Variant Effect Predictor Tutorial
NOTE: If you're using a UNIX or Mac system, you can dive straight into this tutorial by opening your favourite terminal application. If you're on Windows you might like to have a look at the guide for Windows users before starting.
Have you downloaded the VEP yet? Click this link to download if you haven't already. If you prefer to do things on the terminal, you can use wget to grab the VEP archive:
> wget "https://github.com/Ensembl/ensembl-tools/archive/release/75.zip"
If you clicked the download link, let's find the file and unpack it ourselves. Typically the file will be in your Downloads folder:
> cd ~/Downloads
Now let's unpack it and navigate to the VEP's sub-directory:
> unzip ensembl-tools-release-75.zip > cd ensembl-tools-release-75/scripts/variant_effect_predictor/
NOTE: depending on your setup the archive file may instead be named 75.zip
The VEP uses "cache files" or a remote database to read genomic data. Using cache files gives the best performance - let's set one up using the installer:
> perl INSTALL.pl Hello! This installer is configured to install v75 of the Ensembl API for use by the VEP. It will not affect any existing installations of the Ensembl API that you may have. It will also download and install cache files from Ensembl's FTP server. Checking for installed versions of the Ensembl API...done It looks like you already have v75 of the API installed. You shouldn't need to install the API Skip to the next step (n) to install cache files Do you want to continue installing the API (y/n)?
If you haven't yet installed the API, type "y" followed by enter, otherwise type "n" (perhaps if you ran the installer before). At the next prompt, type "y" to install cache files
Do you want to continue installing the API (y/n)? n - skipping API installation The VEP can either connect to remote or local databases, or use local cache files. Cache files will be stored in /nfs/users/nfs_w/wm2/.vep Do you want to install any cache files (y/n)? y Downloading list of available cache files The following species/files are available; which do you want (can specify multiple separated by spaces): 1 : ailuropoda_melanoleuca_vep_75.tar.gz 2 : anolis_carolinensis_vep_75.tar.gz 3 : bos_taurus_vep_75.tar.gz ... 24 : homo_sapiens_vep_75.tar.gz ... ?
Type "24" (or the relevant number for homo_sapiens) to install the cache for human. This will take a little while to download and unpack! By default the VEP assumes you are working in human; it's easy to switch to any other species using --species [species].
? 24 - downloading ftp://ftp.ensembl.org/pub/release-75/variation/VEP/homo_sapiens_vep_75.tar.gz - unpacking homo_sapiens_vep_75.tar.gz Success
By default the VEP installs cache files in a folder in your home area ($HOME/.vep); you can easily change this using the -d flag when running the install script. Have a look at the installer documentation for more details.
The VEP needs some input containing variant positions to run. In their most basic form, this should just be a chromosomal location and a pair of alleles (reference and alternate). The VEP can also use common formats such as VCF and pileup as input; these are commonly produced by variant calling packages. Have a look at the Data formats page for more information.
We can now use our cache file to run the VEP on the supplied example file example.vcf, which is a VCF file containing variants from the 1000 Genomes Project exon-sequencing pilot:
> perl variant_effect_predictor.pl -i example.vcf --cache 2013-07-31 09:17:54 - Read existing cache info 2013-07-31 09:17:54 - Starting... ERROR: Output file variant_effect_output.txt already exists. Specify a different output file with --output_file or overwrite existing file with --force_overwrite
You may see this error message if you've already run the script once. The VEP tries not to trample over your existing files unless you tell it to. So let's tell it to using --force_overwrite
> perl variant_effect_predictor.pl -i example.vcf --cache --force_overwrite 2013-07-31 09:19:30 - Read existing cache info 2013-07-31 09:19:30 - Starting... 2013-07-31 09:19:30 - Detected format of input file as vcf 2013-07-31 09:19:30 - Read 173 variants into buffer 2013-07-31 09:19:30 - Reading transcript data from cache and/or database [================================================================================] [ 100% ] 2013-07-31 09:19:31 - Retrieved 3037 transcripts (0 mem, 3106 cached, 0 DB, 69 duplicates) 2013-07-31 09:19:31 - Analyzing chromosome 21 2013-07-31 09:19:31 - Analyzing variants [================================================================================] [ 100% ] 2013-07-31 09:19:32 - Calculating consequences [================================================================================] [ 100% ] 2013-07-31 09:19:32 - Analyzing chromosome 22 2013-07-31 09:19:32 - Analyzing variants [================================================================================] [ 100% ] 2013-07-31 09:19:33 - Calculating consequences [================================================================================] [ 100% ] 2013-07-31 09:19:34 - Processed 173 total variants (43 vars/sec, 43 vars/sec total) 2013-07-31 09:19:34 - Wrote stats summary to variant_effect_output.txt_summary.html 2013-07-31 09:19:34 - Finished!
Note that the VEP has told us it retrieved 3037 transcripts from the cache - this means the cache we downloaded is working OK.
The VEP produces status output as it runs to give you an idea of the progress it is making. You can silence this output using --quiet, though note that errors and warnings will still be printed out.
By default the VEP writes to a file named "variant_effect_output.txt" - you can change this file name using -o. Let's have a look at the output that the script generated.
> head variant_effect_output.txt ## ENSEMBL VARIANT EFFECT PREDICTOR v75 ## Output produced at 2013-07-31 09:19:30 ## Connected to homo_sapiens_core_75_37 on ensembldb.ensembl.org ## Using cache in /nfs/users/nfs_w/wm2/.vep/homo_sapiens/75 ## Using API version 75, DB version 75 ## Extra column keys: ## DISTANCE : Shortest distance from variant to transcript #Uploaded_variation Location Allele Gene Feature Feature_type Consequence ... rs116645811 21:26960070 A ENSG00000260583 ENST00000567517 Transcript upstream_gene_variant ... rs116645811 21:26960070 A ENSG00000154719 ENST00000352957 Transcript intron_variant ...
The lines starting with "#" are header or meta information lines. The final one of these (highlighted in blue above) gives the column names for the data that follows. To see more information about the VEP's output format, see the Data formats page.
We can see two lines of output here, both for the uploaded variant named rs116645811. In many cases, a variant will fall in more than one transcript. Typically this is where a single gene has multiple splicing variants; however, as in this case, it can also be that the variant overlaps transcripts from two different genes. Here our variant has a consequence for the genes ENSG00000260583 and ENSG00000154719.
In the consequence column, we can see two different terms, upstream_gene_variant and intron_variant. These terms are standardised terms for describing the effects of sequence variants on genomic features, produced by the Sequence Ontology (SO). See our predicted data page for a guide to the consequence types that the VEP and Ensembl uses.
Let's try something a little more interesting. SIFT is an algorithm for predicting whether a given change in a protein sequence will be deleterious to the function of that protein. The VEP can give SIFT predictions for any of the missense variants that it predicts. To do this, simply add --sift b (the b means we want both the prediction and the score):
> perl variant_effect_predictor.pl -i example.vcf --cache --force_overwrite --sift b
SIFT calls variants either "deleterious" or "tolerated". We can use the VEP's filtering script to find only those that SIFT considers deleterious:
> perl filter_vep.pl -i variant_effect_output.txt -filter "SIFT is deleterious" | head -n15 ... ## SIFT : SIFT prediction #Uploaded_variation Location Allele Gene Feature ... Extra rs114053718 21:34029195 G ENSG00000159082 ENST00000382499 ... SIFT=deleterious(0) rs114053718 21:34029195 G ENSG00000159082 ENST00000382491 ... SIFT=deleterious(0) rs114053718 21:34029195 G ENSG00000159082 ENST00000322229 ... SIFT=deleterious(0) rs114053718 21:34029195 G ENSG00000159082 ENST00000433931 ... SIFT=deleterious(0)
Note that the SIFT score appears in the "Extra" column, as a key/value pair. This column can contain multiple key/value pairs depending on the options you give to the VEP script. See the Data formats page for more information on the fields in the Extra column.
You can also configure how the VEP writes its output using the --fields flag.
You'll also see that we have multiple results for the same gene, ENSG00000159082. Let's say we're only interested in what is considered the canonical transcript for this gene (--canonical), and that we want to know what the commonly used gene symbol from HGNC is for this gene (--symbol). We can also use a UNIX pipe to pass the output from the VEP directly into the filtering script:
> perl variant_effect_predictor.pl -i example.vcf --cache --force_overwrite --sift b --canonical --symbol \ --fields Uploaded_variation,SYMBOL,CANONICAL,SIFT -o STDOUT | \ perl filter_vep.pl -format vep -filter "CANONICAL is YES and SIFT is deleterious" ... #Uploaded_variation SYMBOL CANONICAL SIFT rs114053718 SYNJ1 YES deleterious(0) rs2254562 SYNJ1 YES deleterious(0.04) rs4986958 IFNGR2 YES deleterious(0.03) rs16994704 PIGP YES deleterious(0.01) ... rs115264708 PHF21B YES deleterious(0.04)
So now we can see all of the variants that have a deleterious effect on canonical transcripts, and the symbol for their genes. Nice!
Over to you!
This has been just a short introduction to the capabilities of the VEP - have a look through some more of the options, see them all on the command line using --help, or try using the shortcut --everything which switches on almost all available output fields! Try out the different options in the filtering script, and if you're feeling adventurous why not use some of your own data to annotate your variants or have a go with a plugin or two.