Demo: Exploring species and genome assemblies in Ensembl Plants

Homepage

The front page of Ensembl Plants is found at plants.ensembl.org. It contains lots of information and links to help you navigate Ensembl Plants:

At the top left you can see the current release number and what has come out in this release.

Available species

Click on View full list of all species.

Click on the scientific name of your species of interest to go to the species homepage. We’ll click on Triticum aestivum.

Species information

Here you can see links to example pages and to download flatfiles. To find out more about the genome assembly and genebuild, click on More information and statistics.

Here you’ll find a detailed description of how to the genome was produced and links to the original source. You will also see details of how the genes were annotated.

The front page of Ensembl Metazoa is found at www.metazoa.ensembl.org/. It contains lots of information and links to help you navigate Ensembl Metazoa.

Demo: Exploring genes and transcripts in Ensembl Plants

You can find out lots of information about Ensembl genes and transcripts using the browser. If you’re already looking at a region view, you can click on any transcript and a pop-up menu will appear, allowing you to jump directly to that gene or transcript.

Alternatively, you can find a gene by searching for it. You can search for gene names or identifiers, and also phenotypes or functions that might be associated with the genes.

We’re going to look at the Arabidopsis thaliana PAI1 gene. From plants.ensembl.org, type PAI1 into the search bar and click the Go button.

The gene tab

Click on PAI1 from the search hits. The Gene tab should open:

This page summarises the gene, including its location, name and equivalents in other databases. At the bottom of the page, a graphic shows a region view with the transcripts. We can see exons shown as blocks with introns as lines linking them together. Coding exons are filled, whereas non-coding exons are empty. We can also see the overlapping and neighbouring genes and other genomic features.

There are different tabs for different types of features, such as genes, transcripts or variants. These appear side-by-side across the blue bar, allowing you to jump back and forth between features of interest. Each tab has its own navigation column down the left hand side of the page, listing all the things you can see for this feature.

Let’s walk through this menu for the gene tab. How can we view the genomic sequence? Click Sequence at the left of the page.

The sequence is shown in FASTA format. The FASTA header contains the genome assembly, chromosome, coordinates and strand (1 or -1) – this gene is on the positive strand.

Exons are highlighted within the genomic sequence, both exons of our gene of interest and any neighbouring or overlapping gene. By default, 600 bases are shown up and downstream of the gene. We can make changes to how this sequence appears with the blue Configure this page button found at the left. This allows us to change the flanking regions, add variants, add line numbering and more. Click on it now.

Once you have selected changes (in this example, Show variants and Line numbering) click at the top right.

You can download this sequence by clicking in the Download sequence button above the sequence:

This will open a dialogue box that allows you to pick between plain FASTA sequence, or sequence in RTF, which includes all the coloured annotations and can be opened in a word processor. If you want run a sequence analysis tool, download as FASTA sequence, whereas if you want to analyse the sequence visually, RTF is best for this. This button is available for all sequence views.

To find out what the protein does, have a look at GO terms from the Gene Ontology consortium. There are three pages of GO terms, representing the three divisions in GO: Biological process (what the protein does), Cellular component (where the protein is) and Molecular function (how it does it). Click on GO: Biological process to see an example of the GO pages.

Here you can see the functions that have been associated with the gene. There are three-letter codes that indicate how the association was made, as well as links to the specific transcript they are linked to.

We also have links out to other databases which have information about our genes and may focus on other topics that we don’t cover, like Expression Atlas or UniProtKB. Go up the left-hand menu to External references:

Demo: The transcript tab

We’re now going to explore the different transcripts of PAI1. Click on Show transcript table at the top.

Here we can see a list of all the transcripts of PAI1 with their identifiers, lengths and biotypes. Click on the ID of the Ensembl Canonical transcript, PAI1-211.

You are now in the Transcript tab for PAI1-211. We can still see the gene tab so we can easily jump back. The left hand navigation column provides several options for the transcript PAI1-211 - many of these are similar to the options you see in the gene tab, but not all of them. If you can’t find the thing you’re looking for, often the solution is to switch tabs.

Click on the Exons link. This page is useful for designing RT-PCR primers because you can see the sequences of the different exons and their lengths.

You may want to change the display (for example, to show more flanking sequence, or to show full introns). In order to do so click on Configure this page and change the display options accordingly.

Now click on the cDNA link to see the spliced transcript sequence with the amino acid sequence. This page is useful for mapping between the RNA and protein sequences, particularly genetic variants.

UnTranslated Regions (UTRs) are highlighted in dark yellow, codons are highlighted in light yellow, and exon sequence is shown in black or blue letters to show exon divides. Sequence variants are represented by highlighted nucleotides and clickable IUPAC codes are above the sequence.

Next, follow the General identifiers link at the left. Just like the External References page in the gene tab, this page shows links out to other databases such as RefSeq, UniProtKB, PDBe and others, this time linked to the transcript or protein product, rather than the gene.

If you’re interested in protein domains, you could click on Protein summary to view domains from Pfam, PROSITE, Superfamily, InterPro, and more. These are all plotted against the transcript sequence, with the exons shown in alternating shades of purple at the top of the page. Alternatively, you can go to Domains & features to see a table of the same information.

Demo: Exploring genes and transcripts in Ensembl Metazoa

We’re going to look at the AAEL026647 gene in Aedes aegypti (Yellow fever mosquito, LVP_AGWG) to find out information about it and its transcript.

Demo: Exploring comparative genomics data for cultivars in Ensembl Plants

To see the list of cultivars available for your chosen species, select the reference genome for that species. We are going to select Oryza sativa Japonica Group which will take you to the genome’s home page:

Within the Genome Assembly area there will be a link to ‘View full list of cultivars’ when additional cultivars are available in Ensembl. Clicking this link will take you to a list of all available cultivars, with some overview information and links to access example locations or the genome’s home page for each cultivar.

Returning to the Ensembl Plants homepage, we can search for rice gene Os05g0421300.

The results return links to the gene page, species home page, location (Chromosome 5: 20,663,027-20,668,604) and gene tree. We are going to select the Gene from species “Oryza sativa Japonica Group”.

On the left-hand menu there is a sub-menu called ‘Plant Compara’ and a menu within that for ‘Cultivars’. We will select ‘Gene tree’ which will show a gene tree generated by the Gene Orthology/Paralogy prediction method pipeline. Cultivar gene trees are constructed using one representative protein (typically the longest protein-coding translation) for every gene in each cultivar. Cultivars whose genomes have been assembled into chromosomes and have been independently annotated are included into Cultivar comparative analyses.

The display shows the consensus tree representing the evolutionary history of this gene and an image of the alignments. Subtrees can be expanded by clicking on a node (blue or red squares) and selecting ‘expand this subtree’ from the pop-up menu. The gene of interest is displayed with red text.

For this example we can see that the two ‘circum-Aus’ cultivars cluster with the ‘Xian/indica’ cultivars, and that the ‘circum-Basmati’ clusters with the ‘Geng/Japonica’ cultivars. Additional Oryza wild relatives are also shown, along with outgroup Leersi perrieri. We can also see from the alignments that the gene is highly conserved, but the representative protein in cultivar ‘Gobol Sail’ has some gaps compared to the others.

On the left-hand menu we can select ‘Orthologues’ from the Cultivar sub-menu of the ‘Plants compara’ menu. This will bring up a table of orthologues which have been inferred from the gene tree. The table lists the species, type of orthologue (e.g. 1-to-1, 1-to-many), links to jump to an orthologue’s gene page, region comparison page (to see an alignment image of the two genes) or text cDNA/protein alignments. Information detailing the percentage identity of the query and target sequences, Gene order conservation (GOC) score, whole genome alignment (WGA) coverage and indication of high confidence (Yes for homology with high percentage identity and high GOC or WGA coverage) are also provided.

To visualise whole genome alignments in this region, we can use the left-hand menu to select ‘Genomic Alignments’ from the ‘Plants compara’ sub-menu. We can then select which alignment we want to display. Options include multiple genome alignments using the EPO (Enredo, Pecan, Ortheus) pipeline or progressive cactus for cultivar alignments. Pairwise alignments generated with LASTZ or cactus are also available for comparison to other plant species, or other cultivars. We will select the ’26 rice cultivars cactus’ alignment to display.

The resulting alignment is returned in a series of blocks, arranged from longest (block 1) to shortest. The table can also be sorted by location on the reference genome. To view a block’s alignment, click on the name of the block. A tree view depicts the tree from cactus, with the sequence alignment represented on the right. Below the tree view a list of links to each aligned region is provided, and below that the text representation of the alignment with exons shown in red.

Demo: Exploring comparative genomics data for in Ensembl Metazoa

Navigate to “www.metazoa.ensembl.org”. Select “Anopheles gambiae (African malaria mosquito, PEST)” from the drop-down menu. Enter the gene ID: “AGAP004707”.

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 MCM6 in chicken. Search for MCM6 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.

To view all the sequence variations 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.

You can also filter by SIFT, 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.

You can also see the phenotypes associated with a gene. Click on Phenotype in the left hand menu.

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

Click on 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 QTLs, which cover a locus without being associated with a specific variant. Turn on the following variation tracks.

• All variants on genotyping chips - short variants (SNPs and indels)
• Phenotype annotations (QTLs)

Click on a variant to find out more information. It may be easier to see the individual variants if you zoom in.

Let’s have a look at a specific variant, which happens to fall within the MCM6 gene: rs14625781.

The easiest way to find this variant is if we put rs14625781 into the search box. Click through to open the Variation tab.

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

This variant is found in three transcripts of the MCM6 gene, and is missense in two. SIFT predicts that it is unlikely to affect protein function of either (Tolerated).

Let’s look at population genetics. Either click on Explore this variant in the left hand menu then click on the Population genetics icon, or click on Population genetics in the left-hand menu.

We can see data from EVA study PRJEB44919 showing the frequency of the alleles and genotypes. We can see what animals these genotypes were actually observed in by going to Sample genotypes.

Click on Phylogenetic context to see the variant in other species.

We can see that other birds also have the C alleles as a reference whereas Anolis_carolinensis has an A allele.

(a) Find the page with information for the chicken SNP rs10731268.

(b) What gene(s) does rs10731268 fall within? What is its effect?

(c) Have any papers been written mentioning rs10731268? What are they about?

(d) What allele is at this position in other birds? What is the likely ancestral allele?

The human gene MC4R has been associated with obesity. The SNP rs81219178 has been identified as a variant in the pig MC4R gene.

(a) What is the amino acid change caused by rs81219178 in MC4R of the pig? Is the change likely to alter the protein function?

(b) How many transcripts does this variant affect? What are the consequences of this variant?

We have identified seven variants in pig:
rs319195925, rs80805426, rs81267388, rs80854621, rs711163915, rs321793337, rs792403417

We will use the Ensembl VEP to determine:

• If the variants have been annotated in Ensembl already
• If genes are affected by the variants

Go to the front page of Ensembl and click on Variant Effect Predictor in the Tools section or click on VEP in the top header.

This page contains information about the VEP, including a link for downloading the script version of the tool. Click on the Launch VEP button to open the input form.

Lets input the variants data in VCF format:
Chromosome Position Name Reference Alternative

Put the following into the Input data box:

9 9580742 rs319195925 T C
1 213701082 rs80805426 C A
15 83361856 rs81267388 A G
1 159538854 rs80854621 A G
14 50574184 rs711163915 C A
14 50571223 rs321793337 G A
14 50571474 rs792403417 C T

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

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

Hover over the options to see definitions.

When you have 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 save, edit, share or delete your job at this time. If you have submitted multiple jobs, they will all appear here.

Click on View Results once your job is done.

In your results you will see a graphical and table summary of the data as well as a table with the detailed results.

We have identified a few variants associated with body size in chicken (bGalGal1.mat.broiler.GRCg7b):

chr 6, genomic coordinate 23650222, alleles A/C, forward strand
chr 6, genomic coordinate 23645685, alleles C/A, forward strand
chr 1, genomic coordinate 51237121, alleles C/T, forward strand

(a) Which genes and transcripts do these variants map to?

(b) What are the consequence terms for these variants?

(c) Which regulatory feature is affected by the variants?

Follow these instructions to guide you through BioMart to answer the following query:

You have three questions about a set of chicken genes:
ESPN, MYH9, USH1C, CISD2, THRB, WHRN
(these are HGNC gene symbols. More details on the HUGO Gene Nomenclature Committee can be found on https://www.genenames.org/)

1. What are the NCBI Gene IDs for these genes?
2. Are there associated functions from the GO (gene ontology) project that might help describe their function?
3. What are their cDNA sequences?

Click on BioMart in the top header of the Ensembl website or go to BioMart directly by visiting https://www.ensembl.org/biomart/martview.

You cannot choose any filters or attributes until you’ve chosen your dataset. Your dataset is the data type you’re working with. In this case we’re going to choose genes, so pick Ensembl Genes then Chicken genes from the drop-downs.

Now that you’ve chosen your dataset, the filters and attributes will appear in the column on the left. You can pick these in any order and the options you pick will appear.

Click on Filters on the left to see the available filters appear on the main page. You’ll see that there are loads of categories of Filters to choose from. You can expand these by clicking on them. For our query, we’re going to expand GENE.

Our input data is a list of identifiers, so we’re going to use the Input external references ID list filter. This allows us to input a list of identifiers from different databases. We need to choose what kind of identifier we’re using, so that BioMart can look up the right column in a data table. You can pick these from a drop-down list, which lists the type of identifier with an example of how it looks. For our query, we have a list of gene names, so we need to pick Gene Name(s).

To check if the filters have worked, you can use the Count button at the top left, which will show you how many genes have passed the filter. If you get 0 or another number you don’t expect, this can help you to see if your query was effective.

To choose the attributes, expand this in the menu. There are six categories for chicken gene attributes. These categories are mutually exclusive, you cannot pick attributes from multiple categories. This means that we need to do two separate queries to get our GO terms and NCBI IDs, and to get our cDNA sequences.

The Ensembl gene and transcript IDs, with and without version numbers are selected by default. The selected attributes are also listed on the left.

We can choose the attributes we want by clicking on them. For our query, we’re going to select:

• GENE
  • Gene Name
• EXTERNAL
  • NCBI gene ID
  • GO term accession
  • GO term name
  • GO term definition

We need to select the Gene Name in order to get back our original input, as this is not returned by default in BioMart. The order that you select the attributes in will define the order that the columns appear in in your output table.

You can get your results by clicking on Results at the top left.

The results table just gives you a preview of the first ten lines of your query. This allows the results to load quickly, so that if you need to make any changes to your query, you don’t waste any time. To see the full table you can click on View ## rows. You can also export the data to an xls, tsv, csv or html file. For large queries, it is recommended that you export your data as Compressed web file (notify by email), to ensure your download is not disrupted by connection issues.

You can see multiple rows per gene in your input list, because there are multiple transcripts per gene and multiple GO terms per transcript.

To get the cDNA sequences, go back to the Attributes then select the category Sequences and expand SEQUENCES.

When you select the sequence type, the part of the transcript model you’ve chosen will be highlighted in the grpahic.

Choose cDNA sequences, then expand HEADER INFORMATION to add Gene Name to the header. Then hit Results again.

For more details on BioMart, have a look at this publication: Kinsella RJ, Kähäri A, Haider S, et al. Ensembl BioMarts: a hub for data retrieval across taxonomic space. Database: the Journal of Biological Databases and Curation. 2011; 2011:bar030. DOI: 10.1093/database/bar030. PMID: 21785142; PMCID: PMC3170168.

BioMart is a very handy tool when you want to convert IDs from different databases. The following is a list of 27 IDs of Sus scrofa proteins from the NCBI RefSeq database: NP_001116455,NP_001231885,NP_001230616,NP_001231413,NP_001231746,NP_999129,NP_001231602,NP_001177096,NP_001231419,NP_001230512, NP_001231165,NP_001167636,NP_001172069,NP_001011509,NP_999191,NP_001231786,NP_001231468,NP_001121951,NP_001230557,NP_999413

Generate a list that shows to which Ensembl Gene IDs and to which gene names these RefSeq IDs correspond. Do these 27 proteins correspond to 27 genes?

Find chicken proteins with transmembrane domains located on chromosome 9.

Here are two affymetrix probeset IDs from my microarray experiment that seem to map uniquely to genes in the chicken genome: Gga.12669.1.S1_at, GgaAffx.7784.1.S1_at

(a) Retrieve for the genes corresponding to these probe-sets the Ensembl Gene and Transcript IDs as well as their gene symbols and descriptions.

(b) In order to analyse these genes for possible promoter/enhancer elements, retrieve the 2000 bp upstream of the transcripts of these genes.

(c) In order to be able to study these chicken genes in duck, identify their duck orthologues. Also retrieve the genomic coordinates of these orthologues.