- Open Access
Touring Ensembl: A practical guide to genome browsing
© Spudich and Fernández-Suárez; licensee BioMed Central Ltd. 2010
- Received: 16 November 2009
- Accepted: 11 May 2010
- Published: 11 May 2010
The number of databases in molecular biological fields has rapidly increased to provide a large-scale resource. Though valuable information is available, data can be difficult to access, compare and integrate due to different formats and presentations of web interfaces. This paper offers a practical guide to the integration of gene, comparative genomic, and functional genomics data using the Ensembl website at http://www.ensembl.org.
The Ensembl genome browser and underlying databases focus on chordate organisms. More species such as plants and microorganisms can be investigated using our sister browser at http://www.ensemblgenomes.org.
In this study, four examples are used that sample many pages and features of the Ensembl browser. We focus on comparative studies across over 50 mostly chordate organisms, variations linked to disease, functional genomics, and access of external information housed in databases outside the Ensembl project. Researchers will learn how to go beyond simply exporting one gene sequence, and explore how a genome browser can integrate data from various sources and databases to build a full and comprehensive biological picture.
- Genome Browser
- Hypersensitive Site
- CTCF Binding Site
- Distribute Annotation System
- NCBI RefSeq
The ongoing increase in the number of databases in biological fields provides a large-scale resource. Last year saw the development of nearly 100 new molecular biological databases, bringing the total number of popular databases in this field to over 1,000 . However, different formats and presentations of the GUIs (graphical user interfaces) make it difficult to access data. Collecting biological information from various sources and comparing them can be time consuming for the researcher. Genome browsers provide an aid to the researcher by importing biological data from various sources and presenting these data in an integrated way.
Genome browser and annotation (chordates)
Genome browser and annotation (nonchordates)
Genome browser and annotation (fruit fly)
NCBI Map Viewer
Genome browser and annotation (multi-species)
NCBI Sequence Viewer
Genome browser and annotation (multi-species)
Genome browser and annotation (multi-species)
VISTA Enhancer Browser
Non-coding elements (human)
1000 Genomes Browser
Genome browser (human, multiple individuals)
Visualization tool (multiple sources)
Visualization tool (multiple sources)
DAS sources available
Ensembl DAS list
DAS sources available in Ensembl
Protein and Nucleotides RefSeq
Repository of nucleic acid and protein sequences
Repository of protein sequence (manually curated)
Data mining tool for export of tables and sequences
Open source software for molecular biology
Construction and analysis of phylogenetic trees
Regulatory Features CisRed
Database: Regulatory sequences
DNase I Footprint
Database: Transcription factor binding sites (fly)
Database: miRNA targets (multi-species)
We focus on the Ensembl genome browser in this article, though a similar approach can be used with other genome browsers shown in table 1. The Ensembl project focuses on the chordate genomes, with the inclusion of additional model organisms that have been extensively studied in biological research and have a reliable, manually annotated gene set (Caenorhabditis elegans, Drosophila melanogaster and Saccharomyces cerevisiae). In addition to providing carefully predicted gene sets based on experimental evidence (sequences from UniProtKB/Swiss-Prot , manually-curated sequences from NCBI RefSeq , and sequences from UniProtKB/TrEMBL (Table 1)), Ensembl includes annotation such as sequence variation, comparative associations, mRNA and protein from other databases, predicted features such as CpG islands , and repeats and motifs mapped along the genome. These annotations are graphically depicted along the genomic assembly in order to allow easier visualisation of a gene neighbourhood or a stretch of sequence.
Ensembl and other browsers provide displays of complex data sets that require time and computing power not generally available to the researcher. Homology relationships based on gene comparisons across all annotated species in Ensembl (53 species in release 55), along with whole-genome alignments, such as alignments of 31 mammalian genomes, can be readily viewed in the browser.
In the following four case studies, we use the Ensembl genome browser to demonstrate how to view and predict functional regions in the genome based on existing evidence. First, we examine known regulatory features for the human IL2 gene and discuss how to display these features in Ensembl. These promoter and enhancer-related elements can be readily exported using the BioMart tool [5–7].
In study 2, we use human MYO6, a case in which gene regulation is not well-understood. Using comparative genomics, we show how the location of functional sequences may be predicted. In case study 3, we demonstrate how the information in Ensembl can be extended through DAS (the Distributed Annotation System) to view data from external sources. Finally, in study 4, we explore a variation associated with disease phenotypes.
These case studies aim to show how data from different sources can be viewed and compared for a gene or region in Ensembl. For a walk-through of how to use the browser to view comparative genomics, variations, and other Ensembl resources, please see our videos and previous publications [10, 11]
We investigate IL2, the interleukin 2 gene, in human (ENSG00000109471). Gene regulation has been studied at the 5' end of the IL2 transcript and flanking sequence [12–14]. Within only 200 bp upstream of the translational start site, binding sites for proteins such as NF-κB, AP-1, and NFAT (nuclear factor of activated T-cells), DNase I hypersensitive sites and a TATA box can all be found. These regions have been shown to be involved in the control of T-cell mediated immune response[15, 16].
Pop-up windows reveal more information for each track if a feature is clicked. In figure 1, the pop-up window indicates a CTCF binding site in the regulatory features track. CTCF proteins are highly conserved zinc finger proteins associated with transcriptional activation and repression. Mutations in these genes are associated with invasive breast cancers, prostrate cancers and Wilms' tumours[21, 22]. These sites have been recently and extensively mapped onto the human genome and are included in Ensembl as part of the regulatory build.
Regulatory features can be exported using the BioMart tool, or accessed via the Perl API from the Ensembl functional genomics database. An walk-through of the BioMart web interface  is provided by Smedley et. al. [5–7] Based on this, to download regulatory features, choose the database as "Ensembl functional genomics" and the dataset as the species of interest. Filters can be applied to select by a region (for example chromosome) or a specific type of regulatory feature (such as DNase I hypersensitive site). Attributes output information (such as chromosomal coordinates, or cell type) about these specific features. For more information about feature sources, and the Ensembl regulatory build, see Ensembl documentation .
The "constrained elements" blocks (Figure 1, label 1) are genomic regions that are highly conserved across 33 species, in this example. Constrained elements result from GERP-scoring of each base pair position within a multi-species alignment. High GERP scores represent the most conserved base pairs, and correspond to blocks in the 'conservation' track. The constrained elements in figure 1 align to the 5' and 3' ends of the Ensembl transcript for IL2, and align with regulatory regions, indicating regions of high sequence conservation and thus, possible function.
A third track displays data from 'CisRED' a database of patterns and motifs associated with regulatory regions, 'miRanda' a collection of miRNA targets identified in the genome, and the 'VISTA' enhancer set  (Figure 1, label 2) Features in this track align to the flanking regions to the IL2 coding sequence, and to the conserved sequence blocks.
The alignment display is highly customisable. Numbering can be turned on or off, and exons highlighted. Pairwise comparisons or multiple alignments can be displayed at the nucleotide level. Alignments can be exported using the export data link at the left of the view.
In case 1, we investigated a gene for which there is information already known about promoter and enhancer elements. Although most human genes in Ensembl are labelled as 'known', signifying a good match to a cDNA or protein in a biological database such as UniProt or NCBI RefSeq, many of these genes have un-investigated regulatory sequences. In addition, many proteins have unknown function. How can we predict function for a protein that is not well-understood in terms of its role in the cell?
In this example we consider human MYO6, ENSG00000196586, which has been studied in the mouse model to understand its role in endocytosis and inner-ear development[30, 31]. What is known about this gene? We can first look for mouse homologues for the human MYO6 gene ENSG00000196586. Do so by clicking on the orthologues link at the left of the gene tab for ENSG0000196586. At the time of writing, one mouse orthologue is known for human MYO6 (in Ensembl release 55): ENSMUSG00000033577.
Many GO terms for the human MYO6 transcript have been projected from mouse homologues (one example is shown in figure 4A). Clicking on the mouse protein identifier ENSMUSP00000108893, then on the Gene ontology link at the left shows the GO terms associated to the mouse protein. Protein binding is 'inferred from physical interaction (IPI)' in transcript ENSMUST00000113268.
The same GO term is listed for the human MYO6 gene in figure 4A, based on homology to the mouse Myo6 gene. The evidence code 'IEA' or 'inferred from electronic annotation' demonstrates a projected GO term. They may aid in predicting functions for a protein, based on homology.
Identifying sequences involved in gene regulation is also important in understanding function. In case 1 we looked at the region upstream of the IL2 gene, which is rich with known regulatory regions. For the human MYO6 gene, we can make some predictions using a similar approach to case 1.
The constrained elements track, and CisRED/miRANDA/VISTA features are also selected in this example. These indicate regions that may function in gene regulation.
In addition, more elements associated with regulatory regions can be displayed along the genome in this view. For example, other elements associated with promoters such as CpG islands[4, 40, 41], or those determined with FirstEF  or Eponine can be selected using the configure this page option at the left.
Conclusions from this case study can be drawn from the GO term associations and the putative regulatory regions. Proposed functions for the human MYO6 gene and protein include actin filament binding and regulation of secretion . These are based on the known functions of the human MYO6 gene homologous to mouse Myo6, based on the gene tree. Furthermore, the regulatory build indicates signatures of open chromatin such as CTCF binding sites, DNase I hypersensitive sites, along with histone modification sites. Open chromatin and histone modification sites at the 5' end of MYO6 transcripts suggest a potential regulatory region (Figure 5). This sequence could be further investigated for promoter activity.
The Distributed Annotation System (DAS) allows Ensembl to link out to and display information from external databases in supported formats. DAS transforms Ensembl into a framework where third party annotation can be added and viewed alongside Ensembl annotation. The DAS registry  provides a repository of external sources, and makes it easy for users to select these data to be displayed in Ensembl. These data can be viewed in the browser along the genome, or as annotation for a gene or transcript. This powerful system integrates data from databases around the world, and is available for all species.
To add DAS tracks to the Location views (Such as Region in Detail, shown in figure 6), users can click on the configure this page link at the left. A greater selection of DAS tracks is found upon clicking manage your data at the left, and then following the Attach DAS link to access the DAS registry. In addition to viewing 'live' external data with DAS, users may draw their own tracks along the chromosome. User data can be displayed in Location views, such as Region in Detail, a chromosome or karyotype.
Population variation in Ensembl is imported from NCBI dbSNP, among other sources, and is represented in a variety of views . Clicking on a variation identifier within the Ensembl website opens the variation tab and brings the focus to data for one specific variation, such as a single nucleotide polymorphism (SNP) or insertion-deletion (indel) mutation. Associated data such as allele frequencies from genotype studies done by HapMap  or Perlegen, or the phenotype information described above can be found in this way.
Genome browsers have gone beyond the simple display of genes and transcripts, moving into the integration of biological data. Ensembl pages allow information annotated on a genome to be shown alongside genes in one display. This annotation comes from various sources and includes sequence variation, conserved regions, motifs such as CpG islands and sequences associated with regulatory regions and promoters. DAS allows Ensembl to draw together more information in more databases, displaying data from external sources as an added layer of information. It also allows the biological community to display and publish their data in an integrated framework. Furthermore, Ensembl itself is a DAS server, and other browsers may display Ensembl data as a respective external source.
As demonstrated in the case studies outlined here, experimentalists targeting potential functional regions for a gene could use a quick display of a variety of sequence features to form a basis for such predictions. The whole genome alignments leading to comparison of sequences across species can indicate important functional regions that are highly conserved. Regulatory features and associated motifs can be compared with these conserved regions to direct researchers towards undiscovered, potentially functional sites.
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