As we announced in our e80 release post, we rolled out a prototype display for cis-regulatory interactions, essentially arches connecting any two elements on the same chromosome. This was mainly designed to display your eQTL or Hi-C with little effort, based on the WashU Epigenomics Explorer interaction track:


All you need to do is prepare a tab-delimited interaction file, optionally indexing it with Tabix if it is too large to upload directly. You can use it to represent Intra-chromosomal rearrangements such as the micro-inversion below:


@RNA3DHub even suggested using it to display RNA secondary structure:


You can do what you want really!


Are you looking for whole genomes, protein sequences, alignments or other genome-wide data from Ensembl?

Look no further; our FTP site is the place for you:

  • Download our data from the current release only (i.e. Ensembl 78)
  • Download our data from current and previous releases (including GRCh37)

These are some of our data that can be downloaded in bulk and for free; file types are described in brackets:

  • DNA, cDNA, CDS, ncRNA sequences (FASTA)
  • Annotations of our coding and non-coding genes (GTF)
  • Annotation of regulatory elements for the human and mouse genomes (GFF)
  • Variation data (VCF) for more than 20 Ensembl species
  • RNASeq reads (BAM) aligned against 25 genomes
  • GERP scores to identify constrained elements (BED)
  • Alignments of resequencing data for several species (EMF)
  • Multiple and pairwise genome alignments (MAF)
  • Ensembl databases for local installation (MySQL)

How can the Ensembl FTP foster research?

Let’s look at coiled-coils, simple dimers in protein sequences found in many species and believed to enable protein-protein interaction in a variety of biological processes.


Structure of coiled-coil domain from PDBe. Homohexameric assembly by Li et al. (2014)

Coiled-coil domains differ immensely from their globular counterparts, and distinct evolutionary constraints on them are expected. How conserved are coiled-coils? What has driven their evolution?

Intrigued by these questions, Surkont and Pereira-Leal (2015) set out on an journey to compare different protein sequences across several vertebrates, and the yeast. They show that substitution patterns do differ in coiled-coil versus globular regions, and they developed an evolutionary model to improve the detection of coiled-coils by homology, and their phylogeny inference.

Where did Surkont and Pereira-Leal find these proteomes for their investigation? In our FTP site.

Why not explore the Ensembl FTP site to see what we’ve got in store for you?

Any comments or questions, just get in touch.


In our latest release of Ensembl, we launched a brand new web interface for the VEP (Variant Effect Predictor).


As “it says on the tin”, the VEP predicts the effect of variants (i.e. SNPs, indels and CNVs) on genes and regulatory elements. It tells you where your variants are located (e.g. introns, coding exons, transcription factor binding motifs), what effect they may have on protein coding sequences, and whether these effects might be deleterious or benign.

The VEP does this by mapping your variants against genes, transcripts, translations, and regulatory features that we annotate in Ensembl.
The Variant Effect Predictor can also be run against other gene sets: you can predict the effect of your variants on RefSeq genes too!

What is new?

The new web interface is more user-friendly and has lots of improvements:

Increased number of variants you can input

You can upload up to one million variants in a compressed format, with a 50MB file size limit. To upload these larger files, you simply need to log in. If you do not have an Ensembl account, you are missing out, as there are many perks of registering. It’s easy to do: just provide your name and email address. If you’d rather not register, the upload limit drops down to 5 MB, i.e. around 100,000 variants.

Display of results

We provide a summary statistics table and pie charts illustrating the different SO terms and the classes of coding consequences for the variants you input.


The new web interface provides user-friendly pie charts and summary statistics.

The results preview table with additional details is shown after the pie charts. You can apply a range of filters to any of the data fields and limit the results you see. The full or filtered results can be downloaded as VCF or tab-delimited text for import into Excel.


The new ticket tracker

You can run several jobs at the same time and track them back at a later date via the ticket numbers assigned to them. You can easily edit and re-run previous jobs. These jobs will be kept in our Ensembl servers for 30 days. If you register though, the jobs will be kept for as long as you like.

Slide1Population data from the NHLBI Exome Sequencing Project (ESP)


Population frequency data for the 1000 Genomes and ESP projects.

The VEP provides frequency data for known variants from both the 1000 Genomes and NHLBI exome sequencing projects.

You can also use this frequency data to filter your variants: you may wish to exclude known variants with a frequency above 1%, for example.

VEP results are linked to BioMart

The results table in the VEP is now directly linked to BioMart, a data export tool.
This allows you to retrieve additional data about known variants or the genes your variants affect.

You just need to select the attributes in BioMart, e.g. phenotype, orthologues, Gene Ontology terms, and you are ready to go.

Other ways to access the VEP

If you use a command line, you can run the VEP with our script on your own computer. With the Perl script, you can do everything you can do in the online version plus much, much more! It’s the most powerful way to use the VEP.

A couple of functionalities of the VEP (e.g. fetch variant consequences) are also available in the beta version of our language agonistic Rest API.

Help on the VEP

Have a look at our video on the new online VEP interface and our documentation pages for help on the web interface and script versions of VEP.

If you have questions or comments, please get in touch with us.

Do you have an Ensembl account? If not, why not?

Maybe you think you have to pay for it. Maybe you think you’ll be joining up to a mailing list that will spam you relentlessly. Maybe you didn’t know we have accounts, or you don’t know what you get from them.

Quick reassurance: they’re free and there are no emails, annoying or otherwise – we promise. You now know we have them, but what can they do for you?

The two main perks are saving and sharing. Keep reading to learn more.


The bookmark this page button

Found an interesting page? Bookmark it.

All kinds of data can be saved to your user account, including bookmarks, configurations and your own custom data. Once saved to your account, you can open them from any computer you use, allowing you to access them at work, at home or from a public computer.

Favourite genomes are zebra finch, duck, chicken, turkey and flycatcher.

If you work with birds, why not have avian genomes on your homepage?

You can bookmark any Ensembl page, such as your gene of interest, a variant page or a genomic region. This allows you to jump back to these pages quickly and easily. You can even customise the Ensembl homepage to feature your preferred species.

Many Ensembl pages, such as Region in Detail, can be configured to view your features of interest. These configurations can be saved to your account, so that you can easily change a view to how you want it. This is especially useful if there are a few different ways you like to look at a view and want to switch between them. If there are lots of views you like to look at, you can make sets of these configurations. A set has one configuration for one view in Ensembl, and another configuration for another view. For example you could create a regulation set for a particular cell line, so that for every view with regulation data available, the data for that cell line would be shown.

Region and sequence views configured to show variation data

I’ve configured all these pages to show variation data, and saved it as a configuration set.

There are many ways to upload your own data to Ensembl, whether it’s custom data tracks in the Region in Detail page, BLAST/BLAT searches or variation data using the VEP. All of these data can be saved to your account, allowing you to go back to them at any time.

Table of previous VEP analyses

All of my VEP uploads are saved to my account, so I can go back to them at any time.


If you can save it, you can share it.

You can share views by sending your colleagues and collaborators email links, but Ensembl accounts makes this even easier. If other members of your team have Ensembl accounts, you can create a group. Now anything that you’ve saved to your account (bookmarks, configurations and custom data) can be shared with the group, and all members of the group can access it via their accounts. This is great for working collaboratively on projects (especially long-distance collaborations), standardising analysis in a lab and getting new group members started.

How to do it

Get started right now by clicking on the Login/Register link that you’ll see at the top right of any Ensembl page to set up your account and explore the links you’ll find.

The Login/Register link in Ensembl.

The Login/Register link in Ensembl.

If you get stuck on any of this, there’s a help page on using accounts here.

Following on from previous installation guides, here we walk the seldom-trodden path to a Windows development environment for Ensembl. Linux and Mac OS users are well served by our Installation and Mac guides, so…

How do I install the Ensembl API on Windows?

Caveat: These methods have been tested on Windows 7 64-bit Home Premium

Method 1, “The easiest way” – Use the Ensembl VM

Ensembl builds a complete downloadable Virtual Machine image that provides everything you need to access Ensembl data. For this you need to install VirtualBox, following our guide. If you really struggle with Linux, you may find the virtual machine hard to use, but take a look at the next section before you give up.


Method 2, “The native way” – Install many dependencies

By default, Windows lacks many of the development tools required to use the Ensembl API. It will take some time to get up and running.

Do What I Mean Perl

This bundle contains a full selection of libraries necessary for modern Perl development. There are other Perl distributions if you prefer, but this one is the most all-inclusive.

An editor

DWIMperl above comes with Padre, a pure Perl editor, but many prefer to write code in other software, such as:

An Archive/zip file extractor

Code and data are often shipped in compressed archive formats. You will want something to manage them, e.g. 7-Zip or the gzip and tar tools brought along with Cygwin.

Git (optional)

Ensembl is changing from CVS to Git and migrating to Github to host our development. After release 75, the use of any version of Ensembl besides the most recent release will probably want to use Git to retrieve the API.

Additional Perl libraries

DWIMperl has installed the excellent tool cpan-minus, which will assist you in installing the Perl libraries needed either by Ensembl, or your own scripts. If Perl declares a library is missing at any stage, cpanm can be used to install it without fuss.

BioPerl is required for Ensembl, but sadly the CPAN release is not Windows compatible. Therefore it will have to be downloaded manually. Once downloaded it can be unpacked to a directory (for example c:\Users\Me\src) and used.

Ensembl Source

The API itself can be downloaded from our FTP server or retrieved via GitHub. Unpack or git clone into a handy source directory alongside BioPerl.

Set up the environment

Perl needs to know where to find BioPerl and Ensembl. There are CPAN modules to help you manage this, but you can also do it directly in Windows.

Windows system-wide settings can be found in the Control Panel, see the screenshot below. Add the following to your User environment variables, making sure to include the whole path to your downloaded copies of BioPerl and Ensembl API.

PERL5LIB = src\bioperl-1.2.3;


Getting stuff done

You can now launch Perl from within an editor, or through the command shell (windows-r command). A lesser known tool is PowerShell, (windows-r powershell) which is a little more potent than the standard command shell. Both support auto-complete on the tab key in any event.

Change directory to src\ensembl\misc-scripts and test the installation with perl
It will tell you if you have misconfigured any major components and establish contact with Ensembl databases.

You are now free to work with the Ensembl API as you wish. Good luck with your work!

Appendix: Working with Cygwin

Many developers on the Windows platform use Cygwin to add Unix-like capabilities to Windows. This includes tar, gzip, and an entire Perl installation, not to mention many more. You are free to achieve the same installation as outlined above using Cygwin to manage most of the components required, but will need to manually install many more dependencies. You might still be better off using DWIMperl above to get Perl support along with Cygwin tools.

Why did my gene change?

As a member of the Ensembl Outreach team, who is actively involved with training and user support, I often have to answer the question, “Why did the annotation of my favourite gene change?”

There are a few driving forces behind the changes in the annotation of any given gene.  Two of those are the growing number of sequences that are deposited in sequence databases nightly, and the updates to the genomic assembly of a given species.  Regardless of the reason, changes and improvements will result in a revised and refined annotation of our Ensembl geneset.

However, clinical researchers in particular may prefer to work in a more controlled, less changeable environment. You may wonder then: “Is there annotation in Ensembl that won’t change?”  Yes, there is!

Are there genes that don’t change?

There is a set of gene sequences where changes are strictly prohibited.
These are the LRG records. An LRG or Locus Reference Genomic has a fixed and stable reference sequence for reporting and diagnosing variants that cause diseases in humans.

More than 700 records have been annotated so far. They have been mapped everywhere in the human genome, with the exception of the Y chromosome and the mtDNA.


LRG loci currently annotated in human.

The majority of these 700 records (59%) are publicly available on the LRG website, whereas the remaining are still in the validation phase, carried out manually by LRG curators. The ultimate goal is to provide an LRG record for every single protein-coding gene in the human genome. It’s certainly a mammoth task! So, genes with clinical implications will be prioritised.


Summary information of LRG_293 (BRCA2 gene)

Stable sequences allow clinical geneticists and the research community to report their variation data in a more controlled and stable framework. They will be able to perform consistent comparisons of variants reported in LRG coordinates against other databases and therefore be better equipped when diagnosing diseases.

Viewing LRGs in Ensembl

In addition to the LRG website, clinical geneticists and others can investigate any of the public LRG records in Ensembl too, where they can be viewed in the context of our comprehensive annotation of genes, variants and regulatory features, among many other features.

Use Ensembl to search for an LRG and get all the variants that map to it. You can then check the functional impact of these variants. For more tips on how to investigate LRG records in Ensembl, contact us.


Variation consequences calculated with VEP.

Can I request my own LRG?

You can request an LRG record for a clinically relevant gene. For more details on how to submit the request, have a look at the LRG request page.

We are pleased to announce a new bioinformatics application, WiggleTools, described in a recent Application Note in Bioinformatics. It allows you to quickly and conveniently compute statistics across many (up to the hundreds) of genome-wide datasets.

WiggleTools is first a data summary tool. It collapses into a single summary a large collection of genome wide datasets, such as BigWig, BigBed or BAM files. You can then view on the Ensembl browser a single statistic that combines all the datasets for a given project rather than displaying a pileup of several data tracks.

For example, if you wanted to display the average binding probability for each TF in the ENCODE dataset you could display a huge number of tracks on the browser, one for each TF. It is clearly difficult to interpret the view as one has to scroll up and down endlessly

EnsEMBL_Web_Component_Location_ViewBottom-Homo_sapiens-Location-View-74- (1)

And here is a summary track which recaps all of the data above in a single track (explained below):

EnsEMBL_Web_Component_Location_ViewBottom-Homo_sapiens-Location-View-74- (2)

Overall binding probability for all TF in a single track. Note that you now have room to add other datasets in the ‘Region in detail’ view.

To better handle different types of signal, WiggleTools offers a range of statistics on a set of values, such as mean, median, minimum, maximum, or variance.

Besides boiling down large collections of data into a single track, WiggleTools also allows you to compare groups of datasets. For example, if you have a collection of case and control replicates, you can compare the means of the cases and controls, but you can also apply more advanced statistics as Welch’s T-test (for normally distributed variables) or Wilcoxon’s rank sum test (for other variables).

WiggleTools has been designed with efficiency in mind. Streaming the data keeps memory requirements to a minimum by only storing local information. Functional components communicate directly in memory, without disk access or string passing. Parallel threads keep the system going smoothly regardless of irregularities in disk access. Finally, a novel BigWig file merging tool, bigWigCat, which we contributed to Jim Kent’s C library, allows WiggleTools to make the most of a cluster of computers. For example, to compute the sum of 126 BigWig files (a total of 121 GB) takes less than 17 minutes in total, on 116 CPUs, and fits on less than 5.5 GB of RAM.

A statistics package for genomic datasets

With WiggleTools, you can pretty much play with the BigWig, BigBed and Bam files lying on your filesystem as if they were vectors loaded in R, Numpy or Matlab. A simple language, which resembles LISP, is enough to define the functions that WiggleTools then runs in a single pass through the data.

A use case: we wanted a summary of transcription factor (TF) binding across the genome. For every position in the genome, we had estimated for each TF the probability of observing binding in a random cell type. To compose these datasets, we wanted to compute an overall probability of observing any binding at that position. We therefore wanted to compute:


WiggleTools can create the appropriate function on the fly and compute the result in a single pass through the files. Total run time: 34s, max memory: 20MB.

For more information on WiggleTools, have a look at our paper in Bioinformatics, and our code on Github.

Over the past few months a number of users have asked us how to install Ensembl and its dependencies on OSX. Over the past 4 years I have had to do this quite a number of times and thought it best to share my personal best practice. There are alternatives to this methodology involving supplementing the stock OSX Perl with extra libraries or using ActiveState for OSX. I recommend neither method. Apple never developed a package management tool that works well with Perl libraries and so upgrades carry a level of risk. As for ActiveState they do not currently support DBD::mysql on OSX. Instead we will install a new version of Perl using Perlbrew; a Perl installation management tool.

This guide will require admin rights on your mac and assumes some understanding of the terminal. If you do not feel confident enough then try using our Virtual Machine instead.

Pre-Flight Checks

You must have Xcode and GCC installed on your mac. Check by running the following command and see if you get a response similar to the one pasted below

> gcc -v
Using built-in specs.
Target: i686-apple-darwin11
Configured with: /private/var/tmp/llvmgcc42/llvmgcc42-2336.11~182/src/configure --disable-checking --enable-werror --prefix=/Applications/ --mandir=/share/man --enable-languages=c,objc,c++,obj-c++ --program-prefix=llvm- --program-transform-name=/^[cg][^.-]*$/s/$/-4.2/ --with-slibdir=/usr/lib --build=i686-apple-darwin11 --enable-llvm=/private/var/tmp/llvmgcc42/llvmgcc42-2336.11~182/dst-llvmCore/Developer/usr/local --program-prefix=i686-apple-darwin11- --host=x86_64-apple-darwin11 --target=i686-apple-darwin11 --with-gxx-include-dir=/usr/include/c++/4.2.1
Thread model: posix
gcc version 4.2.1 (Based on Apple Inc. build 5658) (LLVM build 2336.11.00)

Later versions of OSX (Mavericks 10.9) are intelligent enough to instal the GCC tools automatically once you have confirmed. If you are on an earlier version of OSX and do not get this prompt then following the instructions below (this will require admin privileges)

  1. Install Xcode from the Apple AppStore
  2. Run it Applications
  3. Install the command line utilities by clicking on Xcode in the menu
    • Preferences
    • Downloads
    • Click “Install” by the Command Line Tools section

Additional information (with screenshots) is available from this Stack Overflow answer.

Installing Perlbrew

Firstly you need to install Perlbrew. This will create a directory called perl5 in your home directory. It will also ask to add commands to your shell’s profile (either .bashrc, .cshrc or .bash_profile) to bring the perlbrew binary onto your path. To install use the following commands:

> curl -L | bash

# Add this to the end of your ~/.bash_profile
> echo 'source $HOME/perl5/perlbrew/etc/bashrc' >> ~/.bash_profile

Now install Perl 5.14.4 (you will have to wait a bit). The following command was run on a Mountain Lion installation (10.8.4). You must install a new version of Perl. Modifying the system version of Perl (including installing module updates) on OSX is a very bad idea and can cause unintentional side effects. To be safe always install your own version:

> perlbrew install -j 5 --as 5.14.4 \
--thread --64all -Duseshrplib perl-5.14.4

Fetching perl 5.14.4 as /Users/user/perl5/perlbrew/dists/perl-5.14.4.tar.bz2
Installing /Users/user/perl5/perlbrew/build/perl-5.14.4 into ~/perl5/perlbrew/perls/5.14.4

This could take a while. You can run the following command on another shell to track the status:

tail -f ~/perl5/perlbrew/build.perl-5.14.4.log

5.14.4 is successfully installed.

Later versions of OSX and Perl can sometimes fail during this compilation process citing issues with locale settings. Should you see this run the following command (stops any testing against the new Perl binary):

> perlbrew install --notest --as 5.14.4 --thread \
--64all -Duseshrplib perl-5.14.4

Now install cpanminus. This is our CPAN package manager and makes working with it a breeze.

> perlbrew install-cpanm

Now we will switch to using the new version of Perl by default and ensure that the switch worked.

> perlbrew switch 5.14.4
> perl -v | grep 'This is'
This is perl 5, version 14, subversion 4 (v5.14.4) built for darwin-thread-multi-2level

Installing MySQL Client Libraries

DBD::mysql requires access to libmysqlclient.18.dylib and the MySQL C headers to compile. MySQL’s Connector/C distribution ships with these files. However I have always found more success using a server installation and like having a personal MySQL server to develop against. This guide will only cover using a MySQL Server installation.

  1. Go to
  2. Select a version compatible with your Mac
  3. I selected Mac OS X ver. 10.7 (x86,64bit), DMG archive MySQL Community Server 5.6.12. You may find a later version. Make sure to change all other commands accordingly
  4. Mount the DMG
  5. Install mysql-5.6.12-osx10.7-x86_64.pkg and double click the MySQL.prefPane
  6. Check you can start-up MySQL (required for DBD::mysql installation tests)
  7. Go to System Preferences > MySQL > Start MySQL Server
  8. Enter your system admin password

Installing core dependencies

Basic dependencies can be installed using the cpanm command. For core Ensembl that amounts to database bindings so lets bring in DBI.

> cpanm DBI

Should you wish to run any core test suites you will also need the following packages:

> cpanm Test::Differences Test::Exception Test::Perl::Critic

Installing DBD::mysql

Congratulations on getting this far. Now for the tricky bit. By default the required dynamic library is not available on OSX’s default search paths. You can solve by using one of the following 3 options. Once the library is available to OSX you can install DBD::mysql with the following command (ensure your MySQL server is running otherwise the library’s test suite will fail). I prefer to use the second option and symbolically link the library into /usr/lib but this does require admin rights.

> cpanm DBD::mysql

Option 1). Add MySQL’s lib directory onto the DYLD_LIBRARY_PATH

Works well for all command line terminals, does not require admin but will not work if you’re going to use a GUI based application to run Ensembl scripts.

> export DYLD_LIBRARY_PATH=/usr/local/mysql/lib/:$DYLD_LIBRARY_PATH

Option 2). Symbolically link the required library into /usr/lib

Works well for all applications but requires admin rights to create the symbolic link in /usr/lib

> sudo ln -s /usr/local/mysql/lib/libmysqlclient.18.dylib /usr/lib/libmysqlclient.18.dylib

Option 3). Add the library to install_name_tool

A more official OSX way of doing it but will require re-updating the library whenever you upgrade your MySQL installation. Also requires admin rights.

> sudo install_name_tool -id /usr/local/mysql-5.6.12-osx10.7-x86_64/lib/libmysqlclient.18.dylib /usr/local/mysql-5.6.12-osx10.7-x86_64/lib/libmysqlclient.18.dylib

Installing Ensembl

Nearly there. My best advice is to follow the installation instructions hosted on the Ensembl website. Once finished you should verify the installation is good. Ensembl ships with a program called We will use this to check we can connect to Ensembl’s UK based MySQL servers and can find the species human.

> perl ~/src/ensembl/misc-scripts/
Installation is good. Connection to Ensembl works and you can query the human core database

The script will also try to diagnose any problems with missing dependencies. Remember should you need to install any additional dependencies use cpanm.


If you made it this far you should have a fully functional installation of Perl able to query Ensembl. More information on the API is available from our website along with tutorials covering the core, variation, comparative genomics and regulation APIs.

Should you have any issues then please do not hesitate to contact Helpdesk or follow our debug my Ensembl installation guide.

It has been quite a while since we’ve blogged about the VEP (Variant Effect Predictor), and in that time we’ve added a whole load of new features, particularly to the downloadable script version.

Structural variants

The VEP now supports finding the consequences of structural variants, with input either in VCF or tab-delimited format. Using the web interface to the VEP you can visualise which transcripts and features your structural variants overlap by clicking through to the Region in Detail view:

Screen Shot 2013-04-19 at 15.14.23 copy

The cache

We’ve really pushed the VEP script’s capabilities when using local “caches” (as opposed to using remote databases). Almost every feature of the VEP is now available when using the cache in offline mode. You can use a local FASTA file to quickly retrieve the sequences required to construct HGVS notations. You can even construct your own cache from a GTF file if your species isn’t supported by Ensembl.

Our cache for human now contains allele frequency data from phase 1 of the 1000 Genomes Project, and you can use these frequencies to filter your input (for example, you might want to filter out variants that are common in the combined European (EUR) population). We also now provide SIFT predictions for 8 species – human, mouse, zebrafish, pig, cow, chicken, rat and dog.


We’re always trying to add new and useful features to the VEP, but we also recognise that other users have great ideas that they’d like to implement. The VEP script enables the use of plugins; these are bits of code that add extra functionality to the VEP. They can be used to retrieve data from remote sources, run external tools, filter output; pretty much anything you can think of can be accomplished in a plugin!

It’s easy to get started, and a basic plugin can be just a few lines of code – have a look at some of the examples we’ve created.

I recently added a plugin to retrieve data from dbNSFP – this is a great resource created by Liu et al in Houston, TX. They have, for every possible missense substitution in the human genome, pre-calculated pathogenicity scores, frequencies, conservation scores and a plethora of other things, and made all of this available as an easily downloadable file. To use this with the VEP, you just download the file and the plugin, run a couple of commands to get the data into the right format, and away you go – the VEP can now provide you with scores from LRT, MutationAssessor, MutationTaster, FATHMM and more for any missense substitution in your input.

Summary and HTML output

We had a number of requests for the VEP to provide summary statistics at the end of each run, and who are we to disappoint our loyal users?!? The VEP now writes a pretty HTML summary:
Screen Shot 2013-04-03 at 13.35.45 You can also view your output as HTML using the –html flag, which allows you to sort, filter and analyse your output on the fly.

Don’t hesitate to get in touch with us about the VEP – our developer mailing list is the best place for technical questions, with helpdesk for everything else.


From release 70 we store and display information on the type of consequence a variant has on overlapping regulatory regions (Ensembl regulatory features and Ensembl motif features) for human and mouse.

Web display
Consequence types for variations overlapping regulatory features in region in detail view
One of the major benefits of this is that we can highlight the predicted consequence types for a variant overlapping regulatory regions in the region in detail view. The Variation – Genes and regulation page gives more information on the type of consequence a single variant has on a specific regulatory region.

We store the data in two new tables: regulatory_feature_variation and motif_feature_variation. Both are populated in a similar way to the transcript_variation table. You can find further information on the table structures on our Variation database schema description page.

You can access the data using the Ensembl Variation Perl API. Please check the API documentation for examples of how to use the RegulatoryFeatureVariationAdaptor and the MotifFeatureVariationAdaptor. These new modules allow you to fetch MotifFeatureVariations or RegulatoryFeatureVariations on a VariationFeature, MotifFeature or RegulatoryFeature. This is in addition to the existing functionality for getting all RegulatoryVariationFeatures and MotifFeatureVariations using the VariationFeatureAdaptor.

If you have any questions please email helpdesk.