VariantGrid documentation¶
VariantGrid is an open source variant database and web application for analyzing genetic data.
Intro¶
VariantGrid has a number of installations. Please visit the individual sites for login/registration details.
Cloud servers¶
variantgrid.com - Research cloud server
runx1db - Rare disease exome sharing
Shariant - Australian Genomics variant classification sharing platform
Private server¶
There is a VariantGrid private server inside SA Pathology, the public pathology provider to the South Australian Health.
The advantages of a private server are being restricted to a private intranet, and being able to analyse private patient data without worrying about it being on the cloud.
To install a local copy of VariantGrid, please see the GitHub page.
Analysis Intro¶
Create custom variant filters by connecting together nodes representing sources or filters of variants. See analysis nodes
Other variant databases allow similar creation of filters, but VariantGrid can constuct nodes in real-time, enabling rapid exploration of large and difficult genomic data sets.
Analysis Nodes¶
Sample Node connected to a Population Filter Node
The top node is configured to show a particular patient exome (from an uploaded VCF).
These variants are then filtered to those that are less than 1% of the population.
Connecting Nodes
To add a node, select the node type from the drop down menu in the top left of the screen and click the 
add button
Click and drag a node to move it around. You can select multiple nodes by drag-selecting a box around them. This allows you to copy, delete or move them as a group. Delete selected nodes by pressing DELETE, or click the 
delete button.
Analysis screen¶
The screenshot above shows the VariantGrid analysis screen. The node graph is on the left part of the screen, showing the user built filters.
Click a node to select it. This loads the node editor (top right) and a grid of the variants (see section below) in the node (bottom right).
Clicking on the node loads this editor window. The node editor is different depending on the type of node.
Analysis Grid¶
The 1st column (ID) is special and contains a check box, a numbered link and an IGV logo. The check box is used to select rows manually. The link loads detailed information about that variant above the grid. The IGV link will view the locus in IGV (loading bam files associated with samples). See IGV Integration page. Clicking on a row highlights it. Select the “tagging” tab, then click on a label to tag/colour the row.
Analysis Nodes¶
Filter Nodes¶
These nodes filter variants connected to the top of them
Built In Filter¶
Built in filters used in node counts eg High or Moderate Impact / OMIM / ClinVar Pathological
Population¶
Filter on population frequencies in public databases (gnomAD/Exac/1KG/UK10K) or number of samples in this database.
Analysis - advanced¶
Analysis settings¶
In an analysis click the 
Settings icon to open the analysis settings page.
Analysis settings screenshot
Genome build - Cannot be changed. Only data (eg VCF samples) from this build can be used in the analysis.
Analysis type - One of (Singleton/Cohort/Trio/Pedigree) set at creation if using an auto-analysis.
Custom columns - Columns to use - from customise columns. Default set in user settings
Default sort by column - Can be used for example to make the grid always sort by gene.
Annotation Version - The Annotation Version used.
Node Counts¶
The numbers below a node are counts of variants that meet a certain criteria. The colours correspond to names in bottom left hand legend, eg in the image below, there are 32 ClinVar (Likely) Pathogenic variants in that node.
Node with counts
Click on a count to load the variants in the node that meet that criteria, eg clicking on the red 32 would just load the ClinVar variants.
To edit which node counts are shown, open analysis settings, then select the “node counts” tab.
Settings/Node counts
Drag and drop the node counts to show/hide them and change the order.
Column Summary¶
Node Summary
The second tab (Summary) is used to view what values are in a column. Qualitative data is counted and shown in a grid, such as snpEFF Effect in the screenshot below:
Clicking on the link in the 1st column creates a child node filtering to that value. This is useful for getting an overview then drilling down into your data.
The screenshot shows 396 entries under “frameshift variant”, and the filter node created underneath the current (red bordered) node, which is configured to filter to snpeff_effect = frameshift variant, and also has 396 variants after filtering.
Quantative data (numbers, such as for the af_1kg column (1000 Genomes Alt Frequency)) is shown as a box-plot.
Variant Tagging¶
A tag is a label (such as “Cancer” or “Investigate”) which you can use to label and track variants in an analysis.
Tagging variants¶
In an analysis, click the Add icon in the “tags” column then auto-complete your tag.
Adding a tag
To remove a tag - clicking on the tag. The tag will grow in size, and a delete symbol will appear. Click it to remove the variant tag.
Removing a tag
Using tags¶
Click the 
tag icon on the toolbar to view all Tags in an analysis 
To filter to specific tags - add a tag node, and use it like any other node to filter variants to just those that have been tagged.
You can view all tagged variants on a page, via menu: [analysis] -> [Tagged Variants]]
Analysis Classification¶
Recommended workflow to create a classification from a variant in an analysis:
Tag the variant with the “RequiresClassification” tag.
Click the
tags button, then then “Classification” tab.Select the sample, then click the [classify] button.
Karyomapping¶
Background¶
We handle the simpler case of a Trio with an affected child (ie proband/mother/father).
Variants are assigned to the following bins
F1ALT: Paternally inherited, in phase with affected child, ALT variant. F1REF: Paternally inherited, in phase with affected child, REF variant. F2ALT: Paternally inherited, out of phase with affected child, ALT variant. F2REF: Paternally inherited, out of phase with affected child, REF variant.
And vice versa for the mother. The only variants that fall into each of these situations are:
| Child GT | Father GT | Mother GT | Bin |
|---|---|---|---|
| 0/1 | 0/1 | 0/0 | F1ALT |
| 0/1 | 0/0 | 0/1 | M1ALT |
| 0/1 | 0/1 | 1/1 | F1REF |
| 0/1 | 1/1 | 0/1 | M1REF |
| 0/0 | 0/1 | 0/0 | F2ALT |
| 0/0 | 0/0 | 0/1 | M2ALT |
| 1/1 | 0/1 | 1/1 | F2REF |
| 1/1 | 1/1 | 0/1 | M2REF |
Gene analysis¶
Menu: [analysis] -> [karyomapping]
Enter a gene name and click [Karyomap Gene] button.
Genome-wide analysis¶
A genome wide karyomap count is performed when you create a trio. This is useful for finding sample mixups.
This is summarised as Proband phase: 50.74% mum / 49.26% dad. Mum: 54.96%. Dad: 51.69%. and is visible on the gene analysis screenshot above and the Trio page.
Proband phase shows the child’s marker percentage from each parent. Mum%/Dad% = Percent of parent markers that are in phase in proband.
Here are some examples for various Trios:
| Description | PP mum | PP dad | Mum % | Dad % |
|---|---|---|---|---|
| Real Trio 1 | 53% | 47% | 52.1% | 45.9 |
| Real Trio 2 | 52.3% | 47.7% | 46.1% | 45.9% |
| Bad Trio (Trio 1 with random dad) | 60.2% | 39.8% | 52.1% | 25.7% |
| Bad Trio (unrelated samples) | 48.5% | 51.6% | 30.8% | 29.8% |
| Bad Trio (mother/proband swapped) | 60.8% | 39.2% | 86.9% | 36.1% |
As a rough rule, you’d expect a minimum of 40% for an actual child.
Annotation Details¶
Annotation refers to all of the information about a variant, it is made from different components, including:
Variant-level annotation: Information specific to a base change. Examples include computational predictions and effects, and existing database entries (such as population frequency for the variant)
Gene-level annotation: Information about the gene (from RefSeq/Ensembl + other sources), matched from the variant’s assigned transcript_id.
ClinVar: Clinical variant classifications from ClinVar
To see a description of each field, use menu: [annotation] -> [descriptions]
Annotation is shown on the variant details page, and in an analysis, where it is used in filters and shown on the grid (see customise columns)
Variant Level Annotation¶
The first time we see a variant, it is annotated by the variant annotation pipeline.
Annotation Versions¶
Each annotation component above is versioned and can be upgraded separately by the site administrator. To see the versions via menu: [annotation] -> [versions]
VariantGrid can store multiple annotation versions, which allows us to load historical analyses which return the same results as when they were first analysed, as well as updating from new sources regularly.
IVAT¶
VariantGrid uses IVAT developed by Jinghua (Frank) Feng from the CCB ACRF Cancer genomics facility.
SACGF Tiers¶
Tier 1¶
Novel variants, with evidence of being strongly damaging, and without any evidence of being artificial:
Not in dbSNP, 1KG, UK10K, ExAC or ESP
HIGH or MODERATE snpEFF impact, or mutating at branch point, at miRNA binding site, or at transcription factor binding site
For a SNV: GERP > 4 or CADD > 30
For an INDEL: not in LowComplexRegion
Not in SegmentDup region
No multi-ALT alleles were called
Tier 2¶
Extremely rare variants, with evidence of being strongly damaging, and without any evidence of being artificial:
Not Tier 1
Minor allele frequency (MAF) < 0.05% in 1KG, UK10K and ExAC.
HIGH or MODERATE snpEFF impact, or mutating at branch point, at miRNA binding site, or at transcription factor binding site
For a SNV: GERP > 3 or CADD > 20
For an INDEL: not in LowComplexRegion
Not in SegmentDup region
No multi-ALT alleles were called
Tier 3¶
Very rare variants, with evidence of being potentially functional, and without any evidence of being artificial:
Not Tier 1 or 2
MAF < 0.2% in 1KG, UK10K and ExAC.
HIGH, MODERATE or LOW snpEFF impact, or mutating at branch point, at miRNA binding site, or at transcription factor binding site
For a SNV: GERP > 2 or CADD > 20
For an INDEL: not in LowComplexRegion
Not in SegmentDup region
No multi-ALT alleles were called
Tier 4¶
Rare variants, with evidence of being potentially damaging. They can locate within the SegmentDup regions, and hence are with increased chance of being artificial:
Not Tier 1, 2 or 3
MAF < 0.5% in 1KG, UK10K and ExAC.
HIGH, MODERATE or LOW snpEFF impact, or mutating at branch point, at miRNA binding site, or at transcription factor binding site
For a SNV: GERP > 2 or CADD > 20
For an INDEL: not in LowComplexRegion
Tier 5¶
Uncommon variants with potential damage effect, and can located in SegmentDup and LowComplexRegion and hence with significantly increased chance of being artificial:
Not Tier 1, 2, 3 or 4
MAF < 1% in 1KG, UK10K and ExAC
Satisfying *any one of the three criteria below:
Annotated with HIGH, MODERATE or LOW snpEFF impact (aka. altering the exon or splice region)
Altering splicing branchpoint, miRNA binding site, or transcription factor binding site
GERP > 2 or CADD > 20
Tier 6¶
Not Tier 1, 2, 3, 4 or 5
Notes:
A variant is classified as Tier 6, when all your samples are HOM-ALT at the variant and that ALT allele is common in 1KG, UK10K and ExAC (i.e. The frequency of the ALT allele is > 0.5 in anyone of 1KG, UK10K and ExAC). This applies before all the tiering above. From a trio sequenced with the Medical Exome Capture on our NextSeq machine in September 2016, below are the numbers of variants (called by GATK, mostly germline) for each tier:
| Tier | # Variants |
|---|---|
| 1 | 96 |
| 2 | 233 |
| 3 | 246 |
| 4 | 282 |
| 5 | 3343 |
| 6 | 223008 |
Variant Details¶
This page shows the annotation and other information about a variant.
The top of the page has an IGV link, and a link to the allele for this variant:
An allele is genome build independent - ie hg19 and hg38 variants for same change point to same allele. The ID (CA9034) is from the ClinGen Allele Registry
Classifications¶
Variant Details - Classification section
This shows internal classifications for an allele (may have been classified against a different genome build)
The far right column contains Classification Flags
Transcripts¶
Variant annotation is calculated for each transcripts overlapping a variant. You can select each of the different transcripts to change which is being displayed.
Samples¶
At the bottom of the page is a grid of samples that contain the variant (and the zygosity and read information). Only samples you have permissions to view are shown, but a warning will be shown informing you that samples you don’t have permission to see exist.
Representative Transcript¶
SnpEff calculates the damage effects for each transcript. The representative transcript is chosen as:
The most damaging transcript
If equally damaging, the canonical transcript defined by Ensembl is selected
If no canonical transcript exists, the longest transcript is selected. If more than one canonical transcript exists, the longest canonical transcript is selected.
Uploading Data¶
Menu: [data] -> [upload]
You can either drag & drop files onto the page, or by selecting the [Add Files] button.
After the file has been transferred to the server, a spinning icon (
) will appear as the file is processed. The large link (eg “AS-145_WES_HiSeq_Variants.vcf”) takes you to the import processing page if you’d like to monitor the progress.
Once it has been successfully imported, a link will appear beneath the file (eg the “VCF” links above) allowing you to jump to the data page for this file.
Managing data¶
Menu: [data]
The data page displays all of your uploaded data such as (VCFs, Bed files, Pedigree Files etc)
Data is displayed in grids, with each data type in a separate tab.
You can enter parts of the name into an autocomplete search box to quickly find your files:
Click the link on the grid to view the file details page.
Sharing data¶
Users belong to groups (see user settings) that can share data. Ticking the Show Group Data checkbox will show this on a grid.
By default, you automatically share data (read-only) with your group.
To change data permissions, click the [Data/Sharing] tab:
logged_in_users is a special group - and means everyone who has a VariantGrid account.
Search¶
Enter text into the search box in the top right hand corner and press enter or click Go.
Accepted inputs:
| Name | Example |
|---|---|
| Locus | chr1:169519049 |
| Variant | 1:169519049 T>C |
| ClinGenAllele | CA285410130 |
| dbSNP ID | rs6025 |
| HGVS | "NM_001080463.1:c.5972T>A", "NM_000492.3(CFTR):c.1438G>T", "NC_000007:g.117199563G>T" |
| Gene | GATA2, ENSG00000179348 |
| Sample | hiseq_sample_2 (case insensitive search match in name) |
| Flowcell | 160513_NB501009_0029_AH3FFJBGXY |
Somatic data¶
Somatic VCFs detected as somatic only (tumor minus normal) are analysed for mutational signatures
Allele Frequency¶
We do not import the AF value from the VCF, but instead normalize the data then recalculate AF to be AD / sum(AD for all variants at locus)
In an analysis, Sample, Cohort and Trio nodes can filter by allele frequency. For the Cohort and Trio nodes, all or any refers to requiring all samples to have allele frequency within the ranges or just one or more sample.
Mutational signatures¶
Different types of cancer can have consistent somatic variants, see Signatures of mutational processes in human cancer, Alexandrov et al 2013
Mutational signatures are calculated during VCF import when the sample is detected as somatic only
Menu: [data] -> Sort samples grid by “Mutational Signature” column -> Click on entry.
Or click on the link in the “Mutational Signatures” at the bottom of the sample page.
Thanks to Paul Wang from the ACRF Cancer Genomics Facility for the code.
VCF / Samples¶
VCF import¶
Variants are normalized upon import. We only import variants, filters and genotypes (we don’t use INFO as we do our own annotations)
The VCF format can vary a lot, we have tested VCFs from the following variant callers:
GATK
FreeBayes
Each sample is assigned a “variants type” of Unknown, Germline, Mixed (single sample) or Somatic only (tumor minus normal).
This is determined by looking at the “source” entry in the VCF header, and matching it to an entry in VCFSource object (setup by your administrator)
Samples with variants type of_somatic only_ are checked for mutational signatures
Multi-sample VCFs¶
Multi-sample VCF files combined using bam files record the genotype for all samples at each variant position.
This allows you to differentiate between reference calls and no coverage - and is extremely important for Trios so that you can make correct calls about inheritance and denovo variants
You must use bam files, to re-call the genotypes for each position.
Consider 3 VCF files:
| Proband | Mum | Dad |
|---|---|---|
| HET | (not present) | (not present) |
There’s no way to tell if a variant not being present in a single sample VCF is due to having the reference allele or no coverage.
Merging just the VCFs (without supplying the bams) will give the genotypes of:
| Proband | Mum | Dad |
|---|---|---|
| HET | ./. | ./. |
If you merge them using GATK/Picard using bam files - the caller will re-examine the reads over the locus, and make the genotype call.
Thus, if both parents had reference bases, the calls would be:
| Proband | Mum | Dad |
|---|---|---|
| 0/1 (HET) | 0/0 (HOM_REF) | 0/0 (HOM_REF) |
And you can be confident that it is a denovo variant, rather than just lacking coverage in one of the parent samples.
Search¶
TODO
Gene Page¶
Menu: [genes] -> [genes] then autocomplete a gene name.
You can also enter a gene name such as “GATA2” or “RUNX1” into the search box, or click on a link in GeneGrid
If you have gene coverage data, boxplots will be shown.
Gene Lists¶
Menu: [genes]
Creating Gene Lists¶
Ways to create a gene list include:
Click on New GeneList
Enter name, genes and click save
Gene Grid¶
Menu: [genes] -> [gene grid]
GeneGrid allows quick comparisons between gene lists and adding/removing genes from them. Genes are rows and gene lists are columns.
GeneGrid screen¶
You can copy/paste the URL at any time to re-create a particular comparison.
Choose lists from the top left select boxes, or manually paste in gene names into the Custom Gene List text entry box. Click the 
red delete button to remove a gene list column.
In the top right are optional evidence columns which provide information about genes.
See Gene Coverage for details on how the % at 20x values in the Enrichment Kit columns are calculated. Enrichment kits are automatically added when a pathology test that uses it is added to the grid.
Gene Info¶
Small icons next to gene names on the left of the grid indicate the gene has one of these attributes:
Alternative Haplotype
Pseudogenes
Triplet repeat disorders
Gene Coverage¶
Gene Coverage refers to how well a gene was covered by high throughput sequencing reads. This is useful to know how confident you can be about a lack of variant calls in a region.
Having gene coverage associated with a VCF sample allows you to be warned in an analysis when a gene in a gene list is below a threshold (default: 20x) and you may be missing some variants. The node will flash yellow, and the “genes” tab will be highlighted yellow so you can view which genes have low coverage.
Boxplots of sample coverage for genes are on the gene page
Canonical Transcripts¶
Many genes have multiple transcripts, but people want only one value for each gene.
This is achieved by choosing a single (representative or canonical) transcript, and use that transcripts value for the gene.
A CanonicalTranscriptCollection is a list of gene:transcript mappings imported into the system. The administrator can import different collections, linking them to EnrichmentKits and setting a system default.
Sample QC metrics¶
You can upload gene coverage files (.txt files) which use the system default canonical transcripts. You can then associate them with a sample from a VCF
Sample QC coverage loaded via sequencing features - and automatically choose transcripts based on EnrichmentKit
GeneGrid EnrichmentKit coverage¶
The per-gene QC metrics for an EnrichmentKit on the GeneGrid page are from Gold Standard Runs, using the canonical transcripts for that EnrichmentKit.
Pathology Tests¶
Menu: [tests] -> [manage tests]
Pathology Tests are curated, versioned gene lists offered as a diagnostic test. There can be multiple versions of a test.
A Pathology Test Version is a specific versions of a pathology test.
Active tests¶
Each pathology test has at most one currently active test - the one available for test orders.
An active test is the most recent confirmed version of a pathology test.
Active test logo
All other versions of tests
The curator confirms & adds a time-stamp by clicking the Confirm Test button. Once a test has been confirmed it cannot be modified, and any further changes must create a new test version.
Requesting gene changes¶
Only the curator can modify a test, everyone else can make modification request but these must be approved by the curator. Contact an administrator to change curator for a test.
Make gene modification requests on the GeneGrid page.
The gene symbols in the pathology test column are always what is in the test. The +/- numbers (green background for add, blue for delete) in the image above are counts of requested additions/removals for that gene.
To request a gene addition: Add genes to the GeneGrid, then click on an empty space where the gene should be. To request a gene deletion: Click on an existing gene, then the red delete symbol which appears.
In both cases a box will appear where you can enter a brief justification of the request. Only put a brief summary - please put in depth evidence such as linking a disease with a gene or adding literature on the gene page (click on the the gene name on the left column of the grid to open gene page in a new window).
Accepting gene changes¶
The curator can see any pending requests on the pathology test version page, where they can accept/reject them.
Any genes added will have the user, date and brief justification comment from the addition request stored on the “Modification info column” which you can see on the grid of genes for a pathology test version.
The outcomes for any processed requests can be seen by all users at the bottom of the page:
Test Ordering¶
Patients¶
Menu: [patients]
Create patients to store phenotype information and link multiple samples (eg tumor/normal) together.
Searching¶
You can search by name, code or free text in the phenotype description.
Click the graph of phenotype terms to filter the grid to patients with that phenotype.
Patients grid filtered to microcephaly
Patient records¶
Import a CSV to create patients in bulk. Click the patient record imports link at the top of the page, then can select to download an example CSV with your samples pre-filled, so it’s easy to match your patients to your existing data.
You can also create patients one at time via a form, by clicking the Create New Patient link just above the grid.
Other sources of patients¶
Patients can be created via the patholoy test ordering system.
On a private server (eg diagnostic lab intranet), patient records can be automatically created via your LIMS/Patient records system (speak to your administrator)
Other¶
Family Code is useful for linking together patients
The system can be configured to show/hide names, or convert birthdates to years depending on your privacy needs.
Phenotypes¶
It is useful to store phenotypes, diseases and genes for a patient. Having this information well structured and using controlled terms is very useful as it allows us to:
Filter variants to genes associated with a disorder
Know phenotypes for patients that share variants
Perform analyses across disease cohorts (is the same variant or gene responsible for the disease or are they different?)
Track per-disease solve rates
Assigning Terms to Patients¶
You can auto-complete terms in the boxes, which will be added to the bottom of the patient description.
Or, you can type plain text and we’ll automatically match your words to Human Phenotype Ontology, OMIM and Gene Names.
Matched terms will be highlighted to the right of the description box.
Patients grid filtered to microcephaly
How phenotype term matching works¶
Everything after “–“ on a line is ignored and can be used for comments.
The text is broken up into sentences based on punctuation and new lines.
The sentence is separated into words, and then sub sets of the words in order are created, and sorted largest to smallest. For instance:
The cat sat on the mat
cat sat on the mat
The cat sat on the
sat on the mat
cat sat on the
The cat sat on
The cat sat
on the mat
sat on the
cat sat on
the mat
cat sat
The cat
on the
sat on
mat
the
sat
cat
The
on
This allows us to find the biggest matches first. If a match occurs, the unmatched parts of the sentence continue to be searched until there is nothing left. If no match occurs for a sentence, we try the next smaller one.
Some filtering is done to avoid matching to common words and terms. For instance “Trio” is a gene name, but we will not match it as a gene if the sentence also contains the name of a enrichment_kit or one of the words: “exome”, “WES”, “father” or “mother”.
Matching occurs first against Human Phenotype Ontology terms and synonyms, and OMIM terms and aliases.
If no exact match is found, we try again using mismatches - 1 mismatch (including insertions/deletions) is allowed for two or more words.
For single words, we only allow mismatches if the word is more than 5 letters long and made entirely of letters (ie no digits or symbols).
Single words are then matched (exact with no mismatches) to gene names.
Sometimes there will be multiple matches, eg “PKD1” will map to both the OMIM term PKD1 (POLYCYSTIC KIDNEY DISEASE 1) and the gene PKD1. This is usually what people want as the gene is associated with the disorder.
Cohorts¶
Menu: [patients] -> [cohorts]
A cohort is a collection of samples, which you can analyse as a group. A multi-sample VCF automatically becomes a cohort, but you can create your own to organise your own samples.
Create a new cohort¶
From the cohort page, enter the name of a cohort and click the Create button.
This opens the Add/Remove samples tab. Add samples to your cohort by auto-completing sample names in the Enter to add box, or filter the grid, select the checkbox to the left of a sample, and click the green arrow to add, or red button to delete.
Once you have finished adding/removing samples, click save. This processes the cohort so it can be used in analyses.
Create from a larger cohort¶
You can create a smaller cohort from a larger one. Select at least 2 samples then click the [Create cohort from selected samples] button. Selecting exactly 3 samples allows you to create a Trio which allows for simpler analyses.
Creating a sub-cohort
Cohort Analyses¶
Use the Cohort Node to filter by counts within the cohort (eg in 7 out of 8 of the samples) or zygosity. (see screenshot below).
Cohort Node filtering by zygosity
Quickly create an analysis using the cohort by clicking “Create new analysis for cohort” on the details tab of the cohort page.
There are some other analyses you can perform from the cohort/VCF page, eg:
Gene/Sample Matrix
Cohort Hotspots graph
Trios¶
Menu: [patient] -> [trios]
A trio is a collection of 3 samples (mother/father/proband) which are frequently analysed together in high throughput sequencing, as they have a number of standard analyses.
Creating a trio¶
It is far better to upload a trio within the same multi-sample VCF. If not, you must first create a cohort containing the 3 samples/
View the VCF or cohort, select exactly 3 samples then click the [Perform Trio Analysis using template] button.
Creating a Trio
The Trio wizard will now open, showing the 3 samples and patient / phenotype info. Assign samples (1 each to mother/father/proband) and check mother or father affected if they also have the disorder.
Digital karyomapping¶
By checking a trio’s zygosity, it’s possible to perform a number of relatedness calculations, see karyomapping.
A genome-wide count is automatically performed, and a summary provided on the trio page - this is useful for checking for sample mix-ups.
Trio inheritance analysis¶
An analysis is created using different inheritance models (see below). If either parent is affected it will also use an autosomal dominant inheritance model.
Trio inheritance analysis
The phenotype at the bottom uses the proband patient phenotypes, and sample gene lists.
Require Zygosity Calls¶
By default, the filters are strict and require zygosity calls in all patients - for instance the recessive inheritance model requires a variant to be HOM in proband and HET in both parents.
However that may be overly strict - one parent may have low coverage, with no variants recorded at that locus.
Click on an Trio node to open the editor - unchecking the require zygosity calls box is less strict and allow for variants that are missing due to low coverage.
Compound Het filter¶
Compount heterozygous means 2 variants in the same gene from different parents.
The C. Het node in the bottom right of the screenshot above is a filter node - ie it has another node connected to the top, while the other inheritance models do not.
This is because you probably don’t want every gene with >=2 variants, but rather only >=2 damaging/rare ones. Adjust the filters above the C.Het node to adjust this.
Modify the analysis as per instructions below to filter to all of them.
Sequencing Runs¶
When VariantGrid has access to a network drive (eg a diagnostic lab intranet) it can scan disks for sequencing runs to collect QC metrics, gene coverage and automatically load VCFs.
Sequencing Samples over time
Automatically loaded sequencing runs + VCFs
A Sequencing Run
We collect Sequencing QC metrics and display them with interactive graphs. Collecting data over time allows us to see how this run compares to other runs over time (or vs gold standard runs).
Gold Standard Runs¶
The administrator can mark a sequencing run as “Gold Standard” - which means it has passed validation / is of sufficient quality to be used as a benchmark for other runs.
Gold standard runs have an icon (
) on the sequencing run grid.
Gold runs for an enrichment kit are used:
In boxplots on QC metrics pages for a sequencing run or other sample QC graphs.
To calculate average gene coverage on the GeneGrid page.
Finding sequencing data¶
Sequencing Runs are found by searching for the file ‘RTAComplete.txt’ on the server disks. You can ignore flow cells by putting a file “.variantgrid_skip_flowcell” in the directory.
Customise columns¶
You can customise grid columns on the Customise Columns ([user]->[customise columns]) page.
IGV integration¶
Click the 
IGV link to automatically jump to your variants + BAM files in IGV.
IGV Configuration¶
IGV needs to be running, and have the Enable Port option ticked.
To check this open preferences in the IGV menu: [View] -> [Preferences] -> [Advanced] Tab.
VariantGrid Configuration¶
If the value of the IGV port is different from 60151 (default), you need to change the IGV Port option in your User Settings page.
Clicking the IGV link (IGV link) will jump to the locus, and show BAM files associated with input samples (Sample or Cohort ancestors). These are the same samples that have their zygosities/allele depth shown on the grid.
Each sample has a bam file path entry. If your samples were automatically loaded from a server, this is probably already set. Otherwise you can change it on the Sample or VCF (VCF) page.
You can set all the samples in a VCF file at once in the vcf page, click Bulk Set Fields to set all samples according to a pattern based on the sample name.
Network drives and File Servers¶
Many labs access data via servers, or network shares. These can be different on different computers.
It is recommended that you set bam file path to be the location on the server, so that it is consistent between users.
Different data access methods on different computers can then be managed by having users change their configuration on the IGV Integration page.
Variant Classifications¶
Creating Classifications¶
From an analysis (see analysis classification workflow)
From the variant details page
Via API (See Shariant API docs)
Autopopulation¶
When you create a classification from inside the system, a number of fields are auto-populated from annotation and sample information.
Variants created from the external API are not auto-populated with values from annotation.
Editing¶
See the Classification Form.
Configuring Fields¶
An administrator can add/remove EvidenceKeys which are used to create fields.
They can also hide visible fields on a per-lab basis.
Variant Classification Form¶
The Classification Web Form can be used to create and edit classifications directly within VariantGrid.
View¶
To quickly see all fields that have values for a classification, enter “*” into the filter box at the top of the classification. To see all possible fields, enter “**” in the filter box. To find an individual field, start typing the label of the field into the filter e.g. “gnomad”.
Identify Errors¶
A record might not be shared as there are outstanding validation errors. In the Messages box on the form it will list any errors. If possible fix those errors in your curation system and then they should be fixed on the next sync.
Change History / Diff¶
Each version of a record published in VariantGrid is recorded, by clicking on “Compare historical versions of this record”.
If there are other classifications for the same variant, there will be a link to compare them there too.
ACMG Guidelines¶
The classification form has fields for the ACMG Guidelines, e.g. PM4, BA1 - the meaning of each is given in the help. See Guidelines
VariantGrid displays a grid of ACMG fields with each row being a category of data, and each column representing the strength of evidence for benign or pathogenic.
The number of met criteria for a given box will be shown as a number.
Explicitly unmet criteria will show as “/”s.
Criteria not yet marked as met or unmet will show as “?”s.
The various values will be plugged into the ACMG formulae and a recommended overall clinical significance will be displayed. This calculated value has no affect on any of the data, the user is still able to set the overall clinical significance to whatever (hopefully justifiable) value they like.
Actions¶
At the bottom of the form there will be a list of action buttons.
The Tick icon re-submits the classification at its current change level. For any manual changes to be seen, this button will need to be ticked.
Next to it is a Share button that allows you to increase the scope of who can see the classification. Important, increasing the Share level is not un-doable. The share levels are
Just your lab
Anyone within your organisation (if your organisation has multiple labs)
All Shariant Users
3rd Party Databases (this will allow us to upload the record to Clinvar at a later date)
Delete / Withdraw¶
If the classification has only been shared at the lab or organisation level, you are able to perform a hard delete on the record. If it has been shared, instead you have the option to “withdraw”. This will remove the record from most listings and search results, but will not remove it from any Discordance Reports that it had been involved in (it will no longer be a part of discordance calculations).
When a record has been withdrawn it can be unwithdrawn by clicking the same button (it should look like a rubbish bin with a raised lid now).
Export¶
You can also export the single record as CSV, a preview of the Clinvar format or as a report. (The report does require that your lab has a report template pre-configured.)
Literature Citations¶
Any PMID references in the form of PMID:123456 from anywhere within the classification will be summed together and listed at the bottom of the classification.
Classification Flags¶
Each classification flag indiciates that there is an action that needs to be performed against the classification.
Many of the flags will be automatically raised by Shariant, though some of them you will be able to open yourself.
To look at the details of a specific open flag, simply click on it to be taken to the flag dialog.
Flag Dialog¶
From the flag dialog you can view summaries about what flags are currently open, see a list of flags that have been resolved as well as raise new ones. Note that only important flags still show up when closed, e.g. suggestions and internal reviews and a few others.
In the provided screenshot we can see we have an open flag asking us to share the classification, a completed internal review, an accepted suggestion and a rejected suggestion, as well as the buttons to create new internal reviews and suggestions.
You can visit the details of an open flag, or a closed one by clicking on the icon.
From the details page of an open flag, depending on the type of flag, you can add a comment and potentially change the status of a flag.
You can raise a new flag by clicking on one of the icons near the bottom with a plus button.
(The kinds of actions you can take on flags will depend on if you’re looking at a classification from your lab or another lab.)
See below for flags and how to solve them:
Flag Types¶
Discordance¶
This classification is in discordance with one or more classifications.
Ensure that you have completed an internal review of your lab’s classification recently (within the last 12 months is recommended). If not, raise the internal review flag and complete an internal review of your lab’s classification.
Review any outstanding suggestions against your lab’s classification.
View the other classifications in the discordance report and view the evidence differing between multiple records via the diff page. If appropriate, raise suggestions against other lab classifications.
This Discordance flag will automatically be closed when concordance is reached.
This is discussed in the Classification Discordance page.
Internal Review¶
This classification is marked as currently being internally reviewed.
Once the internal review is complete, ensure you update the classification in your curation system.
Mark the internal review as Completed.
This is discussed in the Classification Discordance page.
Matching Variant¶
This variant has not been seen in this system previously. It should be linked to a variant given time.
Matching Variant Failed¶
We were unable to normalise the variant provided based on the c.hgvs and genome build values.
Please contact Shariant support for help in resolving this.
Outstand Edits¶
Edits have been made to this classification that are not included in a published version.
From the classification form, ensure there are no validation errors stopping this record from being published.
At the bottom of the form, click the tick to submit the outstanding changes.
Significance Changed¶
This classification has changed it’s clinical significance compared to a previously published version.
Set the status of this flag to reflect the primary reason behind the change in classification.
Please also add a comment providing some context.
This is discussed in more detail on the Classification Discordance page.
Suggestion¶
Someone has raised suggestion(s) against this classification.
Review the contents of each suggestion.
If appropriate, make changes in your curation system and mark the suggestion as Complete.
If you decline the suggestion, mark it as Rejected.
Unshared Classification¶
This classification is not yet shared outside of your lab or institution.
From the classification form, ensure there are no validation errors stopping this record from being published.
Review the content of the classification to make sure it’s ready to be shared.
At the bottom of the form, click the Share to submit at a higher share level.
Withdrawn¶
This classification has been marked as withdrawn. It will be hidden from almost all searches and exports.
If the classification is not of high enough quality or in error, you may leave it as “withdrawn” indefinately.
If you wish to un-withdraw the classification, click the open bin icon in actions from the variant classification form. (Note you can’t open a Withdrawn flag, but you can Withdraw/Unwithdraw from the classification form)
Variant Classification Report¶
Running the report¶
To generate the report from a classification, open the classification and scroll to the bottom. You will see a button called “Report”. Click on it and you will then be able to copy & paste the report contents into a document.
Configuring the report¶
The report can only be configured by admin users. Each “organisation” within variantgrid uses its own report. To edit it go to the admin view, Organisations, (your organisation), and then edit the Classification report template.
The template is run using Django template and produces HTML
Values available for the report¶
Evidence Keys¶
All the fields in the classification are exposed here, see the Evidence Keys admin for a list of possible values, e.g. zygosity, mechanism_of_disease, mode_of_inheritance.
In addition you can also suffix _raw or _note e.g.
The raw value for Mode of Inheritance is {{ mode_of_inheritance_raw }} and the note for it is {{ mode_of_inheritance_note }}
{% if mode_of_inheritance_raw == 'x_linked' %}
Special case for X Linked
{% endif %}
Typically you’ll only want to refer to the _raw value if you’re doing some logic for a specific drop down value. If you ommit the _raw then you will get the human friendly label for the value which might subtly change in the future.
p.hgvs¶
You can reference the full p_hgvs or breakdown
full p.hgvs = {{ p_hgvs }}<br/>
p amino acid from = {{ p_hgvs_aa_from }}<br/>
p hgvs codon = {{ p_hgvs_codon }}<br/>
p hgvs amino acid to = {{ p_hgvs_aa_to }}
c.hgvs¶
You can reference the full c_hgvs or breakdown
full c.hgvs = {{ c_hgvs }}<br/>
c hgvs transcript = {{ c_hgvs_transcript }} or {{refseq_transcript_id}}<br/>
c hgvs gene symbole = {{ c_hgvs_gene_symbol }} or {{ gene_symbole }}<br/>
c hgvs short = c.{{ c_hgvs_short }} (this is the value in c_hgvs after "c.")
Evidence weights¶
A summary of the strength of ACMG critieria met can be accessed with
Evidence weights = {{ evidence_weights }}
Citations¶
PMIDs put anywhere in the classification can be accessed, and then specific attributes of those citations can be referenced. citations is an array that you must loop through, e.g.
{% for cit in citations %}
<tr>
<td>{{ cit.source }}</td>
<td>{{ cit.citation_id }}</td>
<td>{{ cit.citation_link }}</td>
<td>{{ cit.journal }}</td>
<td>{{ cit.journal_short }}</td>
<td>{{ cit.title }}</td>
<td>{{ cit.year }}</td>
<td>{{ cit.authors }}</td>
<td>{{ cit.authors_short }}</td>
<td>{{ cit.abstract }}</td>
</tr>
{% endfor %}
The example here is in a table but you can display it however you’d like, e.g.
{% for cit in citations %}
{{ cit.source }}:{{ cit.citation_id }}
{% endfor %}
Which would give you PMID:12334 PMID:4555 etc
Variant Classification REDCap¶
Variantgrid supports the exporting of Variant Classification data into REDCap files. Note that this is currently the full extent of REDCap integration with Variantgrid, there is no support for importing REDCap records or exporting any other kinds of records in a REDCap format.
There are two parts to the REDCap export.
REDCap Definition¶
The data definition is available by opening the page help on the classification page.
The definition is dynamically generated from the variant classification evidence key configuration. We do our best to ensure that changes to evidence keys are backwards compatible for REDCap definitions.
The definition is laid out in such a way that up to 10 records can be grouped together in one record
e.g. vc_zygosity_1, vc_zygosity_2, vc_zygosity_3 up to vc_zygosity_10
This is so that variants for the same patient can be consolidated.
Note that the REDCap definition is primarily used as a read only representation of the data, doing large edits of data in REDCap is not recommended.
REDCap Rows¶
Important: Variant Classifications will ONLY be exported if REDCap Record ID has a value.
All rows that do not have a value for REDCap Record ID will be ignored in the export.
At the bottom of the classification table there will be a CSV and REDCap download button. Clicking the REDCap download will download records that are:
Available in the current filter (if the results are split over multiple pages all will be downloaded). For example if you filter to show “Mine” the records in the download have to belong to you.
Have a value for
REDCap Record ID.
Records that have the same REDCap Record ID, regardless of any other factors, will be grouped together as described earlier, re vc_zygosity_1, vc_zygosity_2 etc
Technical Specifics¶
| Evidence Keys | REDCap type |
|---|---|
| boolean | yesno |
| select or ACMG criteria | dropdown |
| textarea | notes |
| date | text (with formatted as dmy with validation) |
| everything else (including multi-select fields) | text |
This means while single drop down fields work as you’d expect, multi-drop downs produce text that’s harder to report on.
The evidence key definitions for selects have an explicit index for each drop down option. If adding more options (regardless of insertion order) a new index should be assigned and existing options should retain their index. This is to help keep newer REDCap definitions compatible with older REDCap records.