Published online before print
March 12, 2003, 10.1101/gr.461403
Vol 13, Issue 4, 662-672, April 2003
METHODS
The Gene Ontology Annotation (GOA) Project: Implementation of GO in SWISS-PROT, TrEMBL, and InterPro
Evelyn Camon1,3,4,
Michele Magrane1,3,
Daniel Barrell1,
David Binns1,
Wolfgang Fleischmann1,
Paul Kersey1,
Nicola Mulder1,
Tom Oinn1,
John Maslen1,
Anthony Cox2 and
Rolf Apweiler1
1EMBL Outstation—European Bioinformatics Institute,
Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK;2
Sanger Institute, Wellcome Trust Genome Campus, Hinxton,
Cambridge, CB10 1SA, UK
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ABSTRACT
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Gene Ontology Annotation (GOA) is a project run by the European
Bioinformatics Institute (EBI) that aims to provide assignments of
terms from the Gene Ontology (GO) resource to gene products in a number
of its databases (http://www.ebi.ac.uk/GOA). In the first stage of this
project, GO assignments have been applied to a data set representing
the complete human proteome by a combination of electronic mappings and
manual curation. This vocabulary has also been applied to the
nonredundant proteome sets for all other completely sequenced organisms
as well as to proteins from a wide range of organisms where the
proteome is not yet complete.
Continual advancement in proteome research has led
to an increase in sequences from a wide range of species requiring
addition to the SWISS-PROT Protein Knowledgebase and its supplement,
TrEMBL (Bairoch and Apweiler 2000 ), the majority of these lacking
functional characterization. To fully exploit the potential of this
vast quantity of data, the SWISS-PROT group has intensified its efforts
to capture all available biological information related to these
sequences and, in particular, to the human proteome.
Crucial to this work is the integration of in-house resources with
those of external database groups. Integration and data exchange
involve resolving the complexities that exist between databases. For
example, the use of different vocabularies to describe gene function
can hinder searching across multiple proteins and species for common
characteristics. The use of a common vocabulary facilitates the
identification of relationships and common properties between gene
products from different species.
This problem has been addressed by the creation of the Gene Ontology
(GO) resource (The Gene Ontology Consortium 2001), a dynamic,
controlled vocabulary that can be applied to all organisms even as
protein knowledge is accumulating and changing. The GO Consortium has
developed three separate ontologies: molecular function, biological
process, and cellular component. These help to describe gene products
in a standardized way and allow the annotation of molecular
characteristics across species. Each vocabulary is structured as a
directed acyclic graph (DAG), wherein any term may have more than one
parent as well as zero, one, or more children. This makes attempts
to describe biology much richer than would be possible with a
hierarchical graph. Currently, the GO vocabulary consists of >13,000
terms, which will, in time, all have strict definitions of their
intended usage.
SWISS-PROT has joined the GO Consortium and has adopted its structured
vocabulary to characterize the activities of proteins in the
SWISS-PROT, TrEMBL, and InterPro (Apweiler et al. 2001a) databases. It
has initiated the Gene Ontology Annotation (GOA) project to provide
assignments of GO terms to gene products for all organisms with
completely sequenced genomes by a combination of electronic assignment
and manual annotation. By annotating all characterized proteins with GO
terms and facilitating the transfer of this knowledge to similar
uncharacterized proteins, the SWISS-PROT group will make a valuable
contribution to biological and biotechnological research through a
better understanding of all proteomes.
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METHODS AND RESULTS
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Automatic GO Annotation of SWISS-PROT, TrEMBL, and InterPro
The first phase of the GOA project involved the large-scale
assignment of GO terms to SWISS-PROT and TrEMBL entries using
electronic methods. This strategy was based on the use of existing
properties of the entries including the presence of keywords and Enzyme
Commission (EC) numbers. Mapping of InterPro entries to GO also allowed
further associations of GO terms to entries to be made, based on the
presence of InterPro cross-references in SWISS-PROT and TrEMBL.
"Mapping" is used here to refer to the linking of various
classification systems to GO terms, while the word
"association" refers to a connection between a database object
(which may represent a gene, transcript, or protein) and a GO term
that describes the gene product. The electronic mappings described in
detail below were used to generate associations in SWISS-PROT and
TrEMBL.
Mapping SWISS-PROT Keywords to GO
SWISS-PROT and TrEMBL entries contain keywords that serve as a
subject reference for each sequence and assist in the retrieval of
specific categories of data from the database. Currently, SWISS-PROT
maintains a controlled list of  840 keywords, each with a definition
to clarify its biological meaning and intended usage. This list is
available at http://www.expasy.org/cgi-bin/keywlist.pl and is
updated on a regular basis. Seventy-four percent of SWISS-PROT keywords
have been manually mapped to a high-level GO term. Keywords that were
not mapped include those that have multiple usages, have no equivalent
GO term, or are beyond the scope of the GO project, such as keywords
describing domains.
To enable data transfer, an index file containing the SWISS-PROT
keyword to GO mappings (spkw2go) is shared on the GO home page
(http://www.geneontology.org/external2go/spkw2go). This is frequently
updated with the latest version, helping users to keep track of changes
for local use. During these updates, more specific GO mappings may be
added and obsolete GO terms and SWISS-PROT keywords removed.
During manual annotation of a SWISS-PROT entry, curators assign
keywords based on literature and sequence analysis checks. Keywords are
also added to TrEMBL entries during automatic annotation of the TrEMBL
database (Apweiler 2001). This procedure utilizes a novel system of
standardized transfer of annotation from well-characterized proteins in
SWISS-PROT to unannotated TrEMBL entries (Fleischmann et al. 1999).
Consequently, the accuracy of the association of GO terms to SWISS-PROT
and TrEMBL entries based on the keywords in the entries is assured by
the annotation quality standards already existing in SWISS-PROT. To
associate GO terms to SWISS-PROT and TrEMBL entries, the spkw2go
mapping is combined with a mapping of protein accession numbers to
SWISS-PROT keywords. In-house annotation tools and browsers are updated
automatically as the new data is loaded.
The application of SWISS-PROT keywords in the electronic assignment of
GO terms to gene products continues to be a large-scale success. As of
November 2002, spkw2go has been used to generate over 1,023,969 GO
associations with 376,845 SWISS-PROT and TrEMBL entries (see
http://www.ebi.ac.uk/GOA/SPTR_release.html). It has also been used
successfully by a number of external databases such as the Mouse Genome
Database (MGD) (Hill et al. 2001).
Mapping of EC Numbers to GO
A second electronic strategy takes advantage of an existing mapping
(ec2go) of GO terms from the molecular function ontology to the
nomenclature of the EC as contained in the ENZYME database (Bairoch
2000). EC numbers are consistently annotated in SWISS-PROT and TrEMBL
enzyme entries as part of the description line. To associate GO terms
to the SWISS-PROT and TrEMBL data, the ec2go mapping available from the
GO home page was updated and combined with a mapping of protein
accession numbers to EC numbers. This strategy was very successful,
generating 164,205 GO associations in 72,496 SWISS-PROT and TrEMBL
proteins.
Mapping of the InterPro Resource to GO
InterPro is an integrated documentation resource for protein
families, domains, and sites that combines the complementary efforts of
the PROSITE (Falquet et al. 2002), PRINTS (Attwood et al. 2002),
Pfam (Bateman et al. 2002), ProDom (Corpet et al. 2000), SMART (Letunic
et al. 2002), and TIGRFAMs (Haft et al. 2001) databases. Individual
signatures from the member databases, which describe the same protein
family or domain, are grouped together into a single InterPro entry.
Each entry provides comprehensive annotation describing a set of
related proteins, some of which may have identical molecular functions,
be involved in the same processes, and perform their function in the
same cellular locations. Furthermore, each entry also contains a match
list of the SWISS-PROT and TrEMBL proteins that hit the signatures in
that entry. Mapping InterPro entries to GO terms thus provides an
automatic means of assigning GO terms to the protein sequences that
form the match table of a particular InterPro entry. An additional
advantage is that multifunctional proteins can be mapped to multiple GO
terms through associations with more than one InterPro entry.
The assignment of GO terms to InterPro entries was performed manually
by inspecting all available information. In each case, the abstracts
and the annotation of proteins within the match lists were read and an
appropriate GO term was mapped if it applied to the whole protein. Some
entries could be mapped to very deep level (specific) GO terms, while
entries describing wider families or common domains could only be
mapped to higher level terms or could not be mapped at all. The
associated GO term therefore applies to all proteins with true hits to
all signatures in the InterPro entry. As of November 2002, the
electronic application of these InterPro mappings has led to 1,333,215
GO associations with 442,293 SWISS-PROT and TrEMBL proteins. The
integrity of the InterPro to GO mappings is maintained by running
regular sanity checks on the data. These checks include searching for
mappings from secondary or deleted InterPro accession numbers and
mappings to obsolete or nonexistent GO terms. The reports are manually
verified and corrected.
For each associated term, the name of the term and GO accession number
is given, and these are available in InterPro entries directly from the
database at http://www.ebi.ac.uk/interpro/ (Fig.
1). A file listing InterPro entries and
their corresponding GO terms is also available from the EBI FTP site at
ftp://ftp.ebi.ac.uk/pub/databases/interpro/interpro2go and, on the GO
home page at http://www.geneontology.org/interpro2go. InterPro includes
a sequence search facility that allows users to search a sequence
against the database and to retrieve all InterPro matches for that
sequence. As well as an SRS-based version, there is also a stand-alone
Perl-based package available for local installation that returns GO
terms as part of the results.
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Figure 1. InterPro entry IPR000040 (acute myeloid leukemia 1 protein [AML
1]/Runt) that shows the GO terms that have been manually mapped to the
entry.
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GO Annotation of the Human Proteome
As part of a consortium agreement to fast-track GO annotation of
human data, SWISS-PROT curators have manually assigned GO terms to a
SWISS-PROT/TrEMBL/Ensembl nonredundant human proteome set. The dataset
was created by combining all human protein sequences in the SWISS-PROT
database and supplementing these with a nonredundant selection of human
proteins from the TrEMBL and Ensembl (Hubbard et al. 2002) databases.
Full details of the preparation of the set are available at
http://www.ebi.ac.uk/proteome/SPTREnsembl.html. This set contained
28,736 sequences, of which 7146 came from SWISS-PROT, 14,659 from
TrEMBL and 6931 from Ensembl.
Before manual annotation, the dataset was annotated with GO terms using
the various electronic methods described above. Next, assignments of GO
terms to LocusLink (Pruitt and Maglott 2001) were added. LocusLink is a
database of genetic loci, many of which had GO terms assigned to them
in a one-off annotation marathon by Proteome Inc. LocusLink loci were
therefore mapped to SWISS-PROT and TrEMBL entries by identifier
tracking, and relevant assignments were transferred.
Entries for which no assignments were available or for which only
electronically applied GO terms existed were identified as potential
candidates for manual annotation. This set was then filtered to remove
those entries with references, which were unlikely to contain useful
content. For example, entries whose only references describe a DNA
sequencing project were not considered for manual annotation at this
stage. A reduced set of 3042 entries was thus identified for priority
manual annotation by the SWISS-PROT curation team.
The assignments to this set of 3042 entries were based initially on
abstract information. GO terms from each of the three ontologies were
assigned to each entry in the set, using the GO evidence codes
described at http://www.geneontology.org/GO.evidence.html. During
this initial manual annotation phase, a large number of new GO terms
were requested by the SWISS-PROT and InterPro curators, thus extending
the coverage of GO and increasing its utility in the analysis of human
proteins. At the end of this phase of the project, 9927 manual
associations of GO terms to the human proteome set had been made.
Together, these assignments represent the first stage of the GOA
project at EBI, released in November 2001. Complete references for each
entry are now being read so that additional or deeper-level GO terms
can be assigned. Current numbers of electronic and manual assignments
to the human data set are shown at
http://www.ebi.ac.uk/GOA/release.html. The SWISS-PROT group at EBI will
continue to prioritize the fast-tracking of human GO annotation.
Manual Annotation of GO to Proteins From All Organisms
One of the distinguishing features of the SWISS-PROT database is the
high level of annotation it provides in each entry. This is achieved by
a team of biologists who extract up-to-date information from a variety
of sources, including published literature and compile this information
into a concise but comprehensive report. SWISS-PROT curators are
therefore well placed to contribute to the work of the GO consortium by
assigning GO terms during the annotation process and now assign GO
terms to every entry that they annotate. As these entries come from a
wide range of organisms (50,000 different species), they also continue
to contribute to the expansion of the ontologies by requesting new
terms when necessary, thus extending the scope of the GO ontologies
beyond those terms required to describe the proteins of the model
organism databases, SGD (Dwight et al. 2002), FlyBase (The FlyBase
Consortium 2002), and MGD (Blake et al. 2002), the founding members of
the GO consortium.
Data Searching and Retrieval
The EBI contributes to functional studies by distributing and
updating GO mappings and associations generated in-house. This data is
displayed via the QuickGO browser, Gene Association file, EBI and GO
FTP servers, SRS and Proteome Analysis pages as detailed below.
QuickGO
QuickGO (http://www.ebi.ac.uk/ego/) is a fast, Web-based browser
that was developed at the EBI to allow users to search and browse GO
data and associated links to other data sets. It has access to the core
GO data comprising the terms of the three GO ontologies, the
relationships between these terms, their synonyms, and definitions
where such exist. In addition, QuickGO accesses the manually curated
annotations and mappings of SWISS-PROT keywords, InterPro entries, and
the EC and Transport Commission (http://tcdb.ucsd.edu/tcdb/)
classification schemes to GO terms, as well as electronically and
manually curated associations of GO terms to SWISS-PROT and TrEMBL
entries (GOA). There are also links to the Expression Profiler GO
browser (EP:GO) (http://ep.ebi.ac.uk/EP/GO/), which allows the
extraction of genes associated with each GO category and the analysis
of gene expression, regulatory sequence, and protein–protein
interaction data for these genes. QuickGO is updated on a weekly basis
so that all electronic and manual mappings and associations displayed
reflect the current status.
The QuickGO search interface has recently been updated. For example,
querying by protein accession number will show all terms mapped to that
SWISS-PROT entry and the source of each term association (Fig.
2A). The
default setting will retrieve all associations but it is also possible
to display only manually assigned GO terms. Searches may return
multiple results, in which case an exploded view of the subset of GO
that contains all or selected results can be seen according to their
position within the DAG structure (context view).
The GO term page displays all information currently held at the EBI for
that term including the term name, term ID, and definition. Two
different views for each term are available: a denormalized tree view
of the GO structure ascending from the term (Fig. 2B) or a graphical
tree view (Fig. 2C), which makes it easier to visualize the position of
a GO term within the hierarchy. The concise, denormalized view can be
selected as the default view. New GO users may prefer the graphical
output for tracing more complex paths.
Another useful and unique feature of the QuickGO browser is its display
of common concurrent assignments, that is, GO terms that are frequently
assigned in tandem. Although, as an explicit part of the GO design
there are no relations that span the three ontologies, there are
clearly links between terms in different ontologies. By applying
data-mining techniques, a large number of pairs of GO terms that are
commonly associated with one another were found (S. Clelland, unpubl.
2001). For example, "heavy-metal ion transporter", which is part of
the process ontology and "heavy-metal ion transport", which is part
of the function ontology, are often found assigned together. Therefore,
the QuickGO entry for the GO term "heavy-metal ion transporter"
lists "heavy-metal ion transport" as a common concurrent
assignment. These data act not only as a curation guide but also point
to potential problems with the GOA data in its current state.
Gene Association File
The most common form of data transfer within the GO Consortium is a
tab-delimited file of the associations between gene products and GO
terms referred to as a gene association file. Because SWISS-PROT
annotates proteins rather than genes, the semantics of some fields are
slightly different to gene association files produced by other
consortium members, and these details are fully documented at
http://www.ebi.ac.uk/GOA. Currently, the SWISS-PROT group at EBI
produces two GOA files: GOA-Human contains the GO assignments for the
proteins in the SWISS-PROT, TrEMBL, and Ensembl nonredundant human
proteome set, and GOA-SPTR contains all GO annotations to SWISS-PROT
and TrEMBL. For each association, cross-references are supplied to
SWISS-PROT, TrEMBL, Ensembl, the International Protein Index (IPI)
(http://www.ebi.ac.uk/IPI/), and GO, along with evidence for its
annotation according to GO evidence codes. Data provided by the GOA
project is available on the EBI FTP site at
ftp://ftp.ebi.ac.uk/pub/databases/GO/goa/ as well as the GO FTP site
at ftp://ftp.geneontology.org/pub/go/gene-associations/. In addition to
the human gene association file, the EBI releases a file of cross
references that displays the relationship between the entries in the
GOA data set with other databases, such as the EMBL-Bank/GenBank/DDBJ
nucleotide sequence databases (Stoesser et al. 2003), HUGO, and
LocusLink and RefSeq (Pruitt and Maglott 2001) at the NCBI. This
tab-delineated file is available at
ftp://ftp.ebi.ac.uk/pub/databases/GO/goa/HUMAN/xrefs.goa and is updated
with each GOA-Human release. Monthly releases of GOA will include the
regular replacement of electronic associations with experimentally
verified evidence codes. In between GOA releases, we recommend use of
the QuickGO browser for the latest curated associations.
Sequence Retrieval System (SRS)
The EBI's SRS server (Zdobnov et al. 2002) at
http://srs.ebi.ac.uk/ acts as a central resource for biological
databases and now includes the EBI's gene association file of the GOA
project as well as a mirror of the GO Consortium repository. This is a
welcomed development to the query form, as it allows more functional
questions to be asked across a range of databases, quickly and
efficiently. Under SRS, the gene association file can be searched using
a range of fields including GO ID, SWISS-PROT, or TrEMBL accession
number, and GO evidence type. The sample query shown in Figure
3A illustrates how a user asks the
question, "How many protein sequences in GOA have been manually
assigned the GO function ‘electron transfer flavoprotein’?" The
user can simply create a query that searches for all proteins linked to
the GO term "electron transfer flavoprotein" (GO:0008246) and
filters out any associations that have an IEA evidence code. A further
facility of SRS is its ability to link to databases that do not contain
direct references to each other. As such, the user can perform a second
search to gather all the sequences in his previous search with
accession numbers for the EMBL/GenBank/DDBJ nucleotide databases (Fig.
3B,C,D).
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Figure 3. Searching Gene Ontology Annotation (GOA) database with Sequence
Retrieval System (SRS). (A) To find all annotated proteins
that function as electron transfer flavoproteins and that have an
experimental evidence code (Non-IEA), the "goid" field is searched
for the GO identifier "0008246"in the GOA database. In the
"combined searches with" section of the tool bar, the "BUTNOT"
option is selected and "IEA" (the GO evidence code for "inferred
from electronic annotation") is entered in the "evidence" field.
(B) This produces a query result, which displays all proteins
manually assigned the function of "electron transport flavoprotein"
using published literature. Associations made by electronic inference
are filtered out and results displayed in the "gene association
file" format. (C, D) A further facility of SRS is its
ability to link to databases that may or may not contain direct
references to each other. As such, the last search can be extended to
display EMBL/GenBank/DDBJ accession numbers by selecting the "link"
option and choosing the EMBL database and "submit link."
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Applications of GOA in Proteome Analysis
An application of the GO mappings at the EBI can be seen on the
Proteome Analysis pages (http://www.ebi.ac.uk/proteome/) produced by
the SWISS-PROT group, where GO is used for classification of proteins
belonging to each complete proteome. The aim of the Proteome Analysis
project (Apweiler et al. 2001b) is to provide proteome sets for whole
genomes with comprehensive statistical and comparative analyses.
Nonredundant sets of SWISS-PROT and TrEMBL entries are produced for
each complete proteome, based on genome sequence submissions and
additional knowledge researched by SWISS-PROT curators. The statistical
and comparative analyses are compiled using InterPro, CluSTr
(Kriventseva et al. 2001), and GO and contain structural information
derived from the HSSP (Dodge et al. 1998) and PDB (Westbrook et al.
2002) databases.
For each proteome, there is an automatically generated table showing
the general statistics for the number of proteins that can be assigned
to a selection of high-level terms from each of the GO ontologies.
These terms have been selected to cover most aspects of the ontologies
without overlapping in paths in the GO hierarchy and are described as
"GO Slim". Using the mappings of proteins within a proteome set to
GO, which are derived from assignments based on InterPro, SWISS-PROT
keywords, and EC numbers as well as from manual assignments, the
mappings are collapsed to the selected high-level terms. The number of
proteins mapped to each selected term is calculated to provide a table
of statistics of the relative percentage of proteins in the proteome
mapped to each term (Fig. 4). Because
proteins may be assigned to more than one GO term, some proteins will
have been counted more than once. The GO Slim used by EBI is archived
at ftp://ftp.geneontology.org/pub/go/GO_slims/goslim_goa.2002.
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Figure 4. Example of a table from the proteome analysis database showing the
general statistics for the number of proteins in the human proteome
that can be assigned to a selection of high-level terms (GO Slim) from
each of the three gene ontologies.
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The functional classification and mapping of InterPro families and
domains, as well as SWISS-PROT keywords and EC numbers, to GO provides
a simple method for determining whole proteome composition and provides
a basis for comparative analysis. In addition, the CluSTr database has
links to InterPro and, from there, to the corresponding functional
classification codes and GO terms, making it is possible to identify
protein functions within clusters.
Applications of GOA in Genome Analysis
To support the mapping of biological knowledge, and especially to
facilitate the interpretation of genomic data, the GOA project
annotations have been cross linked to the coding regions directly in
EMBL-Bank flatfiles, which contain the nucleotide sequences of the
international collaboration EMBL-Bank/GenBank/DDBJ. GOA project
annotations have also been integrated into Ensembl, a joint project
between EMBL-EBI and the Sanger Institute, which produces automatic
annotation on eukaryotic genomes. GOview provides an interface to the
Ensembl gene database via the GO hierarchy. GO annotations (GOA-Human)
have been mapped to Ensembl human genes and the GO hierarchy can be
navigated directly or searched to identify matching loci. The resulting
gene matches are displayed in their tribe families and graphically as
locations on the human genome. Ensembl to GOA mappings are currently
only available for human, although these will be extended to mouse at
the next release. As GOA and Ensembl releases are not synchronized,
users should check the version of GOA-Human, which has been used within
GOview. An example GOview can be seen at
http://www.ensembl.org/Homo_sapiens/geneview?gene=ENSG00000139618.
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DISCUSSION
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Manual annotation produces reliable GO annotation but is an
inefficient approach to tackling the vast amounts of data already
accumulated in SWISS-PROT and TrEMBL from the various genome projects.
On the other hand, electronic techniques offer a much quicker approach
to the assignment of GO terms to new data while enabling a retrofit of
GO annotation to previously curated data. To date, 64% of all proteins
stored in the SWISS-PROT and TrEMBL databases have been annotated with
GO terms using electronic methods. This represents 2.5 million
associations covering 544,362 proteins out of a total of 850,795
(http://www.ebi.ac.uk/GOA/SPTR_release.html). In contrast, GO
associations generated by biologists cover just 1% of SWISS-PROT and
TrEMBL entries. The electronic methods are responsible for assigning GO
terms to entries from almost 50,000 different species while manual
methods have assigned GO terms to entries from 182 different species.
By annotating GO terms to such a wide variety of species, the
SWISS-PROT group makes a substantial contribution to the GO Consortium
efforts.
Of the electronic techniques, the InterPro to GO mapping (Interpro2go)
has generated the most associations followed closely by the application
of SWISS-PROT keywords to GO (spkw2go). GO annotation by electronic
techniques assigned GO terms unevenly across the three ontologies (Fig.
5). Interestingly, InterPro associations
showed a strong bias toward the assignment of function (92%) and
process (81%) terms, whereas the use of SWISS-PROT keyword mappings
assigned much fewer function terms (33%) but was a little better than
InterPro at assigning component terms (52%). EC numbers have been
mapped only to terms from the function ontology so no comment can be
made on the success of this method in assigning terms from the process
or component ontologies. However, the average depth of terms assigned
based on mappings of EC numbers to GO is higher than that of either of
the other two electronic methods. The depth of a term is used here to
mean the number of terms from the parent term to the assigned term. The
average depth of predictions based on EC numbers is 10.54 whereas for
InterPro, it is 5.94 and for SWISS-PROT keywords, 4.67. The average
overall depth of terms assigned using electronic methods is 5.73.
Manual annotation assigned GO terms more evenly across the three
ontologies (data not shown) and provided literature references and
information about the type of experiments used through GO evidence
codes. These results indicate that different methods have their merits
and limitations and that combining multiple techniques to assign GO
terms increases annotation coverage, an observation also reported by
others (Schug et al. 2002).
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Figure 5. Percentage of proteins associated with GO terms from each ontology,
using the interpro2go and spkw2go mappings (interpro2go = mapping of
InterPro entries to GO terms; spkw2go = mapping of SWISS-PROT
keywords to GO).
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Although the number of incorrect assignments made electronically was
not directly measured in this study, the number of times the different
electronic techniques predicted GO assignments in the same lineage has
been calculated. In general, electronic techniques assign more general
GO terms (higher in the GO hierarchy) than can be applied by manual
efforts (data not shown). However, of all SWISS-PROT and TrEMBL entries
that had multiple GO associations, 94% had terms assigned by different
electronic methods that were from the same lineage, that is, they had
the same parent term in GO. As a first-round annotation approach, GO
associations by electronic techniques worked extremely well and
provided a useful guide to curators who often found evidence to assign
more precise terms (deeper in the GO hierarchy) within the same or new
lineages. On very few occasions, InterPro GO assignments were
inconsistent with manual curation. This discrepancy was related to the
fact that not all proteins function according to their membership in a
particular family. This membership may only represent their
evolutionary origin. An example of such an occurrence is that of the
cytokine subunit of the IL-12 heterodimer, p40, which evolved from a
primordial IL-6-like receptor (Schoenhaut et al. 1992; Shields et al.
1995) and was assigned the GO term "hematopoeitin/interferon-class
(D200-domain) cytokine receptor" (GO:0004896) through an InterPro
to GO mapping. In fact, IL-12 p40 does not function as a conventional
membrane-bound cytokine receptor. It does, however, bind the second
subunit of IL-12, p35 (relative of IL-6), to form a functional
heterodimer with very different cytokine functions. This shows that GO
terms assigned by electronic means should be treated with a certain
amount of caution. It also highlights the need to find real evidence in
published literature by either manual or text mining efforts and for
users of GO annotation to alert sources of electronic GO associations
when inconsistencies occur. It should equally be noted that for
proteins of unknown function, GO associations made using InterPro can
also offer very precise first-round functional predictions. Such data
may help identify new relatives of biologically important proteins and
possibly identify candidates for further experimental analysis.
Future Work
To promote database interoperability and provide consistent
annotation, the SWISS-PROT group will continue to assign GO terms to
the gene products of the SWISS-PROT knowledgebase and its supplementary
database, TrEMBL. As the EBI hosts the GO editorial office, the
SWISS-PROT curators already work closely with the GO curators in their
efforts to expand and improve the GO resource.
Ongoing refinement of automated procedures in the TrEMBL section of the
SWISS-PROT knowledgebase is paramount to the success of our large-scale
GO annotation. The group will continue to develop these methods and
will resolve any in-house complexities that may arise from the
integration with other database resources. In collaboration with the
Swiss Institute of Bioinformatics (SIB), new GO mappings will be
released in 2003 for SWISS-PROT subcellular location as well as from
PROSITE (Sigrist et al. 2002) and HAMAP databases.
The continuous assignment of GO terms to additional SWISS-PROT and
TrEMBL entries by manual and electronic strategies will be reflected in
subsequent releases of GOA. In each release, electronic associations
are replaced by terms that are based on experimental evidence.
SWISS-PROT biologists will assign these more detailed terms during
literature-based GO curation.
The incorporation of GO data from model organism databases is also
planned. Only data with a non-IEA association and nonreview literature
reference will be considered for inclusion in the SWISS-PROT GOA
releases. In time, we will produce separate GOA files for each
proteome. The group also plans to improve displays of GO data on
proteome analysis pages and to develop the display pages for the
SWISS-PROT/TrEMBL/Ensembl proteome set. The association of GO terms to
the clusters in our CluSTr database are also included in the project
plans.
Currently, 64% of SWISS-PROT and TrEMBL entries have been mapped to GO
terms. We aim to have assigned GO terms to more than 70% of the
SWISS-PROT and TrEMBL records by 2004.
How to Submit Updates to Our GO Annotation
Although a careful one-pass annotation is initially useful, it is
certain that as our knowledge of biology develops, both the SWISS-PROT
annotation and GO vocabulary will grow and change. As such, we envisage
that proteins in SWISS-PROT and TrEMBL will need to be updated
regularly to keep up with this expanding knowledge. The success and
accuracy of our GO annotations rely on frequent electronic and manual
checking. As with SWISS-PROT curation, the group actively encourages
updates or corrections from the scientific community to improve this
shared resource. For all inquiries and corrections to the GOA project,
please use the contact e-mail: goa{at}ebi.ac.uk.
|
WEB SITE REFERENCES
|
|---|
http://www.geneontology.org/external2go/interpro2go; InterPro to GO
mappings.
http://www.geneontology.org/external2go/spkw2go; SWISS-PROT keyword to
GO mappings.
http://www.geneontology.org/GO.evidence.html; GO evidence codes.
http://ep.ebi.ac.uk/EP/GO/; Expression Profiler GO browser.
http://www.ensembl.org; Ensembl Home Page.
http://www.ebi.ac.uk/GOA; GO Annotation at EBI home page.
http://www.ebi.ac.uk/IPI/; International Protein Index.
http://www.ebi.ac.uk/interpro/; InterPro home page.
http://www.ebi.ac.uk/interpro/scan.html; InterProScan.
http://www.ebi.ac.uk/proteome/; Proteome Analysis database.
http://www.ebi.ac.uk/proteome/SPTREnsembl.html; SPTr-Ensembl human
proteome set.
http://www.ebi.ac.uk/ego/; QuickGO browser.
http://srs.ebi.ac.uk/; SRS server at EBI.
http://www.expasy.org/cgi-bin/keywlist.pl; SWISS-PROT keyword list.
http://tcdb.ucsd.edu/tcdb/; Transport Commission database home page.
http://us.expasy.org/sprot/hamap/; High-Quality Automated and Manual
Annotation of microbial Proteomes (HAMAP) database home page.
http://us.expasy.org/prosite; Prosite home page.
|
Acknowledgements
|
|---|
The GO project at EBI is supported by two European Union contracts
BioBabel (QLRT-2000–00981) and TEMBLOR (QLRI-2001–00015, Hermjakob
and Apweiler, 2002) and a supplementary NIH grant (1R01HGO2273–01). We
are grateful to the SWISS-PROT curation team for the manual assignment
of GO terms to SWISS-PROT and TrEMBL entries and to Sarah Clelland for
generating the common concurrent assignments in QuickGO as part of an
undergraduate research project. We also thank John Maslen for recent
improvements to in-house GO annotation tools and GO Consortium members,
Michael Ashburner (FlyBase) and David Hill (MGD) for their improvement
of the SWISS-PROT keyword to GO mapping (spkw2go). We are also very
grateful to Midori Harris, Jane Lomax, Cath Brooksbank, and Amelia
Ireland, the GO editorial team at EBI.
The publication costs of this article were defrayed in part by payment
of page charges. This article must therefore be hereby marked
"advertisement" in accordance with 18 USC section 1734 solely to
indicate this fact.
|
Footnotes
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3 These authors contributed equally to this work. 
4 Corresponding author.
E-MAIL camon{at}ebi.ac.uk; FAX +44 0 1223 494 468.
Article and publication are at
http://www.genome.org/cgi/doi/10.1101/gr.461403. Article published online before print in March 2003.
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accepted in revised format December 30, 2002.
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ABSTRACT
METHODS AND RESULTS
