We have identified five variants on human chromosome nine, C-> A at 128203516, an A deletion at 128328461, C->A at 128322349, C->G at 128323079 and G->A at 128322917.

We will use the Ensembl VEP to determine:

• Have my variants already been annotated in Ensembl?
• What genes are affected by my variants?
• Do any of my variants affect gene regulation?

Go to the front page of Ensembl and click on the Variant Effect Predictor.

This page contains information about the VEP, including links to download the script version of the tool. Click on Launch VEP to open the input form:

The data is in VCF format:
chromosome coordinate id reference alternative

Put the following into the Paste data box:
9 128328460 var1 TA T
9 128322349 var2 C A
9 128323079 var3 C G
9 128322917 var4 G A
9 128203516 var5 C A

The VEP will automatically detect that the data is in VCF.

There are further options that you can choose for your output. These are categorised as Identifiers, Variants and frequency data, Additional annotations, Predictions, Filtering options and Advanced options. Let’s open all the menus and take a look.

Hover over the options to see definitions.

We’re going to select some options:

• HGVS, annotation of variants in terms of the transcripts and proteins they affect, commonly-used by the clinical community
• Phenotypes
• Protein domains

When you’ve selected everything you need, scroll right to the bottom and click Run.

The display will show you the status of your job. It will say Queued, then automatically switch to Done when the job is done, you do not need to refresh the page. You can edit or discard your job at this time. If you have submitted multiple jobs, they will all appear here.

Click View results once your job is done.

In your results you will see a graphical summary of your data, as well as a table of your results.

The results table is enormous and detailed, so we’re going to go through the it by section. The first column is Uploaded variant. If your input data contains IDs, like ours does, the ID is listed here. If your input data is only loci, this column will contain the locus and alleles of the variant. You’ll notice that the variants are not neccessarily in the order they were in in your input. You’ll also see that there are multiple lines in the table for each variant, with each line representing one transcript or other feature the variant affects.

You can mouse over any column name to get a definition of what is shown.

The next few columns give the information about the feature the variant affects, including the consequence. Where the feature is a transcript, you will see the gene symbol and stable ID and the transcript stable ID and biotype. Where the feature is a regulatory feature, you will get the stable ID and type. For a transcription factor binding motif (labelled as a MotifFeature) you will see just the ID. Most of the IDs are links to take you to the gene, transcript or regulatory feature homepage.

This is followed by details on the effects on transcripts, including the position of the variant in terms of the exon number, cDNA, CDS and protein, the amino acid and codon change, transcript flags, such as MANE, on the transcript, which can be used to choose a single transcript for variant reporting, and pathogenicity scores. The pathogenicity scores are shown as numbers with coloured highlights to indicate the prediction, and you can mouse-over the scores to get the prediction in words. Two options that we selected in the input form are the HGVS notation, which is shown to the left of the image below and can be used for reporting, and the Domains to the right. The Domains list the proteins domains found, and where there is available, provide a link to the 3D protein model which will launch a LiteMol viewer, highlighting the variant position.

Where the variant is known, the ID of the existing variant is listed, with a link out to the variant homepage. In this example, only rsIDs from dbSNP are shown, but sometimes you will see IDs from other sources such as COSMIC. The VEP also looks up the variant in the Ensembl database and pulls back the allele frequency (AF in the table), which will give you the 1000 Genomes Global Allele Frequency. In our query, we have not selected allele frequencies from the continental 1000 Genomes populations or from gnomAD, but these could also be shown here. We can also see ClinVar clinical significance and the phenotypes associated with known variants or with the genes affected by the variants, with the variant ID listed for variant associations and the gene ID listed for gene associations, along with the source of the association.

For variants that affect transcription factor binding motifs, there are columns that show the effect on motifs (you may need to click on Show/hide columns at the top left of the table to display these). Here you can see the position of the variant in the motif, if the change increases or decreases the binding affinity of the motif and the transcription factors that bind the motif.

Above the table is the Filter option, which allows you to filter by any column in the table. You can select a column from the drop-down, then a logic option from the next drop-down, then type in your filter to the following box. We’ll try a filter of Consequence, followed by is then missense_variant, which will give us only variants that change the amino acid sequence of the protein. You’ll notice that as you type missense_variant, the VEP will make suggestions for an autocomplete.

You can export your VEP results in various formats, including VCF. When you export as VCF, you’ll get all the VEP annotation listed under CSQ in the INFO column. After filtering your data, you’ll see that you have the option to export only the filtered data. You can also drop all the genes you’ve found into the Gene BioMart, or all the known variants into the Variation BioMart to export further information about them.

Resequencing of the genomic region of the human CFTR (cystic fibrosis transmembrane conductance regulator (ATP-binding cassette sub-family C, member 7) gene (ENSG00000001626) has revealed the following variants. The alleles defined in the forward strand:

• G/A at 7: 117,530,985
• T/C at 7: 117,531,038
• T/C at 7: 117,531,068

Use the VEP tool in Ensembl and choose the options to see SIFT and PolyPhen predictions. Do these variants result in a change in the proteins encoded by any of the Ensembl genes? Which gene? Have the variants already been found?

7	117530985	117530985	G/A
7	117531038	117531038	T/C  
7	117531068	117531068	T/C

We have details of a genomic deletion in a breast cancer sample in VCF format:

13 32307062 sv1 . <DEL> . . SVTYPE=DEL;END=32908738

Use VEP in Ensembl to find out the following information:

1.  How many genes have been affected?

2.  Does the structural variant (SV) cause deletion of any complete transcripts?

3.  Map your variant in the Ensembl browser on the Region in detail view.

Ensembl’s Variant Effect Pedirctor (VEP) is a powerful tool for annotating genomic variants. VEP is accessible via web, REST API and command line options.

In this practical session, we will practice using VEP via command line to annotate a variant call file (VCF).

The VCF you will use contains variant calls for Homo Sapiens chromosome 13 from the IGSR: The International Genome Sample Resource. This file was extracted using Ensembl’s Data Slicer for human variation aligned to GRCh38, with the IGSR British in England and Scotland GBR population samples subsetted. The file is available here

Directories and starting tutorials:

A General Tutorial for command line VEP is available to try out or compare to if you needsome guidance.

You can use the options guide at Ensembl VEP Options to help find the commands you need.

Launch a command prompt. VEP is installed already and should work directly from the terminal command line. Navigate to /home/ubuntu/Course_Materials/VEP_CLI/ to carry out the exercises.

The subsetted IGSR VCF file name is: 13.32315086-32400268.ALL.chr13_GRCh38.genotypes.20170504.GBR.vcf

It should already be available in the course materials area, but if not you can download it:

wget https://ftp.ebi.ac.uk/pub/databases/ensembl/training/output_files/13.32315086-32400268.ALL.chr13_GRCh38.genotypes.20170504.GBR.vcf

Exercises:

1. Explore the VCF file with any text viewer to explore its contents. What do lines denoted by “##” represent? What are the key headers in the file?

You can use VCF file specifications as a reminder.

1. Use the command-line VEP tool to annotate the variants in the IGSR VCF file and output to VCF format. Here is an example command:

vep -i [input_file] -o [output_file] --cache --offline

We recommend using the default VEP output format, as it’s easier to read on screen, but you can preserve the output as VCF by using --vcf.

1. Explore the output of VEP in any text viewer to explore the contents. What genes are affected by the variants in the file? Why are there multiple results for each variant?

2. The VEP cache contains Allele Frequency (AF) data available for known variants from multiple sources. As this data is from the 1000 genomes it already contains allele frequency data from that resource. Use the appropriate option to add AF data from gnomAD genomes, then write the output to a new file (something like -o chr13_with_AF.txt).

3. Use the filter_vep tool on the output of step 6 to find variants that are:

a. in BRCA2 and

b. are a MANE_SELECT transcript and

c. which have an AF of 0.01 or less in the overall gnomAD genomes population gnomADg_AF.

• Why do you think we have used an AF filter here?
• From the original output of exercise 2, what has been the overall effect of adding annotations and then filtering using that information?
• Are there any missense variants left?

Bonus!

If you finish all that, try pasting the variant below into Web VEP and select the alphamissense, revel, CADD, mavedb and mutfunc options.

What do you see in the results?

13 48040982 . A T