D158–D169 Nucleic Acids Research, 2017, Vol. 45, Database issue Published online 28 November 2016
doi: 10.1093/nar/gkw1099
UniProt: the universal protein knowledgebase
The UniProt Consortium1,2,3,4,*
1
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome
Campus, Hinxton, Cambridge CB10 1SD, UK, 2
SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1
rue Michel Servet, 1211 Geneva 4, Switzerland, 3
Protein Information Resource, Georgetown University Medical
Center, 3300 Whitehaven Street NW, Suite 1200, WA 20007, USA and 4
Protein Information Resource, University of
Delaware, 15 Innovation Way, Suite 205, Newark DE 19711, USA
Received September 19, 2016; Revised October 25, 2016; Editorial Decision October 26, 2016; Accepted November 05, 2016
ABSTRACT
The UniProt knowledgebase is a large resource of
protein sequences and associated detailed annotation.
The database contains over 60 million sequences,
of which over half a million sequences
have been curated by experts who critically review
experimental and predicted data for each protein.
The remainder are automatically annotated based on
rule systems that rely on the expert curated knowledge.
Since our last update in 2014, we have more
than doubled the number of reference proteomes to
5631, giving a greater coverage of taxonomic diversity.
We implemented a pipeline to remove redundant
highly similar proteomes that were causing excessive
redundancy in UniProt. The initial run of this
pipeline reduced the number of sequences in UniProt
by 47 million. For our users interested in the accessory
proteomes, we have made available sets of pan
proteome sequences that cover the diversity of sequences
for each species that is found in its strains
and sub-strains. To help interpretation of genomic
variants, we provide tracks of detailed protein information
for the major genome browsers. We provide
a SPARQL endpoint that allows complex queries of
the more than 22 billion triples of data in UniProt
(http://sparql.uniprot.org/). UniProt resources can be
accessed via the website at http://www.uniprot.org/.
INTRODUCTION
Protein science is entering a new era that promises to unlock
many of the mysteries of the cell’s inner workings. Next
generation sequencing is transforming the way that we access
DNA information and, as the variety of protein assays
that can be linked to a DNA or RNA read-out grows, we are
gaining protein information at an increasing rate. For example,
ribosomal profiling experiments are telling us when and
where ribosomes bind for translation while CHIP-seq and
CRAC give us information on the DNA and RNA binding
properties of proteins. We are also gaining new insights into
the mechanics of large assemblies of proteins through the incredible
strides being made in electron microscopy technology.
However, this wealth of molecular data will be worth
little without it being available to and interpretable by the
scientific community.
UniProt is a long-standing collection of databases
that enable scientists to navigate the vast amount of
sequence and functional information available for proteins.
The UniProt Knowledgebase (UniProtKB) is the
central resource that combines UniProtKB/Swiss-Prot
and UniProtKB/TrEMBL. UniProtKB/Swiss-Prot contains
over 550 000 sequences that have been created by our
expert biocuration team. For these entries experimental information
has been extracted from the literature and organized
and summarized, greatly easing scientists access to
protein information. UniProtKB/TrEMBL provides a further
60 million sequences that have been largely derived
from high throughput sequencing of DNA. These entries
are annotated by our rule based automatic annotation systems.
We also provide a series of UniRef databases that provide
sequence sets trimmed at various levels of sequence
identity (1,2). Finally we provide the UniProt Archive (UniParc)
that provides a complete set of known sequences, including
historical obsolete sequences (3).
In this article we describe the major developments that we
have made since our last update published in this journal in
2014 (4). These updates have helped to improve the scalability
and usability of UniProt for our end users. We also
include a call to all life science authors to include UniProt
accession numbers for proteins within their papers to help
improve the connections between the literature and scientific
databases.
*To whom correspondence should be addressed. Tel: +44 1223 494 100; Fax: +44 1223 494 468; Email: agb@ebi.ac.uk
Present address: Alex Bateman, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton,
Cambridge CB10 1SD, UK.
C The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which
permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Nucleic Acids Research, 2017, Vol. 45, Database issue D159
Figure 1. Growth of the number of sequences in UniProt databases. The
blue line shows the growth in UniProtKB/TrEMBL entries from January
2010 to date. The sharp drop in UniProtKB entries corresponds to the proteome
redundancy minimization (PRM) procedure implemented in March
2015. Note that the post-PRM growth in UniProtKB is no longer exponen-
tial.
PROGRESS AND NEW DEVELOPMENTS
Identifying redundant proteomes
The accelerating growth of sequenced genomes poses great
challenges for databases with their major growth being
driven by sequencing of very similar and almost identical
strains of the same bacterial species. UniProtKB saw an exponential
growth in the 2014–2015 time period, with April
2014 seeing a very high increase, reaching a peak of 90 million
sequences with a high level of redundancy (e.g. 4080
proteomes of Staphylococcus aureus comprising 10.88 million
proteins), see Figure 1. While this wealth of protein
information presents our users with new opportunities for
proteome-wide analysis and interpretation, it also creates
challenges in capturing, searching, preserving and presenting
proteome data to the scientific community. In 2015,
we introduced a method to identify and remove highly redundant
bacterial proteomes within species groups, for example,
strains of Escherichia coli, in UniProtKB while still
making them available for users in UniParc. Briefly, the
method identifies redundant proteomes by performing pairwise
alignments of sets of sequences for pairs of proteomes
and subsequently applies graph theory to find dominating
sets that result in removal of proteomes that cause a minimal
loss of information. Approximately 47 million proteins were
removed from UniProtKB in March 2015. If this procedure
were not being applied UniProtKB would now contain over
217 million sequences compared to the current 65 million.
We have recently extended this approach to fungal species
where there has been a similar if not as large (as yet) explosion
in sequencing of highly similar genomes. As a result of
this, ∼1 million fungal UniProtKB entries were deprecated
in release 2016 08.
Growth of reference proteomes
The UniProt proteome pages (http://www.uniprot.org/
proteomes/) provide access to proteomes for over 56
000 (56 317, release 2016 07) species with completely sequenced
genomes. The majority of these proteomes are
based on the translation of genome sequence submissions
to the International Nucleotide Sequence Database
Consortium (INSDC) but also include predictions from
Ensembl, RefSeq reference genomes and vector/parasitespecific
databases like VectorBase (5) and WormBase ParaSite
(6). Some proteomes may also include protein sequences
based on high quality cDNAs that cannot be
mapped to the current genome assemblies (due to sequencing
errors or gaps) and have been manually reviewed following
supporting evidence, and/or careful analysis of homologous
sequences from closely related organisms. Proteomes
can include both manually reviewed (UniProtKB/SwissProt)
and unreviewed (UniProtKB/TrEMBL) entries. The
proportion of reviewed entries varies between proteomes,
and is obviously greater for the proteomes of intensively
curated model organisms. For example, the proteomes for
Saccharomyces cerevisiae 288C and E. coli strain K12 consist
entirely of reviewed entries. The unreviewed records
are updated by our automatic annotation systems for every
release, ensuring consistency and up-to-date annotations.
To help in tracking and displaying proteomes, we use
a proteome identifier that uniquely identifies the set of proteins
corresponding to a single assembly of a completely sequenced
genome. An example of a proteome can be found
here: http://www.uniprot.org/proteomes/UP000005640.
It is important to realize that there may be additional
records for a particular species which do not belong to
the defined proteome. For more information, please read
http://www.uniprot.org/help/proteome.
As the number of new proteomes increases, it is important
to organize the data in a way that allows users
to effectively navigate the proteomes while keeping relevant
and pertinent information easily accessible. To achieve
this, UniProt provides reference proteomes to give a selection
of species representing a broad coverage of the tree of
life. These reference proteomes are selected via consultation
with the research community or computationally determined
from proteome clusters (7) using an algorithm that
considers the best overall annotation score, an automatically
calculated score which provides a heuristic measure of
the annotation content of a proteome. There are currently
5,631 reference proteomes representing a cross-section of
the taxonomic diversity found in UniProtKB (Figure 2).
They are the focus of both manual and automatic annotation,
aiming to provide the best annotated protein sets
for the selected species. They include model organisms and
other proteomes of interest to biomedical and biotechnological
research. We very much welcome feedback from our
user community on our current list of reference proteomes
and suggestions for new candidates.
Pan proteomes
To complement the growing set of reference proteomes,
UniProt has developed pan proteomes analogous to the
pan genome concept (8). A pan proteome is the full set
D160 Nucleic Acids Research, 2017, Vol. 45, Database issue
Figure 2. The distribution of proteomes and reference proteomes across the tree of life.
of proteins expressed by a group of highly related organisms
(e.g. multiple strains of the same bacterial species). Pan
proteomes provide a representative set of all the sequences
within a taxonomic group and capture unique sequences
not found in the group’s reference proteome. UniProtKB
pan proteomes encompass all non-redundant proteomes
and are aimed at users interested in phylogenetic comparisons
and the study of genome evolution and gene diversity.
For each reference proteome cluster, also known as representative
proteome group (7), a pan proteome is the set of
all the sequences in the reference proteome plus the unique
protein sequences that are found in other species or strains
of the cluster but not in the reference proteome. Details
on how we compute pan proteomes can be found here:
http://pir.georgetown.edu/rps/pp.shtml.
Pan proteomes are available as files of FASTA formatted
sequences on the FTP site. On the UniProt website’s proteomes
pages, you will see a link to download the pan proteome
file when a proteome has proteins that are part of a
larger pan proteome.
Manual curation progress
Expert literature-based curation is stored in the
UniProtKB/Swiss-Prot section of the UniProt Knowledgebase
(UniProtKB) and constitutes a cornerstone of
UniProt. UniProtKB/Swiss-Prot provides high quality
annotation for experimentally characterized proteins and
represents the world’s most comprehensive catalogue of
information on proteins. It is composed of more than
550 000 curated proteins, including all protein-coding
genes for a number of key organisms such as human, S.
Nucleic Acids Research, 2017, Vol. 45, Database issue D161
cerevisiae, Schizosaccharomyces pombe, Bacillus subtilis
and E. coli and contains in-depth information extracted
from more than 210 000 publications that have been fully
curated. In the last year we have added 3183 new entries
to UniProtKB/Swiss-Prot (releases 2015 10 to 2016 09),
which represents the growth in newly characterized
proteins.
In spite of the rise of big data and high-throughput technologies
in recent years, which have shifted a number of
paradigms in the scientific community, expert curation is by
far the most reliable method to report gold-standard information
and provide an up-to-date knowledgebase containing
experimental information. With more than a million articles
indexed every year in PubMed, strict prioritization of
articles and proteins for curation is crucial. A key challenge
is the identification of relevant high-quality articles that allow
for the comprehensive curation of a protein and literature
selection is made according to well-established criteria
which have been documented elsewhere (see (9) for a description
of categories of literature that are prioritized).
Literature curation of post-translational modifications
(PTMs) and their consequences is a priority due to their
crucial role in generating protein complexity and regulating
protein activity, thus controlling many biological processes.
Curation of experimentally determined PTMs from
the literature is important as many PTMs cannot be reliably
predicted by computational tools. Over the years, experimental
PTMs have been catalogued in UniProtKB/SwissProt,
a catalog that can be used both as a high-quality training
set for development and enhancement of bioinformatics
algorithms, and as an essential library for the identification
of proteins by proteomics. Priority is given to articles
that describe new PTMs and/or characterize the effects of
modifications. In addition, to complement this approach,
we have developed a semi-automatic pipeline for integration
of high-throughput proteomics data that is distinct from expert
curation and which adds PTMs from manually evaluated
large-scale proteomics publications (10).
The tubulin-alpha and -beta chains, which regulate microtubule
properties and functions, provide a good example
of the breadth of PTM curation in UniProtKB. A number
of PTMs have been identified on tubulin chains, such as
acetylation, phosphorylation, tyrosination/detyrosination,
nitration, polyglutamylation and polyglycylation. Interestingly,
while these modifications have been known for many
years, the roles they play are only now starting to be understood.
Recent advances in the field suggest the existence of
a tubulin code, similar to the histone code, with a combination
of PTMs that are interpreted by reader proteins (11).
In UniProtKB entries, PTM information is stored in the
‘PTM/Processing’ section, which describes the modified
residues and a summary of what is known about the PTMs.
Figure 3 shows the ‘PTM/Processing’ section for the human
tubulin alpha-1A isotype (UniProtKB Q71U36). Descriptions
of the modified residues are structured in a machinereadable
format with the use of standardized vocabularies
being essential to organize knowledge for subsequent retrieval.
Four annotation types describe different types of
amino acid modifications: modified residue, Disulfide bond,
Cross-link and Glycosylation. Each PTM annotation is associated
with a controlled vocabulary established in collaboration
with the RESID database (12) and PSI-MOD (13).
A list of all terms used in UniProtKB is available in the ptmlist.txt
document (http://www.uniprot.org/docs/ptmlist).
Traceability of data is fundamental. Every new piece of
knowledge is associated with the original source of the information
and the type of supporting evidence, using the
Evidence and Conclusion Ontology (14). In the example
shown in Figure 3, detyrosination, acetylation and nitration
are supported by experimental evidence and the source
references are displayed. In contrast, polyglutamylation (5glutamyl
polyglutamate) is inferred by similarity to a related
protein where the modification has been experimentally determined,
and the source entry (UniProtKB P68369) is indicated.
Interestingly, while polyglutamylation, which consists
of the formation of glutamate side chains on specific
glutamate residues in the C-terminal tail of both tubulinalpha
and -beta chains, is well-known, the precise positions
are still unclear for most proteins. For tubulin-alpha entries,
only one site has been unambiguously identified in mouse.
In addition to describing amino acid modifications on the
protein sequence in a structured format, the ‘PTM’ subsection
provides a comprehensive summary of available PTM
information (Figure 3). We describe, for example, that tubulin
polyglutamylation acts as a rheostat for microtubule severing
by spastin (SPAST, UniProtKB Q9UBP0). Spastin
specifically recognizes and cuts microtubules that are polyglutamylated:
severing activity by spastin increases as the
number of glutamates per tubulin rises from one to eight
and decreases beyond this glutamylation threshold (15). We
also describe the glycylation modification and explain why
human tubulin is only monoglycylated and not polyglycylated
as in other organisms, due to the absence of a functional
TTLL10 enzyme in human (UniProtKB Q6ZVT0).
We attach specific importance to accuracy of knowledge.
Information is reported in appropriate entries, which can
be challenging in the case of tubulins where researchers often
work with extracts containing different isotypes, making
the task of precisely assigning information to the appropriate
protein-coding gene difficult. When experiments are
made on specific tubulin isotypes, information is reported
in the entries corresponding to the relevant isotypes. For
example, Edde et al. determined the protein sequence of a
peptide that is polyglutamylated in mouse (16). The peptide
sequence corresponded to Tuba1a and Tuba1b proteins
and this information is reported in both of these entries
(UniProtKB P68369 and P05213). In cases where experiments
are done with tubulin extracts containing a mixture
of different tubulin isotypes, information is reported in all of
the tubulin isotypes that contain the modification: for example,
Barisic et al. (17) performed experiments in human cells
and information about detyrosination is reportedin all human
tubulin-alpha isotypes that contain a tyrosine residue
at the C-terminus (UniProtKB Q71U36, P68363, Q9BQE3,
Q13748 and Q6PEY2).
Annotation consistency and completeness is essential.
In the course of PTM curation, curators also check that
the annotation content of enzymes that mediate modifications
is up-to-date. For example, detailed information can
be found on proteins that mediate tubulin polyglutamylation
(UniProtKB O95922, Q9BWV7, Q14679, Q6EMB2,
Q8N841, Q6ZT98, Q3SXZ7, Q8NHH1, A6NNM8) and
D162 Nucleic Acids Research, 2017, Vol. 45, Database issue
Figure 3. Screenshot of a part of the ‘PTM/Processing’ section of human TUBA1A entry (UniProtKB Q71U36, http://www.uniprot.org/uniprot/Q71U36)
Figure 4. Growth of automatic annotation rules within UniRule. UniRule integrates rules from HAMAP, PIRSF and RuleBase.
Nucleic Acids Research, 2017, Vol. 45, Database issue D163
acetylation (UniProtKB Q5SQI0). We also describe proteins
that ‘read’ modifications, such as the tubulin-severing
ATPase spastin (UniProtKB accession Q9UBP0), which
specifically acts on microtubules that are polyglutamylated.
The constant effort over the last 30 years has given
rise to a unique resource in terms of PTM information.
In total, more than 450 different types of PTMs are described
in UniProtKB/Swiss-Prot, 45 000 experimentally
proven modification sites are curated and more than 22 000
UniProtKB/Swiss-Prot entries contain experimental information
on PTMs.
Including UniProt accession numbers in scientific papers
The biomedical literature is vast, with over one million papers
being added to PubMed every year. As mentioned
above, UniProt curators triage these and select relevant papers
to create and update our protein entries. This large task
is made more difficult when the curator cannot easily identify
precisely which protein(s) a paper is referring to. This is
often due to a lack of information about the species or strain
of the source organism of the protein being studied. The
simple addition of a UniProt accession number in a paper
could go a long way to helping both UniProt and other resources
to incorporate the protein knowledge from new papers
into their databases. This has many benefits for authors
including increasing the reach and impact of their work. We
request that authors use the following format (UniProtKB
P68369) to describe a protein within the text of a paper.
Using this format can also provide a simple mechanism to
refer to other data resources. Some journals already have
specific formatting requirements for such citations to accessions
and these should be given precedence.
Automatic annotation
We have developed two complementary rule-based systems
to automatically annotate the unreviewed protein sequences
of UniProtKB/TrEMBL with a high degree of
accuracy. The first system, UniRule, consists of annotation
rules which are created by the biocurators as part of
the process of curation of the experimental literature for
UniProtKB/Swiss-Prot. Our priorities for UniRule generation
are (i) to focus on using and annotating new functional
data of interest for proteomes, such as enzymes and
pathways and (ii) to expand our coverage into new taxonomic
and protein families. The developers ensure that the
supporting technical infrastructure enables these rules to be
created, maintained and applied both accurately and efficiently.
UniRule is complemented by the Statistical Automatic
Annotation System (SAAS), a completely automatic
decision-tree-based rule-generating system in which rules
are derived automatically from UniProtKB/Swiss-Prot entries
sharing common annotations and characteristics. Both
UniRule and SAAS use the hierarchical InterPro classification
of protein family and domain signatures (18) as a
basis for protein classification and functional annotation.
These rules share a common syntax that specifies the predicted
annotations––including protein nomenclature, function,
subcellular location and catalytic residues––and necessary
conditions, such as the requirement for conserved functional
residues and motifs. InterPro integrates signatures
from the HAMAP (19) and PIRSF (20) projects within the
UniProt Consortium. Figure 4 illustrates our progress in
UniRule generation to date.
In addition to the ongoing development of our rulebased
systems, we have also extended our automatic annotation
approach in collaboration with the InterPro team
at EMBL-EBI by focussing on enriching the feature annotation
(FT) of UniProtKB/TrEMBL. The SAMs (Sequence
Analysis Methods) are a suite of methods (Coils
v2.2, TMHMM v2.0, Phobius and SignalP v4.0 currently)
from external providers used to automatically generate
sequence features such as Signal, Chain, Transmembrane
and Coil regions. The results of these methods are
combined and refined according to UniProt standards
with the addition of the appropriate UniProtKB annotation.
The new predictions are propagated into all the
UniProtKB/TrEMBL records that do not already have feature
predictions from UniRule. This data was introduced
at UniProt release 2015 10 and is reapplied with each
UniProt release. Another recent development in this collaboration
is the annotation of domain predictions from
the InterPro member databases PROSITE, SMART or
Pfam in UniProtKB/TrEMBL entries. The name of the
domain is taken from UniProtKB/Swiss-Prot annotation
or InterPro entry names. This was introduced at UniProt
release 2015 12 and is reapplied with each UniProt release.
The resulting enrichment of the annotation present
in UniProtKB/TrEMBL has increased significantly with
these complementary approaches, as has the coverage. Annotation
from the automatic annotation systems is labelled
with evidence attribution indicating the specific
source rule/method on both the UniProt website and in
the UniProtKB files available at http://www.uniprot.org/
downloads.
Mapping of proteomic data
High-throughput proteomics experiments deposited in
proteomics repositories are a rich source of annotations
for UniProtKB, providing supporting evidence for
the existence of specific protein isoforms and PTMs.
We have developed a pipeline that provides evidence
annotations to UniProtKB records using proteomics
datasets currently from PeptideAtlas (21) MaxQB (22)
and Encyclopedia of Proteome Dynamics (23). Speciesspecific
lists of identified experimental peptides from
these repositories are uniquely matched to the products
of a single gene by comparison with a database of
in-silico digested peptides within the target UniProtKB
species. Unique peptide identifications are used to generate
annotations in UniProtKB/TrEMBL and to confirm
protein existence. All peptide identifications and their
mapped UniProtKB proteins are available via the UniProt
FTP site (ftp://ftp.uniprot.org/pub/databases/uniprot/
current release/knowledgebase/proteomics mapping/) and
as a dedicated track in the UniProt ProtVista feature viewer
in the website. We have used our pipeline to annotate and
map experimental peptides to UniProtKB proteins in 10
species including Homo sapiens, Mus musculus, Rattus
norvegicus, S. cerevisiae, S. pombe, Caenorhabditis elegans,
Drosophila melanogaster, Danio rerio, Sus scrofa and Bos
D164 Nucleic Acids Research, 2017, Vol. 45, Database issue
Figure 5. The GLA gene (P06280, ␣-galactosidase A) associated with Fabry disease (FD) is shown on the UCSC genome browser using UniProt genome
tracks plus variations from ClinVar, dbSNP and OMIM. Panel (A) shows UniProt annotation for a disulfide bond and an amino acid variation associated
with FD that removes the Cystene required for a structural fold. Similar situations exist in panel (B) where part of the enzyme’s Active Site is disrupted
and panel (C) where an N-linked carbohydrate is located. Only the pathogenic variation in C is annotated in other public resources.
taurus. Future work will focus on the annotation of PTMs
and the extension of mappings to experimental data sets
in the PRIDE repository (24) and the Consortium for Top
Down Proteomics (25).
Mapping the UniProt human proteome to the reference
genome
With the increasing interest and progress in genomic-based
medicine understanding the effects of variation on protein
function is a key component of gene and variant curation
(26,27). Unfortunately much of this information has not
been readily available to medical geneticists. UniProtKB
contains decades of literature-based and semi-automated
curation describing protein function including variation
data (28). Standard NGS annotation tools identify simple
effects like translation stops and missense mutations, but
more subtle effects like disruption of an enzyme active site,
protein-binding site or post-translationally modified site are
often not included. To make this information available to
the genomic community, UniProt in collaboration with Ensembl
has now mapped protein sequence annotation in the
human reference proteome to the GRCh38 build of the human
genome. Figure 5 illustrates pathogenic variants annotated
in UniProt but not yet in ClinVar, dbSNP or OMIM.
The figure also shows how these variants disrupt protein
structural and functional features. Twenty-six positional
annotation tracks and a protein sequence track are provided.
These include: enzyme active sites; modified residues;
protein binding domains; protein isoforms; protein variations;
and more. The mappings and related annotation are
available on the UniProt FTP site as extended BED text files
and binary BigBED files for use on most genome browsers
and also programmatically via a REST API (http://www.
ebi.ac.uk/proteins/api/doc/swagger/index.html). The mapping
and annotation are also available as public Genome
tracks on the Ensembl and UCSC genome browsers and via
the Track Hub Registry (trackhubregistry.org).
Website developments
UniProt follows a user-centered design process, involving
many users worldwide with varied research backgrounds
and use cases, to improve its website and add new features.
The most notable new development is the ProtVista feature
viewer, a BioJS component that brings together sequence
annotations in one compact view. UniProt FTs describe
sites and regions of biological interest, such as an enzyme’s
active site, binding sites, PTMs, domains, etc., which play a
critical role in the understanding of what the protein does.
With the growth in biological data, integration and visualization
become increasingly important for exposing different
data aspects. Similar to genome browsers, ProtVista
uses tracks to display different protein features, providing
an intuitive picture of co-localized elements (see Figure 6).
Each track can be expanded to reveal a more in-depth
view of the underlying data. Users can zoom into and out
of an area and clicking on a feature will trigger a pop-up
Nucleic Acids Research, 2017, Vol. 45, Database issue D165
Figure 6. The ProtVista feature viewer. ProtVista uses tracks to display different protein features providing an integrated intuitive picture. The tracks can
be expanded, as shown in this Figure with the Variants track. Clicking on a feature highlights its position across all tracks so that co-localized elements
can be easily identified. For example here the highlighted site is at the same position as disease correlated natural variants.
with more information about the feature, such as the feature
position, description and any available evidence. The
view can be customized to hide or show feature tracks. The
viewer shows all features of a UniProtKB entry and includes
additional features that are mapped from large-scale studies,
currently for variants and proteomics data. The viewer
is available for every UniProtKB protein entry through the
‘Feature viewer’ link under the ‘Display’ heading on the left
hand side of the entry page.
The UniProt proteome pages (http://www.uniprot.org/
proteomes) were launched in 2015 to enable users to access
proteomes for species with completely sequenced genomes.
One can query the data by taxonomy as well as genome and
proteome identifiers and filter results for reference or nonredundant
proteomes. The results table indicates the number
of UniProt entries for each proteome and allows users
to view or download them in a range of formats.
Individual proteome pages provide a short overview with
details about the organism and genome assembly. The components
of the genome assembly are listed in a table where
it is possible to view or download the UniProt entries for
selected components or the entire proteome. If a proteome
is part of a pan proteome, a download link for the pan proteome
is also provided. The pages for redundant proteomes
link to the corresponding non-redundant proteome. Relevant
publications describing the sequencing of the genome
are also listed. Figure 7 shows the proteome page for B.
subtilis 168––a non-redundant proteome. A short video tutorial
is available on the UniProt YouTube channel (https:
//www.youtube.com/watch?v=ZLt3ug0mZ7A).
We have also added new pages for UniProt’s automatic
annotation systems, UniRule (Unified Rule system) and
SAAS, which are used to automatically annotate unreviewed
UniProtKB/TrEMBL entries in an efficient and
scalable manner. All annotations that are predicted in this
way link back to their source rules and users can now inspect
these rules to understand which conditions have gen-
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Figure 7. Proteome page for Bacillus subtilis 168. Proteome pages contain a short overview with details about the organism and genome assembly, the list
of the genome’s components and references from the sequencing projects.
Figure 8. An example UniRule entry page. Clicking on the conditions highlights the corresponding annotations applied if the conditions hold true and
viceversa clicking on the annotations highlights the corresponding conditions. Clicking on the ‘View all proteins annotated by this rule’ button leads to the
list of proteins that this rule annotates in UniProtKB.
Nucleic Acids Research, 2017, Vol. 45, Database issue D167
Figure 9. The new publications view for UniProtKB entries.
Figure 10. The Peptide search interface.
erated the annotations and to find other proteins that have
been annotated by the same rule (see Figure 8).
UniProtKB protein entries now have an enhanced view
of publications relevant for a protein. UniProtKB contains
more than 350 000 unique publications, with over 210 000
of these fully curated in UniProtKB/Swiss-Prot and the
remainder imported in UniProtKB/TrEMBL. This set is
complemented by more than 640 000 additional publications
that have been computationally mapped from other
resources to UniProtKB entries. The publications annotated
in UniProtKB have previously been displayed in the
entry view and a link provided access to a separate page
that listed the computationally mapped publications. We
have now combined all publications into a new ’Publications’
view that can be accessed from a link under the ‘Display’
section (see Figure 9). This has made it possible to implement
filters to narrow the publication list by categories
that are based on the type of data a publication contains
about the protein (such as function, interaction, sequence,
etc.) and it is easy to see if there are mapped publications
in a category for which no annotated publications are avail-
able.
We have also introduced a new tool called ’Peptide
search’ (see Figure 10) that is available from a link in the
header of the UniProt website. Users can enter a peptide
sequence (for example from a proteomics experiment) into
D168 Nucleic Acids Research, 2017, Vol. 45, Database issue
the search field and the tool quickly finds all UniProtKB
sequences that match 100% with the query sequence (29).
Searches can be restricted to a taxonomic subset of UniProtKB
to further decrease the search time. The tool returns
a results page showing the matched UniProtKB entries in
a design consistent with the UniProtKB text search results
page, including filters on the left, results on the right and an
option to customize the results table through the ‘Columns’
button.
SPARQL endpoint
UniProt has been distributing its databases in RDF format
(https://www.w3.org/RDF/) since 2007. Complete datasets
can be download from the UniProt FTP sites and individual
records and query result sets can be retrieved programmatically
via the UniProt website’s REST API. To allow our
users to also query these RDF data with SPARQL (https:
//www.w3.org/TR/rdf-sparql-query/), we have collaborated
with several database software vendors and provided test
datasets and use cases for performance studies to help
them improve their RDF storage products over the years.
The main challenges for the application of this technology
are the data volume (22 billion triples at UniProt release
2016 07) and the highly variable complexity and number of
user queries (with monthly averages ranging from app. 90
000 to over 4 million in 2015). By 2014, several RDF storage
solutions had reached a stage of maturity that made it
feasible to implement a public SPARQL endpoint for beta
testing. This allowed us to test the technical infrastructure
and also to identify areas for improvements in the data representation.
The http://sparql.uniprot.org/ server has been
running in production since July 2015 and is used by over
1000 unique users every month. It is updated with every
UniProt release and can be linked using SPARQL 1.1’s federated
query capabilities with any remote data resource that
has a SPARQL endpoint.
CONCLUSIONS
Our knowledge of the protein world is rapidly growing, and
the complexity of biological systems is becoming increasingly
clear. UniProt continues to adapt its data gathering,
data processing and data display to improve the availability
and utility of protein information for the benefit of all.
The user community can contact UniProt with feedback
and queries through the ’Contact’ link on the website or
via the e-mail help@uniprot.org. They can also subscribe
to our Twitter feed @UniProt, follow our Facebook page
or follow our blog ’Inside UniProt’ for the latest updates.
ACKNOWLEDGEMENTS
UniProt has been prepared by Alex Bateman, Maria Jesus
Martin, Claire O’Donovan, Michele Magrane, Emanuele
Alpi, Ricardo Antunes, Benoit Bely, Mark Bingley, Carlos
Bonilla, Ramona Britto, Borisas Bursteinas, Hema Bye-AJee,
Andrew Cowley, Alan Da Silva, Maurizio De Giorgi,
Tunca Dogan, Francesco Fazzini, Leyla Garcia Castro,
Luis Figueira, Penelope Garmiri, George Georghiou,
Daniel Gonzalez, Emma Hatton-Ellis, Weizhong Li,
Wudong Liu, Rodrigo Lopez, Jie Luo, Yvonne Lussi, Alistair
MacDougall, Andrew Nightingale, Barbara Palka,
Klemens Pichler, Diego Poggioli, Sangya Pundir, Luis
Pureza, Guoying Qi, Steven Rosanoff, Rabie Saidi, Tony
Sawford, Aleksandra Shypitsyna, Elena Speretta, Edward
Turner, Nidhi Tyagi, Vladimir Volynkin, Tony Wardell,
Kate Warner, Xavier Watkins, Rossana Zaru and Hermann
Zellner at the European Bioinformatics Institute;
Ioannis Xenarios, Lydie Bougueleret, Alan Bridge, Sylvain
Poux, Nicole Redaschi, Lucila Aimo, Ghislaine ArgoudPuy,
Andrea Auchincloss, Kristian Axelsen, Parit Bansal,
Delphine Baratin, Marie-Claude Blatter, Brigitte Boeckmann,
Jerven Bolleman, Emmanuel Boutet, Lionel Breuza,
Cristina Casal-Casas, Edouard de Castro, Elisabeth Coudert,
Beatrice Cuche, Mikael Doche, Dolnide Dornevil, Severine
Duvaud, Anne Estreicher, Livia Famiglietti, Marc
Feuermann, Elisabeth Gasteiger, Sebastien Gehant, Vivienne
Gerritsen, Arnaud Gos, Nadine Gruaz-Gumowski,
Ursula Hinz, Chantal Hulo, Florence Jungo, Guillaume
Keller, Vicente Lara, Philippe Lemercier, Damien Lieberherr,
Thierry Lombardot, Xavier Martin, Patrick Masson,
Anne Morgat, Teresa Neto, Nevila Nouspikel, Salvo
Paesano, Ivo Pedruzzi, Sandrine Pilbout, Monica Pozzato,
Manuela Pruess, Catherine Rivoire, Bernd Roechert,
Michel Schneider, Christian Sigrist, Karin Sonesson, Sylvie
Staehli, Andre Stutz, Shyamala Sundaram, Michael Tognolli,
Laure Verbregue and Anne-Lise Veuthey at the SIB
Swiss Institute of Bioinformatics; Cathy H. Wu, Cecilia N.
Arighi, Leslie Arminski, Chuming Chen, Yongxing Chen,
John S. Garavelli, Hongzhan Huang, Kati Laiho, Peter McGarvey,
Darren A. Natale, Karen Ross, C. R. Vinayaka,
Qinghua Wang, Yuqi Wang, Lai-Su Yeh and Jian Zhang
at the Protein Information Resource.
FUNDING
National Institutes of Health [U41HG007822, U41HG0
02273, R01GM080646, P20GM103446, U01GM120953];
British Heart Foundation [RG/13/5/30112]; Parkinson’s
Disease United Kingdom [G-1307]; Swiss Federal Government
through the State Secretariat for Education, Research
and Innovation and European Molecular Biology Laboratory
core funds. Funding for open access charge: National
Institutes of Health [U41HG007822].
Conflict of interest statement. None declared.
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