The Ensembl regulation resources FTP site saw a facelift in release 87. The directory structures have been modified to make it easier to find files- the file names have become more descriptive and we now also provide our data in a greater variety of file formats. All data files on our FTP site now adheres to a naming convention, which is described in greater detail here. The filenames include the following information separated with a dot (‘.’):

  • species
  • assembly version
  • cell type (if applicable)
  • feature type (if applicable)
  • analysis name
  • results type
  • data freeze date
  • file format.

E.g.: homo_sapiens.GRCh38.K562.Regulatory_Build.regulatory_activity.20161111.gff.gz

The data available on our FTP site include:

Peaks: The set of peaks for transcription factors, histone modifications and variants that are part of our regulatory resources. In previous releases these used to be collated in one file, called ‘AnnotatedFeatures.gff.gz’, but with our recent expansion to 88 human cell types with ChIP-seq data, the file became too big. Therefore, we split it into separate files by cell and feature type in the ‘Peaks’ subdirectory. The peaks are now available in gff, bed and bigBed format.

Quality scores: The outcome of our quality checks from processing the ChIP-seq data that yielded the peaks. They are in JSON format in the ‘QualityChecks’ subdirectory:

  • the number of mapped reads
  • the estimated fragment length, the NSC and RSC values using phantompeakqualtools
  • the proportion of reads in peaks
  • the enrichment of the ChIP over the Input using CHANCE.

Regulatory build: The current set of regulatory features along with their predicted activity in every cell type. We provide one gff file per cell type in the ‘regulatory_features’ subdirectory.

Transcription factor motifs: The transcription factor motifs identified using position weight matrices from JASPAR in enriched regions identified by our ChIP-seq analysis pipeline in gff format.

Due to essential maintenance, the Ensembl helpdesk email will be shut down for approximately 48 hours, beginning at 9 am (GMT) on 25th November. Any emails sent during this time will be held in a queue, and we will respond to them when the system is up and running again, although there may be some delay. You will not receive any confirmation of your email, as this is automatically generated by the system.

You can also post queries to the Ensembl dev list and BioStars (please add “Ensembl” as a tag).

We will update you when the system is back.

We apologise for any inconvenience this may cause.

What’s new?

Ensembl Plants takes centre stage in the release of Ensembl Genomes 33, with a variety of new data available for a number of different species:

  • Incorporation of the Araport 11 gene model annotation for Arabidopsis thaliana
  • Addition of mitochondrial and plastid genome sequences to the current maize (Zea mays) chromosomal assembly (AGPv4)
  • Alignment between the A, B and D genomes of bread wheat (Triticum aestivum) updated to use TGACv1 genome assemblies
  • Whole genome alignment between bread wheat and Brachypodium distachyon

Other News

You can find more details in the release notes.

What’s New in e86:

Mouse strain genomes

In Ensembl 86, you will now be able to view the annotated genome assemblies, variation data and comparative analyses of 16 different mouse strains, produced by the Mouse Genomes Project. While the GRCm38 assembly (produced from Mus musculus strain C57BL/6J) remains the reference assembly, variants and comparative analyses for the other strains can be viewed through the Gene tab and the Location tab. You can find the gene trees and orthologue/paralogue predictions for the mouse strains through the Strains option in the menu in the Gene tab. The mouse strain gene tree depicts the evolutionary history of genes (left) and protein alignment (right) for the individual mouse strains and rat. mouse strain treemouse strain orthologues You can find the variants between these mouse strains through the Strain table option in the menu in the Location tab. The strain table displays the alleles identified at variant positions across the 16 mouse strains. strain variant table

Updated assemblies, gene sets and annotations

In Ensembl 86, there will also be a number of updates to the assemblies and gene sets for a number of different species:

  • Human: updated cDNA alignments and RefSeq import
  • Mouse: updated cDNA alignments and RefSeq import
  • Zebrafish: updated gene set and RefSeq import
  • Chicken: updated to the Galgal_5.0 assembly
  • Mouse lemur: updated to the Mmur_2.0 assembly
  • Macaque:  updated to the Mmul_8.0.1 assembly

New lincRNA data

New Mobile Site Views

As of release 86, you can now view transcripts on the mobile version of Ensembl. You can also view exon sequence, cDNA sequence and protein sequence by clicking on the lefthand arrow.

mobile site- transcript[1]mobile site- transcript[2]

The gene sequence is also now available to view on mobile devices. Just go to any gene page and click on the left hand arrow and then choose sequence.


Other News

  • Variation and phenotype databases updated
  • You can now select ‘Manhattan plot’ as an option when configuring bigWig files

A complete list of the changes can be found on the Ensembl website

Find out more about the new release and ask the team questions, in our free webinar: Tuesday 11th October, 4pm BST. Register here.

Ensembl 86 is scheduled for September 2016, highlights include:

New mouse strains

  • Annotated genome assemblies, variation data and comparative analyses of 16 different mouse strains will be included in Ensembl 86.

Updated assemblies, gene sets and annotations

  • Human: updated cDNA alignments and RefSeq import
  • Mouse: updated cDNA alignments and RefSeq import
  • Zebrafish: updated gene set and RefSeq import
  • Chicken: updating to the Galgal_5.0 assembly
  • Mouse lemur: updating to the Mmur_2.0 assembly
  • Macaque:  updating to the Mmul_8.0.1 assembly

New lincRNA data

New GRCh37 tools converted from 1000 Genomes Project

A number of tools previously developed for use in the 1000 Genomes Project browser have now been converted for use with the GRCh37 assembly in Ensembl:

  • Dataslicer tool- This tool allows you to get a subset of data from a BAM or VCF file.
  • Variation pattern finder tool- This tool allows you to identify variation patterns in a chromosomal region of interest for different individuals.
  • Forge analysis tool- This tool takes a list of variants and analyses their enrichment in functional regions from the ENCODE or Roadmap Epigenome project on a tissue specific basis.

Other updates and highlights

  • Variation and phenotype databases updates

For more details on the declared intentions, please visit our Ensembl admin site. Please note that these are intentions and are not guaranteed to make it into the release.

The CRISPR/Cas9 system has revolutionised scientific research over the last few years, offering an efficient method of genome editing. CRISPR/Cas9 utilises the cellular machinery used by bacteria to recognise and edit the DNA of invading viruses. It is formed of two key components: Cas9, an enzyme that can cut a double DNA strand at a precise point; and CRISPR, a short strand of RNA that guides the Cas9 enzyme to recognise and cleave at specific DNA sites.

Cas9 restricts DNA at specific Protospacer Adjacent Motifs (PAMs), which is species-dependent (for example, 5′ NGG 3′ for Streptococcus pyogenes Cas9). Therefore, by coupling a custom CRISPR polymer (gRNA), Cas9’s restriction activity can be targeted to specific locations in the genome that contain a PAM region.

The latest release of Ensembl (Ensembl 85, July 2016) now includes annotated CRISPR/Cas9 sites predicted by the Wellcome Trust Sanger Institute Genome Editing (WGE) group for human and mouse genomes.

The WGE group have predicted CRISPR sites and developed an accompanying database to help you design genome editing experiments, and you can view these WGE-predicted sites by adding the ‘WGE CRISPR sites’ track to any ‘Region in Detail’ view for human or mouse in Ensembl. Click on the ‘Configure this page’ option from the menu on the left hand side of the page, and then add the track, which Configure this page buttoncan be found in the ‘Other regulatory regions’ category, by clicking the empty box and selecting the track style from the pop-up window:Add CRISPR track option

Below, you can see an example of the WGE-predicted CRISPR site track added (to both the forward and reverse strand) of the genomic region containing the human BRCA2 gene in the ‘structure’ style. Each CRISPR site is labelled as a single green box, which appears as a single vertical line when viewing a large genomic region.CRISPR site track

From the example above, we have now zoomed into a specific region of interest. You can see the structure of each CRISPR site, with the filled green box matching up with the PAM motif and the un-filled box representing the potential gRNA binding sequence. Clicking on any of these individual CRISPR sites will open a pop-up window that provides you with more information about the specific genomic co-ordinates of the CRISPR site as well as a link to the WGE database.CRISPR pop up

You can find more information about the CRISPR site prediction method in the published description of the WGE database

Continued HapMap variation data access through Ensembl

NCBI have recently released plans to immediately retire their HapMap interface, however, data from the HapMap Project will continue to be freely accessible through Ensembl. There is lots of help and documentation as well as video tutorials to help you learn how to access variant data in Ensembl. This post aims to complement those materials to highlight the methods for accessing the HapMap Project variant data specifically.

Finding HapMap variants by ID

You can find data from the HapMap project relating to specific variants by searching for the variant rsID itself. In Ensembl, you can find information related to variants identified in the HapMap Project, which includes population genetics statistics:Population Genetics HapMap

However, as you can see from the example above, some of the populations represented in the HapMap Project have two separate entries in the Population Genetics table. This is because the HapMap Project was completed in a number of phases. In the first phase, a number of different groups used different genotyping platforms to type variants from a number of population panels (CEU, YRI, HCB, JPT). In a later phase, a larger set of samples were added to the samples from the initial phase and submitted as HapMap3. The two entries refer to the two submitted phases of the HapMap Project, where the number in brackets next to the allele frequency indicates the number of samples in that population.

It is also possible to view HapMap Project results by gene of interest by searching the Variant Table. The Variant Table can be filtered by ‘Evidence’ type so you can choose to see only HapMap Project variants, for example.Variant Filter HapMap

Filtering the Variant Table by ‘Evidence type = HapMap’ will filter the displayed variants to those identified in the HapMap project. This will be denoted by theevidence HapMapin the Evidence column.Filtered variant Table Hapmap

Finding HapMap Project variant data using BioMart 

HapMap SNP data can also be retrieved using BioMart. There is help and documentation and a video tutorial to help you while using BioMart.

When querying the Homo sapiens short variants dataset in BioMart, you can access HapMap variant data specifically by using the ‘Variant Set Name’ filter and selecting the HapMap populations that are relevant for your research.HapMap variation Mart

Finding HapMap variants using the Ensembl API

It is also possible to access variation data through the Ensembl APIs. Using the Perl API, for example, it is possible to retrieve variation data specifically related to the HapMap Project variant set, either as the whole HapMap variant set, or as individual populations represented in the HapMap Project.

A mysteriously common debilitating genetic disorder. A deadly tropical disease. One of my favourite stories in the history of genetics weaves together these two elements – it’s a good one and it always deserves a re-telling – that of malaria and sickle cell anaemia.

This story captures my attention and inspires me in the power of scientific observation, curiosity and experiment. I’m sure you are all aware of the details of this worn-out tale: it is used as an example in classrooms and lecture theatres every year to explain Mendelian genetics, haploinsufficiency, physiology, disease and protein structure and function to young scientists. To mark the coincidental coinciding of DNA day and Malaria day, we wanted to re-visit this ‘historical’ example of how scientific observation and experimental approaches have led to the understanding of how a disease as debilitating as sickle cell anaemia paradoxically persists in the human population.

Molecular biology and bioinformatics have transformed the face of biological research over the last few decades. The speed that scientists can sequence and analyse DNA means that global collaborations that study thousands of individuals are beginning to shed light on a range of different diseases.

Sickle-cell anaemia is a disease in which red blood cells form an abnormal crescent (or sickle) shape. It is an inherited disorder, and was the first ever to be attributed to a specific genetic variant (rs334, see it here in Ensembl).


In 1949, ‘Sickle Cell Anaemia, a Molecular Disease’, from Pauling et al. identified a difference in the electrophoretic mobility between haemoglobin from healthy individuals and those with sickle-cell anaemia caused by a change in molecular structure of haemoglobin responsible for the sickling process [1]. The genetic variant (A, Reference:T) that causes cell sickling results in the substitution of a conserved glutamic acid residue at position 7 in beta chain of haemoglobin to a valine [2].

You can find this information in the Genes and regulation section for this variant. In the table below, which has been filtered to see only missense variants, the ‘Allele (transcript allele)’ column describes the variant allele (A) and the  transcript allele (T, as the HBB gene is located on the reverse strand). You can also see the nature and location of the variant on the transcript in the ‘Position’, ‘Amino acid’ and ‘Codons’ columns. The SIFT and Polyphen algorithms predict the effect of the amino acid change on protein structure and function. Interestingly, only the SIFT algorithm predicts that the T/A variant would have deleterious effect on haemoglobin structure and function, confirming that predictions can never be as accurate as experimental evidence.


Only those individuals that are homozygous for the variant allele develop sickle cell anaemia, although heterozygous individuals do have the much more manageable sickle cell trait. If untreated, individuals with sickle cell anaemia have a shorter than normal life expectancy, experiencing lethargy and breathlessness throughout their lives, with increased risk of stroke and pulmonary hypertension, as well as increased vulnerability to infection. Individuals with the milder sickle cell trait can experience problems in low oxygen or as a result of severe physical exercise, but can mostly be expected to live normal lives.

As such it would be expected that this variant would be rare in human populations. However, observations made in mid-20th century revealed that this variant is, in fact, surprisingly common in African, African American and Caribbean populations (you can see this in the 1000 Genomes allele frequencies available under Population genetics in Ensembl). Coincidentally, these were people descended from those who came from areas where malaria is prevalent [3]. Why was this happening?


Individuals carrying just one copy of the variant allele were known not to develop sickle cell anaemia, leading rather normal lives. However, it was found that these same individuals, were in fact highly protected against malaria. It turned out that, quite bizarrely, having alternate alleles at this loci simultaneously prevented infection from the malaria parasite with entirely manageable sickle manifestations! Therefore, individuals with one copy of each allele have a greater chance of survival in geographical areas where malaria is endemic, preserving both alleles in the population.

Understanding this relationship has led to a deeper understanding of the infective lifecycle of the malaria parasite and novel approaches in combating malaria [4-5], but also an appreciation of the genetic factors leading to sickle-cell anaemia.

This story exemplifies how observation, epidemiology and scientific investigation can uncover the mysteries of a human disease and provide important insights for its treatment. Nowadays, this gold standard of studying single genetic disorders has been multiplied and sped up on an unprecedented scale. There are now numerous projects that are aimed at sequencing the DNA of many individuals with different diseases and using the power of bioinformatics to analyse how genetic variation might lay at the foundations for previously poorly understood diseases.

[1] Pauling L. et al. Sickle cell anemia a molecular disease Science, 1949 Nov 25;110(2865):543-8

[2] Ingram VM et al. Abnormal human haemoglobins. III. The chemical difference between normal and sickle cell haemoglobins Biochim Biophys Acta 1959 36: 543–548

[3] Allison AC et al. Protection Afforded by Sickle-cell Trait Against Subtertian Malarial Infection 1954 Br Med J 1 (4857): 290–294

[4] Mounkaila A. et al. Sickle Cell Trait Protects Against Plasmodium falciparum Infection American Journal of Epidemiology, 2012 176 175-185

[5]  Gregory LaMonte et al. Translocation of Sickle Cell Erythrocyte MicroRNAs into Plasmodium falciparum Inhibits Parasite Translation and Contributes to Malaria Resistance Cell Host & Microbe, 2012 12 187-199


What’s new in Ensembl Genomes 31?

There are legs and tentacles everywhere in this release of Ensembl Metazoa, as ten new species scuttle, swim and slither into our databases. From the Antarctic midge to the California two-spot octopus, the new species illustrate the diversity of metazoa. Our new Metazoan species also include dog and rat parasites (the itch mite and a nematode), as well as species that pose significant problems for agriculture (Australian sheep blowfly) and aquaculture (the salmon louse and a myxosporean). The common bumblebee is an important pollinator, a brachiopod represents a new phylum in Ensembl Metazoa, while the African social velvet spider is a fascinating model of sociality and is the first spider in Ensembl Genomes.


Not to be outdone, Ensembl Protists is now updated to 158 genomes from 104 species and Ensembl Bacteria has been updated to include the latest versions of 39,584 genomes (39,183 bacteria and 401 archaea) from the INSDC archives.

Other news

Fungi: Updated annotations based on PHI-base 4.0 have been included. New variation data for Schizosaccharomyces pombe.

Protists: Addition of 4 protist species for pan-taxonomic comparative analysis (Monosiga brevicollis, Thecamonas trahens, Cryptomonas paramecium and Chondrus crispus), meaning that Ensembl Compara now includes protists from all the major Eukaryotic clades.

Plants: There are now 350,000 new rice variations across 3,000 rice accessions from 89 different countries as well as track hubs for more than 900 public RNA-Seq studies, totalling more than 16,000 tracks across 35 different plant species.

MetazoaUpdated gene sets for the leaf cutter antred fire ant and the two-spotted spider mite as well as updated gene sets from VectorBase and WormBase.

Check out all the changes on our Ensembl Genomes website.

Any questions or comments? Email us.

What’s new in e84:

  • Human: Incorporation of BLUEPRINT Epigenome data and methylation data
  • Pairwise Linkage Disequilibrium (LD) calculation on LD variant page
  • Track hub registry interface
  • Transcript haplotype view

Incorporation of BLUEPRINT Epigenome data

BLUEPRINT is a large scale research project aimed at deciphering the epigenome of blood cells. ChIP-seq and DNase hypersensitivity data from the BLUEPRINT project has now been incorporated into Ensembl. All of the cell types analysed in the BLUEPRINT project are listed here. In Ensembl 84, we are including BLUEPRINT data for the following 20 independent cell types, divided based on cell lineage and tissue source:

CD14+ CD16- monocyte from Venous Blood
CD14+ CD16- monocyte from Cord Blood
CD4+ ab T cell from Venous Blood
CD8+ ab T cell from Cord Blood
CM CD4+ ab T cell from Venous Blood
eosinophil from Venous Blood
EPC from Venous Blood
erythroblast from Cord Blood
HUVEC prol from Cord Blood
M0 macrophage from Cord Blood
M0 macrophage from Venous Blood
M1 macrophage from Cord Blood
M1 macrophage from Venous Blood
M2 macrophage from Cord Blood
M2 macrophage from Venous Blood
MSC from Venous Blood
naive B cell from Venous Blood
neutro myelocyte from Bone Marrow
neutrophil from Cord Blood
neutrophil from Venous Blood

This data can be viewed alongside other tracks in Ensembl by using the ‘Configure this Page’ option and selecting your cells of interest.  configure this pageBLUEPRINTex2

Pairwise LD calculation

You are now able to calculate linkage disequilibrium (LD) between any two variants in Ensembl. To calculate the r2 and D’ values for LD between two specific variants, enter the ID of any variant into the LD calculation text box on the specific page of the reference variant. This feature can be found by clicking on ‘Linkage Disequilibrium’ from the menu on any variant page.


Track Hub registry interface

With the arrival of the new Track Hub Registry, we have added a feature that allows you to search for track hubs of interest and attach them directly to Ensembl. Just click on the ‘Add your data/Manage your data’ button on any Ensembl page, and select ‘Track Hub Registry Search’ from the lefthand menu. manage your dataTrackHubRegistryInterface

The interface will only search for hubs that have assemblies available for the site you are on; to see the full range of species and assemblies, visit the Track Hub Registry site directly.

Transcript haplotype view

The transcript haplotype view is a new data view we have implemented that allows you to explore observed transcript sequences that results from variants identified from resequencing data from the 1000 Genomes Project. By clicking on the ‘Haplotypes’ link on any transcript page, you are able to view protein consequences, population frequencies and protein alignments of all the haplotypes for that particular transcript.

Transcript_haplotype_view Screen Shot 2016-03-02 at 11.01.34Screen Shot 2016-03-02 at 11.02.04

Other news

  • Mouse: update to GENCODE M9 annotation
  • Zebrafish: updated gene set, including manually annotated HAVANA annotation
  • Baboon: lincRNA model update
  • Latest sequence variants from dbSNP build 146 for human, cow and dog
  • Import of COSMIC 75 cancer data
  • New and updated studies from DGVa for several species such as human, mouse, zebrafish, macaque, cow and dog
  • Gene trees: new option to prune by target species/ taxon in the REST API
  • Ensembl Families now defined by an HMM library, based upon the Panther database.
  • Alignments in CRAM format
  • DAS support ended
  • Regulatory segments retired from the Ensembl regulation BioMart, but now available in bigbed format through the ftp site

A complete list of the changes can be found on the Ensembl website.

Find out more about the new release, and ask the team questions, in our free webinar. Wednesday 16th March, 4pm GMT. Register here.