Paul Flicek1,2,*, Ikhlak Ahmed1, M. Ridwan Amode2, Daniel Barrell2, Kathryn Beal1,
Simon Brent2, Denise Carvalho-Silva1, Peter Clapham2, Guy Coates2, Susan Fairley2,
Stephen Fitzgerald1, Laurent Gil1, Carlos Garcı ´a-Giro ´n2, Leo Gordon1, Thibaut Hourlier2,
Sarah Hunt1, Thomas Juettemann1, Andreas K. Ka ¨ha ¨ri2, Stephen Keenan1,
Monika Komorowska1, Eugene Kulesha1, Ian Longden1, Thomas Maurel1,
William M. McLaren1, Matthieu Muffato1, Rishi Nag2, Bert Overduin1, Miguel Pignatelli1,
Bethan Pritchard2, Emily Pritchard1, Harpreet Singh Riat2, Graham R. S. Ritchie1,
Magali Ruffier1, Michael Schuster1, Daniel Sheppard2, Daniel Sobral1, Kieron Taylor1,
Anja Thormann1, Stephen Trevanion2, Simon White2, Steven P. Wilder1,
Bronwen L. Aken2, Ewan Birney1, Fiona Cunningham1, Ian Dunham1, Jennifer Harrow2,
Javier Herrero1, Tim J. P. Hubbard2, Nathan Johnson1, Rhoda Kinsella1, Anne Parker2,
Giulietta Spudich1, Andy Yates1, Amonida Zadissa2and Stephen M. J. Searle2
1European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton Cambridge CB10 1SD, UK and
2Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK
Received October 11, 2012; Revised October 31, 2012; Accepted November 1, 2012
provides genome information for sequenced chord-
ate genomes with a particular focus on human,
mouse, zebrafish and rat. Our resources include
evidenced-based gene sets for all supported spe-
cies; large-scale whole genome multiple species
alignments across vertebrates and clade-specific
alignments for eutherian mammals, primates, birds
and fish; variation data resources for 17 species and
regulation annotations based on ENCODE and other
data sets. Ensembl data are accessible through the
genome browser at http://www.ensembl.org and
through other tools and programmatic interfaces.
Ensembl (http://www.ensembl.org) collects, creates, or-
ganizes and distributes data resources in support of
research into the genetics and genomics of chordates.
We currently support 70 species with a focus on human
in additional to agricultural animals and major vertebrate
model organisms such as mouse, zebrafish and rat. We
support a full range of researchers in genomics from
bench biologists interested in looking up specific details
about their genes or loci of interest using a graphical
web interface to advanced bioinformatics programmers
looking to do complex analysis or build new tools that
leverage the Ensembl infrastructure. As such, we provide
Apache-style license and release all of our data without
restriction. Ensembl data are distributed from our genome
Interface (API), direct MySQL access, Amazon Web
Services Public data sets (http://www.ensembl.org/info/
data/amazon_aws.html) and via full data download.
Ensembl aims to be a hub of genome information by
linking identifiers and information between external bio-
logical resources and data within Ensembl or importing
essential information from other resources so that it can
be found within Ensembl and linked back to the original
resource as necessary. For example, we provide up to date
external database references to gene names from the
HUGO Gene Nomenclature Committee (HGNC) (1),
the Universal Protein Resource (UniProt) (2), Orphanet
portal for rare diseases and orphan drugs (3), the Online
Mendelian Inheritance in Man (OMIM) database (4), the
RefSeq collection of Reference Sequences from NCBI (5),
the UCSC Genome Browser (6), the Protein Data Bank
structures (7) and many other resources.
We participate in or work closely with a number of
large-scale international projects including the 1000
*To whom correspondence should be addressed. Tel: +44 1223 492581; Fax: +44 1223 494494; Email: firstname.lastname@example.org
Nucleic Acids Research, 2013, Vol. 41, Database issuePublished online 30 November 2012
? The Author(s) 2012. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/3.0/), which
permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact
at Wellcome Trust Genome Campus on September 16, 2013
Genomes Project (8), ENCODE (9), the International
Cancer Genome Consortium (ICGC) (10) and the
Participation in these efforts helps ensure that we
produce timely and valuable resources through direct sci-
entific engagement with the communities that we are
trying to serve. In addition, we actively develop and
provide key pieces of large-scale bioinformatics infrastruc-
ture including the eHive workflow management system for
genomic analysis (12).
Full incorporation of the data types resulting from the
myriad of experimental assays now leveraging next gener-
ation sequencing technology remains an important area of
development for the project. During the past year, we have
made considerable progress in a number of ways including
a greater incorporation of RNA-seq data into our gene
annotations and ChIP-seq data into our regulatory anno-
tations. In general, we believe that the most useful re-
sources provide integrated summary information that
transforms the raw sequencing data into biological know-
ledge that can provide a foundation for further biological
research. Thus, we believe that the display of the called
variants from the 1000 Genomes Project or regulatory
region annotations supported by specific histone modifi-
cation or transcription factor (TF) binding sites are more
useful as resources for the community than a display of
the raw aligned sequence reads. However, Ensembl does
support the upload and visualization of read alignment
data (e.g. alignment files in BAM format) and provides
signal files for our ChIP-seq and alignment files for
RNA-seq data within the browser for those users
needing direct access to the supporting data. Indeed,
Ensembl’s API development this year included increasing
support for file-based data access to enable integration of
very large BAM and other file-based data sets into the
This report highlights the new data we have released
and the new mechanisms of data access that we have
deployed during the past year since our previous report
(13). We describe how these new features extend the
existing capabilities of the project, which will be explained
As of release 69 (October 2012), Ensembl supports 70
species including 61 species fully supported on our main
site. Of these, we have created full gene annotations for 58
chordates (43 with high-coverage genome sequences and
15 with low-coverage) and have imported annotation data
for three non-chordate model organisms (Saccharomyces
melanogaster) to facilitate comparative analysis. Five
new species were included during the past year with full
(Latimeria chalumnae), ferret (Mustela putorius furo),
Nile tilapia (Oreochromis niloticus) and Chinese softshell
turtle (Pelodiscus sinensis). An additional nine species are
currently available with limited support on the Ensembl
Pre! site (http://pre.ensembl.org) including the following,
which were newly added in the past year: budgerigar
(Melopsittacus undulates), Chinese hamster CHO cell line
(Cricetulus griseus), painted turtle (Chrysemys picta bellii),
spotted gar (Lepisosteus oculatus), collared flycatcher
BLAST and genome visualization, but do not provide a
complete gene build. For specific genomes, we also
provide downloadable data on the preview site.
We update the human gene set for every Ensembl
release via a merge of the Ensembl evidence-based auto-
matic annotation and Havana manual annotation (14) to
produce an updated GENCODE gene set (9,15). This set
also includes all current human Consensus Coding
Sequence (CCDS) gene models (16). Manual annotation
from Havana is also incorporated into our gene sets on
alternate releases for mouse and zebrafish. In addition, pig
now includes manual annotation from Havana on selected
regions of the genome.
The human genome assembly is updated regularly by
the Genome Reference Consortium (GRC) to include al-
ternate sequences in the form of ‘fix’ and ‘novel’ assembly
patches (17), and we continue to include these additional
alternate sequences and annotate them with genes and
(October 2012) included GRCh37.p8 (i.e. the eighth
patch release of the GRCh37 assembly). The mouse
genome annotation, which also incorporates all current
mouse CCDS models, was updated for Ensembl release
68 (July 2012) to reflect the new GRCm38 assembly.
Other species previously available on our website also
saw updates in the past year including new primary
assemblies and gene sets for chimpanzee, dog, pig,
ground squirrel, bushbaby and Ciona intestinalis. The
gene sets for orang-utan, opossum and platypus were
also updated using RNA-seq data.
The whole genome multiple and pairwise alignments
have been re-run in conjunction with the incorporation
of new or updated genomes. In addition to cross-species
alignments, we now provide self-alignments for the human
genome and also use the Ensembl comparative genomics
infrastructure for the comparison of fix and novel patches
alongside the reference human genome (Figure 1).
The year 2012 has seen the inclusion of RNA-seq data
provided by several different groups (18–20) as supporting
evidence for our gene annotations. Thirteen species cur-
rently incorporate RNA-seq data including zebrafish,
chimpanzee, Nile tilapia, dog, Chinese softshell turtle,
orang-utan, opossum and platypus. For some of these
species, the RNA-seq data were added after a standard
gene annotation process (21), whereas for other species,
the data were added as an integral part of the genebuild
process. Some species also include tissue-specific RNA-seq
data that enables the exploration of tissue-specific expres-
sion. In addition, the Illumina Human BodyMap 2.0
E-MTAB-513) have been re-processed using our enhanced
Nucleic Acids Research, 2013,Vol.41, Database issueD49
at Wellcome Trust Genome Campus on September 16, 2013
pipeline to produce updated gene models and new BAM
RNA-seq data are now routinely used in gene annota-
tion in a number of ways, and we anticipate that RNA-seq
data will be used in almost all gene annotation projects for
the foreseeable future. Briefly, our current procedure
starts with raw-sequencing reads that are aligned to the
genome and processed to produce RNA-seq-based gene
models, BAM files and intron features that are supported
by intron-spanning reads. Intron-supporting evidence
helps to quantify intron predictions in RNA-seq transcript
sets. The intron features and RNA-seq-based gene models
are used alongside cDNA and EST alignments to compare
and filter the preliminary set of protein-coding models
against a set of highly supported splice sites. In addition,
the RNA-seq-based gene models are used to provide al-
ternate isoforms and fill in gaps between models identified
by the standard Similarity Genewise component of our
annotation system, which aligns protein sequences to the
genome, and to add untranslated regions to the protein
We have also developed an RNA-seq update pipeline
that allows an existing Ensembl gene set to be updated
through incorporation of new RNA-seq data. The
RNA-seq update pipeline takes in the results of the
standard Ensembl gene annotation method and also
RNA-seq-based models produced by the pipeline previ-
ously described (20). The two sets of input models are
compared and merged to produce an updated gene set.
This new method was used to improve the existing
Ensembl release 69 (October 2012). The method is
particularly effective for species that are distantly related
to the well-annotated mammals and those with little
species-specific sequence data available at the time of
RNA-seq update pipeline include lengthening truncated
genes, merging adjacent gene fragments and splitting
artificially merged genes. RNA-seq-based data are also
useful for higher primate species that have previously
relied largely on human sequence data for annotation, as
it allows for the identification of non-human primate-
specific gene expression.
We create variation resources for 17 species by importing
and merging data from many different sources through
our pipeline (22). The current list of variation data is
provided at http://www.ensembl.org/info/docs/variation/
sources_documentation.html. Most of our SNP and
in-del data (rsIDs, locations, allele frequencies and geno-
types) come from dbSNP (23). This year, we have updated
the Ensembl Variation databases for human, rat, chim-
panzee, orang-utan, zebrafish, pig, dog and macaque.
We have also remapped the variation data for mouse
onto the newGRCm38
GRCm38 mappings were provided by dbSNP and
provided the same update for new dog assembly.
Available structural variation data have increased consid-
erably, and we have data for human, mouse, horse,
zebrafish, cow and macaque largely provided by the
DGVa database of copy number and structural variation
(24). The human structural variation data are more
Figure 1. A region of the GRCh37 human assembly showing the complete APBA1 gene. The top panel displays the GRCh37 reference sequence as
originally released, and the bottom panel displays the region after the inclusion of the novel patch HSCHR9_1_CTG35. The region of difference is
highlighted and marked by the ‘Assembly exception’ track, whereas the pink regions of LASTZ self-alignment provide more details about what
has changed in the patch including the addition of new sequence that was missing in the originally released assembly. The green areas show the
mapping between the original and the alternative sequences and demonstrate a corrected inversion at the left hand side of the patch. The patch
changes the annotation such that the RNA gene RP11-548B3.3 (in purple) moves from 50of the APBA1 gene to within the second intron. As can
be seen in the right hand side of the figure, the existence of the patch does not alter the annotation downstream of the change. Figure based on
D50Nucleic Acids Research,2013, Vol.41, Database issue
at Wellcome Trust Genome Campus on September 16, 2013
comprehensive than all other species combined and
include >6 million variants of which 5624 are somatic.
The variation database infrastructure storing geno-
types has also been redeveloped to improve the respon-
siveness of our displays and to support non-diploid
The human variation data also include genotypes
imported from the 1000 Genomes Project and the
mutation data locations provided by HGMD (26),
clinical variants on LRGs (27) and >135000 somatic
mutation positions from COSMIC (28). We have also
added mitochondrial variants, information on clinical
significance and global minor allele frequencies from
variants from OMIM (4), the European Genome-
phenome Archive (EGA) and the NHGRI GWAS
catalog (29). We denote those variants present on
three Affymetrix genotyping chips (GeneChip 100K
Array, GeneChip 500K Array, GenomeWideSNP_6.0)
and nine Illumina chips (CytoSNP12v1, Human660-
indicate those variants curated by UniProt (2).
For all species, we calculate the effect of each variant
allele on overlapping Ensembl transcripts and whether the
variant falls within an Ensembl regulatory feature, TF
binding motif or a high information position within the
motif. Our consequence annotation now uses defined
Sequence Ontology (SO) terms (30) for all descriptions,
which enable querying of ontological relationships in
BioMart. More detailed consequence information is also
provided for SNPs and in-dels in specific genomic loca-
tions such as splice sites. These SO terms have also been
adopted by both the UCSC genome browser and ICGC
providing a standard to enable easy comparison of vari-
Other resources supporting human variation include
calculated linkage disequilibrium values and tag SNPs,
in addition to SIFT (31) and PolyPhen (32) predictions
for amino acid changes. This year we have switched to
using the Ensembl comparative genomics pipeline to
provide the ancestral alleles of SNPs and short deletions
for human, orang-utan, chimpanzee and macaque (previ-
ously this was imported from dbSNP). We have also ex-
tensively improved our quality control (QC) procedures,
which leverage the eHive software and have been extended
to include structural variations.
As a result of our effort to provide the most useful
possible summaries of large data sets to our users, we
have added new tracks for 1000 Genomes Project
common variants and also tracks for each global 1000
Genomes population. Additionally, appropriate pheno-
type data have been collected into a dedicated section on
the Ensembl gene pages. Finally, the documentation
section of the website has also been extended and
improved for all areas of Ensembl Variation especially
for the Variant Effect Predictor (VEP), SO consequences,
QC pipeline and API diagrams.
Ensembl web interface
During the past year, development on the Ensembl web
interface has continued a combined strategy of small in-
cremental improvements on the website while making sub-
stantial progress on a number of major infrastructure-level
On the data display front, we are now able to show
alignments of human assembly patches to the reference
assembly (Figure 1) and have renamed the ‘Multi-species
view’ as ‘Region comparison’ to reflect its wider applic-
ability. We have also added a transcript variation page,
similar to the gene variation page but showing only one
transcript at a time, which is particularly helpful in the
case of large, well-annotated genes that are challenging
to display quickly or interpret easily due to their data
density. Other additions to the user interface include a
new online tool, Region Report, which provides graphical
access to the API script of the same name to export
sequence, genes and other annotation from one or more
regions. We have also re-introduced the ability to save
configurations on images: users can turn their choice of
tracks on and off and then save this selection in either the
browser session or their personal accounts and then
quickly return to the same layout at a later time. These
configurations can also be grouped into sets (e.g. to
combine a set of favourite variation tracks with a set of
gene tracks) for even quicker reconfiguration of images.
We have started to refresh the look and feel of the
website. For example, our icon set was previously
created from various sources and has now been replaced
with a single matching set. We have adapted the layout
and colour scheme for increased readability, and we are
continuing the process of replacing text-heavy pages with
simpler, more user-friendly layouts where appropriate.
scheduled for release by the end of 2012 include a
Genoverse that will be incorporated into our location
displays for Ensembl release 69 (October 2012) and
support for UCSC-style datahubs, which can contain
sets of preconfigured tracks or a user-supplied collection
of remote resources. Additional work underway includes a
top-to-bottom rewrite of our BLAST/BLAT search using
the Ensembl eHive job management system supporting a
new web frontend, which will be tested on our beta site
(http://beta.ensembl.org) before rolling out into a major
Ensembl release in 2013.
During the past year, we have significantly updated and
increased the amount of data available from the Ensembl
regulation database. As of Ensembl release 69 (October
2012), there are 532 ChIP-seq and DNase-seq data sets
from 13 human and five mouse cell lines. In total, these
data sets represent information about the genomic loca-
tions of 49 different histone modification types and the
binding regions of 113 different TFs. Forty of these TFs
have binding matrices available through the JASPAR
database (33), and we have incorporated these motif data
as positions of high probability TF-binding sites (5% False
Nucleic Acids Research, 2013,Vol.41, Database issueD51
at Wellcome Trust Genome Campus on September 16, 2013
Discovery Rate) within the binding regions. We have also
created a dedicated experimental summary page providing
information on individual experimental details and
reads available in the European Nucleotide Archive (34).
The data underlying the Ensembl Regulatory Build cur-
rentlyinclude experiments in 13cell lines. RegulatoryBuild
coverage has increased by 15% in the past year and now
annotates 270Mb of the human genome in 518020 regula-
tory features. In Ensembl release 65 (December 2011), we
introduced the combined Segway (35) and ChromHMM
(36) segmentation analyses developed for ENCODE (9),
which classifies the genome into regions based on 12
specific assays to obtain a single-track summary of the
functional architecture of the human genome. The segmen-
GM12878, K562, H1-hESC, HepG2, HeLa-S3 and
HUVEC. The segmentation tracks are displayed with
in the Ensembl browser (Figure 2).
The Ensembl Regulation database and web views
continue to provide various other data resources including
the following: mapping of probe sets for all the common
microarray platforms, DNA methylation from various
projects including ENCODE, high profile externally
curated data sets such as cisRED motifs (37) and an
updated VISTA enhancer set (38).
New species added in the past year such as coelacanth and
lamprey have provided our gene trees with representatives
of new taxonomic groups. These species define additional
branching points in the phylogenetic trees, enable splitting
long branches and provide us with more taxonomic power
to better resolve the gene trees. Further information on the
evolution of the gene families is now provided by supple-
menting our phylogenetic analysis with a calculated as-
sessment on the possible expansions and contractions in
each family using the CAFE tool (39).
Our data model for gene trees has been modified to
handle both protein and ncRNA gene trees. During that
process, we also improved our support for protein
super-trees, which are used in the resolution of very
large protein families. These are split in sub-families,
and the super-protein tree represents the relationship
between these sub-families. We have developed a better
identification and annotation of split genes that usually
arise because of assembly errors (40). In our current im-
plementation, the enhanced gene tree pipeline (41) detects
gene split events after building the protein multiple align-
ment, and the resulting nodes of the tree can be annotated
as gene split events when they relate to partial proteins
that could be concatenated to form a full gene.
Ensembl tools and software
During the past year, we have made significant improve-
ment to the Ensembl VEP (42) and launched a beta im-
plementation of a new Ensembl REST API. The VEP
provides comprehensive analysis of SNP, in-del or struc-
tural variation data including reports of which gene, tran-
script, protein or regulatory region overlap the variants of
interest and if there is any change in amino acid sequence.
Figure 2. Combined Segway and ChromHMM segmentation analyses within Ensembl in the region around the SLC18B1 gene on human chromo-
some 6. The combination process results in seven annotated segments: CTCF enriched, Predicted Weak Enhancer/Cis-reg element, Predicted
Transcribed Region, Predicted Enhancer, Predicated Promoter Flank, Predicted Repressed/Low Activity or Predicted Promoter with TSS. Six of
the seven segment types are shown with variability in predicted enhancer activity between the assayed cell lines. Figure based on http://e68.ensembl.
D52 Nucleic Acids Research,2013, Vol.41, Database issue
at Wellcome Trust Genome Campus on September 16, 2013
It also includes information about SIFT and PolyPHEN
predictions in human, protein domains, exon/intron
numbers, minor allele frequencies and other information.
The VEP works with many different file formats and can
in fact convert variant positions between different coord-
inate systems (Ensembl, RefSeq, LRG and HGVS). We
have also written plugins to report on degree of conserva-
tion, presence of the variant in an LOVD database in a
Locus Specific Database (LSDB) using the Leiden Open
Variation Database (LOVD) software (43) and other
capabilities.Our VEP plugins
ensembl-variation github repository (https://github.com/
ensembl-variation/VEP_plugins), and we encourage users
to share their own plugins.
The REST API web service was released as a beta ap-
Although we have a fully supported Perl API to all of
the Ensembl data (44), the REST API addresses those
users who wish to access Ensembl data in a language-
agnostic manner. The web service is built using the Perl
web framework Catalyst, Catalyst::Action::REST and our
existing Perl API providing a rapid development environ-
ment and lowering the cost of creating new endpoints.
Output is a combination of bioinformatics and program-
matically relevant formats such as FASTA and JSON. We
provide access to sequences, assembly mapping, homo-
logues and integration of the VEP with support for
genomic features. The REST service, like all Ensembl
software, is free to download from our CVS server
allowing users to deploy over their local Ensembl
Data access and data mining
Each Ensembl release provides a full rebuild of seven
BioMart (45,46) databases. Four of these BioMart data-
bases (Ensembl Gene, Ensembl Variation, Ensembl
Regulation and VEGA) are visible on the Ensembl
BioMart interface, and the remaining three BioMart data-
bases are hidden from view but are accessed through fed-
eration with visible BioMart databases to provide
Performing a complete rebuild each release ensures the
availability most up to date integrated data from across
the Ensembl project. Users can access these data via the
MartView (web interface) and MartService (BioMart Perl
API, DAS server, SOAP, REST, BioConductor biomaRt
Each Ensembl BioMart release includes the addition of
any new species, updated assemblies, updates to the
germline and somatic variation and structural variation
data sets as well as updates to the regulation data. One
can now obtain our SIFT and PolyPhen predictions and
scores from the Ensembl variation BioMart and from the
variation ‘filter’ and ‘attribute’ sections of the Ensembl
gene BioMart. It is also possible to select specific mouse
strain information from the mouse structural variation
data set, and one can filter on the source and study acces-
sion of interest in the structural variation data sets avail-
able for cow, zebrafish, horse, human, mouse and
macaque. A new human somatic structural variation
genomic feature data.
dataset has been added containing data from COSMIC
(28). The ability to search multiple chromosomal regions
at once has been added to the Ensembl Regulation mart.
In addition to this, users can query human regulatory seg-
mentation features using the newly added regulatory
segments filter section and attribute page.
User training and support
Ensembl supports new and existing users in a variety of
ways from a strong and increasing on-line presence to
direct face-to-face training at universities and other insti-
tutions worldwide. This year, we held one-day workshops
on five continents and launched new virtual initiatives
available to all including those further afield or without
the means to host a one-day workshop.
We provide extensive free and user-driven tutorials via
the Ensembl YouTube (http://www.youtube.com/user/
EnsemblHelpdesk) and YouKu (http://i.youku.com/u/
id_UMzM1NjkzMTI0) channels and e-learning course
channel has >165 subscribers and >91000 video views,
now hosts >20 videos including navigation ‘how-to’
guides. This year, we have added more advanced videos
covering subjects such as patches and haplotypes on the
human assembly, API installation and how RNA-seq data
are used in the genebuild. In 2012, the top 20 countries
accessing our on-line training reflect a worldwide audience
from the USA, Europe, India, Japan, Australia, Pakistan,
Taiwan, Mexico, South Korea and Brazil, and our most
popular videos have been viewed hundreds or thousands
updates and new features using the Ensembl blog
(http://www.ensembl.info/), Facebook page (http://www.
facebook.com/Ensembl.org) and Twitter account (http://
ensembl.org) continues to provide email support for
>100 questions monthly, and we are exploring webinars
as a vehicle for more interactive long-distance learning
and plan to offer more of these events in 2013.
The authors are consistently grateful to their users and
especially to those who take the time to contact us
through our mailing lists, blog and other avenues. They
large-scale projects that have provided data to Ensembl
before publication under the understandings of the Fort
Lauderdale meeting discussing Community Resource
Projects and the Toronto meeting on pre-publication
The Wellcome Trust provides majority funding for the
Ensembl project [WT062023 and WT079643] with add-
itional funding from the National Human Genome
Research Institute [U01HG004695, U54HG004563 and
Nucleic Acids Research, 2013,Vol.41, Database issueD53
at Wellcome Trust Genome Campus on September 16, 2013
U41HG006104] the BBSRC [BB/I025506/1], and the
European Molecular Biology Laboratory. Additional
support for specific project components as specified:
Funded by the European Commission under SLING,
grant agreement number 226073 (Integrating Activity)
within Research Infrastructures of the FP7 Capacities
Specific Programme; The research leading to these
Community’s Seventh Framework Programme (FP7/
author’s views and the European Community is not
liable for any use that may be made of the information
contained herein; The research leading to these results has
Seventh Framework Programme (FP7/2007-2013) under
grant agreement number 200754 – the GEN2PHEN
project; The research leading to these results has
received funding fromthe
Seventh Framework Programme (FP7/ 2007-2013) under
the grant agreement no223210 CISSTEM; The research
leading to these results has received funding from the
(FP7/2007-2013) under grant agreement no282510 –
BLUEPRINT. Funding for open access charge: The
Conflict of interest statement. None declared.
1. Seal,R.L., Gordon,S.M., Lush,M.J., Wright,M.W. and
Bruford,E.A. (2011) genenames.org: the HGNC resources in 2011.
Nucleic Acids Res., 39, D514–D519.
2. UniProt Consortium. (2012) Reorganizing the protein space at
the Universal Protein Resource (UniProt). Nucleic Acids Res., 40,
3. Rath,A., Olry,A., Dhombres,F., Brandt,M.M., Urbero,B. and
Ayme,S. (2012) Representation of rare diseases in health
information systems: the Orphanet approach to serve a wide
range of end users. Hum. Mutat., 33, 803–808.
4. Amberger,J., Bocchini,C. and Hamosh,A. (2011) A new face and
new challenges for Online Mendelian Inheritance in Man
(OMIM(?)). Hum. Mutat., 32, 564–567.
5. Pruitt,K.D., Tatusova,T., Brown,G.R. and Maglott,D.R. (2012)
NCBI Reference Sequences (RefSeq): current status, new features
and genome annotation policy. Nucleic Acids Res., 40,
6. Dreszer,T.R., Karolchik,D., Zweig,A.S., Hinrichs,A.S.,
Raney,B.J., Kuhn,R.M., Meyer,L.R., Wong,M., Sloan,C.A. et al.
(2012) The UCSC Genome Browser database: extensions and
updates 2011. Nucleic Acids Res., 40, D918–D923.
7. Velankar,S., Alhroub,Y., Best,C., Caboche,S., Conroy,M.J.,
Dana,J.M., Fernandez Montecelo,M.A., van Ginkel,G.,
Golovin,A. et al. (2012) PDBe: Protein Data Bank in Europe.
Nucleic Acids Res., 40, D445–D452.
8. 1000 Genomes Project Consortium. (2010) A map of human
genome variation from population-scale sequencing. Nature, 467,
9. ENCODE Project Consortium. (2012) An integrated encyclopedia
of DNA elements in the human genome. Nature, 489, 57–74.
10. International Cancer Genome Consortium. (2010) International
network of cancer genome projects. Nature, 464, 993–998.
11. Adams,D., Altucci,L., Antonarakis,S.E., Ballesteros,J., Beck,S.,
Bird,A., Bock,C., Boehm,B., Campo,E. et al. (2012)
BLUEPRINT to decode the epigenetic signature written in blood.
Nat. Biotechnol., 30, 224–226.
12. Severin,J., Beal,K., Vilella,A.J., Fitzgerald,S., Schuster,M.,
Gordon,L., Ureta-Vidal,A., Flicek,P. and Herrero,J. (2010)
eHive: an artificial intelligence workflow system for genomic
analysis. BMC Bioinformatics, 11, 240.
13. Flicek,P., Amode,M.R., Barrell,D., Beal,K., Brent,S., Carvalho-
Silva,D., Clapham,P., Coates,G., Fairley,S. et al. (2012) Ensembl
2012. Nucleic Acids Res., 40, D84–D90.
14. Wilming,L.G., Gilbert,J.G., Howe,K., Trevanion,S., Hubbard,T.
and Harrow,J.L. (2008) The vertebrate genome annotation (Vega)
database. Nucleic Acids Res., 36, D753–D760.
15. Harrow,J., Denoeud,F., Frankish,A., Reymond,A., Chen,C.K.,
Chrast,J., Lagarde,J., Gilbert,J.G., Storey,R. et al. (2006)
GENCODE: producing a reference annotation for ENCODE.
Genome Biol., 7(Suppl.1), S4.1–S4.9.
16. Harte,R.A., Farrell,C.M., Loveland,J.E., Suner,M.M., Wilming,L.,
Aken,B., Barrell,D., Frankish,A., Wallin,C. et al. (2012) Tracking
and coordinating an international curation effort for the CCDS
Project. Database (Oxford), 2012, bas008.
17. Church,D.M., Schneider,V.A., Graves,T., Auger,K.,
Cunningham,F., Bouk,N., Chen,H.C., Agarwala,R.,
McLaren,W.M. et al. (2011) Modernizing reference genome
assemblies. PLoS Biol., 9, e1001091.
18. Brawand,D., Soumillon,M., Necsulea,A., Julien,P., Csa ´ rdi,G.,
Harrigan,P., Weier,M., Liechti,A., Aximu-Petri,A. et al. (2011)
The evolution of gene expression levels in mammalian organs.
Nature, 478, 343–348.
19. Murchison,E.P., Schulz-Trieglaff,O.B., Ning,Z., Alexandrov,L.B.,
Bauer,M.J., Fu,B., Hims,M., Ding,Z., Ivakhno,S. et al. (2012)
Genome sequencing and analysis of the tasmanian devil and its
transmissible cancer. Cell, 148, 780–791.
20. Collins,J.E., White,S., Searle,S.M. and Stemple,D.L. (2012)
Incorporating RNA-seq data into the zebrafish Ensembl
genebuild. Genome Res., 22, 2067–2078.
21. Curwen,V., Eyras,E., Andrews,T.D., Clarke,L., Mongin,E.,
Searle,S.M. and Clamp,M. (2004) The Ensembl automatic gene
annotation system. Genome Res., 14, 942–950.
22. Chen,Y., Cunningham,F., Rios,D., McLaren,W.M., Smith,J.,
Pritchard,B., Spudich,G.M., Brent,S., Kulesha,E. et al. (2010)
Ensembl variation resources. BMC Genomics, 11, 293.
23. Foelo,M.L. and Sherry,S.T. (2007) NCBI dbSNP Database:
content and searching. In: Weiner,M.P., Gabriel,S.B. and
Stephens,J.C. (eds), Genetic Variation: A Laboratory Manual.
Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY,
24. Church,D.M., Lappalainen,I., Sneddon,T.P., Hinton,J.,
Maguire,M., Lopez,J., Garner,J., Paschall,J., Dicuccio,M. et al.
(2010) Public data archives for genomic structural variation. Nat.
Genet., 42, 813–814.
25. Tennessen,J.A., Bigham,A.W., O’Connor,T.D., Fu,W.,
Kenny,E.E., Gravel,S., McGee,S., Do,R., Liu,X. et al. (2012)
Evolution and functional impact of rare coding variation from
deep sequencing of human exomes. Science, 337, 64–69.
26. Stenson,P.D., Ball,E.V., Mort,M., Phillips,A.D., Shiel,J.A.,
Thomas,N.S., Abeysinghe,S., Krawczak,M. and Cooper,D.N.
(2003) Human gene mutation database (HGMD): 2003 update.
Hum. Mutat., 21, 577–581.
27. Dalgleish,R., Flicek,P., Cunningham,F., Astashyn,A., Tully,R.E.,
Proctor,G., Chen,Y., McLaren,W.M., Larsson,P. et al. (2010)
Locus Reference Genomic sequences: an improved basis for
describing human DNA variants. Genome Med., 2, 24.
28. Forbes,S.A., Bindal,N., Bamford,S., Cole,C., Kok,C.Y., Beare,D.,
Jia,M., Shepherd,R., Leung,K. et al. (2011) COSMIC: mining
complete cancer genomes in the Catalogue of Somatic Mutations
in Cancer. Nucleic Acids Res., 39, D945–D950.
29. Hindorff,L.A., Sethupathy,P., Junkins,H.A., Ramos,E.M.,
Mehta,J.P., Collins,F.S. and Manolio,T.A. (2009) Potential
etiologic and functional implications of genome-wide association
loci for human diseases and traits. Proc. Natl Acad. Sci. USA,
30. Eilbeck,K., Lewis,S.E., Mungall,C.J., Yandell,M., Stein,L.,
Durbin,R. and Ashburner,M. (2005) The sequence ontology: a
tool for the unification of genome annotations. Genome Biol., 6,
D54Nucleic Acids Research,2013, Vol.41, Database issue
at Wellcome Trust Genome Campus on September 16, 2013
31. Kumar,P., Henikoff,S. and Ng,P.C. (2009) Predicting the effects
of coding non-synonymous variants on protein function using the
SIFT algorithm. Nat. Protoc., 4, 1073–1081.
32. Adzhubei,I.A., Schmidt,S., Peshkin,L., Ramensky,V.E.,
Gerasimova,A., Bork,P., Kondrashov,A.S. and Sunyaev,S.R.
(2010) A method and server for predicting damaging missense
mutations. Nat. Methods, 7, 248–249.
33. Portales-Casamar,E., Thongjuea,S., Kwon,A.T., Arenillas,D.,
Zhao,X., Valen,E., Yusuf,D., Lenhard,B., Wasserman,W.W. and
Sandelin,A. (2010) JASPAR 2010: the greatly expanded
open-access database of transcription factor binding profiles.
Nucleic Acids Res., 38, D105–D110.
34. Amid,C., Birney,E., Bower,L., Cerden ˜ o-Ta ´ rraga,A., Cheng,Y.,
Cleland,I., Faruque,N., Gibson,R., Goodgame,N. et al. (2012)
Major submissions tool developments at the European Nucleotide
Archive. Nucleic Acids Res., 40, D43–D47.
35. Hoffman,M.M., Buske,O.J., Wang,J., Weng,Z., Bilmes,J.A. and
Noble,W.S. (2012) Unsupervised pattern discovery in human
chromatin structure through genomic segmentation. Nat. Methods,
36. Ernst,J. and Kellis,M. (2012) ChromHMM: automating chromatin-
state discovery and characterization. Nat. Methods, 9, 215–216.
37. Robertson,G., Bilenky,M., Lin,K., He,A., Yuen,W., Dagpinar,M.,
Varhol,R., Teague,K., Griffith,O.L. et al. (2006) cisRED: a
database system for genome-scale computational discovery of
regulatory elements. Nucleic Acids Res., 34, D68–D73.
38. Visel,A., Minovitsky,S., Dubchak,I. and Pennacchio,L.A. (2007)
VISTA Enhancer Browser–a database of tissue-specific human
enhancers. Nucleic Acids Res., 35, D88–D92.
39. De Bie,T., Cristianini,N., Demuth,J.P. and Hahn,M.W. (2006)
CAFE: a computational tool for the study of gene family
evolution. Bioinformatics, 22, 1269–1271.
40. Dessimoz,C., Zoller,S., Manousaki,T., Qiu,H., Meyer,A. and
Kuraku,S. (2011) Comparative genomics approach to detecting
split-coding regions in a low-coverage genome: lessons from the
chimaera Callorhinchus milii (Holocephali, Chondrichthyes). Brief
Bioinform., 12, 474–484.
41. Vilella,A.J., Severin,J., Ureta-Vidal,A., Heng,L., Durbin,R. and
Birney,E. (2009) EnsemblCompara GeneTrees: complete,
duplication-aware phylogenetic trees in vertebrates. Genome Res., 19,
42. McLaren,W., Pritchard,B., Rios,D., Chen,Y., Flicek,P. and
Cunningham,F. (2010) Deriving the consequences of genomic
variants with the Ensembl API and SNP Effect Predictor.
Bioinformatics, 26, 2069–2070.
43. Fokkema,I.F., Taschner,P.E., Schaafsma,G.C., Celli,J., Laros,J.F.
and den Dunnen,J.T. (2011) LOVD v.2.0: the next generation in
gene variant databases. Hum. Mutat., 32, 557–563.
44. Stabenau,A., McVicker,G., Melsopp,C., Proctor,G., Clamp,M.
and Birney,E. (2004) The Ensembl core software libraries.
Genome Res., 14, 929–933.
45. Smedley,D., Haider,S., Ballester,B., Holland,R., London,D.,
Thorisson,G. and Kasprzyk,A. (2009) BioMart–biological queries
made easy. BMC Genomics, 10, 22.
46. Kinsella,R.J., Ka ¨ ha ¨ ri,A., Haider,S., Zamora,J., Proctor,G.,
Spudich,G., Almeida-King,J., Staines,D., Derwent,P. et al. (2011)
Ensembl BioMarts: a hub for data retrieval across taxonomic
space. Database, 2011, bar030.
Nucleic Acids Research, 2013,Vol.41, Database issueD55
at Wellcome Trust Genome Campus on September 16, 2013