For release 67 we changed how we store the protein function predictions from SIFT and PolyPhen so that they also can be used for more than just Ensembl transcripts, including RefSeq transcripts. We use these tools to compute the predicted effect of every possible amino acid substitution in the human proteome (over 2 billion predictions!). Now, the complete set of predictions for a particular protein are retrieved using the protein sequence itself as an identifier rather than an Ensembl stable identifier (we actually use the MD5 hash of the sequence). This means that you can retrieve predictions for any protein that has the same amino acid sequence as an Ensembl translation. So if you work with RefSeq transcripts, you can now get SIFT and PolyPhen predictions for any missense variants that fall in the 95% of RefSeq transcripts that match an Ensembl transcript exactly, using both the Variant Effect Predictor (VEP) and the Variation API.

New in release 67 are also predictions from both classifier models supplied with PolyPhen. Previously we provided predictions using a classifier trained on the HumVar dataset which is intended to distinguish between severely deleterious alleles against the background of abundant variation with milder effects. This is still the default, but when using the API you can now also opt to use predictions from the classifier trained on the HumDiv dataset which is intended to help evaluate rarer alleles potentially involved in complex disease. For more details on how these datasets are composed, please refer to the PolyPhen website.

The Variant Effect Predictor (VEP) software can predict the consequence of genomic variants using the genomic annotations provided by Ensembl. In release 63 of Ensembl we have added new features to both the script and web versions of the VEP.

Regulatory consequences have made their return; the VEP now reports if a variant falls within a regulatory region or a transcription factor binding motif, and furthermore if the variant falls in a high information locus within the motif.

The VEP now also has a dedicated area of the Ensembl website documentation.

Script version

To improve performance for users in the USA, we have now deployed a mirror of the public database server; to use this simply pass the flag “–host useastdb.ensembl.org” when running the script.

We have also implemented a caching system in the VEP, such that is possible to use almost all of the functionality of the script without the script querying the database at all. Simply download and unpack a pre-built cache, run the script with the flag “–cache”, and hey presto! No more network dependencies.

We have now made “whole genome mode” the default run mode of the script – this code has been rewritten and optimized such that it should be suitable for all use cases. We’ve also improved the status output of the script as it runs, so users with lots of data can easily track their progress.

See the new documentation for further details on all of these new features, or just download the script!

Web version

It is now possible to filter your input variants by their frequency as observed in the 1000 genomes or HapMap populations. You can either include or exclude input variants that are co-located with existing variants, based on frequencies in any particular population or across a range of populations.

As before, you can access the web VEP through the tools page, or via the “Manage your data” link on any species-specific page.

Alongside our website, ensembl provides direct access to our databases through our public MySQL server ensembldb.ensembl.org and as of today, we are pleased to announce the availability of a second MySQL mirror hosted on the east coast of the US. The new server is running on Amazon Cloud with the hostname

useastdb.ensembl.org

it can be directly direct accessed with the mysql client using port 5306 and username anonymous.
eg.

mysql -h useastdb.ensembl.org -u anonymous -P5306

It may also be accessed through our perl API with the following registry incantation:

use Bio::EnsEMBL::Registry;
my $registry = 'Bio::EnsEMBL::Registry';
$registry->load_registry_from_db( -host => 'useastdb.ensembl.org',
                                  -user => 'anonymous');

useastDB will provide the current ensembl release alongside the previous on a rolling basis. This means that useastdb is currently hosting release 63 with 62 databases only, this will then become release 64 with 63 databases after our next release. Our full set of older releases will continue to to be hosted on ensembldb.ensembl.org

We hope that our users enjoy the faster access to our data that this new MySQL mirror should provide.

With some satisfaction, I am happy to announce the arrival of a new documentation resource with Release 63, intended to assist programmers in getting the most out of EnsEMBL.

Using a custom filter and the open source tool Doxygen we now bring you a more pleasing perspective on the EnsEMBL API, with the following features:

1. Search box – find the class you need fast
2. Inherited methods – no more hunting for superclasses
3. Class and dependency diagrams – see how the API is structured
4. Multiple perspectives – view by class, namespace, directory or method

The new reference can be found through the website, so update your bookmarks and have a look around. You might see some artifacts in the automated documentation, but we will be aiming to remove these as part of an ongoing effort to standardise code comments. I hope you enjoy the advantages of this new modern view of our API.

I’d like to introduce you an exciting new data set that we’ve introduced in Ensembl release 62: RNASeq data from Illumina’s Human BodyMap 2.0 project. The data, generated on HiSeq 2000 instruments in 2010, consist of 16 human tissue types, including adrenal, adipose, brain, breast, colon, heart, kidney, liver, lung, lymph, ovary, prostate, skeletal muscle, testes, thyroid, and white blood cells. Raw reads are available for download here. For each tissue, we have aligned the raw reads to the genome and then linked exons into tissue-specific transcript models using the reads that span an exon-exon boundary.

You can view these data in the Region in Detail view. Click on ‘Configure this page’ and choose ‘RNA-Seq’ at the left of the main panel. Enable any or all of the 32 tracks and then close the configuration panel. Out of 32 possible tracks you can draw, 16 are tissue ‘gene model’ tracks, and 16 are ‘intron’ tracks.

The ‘gene model’ track shows you a transcript model. The ‘intron’ track shows you how many raw reads aligned across an exon-exon junction. The higher the intron block, the more highly expressed the transcript isoform is.

In this example, the kidney gene model track shows a transcript (dark blue) with an exon structure that matches the gold-coloured Ensembl transcript AQP6-001. The kidney transcript model includes coding and noncoding exons (in the example above, the empty box is UTR, and the filled boxes are exons).
Click on the kidney intron track to see that 192 raw reads were split between the first and second exons.

This example is interesting because it shows a gene with high expression in kidney tissue, and almost no expression in any other tissue.

The high read coverage for kidney means that the transcript’s exon-intron structure produced for the gene track has a good chance of being correct. When read coverage is very low, it is not always possible to build a full-length transcript model: Look at the colon and brain intron tracks to see that two colon reads and three brain reads have aligned across the transcript’s middle exon-exon junction. Although this read coverage is low, our pipeline has generated a transcript model for brain tissue. The pipeline however was not able to predict the two splice on either side because there were no raw reads from brain aligning over the splice junctions.

Below is a nice example of a gene that seems to be expressed in all 16 tissues, spermidine synthase (SRM).

Try dump_transcripts.pl as an example script to access the RNAseq-based transcript models. Have fun with these new data!

Have you noticed any strange-looking chromosome names when browsing the human data? For example, you might notice sequence region names looking like “Chromosome HSCHR17_2_CTG4: 68,302,419-68,526,413” or “Chromosome HG75_PATCH: 34,442,621-34,976,908”.

The names refer to genomic sequence that differs from the genomic DNA on the primary assembly. These alternate sequences come in two types: Allelic sequence (haplotypes and novel patches) and fix patches. Haplotypes are known variations to the primary assembly, due to variability in the human genome sequence (eg. the highly variable MHC locus containing halpotypes HSCHR6_MHC_COX, HSCHR6_MHC_SSTO, HSCHR6_MHC_APD, HSCHR6_MHC_DBB, HSCHR6_MHC_MANN, HSCHR6_MHC_MCF, and HSCHR6_MHC_QBL).  Novel patches also represent new allelic loci but they are not necessarily haplotypes. Fix patches are where the primary assembly was found to be incorrect, and the patch reflects the corrected sequence.  Haplotypes, novel patches and fix patches are determined by the GRC, not by Ensembl.

In the Ensembl browser, as in the figure below, the allelic sequence (haplotypic regions and novel patches) are coloured red and the fix patches are coloured green. If you have a look at the top image in Region In Detail for chromosome 17, you’ll see examples of both types of alternate sequence.

There are several ways to view alternate sequences in Ensembl:

• If you know the name of the sequence you’re looking for, you can find it by searching in our Search bar.
• You can view alternate sequence regions in the top image of any Location page eg. Region In Detail, Region Overview, Chromosome Summary.
• Some alternate sequences are available through BioMart.
• If you’re comfortable using MySQL, you can access the list through the assembly_exception table as follows:

mysql -uanonymous -hensembldb.ensembl.org -P5306 -Dhomo_sapiens_core_62_37g -e “select sr2.name as chr_name, exc_seq_region_start,exc_seq_region_end,exc_type,sr1.name as alternate_seq_name,seq_region_start, seq_region_end from assembly_exception ae, seq_region sr1, seq_region sr2 where sr1.seq_region_id=ae.seq_region_id and sr2.seq_region_id=ae.exc_seq_region_id order by chr_name,exc_seq_region_start”

Click here for the full list of e62 alternate sequences

• It’s also easy to fetch the alternate sequences using our API. Click here for an example script.

$slices = $slice_adaptor->fetch_all( ‘toplevel’, undef, 1 );

or

$assembly_exception_features = $assembly_exception_feature_adaptor->fetch_all_by_Slice($slice);

When using the API, the primary assembly is known as the ‘reference’ sequence and the alternate sequences are know as ‘non-reference’ sequence.

Enjoy!

Ensembl provides annotations indicating regions in the genome that are experimentally verified to be bound by transcription factors (from ChIP-Seq experiments). Within these regions, we now also provide precise transcription factor binding sites. To generate these binding sites, we make use of publicly available Position Weight Matrices (PWM) from Jaspar.

Transcription factor binding sites can be seen as black boxes in the Regulatory Features track. If you click on a Regulatory Feature you can see information regarding the binding sites contained within that regulatory feature. This includes the binding matrix used and a binding score representing how well a particular site matches the binding matrix. Clicking on a specific black box within the regulatory feature will highlight the corresponding information on the menu (the darker blue line in the figure showing information for a CTCF binding site). Transcription factor binding sites are also displayed as evidence for a regulatory feature (as ‘Core PWM’ entries).

To generate these PWM matches we take Jaspar matrices and find matches throughout the genome. Then, we use experimental binding data to stringently choose high confidence binding sites that fall within regions enriched in ChIP-Seq experiments for the corresponding factor. More details on this process can be found here.

The ENCODE project is generating an immense amount of data.  ChIP-Seq, ChIP-ChIP, and RNA-Seq experiments are yielding an exciting number of sequences that may be involved in gene regulation.  Trying to look at these data individually would be a lot of work, and less powerful, than viewing these data in combination.  The Ensembl Regulatory Build takes data such as CTCF binding sites, DNaseI hypersensitive sites, transcription factor binding sites, and histone modification profiles, and integrates these data into ‘regulatory features’.  The result is for any given regulatory feature in a specific region of the human genome, our users can learn 1) if there is data supporting that sequence as being involved in gene regulation (for example,  is it promoter associated) and  2) how much data supports the sequence as a regulatory feature.

For example, if we zoom in to BRCA2 in the Ensembl region in detail view, the regulatory feature at the 5’ end of the BRCA2-003 transcript is supported by data from numerous experiments (shown in the figure below).  Click on the grey block in the Reg. Feats. track in the region in detail view to see them.

You can even view regulatory features for a specific cell type.  Click on the Configure this page tool button at the left of the region in detail view to do so.  Or, try the regulation tab (get there by clicking on a regulatory feature’s stable ID, such as ENSR00000054736 in the image above).

Questions or comments about these data or views are welcome.  Reply to this post, or email our helpdesk by using the Contact us link at the top right of this blog.

The Ensembl release 61 mouse gene set (released on 1 February 2011) includes updated automatic annotation for mouse NCBIM37 assembly. The automatically annotated transcript models match 94.6% of RefSeq proteins exactly.

We merged the results of this automated annotation with Havana’s manual annotation to produce the final gene set that you see displayed on our website. (We are currently merging in Havana’s mouse annotation every other release.)  The final gene set has also been used as an input set for a new round of CCDS comparisons. As a result, the number of genes in the mouse CCDS set has increased.

Due to feedback on a recent survey, we decided to post some pointers to functionality that may not have been discovered by many of our users.  One specific case is viewing syntenic regions calculated by the Ensembl comparative genomics team (Compara).

How can we view these regions in the Ensembl genome browser?

There are two ways.  The first way is to go to the dedicated Synteny view.  This view, found in the location tab, offers a comparison of syntenic regions between two species.

In the above figure, a chromosome of interest (human chromosome 6)  is depicted in the centre, and any mouse chromosomes with synteny to human chromosome 6 are drawn as smaller chromosomes along the sides.  The colours of the syntenic blocks represent specific mouse chromosomes.

Compare with a different species, or change the central chromosome, using the menus beneath the figure.  A gene list comparing genes within the regions of synteny in both species can also be found below the image.  Read the page help for more.

The second way is to draw syntenic blocks along the chromosome in the Location tab, Region overview.  Click the Configure this page tool button at the left of the view to add synteny for one or multiple species.

If you have a topic you would like to see in a ‘navigation tip’ post, please leave us a comment, or email our helpdesk.