Exploring variants in rice

Visualising variants in the Sequence view

In any of the sequence views shown in the Gene and Transcript tabs, you can view variants on the sequence. You can do this by clicking on Configure this page from any of these views.

Let’s take a look at the Gene sequence view for OS01G0775500 in rice. Search for OS01G0775500 and go to the Sequence view.

If you can’t see variants marked on this view, click on Configure this page and select Show variants: Yes and show links.

Find out more about a variant by clicking on it.

You can add variants to all other sequence views in the same way.

You can go to the Variation tab by clicking on the variant ID. For now, we’ll explore more ways of finding variants.

Viewing variants within a gene in the tabular form

To view all the sequence variants in table form, click the Variant table link at the left of the gene tab.

You can filter the table to only show the variants you’re interested in. For example, click on Consequences: All, then select the variant consequences you’re interested in. For display purposes, the table above has already been filtered to only show missense variants.

You can also filter by the different pathogenicity scores and MAF, or click on Filter other columns for filtering by other columns such as Evidence or Class.

The table contains lots of information about the variants. You can click on the IDs here to go to the Variation tab too.

Visualising variants in the Region in Detail view

Let’s have a look at variants in the Location tab. Click on the Location tab in the top bar.

Configure this page and open Variation from the left-hand menu.

There are various options for turning on variants. You can turn on variants by source, by frequency, presence of a phenotype or by individual genome they were isolated from. You can also turn on genotyping chips.

Exploring a specific variant

Let’s have a look at a specific variant. If we zoomed in we could see the variant rs18335701 in this region, however it’s easier to find if we put rs18335701 into the search box. Click through to open the Variation tab for Oryza sativa Japonica.

The icons show you what information is available for this variant. Click on Genes and regulation, or follow the link on the left.

This page illustrates the genes the variant falls within and the consequences on those genes, including pathogenicity predictors. It also shows data from GTEx on genes that have increased/decreased expression in individuals with this variant, in different tissues. Finally, regulatory features and motifs that the variant falls within are shown.

Let’s look at population genetics. Click on Population genetics in the left-hand menu.

The population allele frequencies are shown by study. Where genotype frequencies are available, these are shown in the tables.

We can see which strains these genotypes were observed in by going to Sample Genotypes. Click on Show for the Duitama et al. 2015 population.

The Arabidopsis thaliana ATCDSP32 protein is a chloroplastic drought-induced stress protein proposed to participate in a process called cell redox homeostasis. Go to Ensembl Plants and answer the following questions:

1. How many variants have been identified in the gene that can cause a change in the protein sequence (i.e. missense variant)?

2. What is the ID of the variant that changes the amino acid residue 60 from Alanine to Threonine (hint: refer to an amino acid codon table)? What is the location of this SNP in the A. thaliana genome? What are its possible alleles?

3. Download the flanking sequence of this SNP in RTF (Rich Text Format). Can you change how much flanking sequence is displayed on the browser?

4. Does this SNP cause a change at the amino acid level for other genes or transcripts?

1. Go to Ensembl Plants and find the Solyc02g084570.3 gene in Solanum lycopersicum (tomato) and go to its Location tab. Can you see the variation track?

2. Zoom in around the last exon of this gene. What are the different types of variants seen in that region? Are any splice region variants mapped in the region? If so, what is/are the coordinate(s)?

1. Search for the variant BA00369602 in Triticum aestivum on Ensembl Plants. Is this variant known by any other names?

2. What gene is affected by this variant? What is the amino acid change?

3. Which cultivars have the alternative base at this locus?

Demonstration of the VEP web interface

Input

We have identified three variants on wheat chromosome 4B:
C -> T at 240206468
C -> G at 240199078
C -> T at 240212229

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?

Click on Tools in the top green bar from any Ensembl Plants page, then Variant Effect Predictor to open the input form:

Click on Add/remove species and search for Triticum aestivum to choose it.

The data is in VCF:
chromosome coordinate id reference alternative

Put the following into the Paste data box:

4B 240206468 var1 C T  
4B 240199078 var2 C G  
4B 240212229 var3 C T  

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

Additional configurations

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.

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.

Results

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. The IDs are links to take you to the gene or transcript 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 and pathogenicity scores. Where the variant is known, the ID of the existing variant is listed, with a link out to the variant homepage. 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.

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.

Running command-line VEP via Docker

If you don’t have root privileges, you can run VEP in a virtualised container via Docker. You can download and install Docker on Mac, Windows and Linux.

Preparing and running Docker

You will need to install or update Rosetta 2 to run Docker:

softwareupdate --install-rosetta

Create a directory for the course:

mkdir /Desktop/VEP/Plants
cd /Desktop/VEP/Plants

Start Docker:

open –a Docker

Download the Ensembl Plants VEP Docker image:

docker pull csicunam/bioinformatics_iamz

Create a Docker working directory to mount to the Docker image (you can save your input files in here and all outputs will be written in this directory):

mkdir vep_data

Some operating systems may require root privileges:

chmod 777 vep_data

Run the Docker image and mount the directory you just created:

docker run -t -i -v $HOME/Desktop/VEP/Plants/vep_data:/data csicunam/bioinformatics_iamz:latest

Exit Docker

exit

Docker options

The following options are available for docker run:

--interactive or -i   Keep STDIN open even if not attached
--tty or -t   Allocate a pseudo-TTY
--volume or -v   Bind mount a volume (this is your local working directory)
--env or -e   Set environment variables

Running VEP via Docker

Download the indexed cache file to your working directory and unpack in your local directory. You can find all available VEP index files on the Ensembl Genomes FTP site:

curl -O http://ftp.ensemblgenomes.org/pub/plants/current/variation/indexed_vep_cache/oryza_sativa_vep_57_IRGSP-1.0.tar.gz
tar xzf oryza_sativa_vep_57_IRGSP-1.0.tar.gz

Run VEP within your Docker image. The directory /data within your Docker image is equivalent to your local working directory:

docker run -t -i -v $HOME/Desktop/VEP/Plants/vep_data:/data csicunam/bioinformatics_iamz:latest \
    vep -i variant_data/rice_variants.vcf -o /data/output.txt --dir /data --cache \
    --cache_version 57 --genomes --species oryza_sativa --force_overwrite --check_existing -offline

View output.txt here.

If you are already within a Docker session, only run the vep code (see an example below). The directory /data within your Docker image is equivalent to your local working directory:

vep -i variant_data/rice_variants.vcf -o /data/output.txt --dir /data --cache \
    --cache_version 57 --genomes --species oryza_sativa --force_overwrite --check_existing --offline

Basic VEP options

You can view a list of all available VEP options here.

Annotation source options (select one):

--cache   Use local data (uses database connections for certain functions)
--offline   Use local data only (forbids external database connections)
--database   Use remote database (default is ensembldb.ensembl.org

Input / output options:

--input_file or -i   Will try to read from STDIN if absent
--output_file or -o   Defaults to variant_effect_output.txt
--force_overwrite   Overwrite existing output file
--tab, --vcf, --json   Different output formats, customise with --fields

Known variants:

--check_existing   Enables checking for variants
--filter_common   Excludes variants that have a co-located existing variant with global allele frequency > 0.01

Pathogenicity predictions:

--sift b   Predicts whether an amino acid substitution affects protein function based on sequence homology and the physical properties of amino acids

VEP plugins

Plugins extend VEP functionality by allowing you to:

• run algorithms
• fetch Ensembl data
• modify parameters

Simply add the --plugin option to your script. You can find a list of all available VEP plugins in this documentation page and on GitHub.

VEP filtering

VEP comes with its own filter tool filter_vep and works with default and VCF output formats. It uses simple query notations, e.g. [field] [operator] [value]. E.g.

filter_vep -i output.txt --filter “consequence is missense_variant”

Queries can be combined with and / or and nested with parentheses. You can resolve consequences types in ontology (--ontology or -y):

[...] -y -f “consequence is coding_sequence_variant or (EXON is 1 and BIOTYPE is protein_coding)”

Let’s filter the variants from the previous output. Filter by using VEP’s filter_vep option and open the file on your local machine:

docker run -t -i -v $HOME/Desktop/VEP/Plants/vep_data:/data csicunam/bioinformatics_iamz:latest \
    filter_vep -i /data/output.txt –o /data/output_filtered.txt \
    --filter "consequence is missense_variant"

View output_filtered.txt here.

You’ll find a VCF file here. This is a small subset of the outcome of Oryza sativa Japonica whole-genome sequencing and variant-calling experiment. Analyse the variants in this file with the VEP tool in Ensembl Plants and determine the following:

1. How many genes and transcripts are affected by variants in this file?

2. Do these variants result in a change in the proteins encoded by any of the Ensembl genes? Which genes are affected? What is the amino acid change?

You have done whole-genome sequencing and variant-calling experiments for Triticum aestivum. You have a VCF file with a small subset of variants from this experiment. Analyse the variants in this file with the VEP tool in Ensembl Plants and determine the following:

1. How many variants were analysed? How many are novel?

2. How many genes and transcripts are affected by variants in this file?

3. Do any of the variants have different consequences for different transcripts?

4. Filter the table to find variants with high impact. How many variants have high impact? Why do you think missense variants are not classified as high impact?

5. Can you export all the results to a VCF file? Compare it to the input VCF file to see what information the VEP adds.

In the Mapping and variant calling practical session, you produced a VCF file with variant calls for Oryza sativa chromosome 10 using sequencing reads from the 3,000 Rice Genomes Project.

1. Use the command-line VEP tool via Docker to annotate the variants in your VCF file. You can use your own file from the previous module, but it can also be found within the Docker image at /home/vep/variant_data/SAMEA2569438.chr10.filt.vcf.gz.

2. Re-run the command-line VEP tool via Docker to annotate the variants in your VCF including SIFT scores and affected protein domains. Save the output of this query into a separate output file.

3. Use the filter_VEP tool to find variants that are located within genes in the Disease Resistance Protein (RP) Panther family.

4. Use the filter_VEP tool to find missense variants that have a deleterious SIFT score and are located within genes in the RP Panther family.

Demo: gene trees and homology predictions

Plants Compara

Gene trees

Let’s look at the homologues of Triticum aestivum (wheat) TraesCS3D02G007500. Open Ensembl Plants, search for the gene and go to the Gene tab.

Click on Plant compara: Gene tree, which will display the current gene in the context of a phylogenetic tree used to determine orthologues, paralogues and homoeologues.

Funnels indicate collapsed nodes. We can expand them by clicking on the node and selecting Expand this sub-tree from the pop-up menu.

We can also see the protein alignment of the sub-tree by clicking on Wasabi viewer, which will open a pop-up:

You can download the tree in a variety of formats. Click on the download icon in the bar at the top of the image to get a pop-up where you can choose your format.

Homologues

We can look at homologues in the Orthologues, Paralogues and homoeologues pages, which can be accessed from the left-hand menu. If there are no orthologues, paralogues or homoeologues, then the name will be greyed out. Click on Plant compara: Orthologues to see the orthologues available in plants.

Choose to see only Eudicotyledons orthologues by selecting the box. The table below will now only show details of Eudicotyledons orthologues. Let’s look at Brassica oleracea.

Here we can see there is a many-to-many relationship between the wheat and B. oleracea orthologues. Links from the orthologue allow you to go to alignments of the orthologous proteins and cDNAs. Click on View Sequence Alignments then View Protein Alignment for the first B. oleracea orthologue.

The paralogue page and homoeologue pages are structured in the same way as the orthologue page.  
 
 

Let’s look at some of the comparative genomics views in the Location tab. Go to the region 1:8000-18000 in Oryza sativa Japonica.

We can look at individual species comparative genomics tracks in this view by clicking on Configure this page.

Select BLASTz/LASTz alignments from the left-hand menu to choose alignments between closely related species. Turn on the alignments for all Oryza species:

The alignment is greatest between closely related species. We can see that many rice species (such as Oryza barthii) are fully aligned across the region, but other species have a region around 1:10700-12500 where a different chunk is aligned (such as Oryza punctata).

We can also look at the alignment between species or groups of species as text. Click on Alignments (text) in the left hand menu.

Select Select an alignment to open the alignment menu.

Select Oryza punctata from the alignments list then click Go.

In this case there are eight blocks aligned of different lengths, some of which correspond to the region we saw unaligned in the image. Click on Block 1.

You will see a list of the regions aligned, followed by the sequence alignment. Click on Display full alignment. Exons are shown in red.

To compare with both contigs visually, go to Region comparison.

To add species to this view, click on the blue Select species or regions button. Choose Oryza punctata again then close the menu.

We can view large scale syntenic regions from our chromosome of interest. Click on Synteny in the left hand menu.

The fumarase gene FUM1 in Arabidopsis thaliana encodes a protein with mitochondrial targeting information. Read more in this UniProt entry. Go to Ensembl Plants to answer the following questions:

1. How many orthologues have been identified for this gene?

2. Which orthologue has the highest sequence similarity? Look at the Query%ID and Target%ID.

Search Ensembl Plants for the gene Lsi1 in Oryza sativa Japonica (rice). This gene is known to code for an aquaporin transporter that facilitates the uptake of silicon and arsenic through the roots. Silicon concentration is highest in grass species, and is associated with defence.

1. From the gene tab, go to the Orthologues page under Plant Compara. Which plant group has the highest number of 1-to-1 orthologues? Is it the same group that has the highest number of 1-to-many orthologues?

2. Reduce the orthologues table to look only at Triticum aestivum (wheat) orthologues. Why are there three results for a 1-to-1 orthologue?

3. Click on the Compare regions link for chromosome 6B region in wheat to go to the Location tab. Scroll to the bottom image. How do the gene models compare between the species? Do they have the same number of exons?

4. Click back to the Gene tab and click on the Gene gain/loss tree page. Which species has the highest number of members of this gene family? Is it a grass? Can you change the view to see a radial tree?