverage and the maximal percentage of bases covered greater than 20x.

There is a link to the desktop IGV browser for poorly covered regions:

1. Click on More details:

1. In the pop-up window click on View on IGV: