Nucleic Acids Research Advance Access originally published online on May 22, 2009
Nucleic Acids Research 2009 37(Web Server issue):W300-W304; doi:10.1093/nar/gkp429
Nucleic Acids Research, 2009, Vol. 37, No. suppl_2 W300-W304
© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
GoGene: gene annotation in the fast lane
Conrad Plake1,*,
Loic Royer1,
Rainer Winnenburg1,
Jörg Hakenberg1,2 and
Michael Schroeder1
1Biotechnology Center, Technische Universität Dresden, 01307 Dresden, Germany and 2Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, USA
*To whom correspondence should be addressed. Tel: +49 351 463 400 60; Fax: +49 351 463 400 61; Email: conrad.plake{at}biotec.tu-dresden.de
Received January 30, 2009. Revised April 21, 2009. Accepted May 11, 2009.
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ABSTRACT
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High-throughput screens such as microarrays and RNAi screens
produce huge amounts of data. They typically result in hundreds
of genes, which are often further explored and clustered via
enriched GeneOntology terms. The strength of such analyses is
that they build on high-quality manual annotations provided
with the GeneOntology. However, the weakness is that annotations
are restricted to process, function and location and that they
do not cover all known genes in model organisms. GoGene addresses
this weakness by complementing high-quality manual annotation
with high-throughput text mining extracting co-occurrences of
genes and ontology terms from literature. GoGene contains over
4 000 000 associations between genes and gene-related terms
for 10 model organisms extracted from more than 18 000 000 PubMed
entries. It does not cover only process, function and location
of genes, but also biomedical categories such as diseases, compounds,
techniques and mutations. By bringing it all together, GoGene
provides the most recent and most complete facts about genes
and can rank them according to novelty and importance. GoGene
accepts keywords, gene lists, gene sequences and protein sequences
as input and supports search for genes in PubMed, EntrezGene
and via BLAST. Since all associations of genes to terms are
supported by evidence in the literature, the results are transparent
and can be verified by the user. GoGene is available at
http://gopubmed.org/gogene.
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INTRODUCTION
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High-throughput gene expression assays are nowadays common practice,
for example, to compare expression levels of genes in a pathological
state (diseased) to those in a control state (healthy) and then
filter for genes that are significantly up or down-regulated
between the two states. The outcome is usually a long list of
genes that requires further investigation. Biologists analyze
these genes, for instance, by exploring known GeneOntology (GO)
annotations. However, manually curated GO annotations do not
cover all genes and also do not cover relevant diseases, mutations,
drugs, anatomical parts, etc. They also do not provide information
to rank genes according to what is new or well-studied already
and not all annotations have links to the literature, which
is important for further study and finding related work.
GoGene associates all genes from different model organisms with concepts of GO and MeSH (Medical Subject Headings), two vocabularies that cover a variety of areas of biomedical research. The hierarchical structure of both vocabularies makes it possible to cluster and summarize long lists of genes. Because most knowledge is contained only in publications and not in databases, GoGene integrates manually curated gene annotations, literature references (GeneRIFs) and textual comments from UniProt and EntrezGene with text-mined annotations from all of PubMed. In doing so, more than 4 000 000 associations between genes and ontology concepts for the model organisms human, mouse, rat, worm, fruit fly, zebrafish, thale cress, baker's yeast, fission yeast and Escherichia coli are made available, thereby increasing the number of known GO annotations by one order of magnitude. Additionally, GoGene provides 
35 000 gene-mutation associations automatically extracted from PubMed abstracts, which are not contained in UniProt. All associations are linked to their origin (i.e. literature or database entries) for further investigation. By scanning the literature, GoGene also compiles publication histories for each gene that are used to rank genes according to what is new, what is widely studied, or what is of high impact (see Usage and examples section). All relevant concepts for a gene list are displayed as a tree that allows for quickly navigating through long lists of genes.
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RELATED WORK
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Many tools exist that facilitate the functional analysis of
genes. Huang
et al. (
1) give a summary of 68 tools to study
the enrichment of gene-related functions. However, those tools
rely on annotation databases that are unlikely to be complete
and not all of them are easily accessible via a web server.
Babelomics integrates different approaches for the functional
analyses of gene expression experiments including text-mined
annotations but does not allow searching the literature directly
(
2). The Panther database contains a collection of protein families
that have been further divided into functional subfamilies (
3).
It can be searched by keywords or gene lists to find related
ontology terms, genes, or subfamilies together with family annotation
information. However, as for Babelomics, no literature search
is supported and neither Panther nor Babelomics provide disease
or other medical annotations. On the other hand, text mining
tools such as iHOP (
4), GOAnnotator (
5), EBIMed (
6), or PolySearch
(
7) allow searching the literature for gene-related concepts,
but they do not integrate known annotations from databases (except
for GOAnnotator) nor do they provide means to filter and cluster
genes based on their associated annotations. GoGene combines
text-mined facts from all the literature in PubMed with annotations
from the databases EntrezGene and UniProt. All annotations are
displayed as a tree following the structure of GO and MeSH that
can be seen as a table of contents to quickly find genes related
to one or more categories.
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USAGE AND EXAMPLES
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GoGene accepts three kinds of queries: lists of EntrezGene identifiers
or gene names, keyword queries that are sent directly to PubMed
to find genes in the matching abstracts, or nucleotide or amino-acid
sequences that are BLASTed against UniProt using its remote
service interface (
8). The type of a query can be specified
by adding one of the following tags at the end: [gene], [pubmed],
or [blast]. If the user does not specify the query type, GoGene
tries to automatically find the best result. First it checks
if the query resembles a nucleotide or amino-acid sequence.
In this case, the BLASTX or BLAST service is invoked, respectively.
Otherwise, the query is sent to both PubMed and EntrezGene and
the larger gene list is returned. For EntrezGene results, genes
are ranked as in the original result list. Results from a PubMed
search are ranked by occurrence frequency in the matching abstracts
in descending order. A BLAST search result is ranked by sequence
similarity, with the most similar gene listed first. Each gene
is associated with the bibliometric features: community, volume,
and novelty. The community value represents the number of authors
in PubMed who published about the gene. The volume is the sum
of journal impact factor points from all publications mentioning
the gene, and novelty is the sum of impact factor points, where
later publications are weighted higher than older ones. We chose
a yearly decrease of 50% towards the past. In GoGene, each gene
list can be re-ranked according to these three bibliometric
features by clicking the links/buttons in the result summary.
A gene list (including all annotations) can also be exported
to a file in different formats (SIF, GML, GraphML). The export
links are located at the bottom of the result page.
Example 1: a search for rat genes related to osteoporosis and bone resorption
Each day PubMed grows by thousands of publications. Thus, keeping track of new developments in a research field can be very time-consuming. GoGene helps to search the literature for discussed genes and gene-related information in PubMed. Consider a biologist who is interested in rat genes related to osteoporosis and bone resorption. A keyword search for ‘osteoporosis bone resorption’ in the Rat Genome Database gives no results. The same query in EntrezGene results in only two rat genes (Pth and Tnfsf11). In PubMed, the query ‘rat osteoporosis bone resorption’ returns 857 publications. Reading 857 abstracts to identify genes is a cumbersome task. GoGene alleviates this task by automatically searching all relevant abstracts for genes and displaying the resulting gene list (here: 142 genes) to the user. In the tree, the biological process, bone resorption, the organism, rat, and the disease, osteoporosis, are listed as top categories. Selecting each as mandatory (green checkbox) augments the query and eventually leaves five rat genes (Pth, Tnfsf11, Ctsk, Tnfrsf11b, Csf1) for which literature references clearly state their role in osteoporosis and bone resorption. (All searches were performed on January 17, 2009.)
Example 2: finding genetic causes for the disease hepatoerythropoietic porphyria (HEP)
Hepatoerythropoietic Porphyria (HEP) is a metabolic disease caused by uroporphyrinogen decarboxylase deficiency in the liver and bone marrow. Searching GoGene for ‘hepatoerythropoietic porphyria’ shows the enzyme-encoding human genes, FECH and UROD, as the top hits. When looking at the detailed gene pages, one sees lists of mutations that where found in PubMed for those enzymes. For FECH, the most recent publication from 2007 reports on a novel missense mutation found in a patient with HEP, where an alanine was substituted by a threonine at position 185. For the gene UROD, one finds a publication from the same year where the authors report on a novel mutation causative for HEP (G168R). (All searches were performed on January 17, 2009.)
Example 3: exploring a pancreatic cancer microarray screen
When plotting the results of a microarray screen one often discovers many deregulated genes. The next step is to find interpretations for the deregulation, for example, by mapping genes to known functional annotations. Here, we consider a set of 400 genes found to be deregulated in pancreatic cancer taken from a genome-wide study by Grützmann et al. (9). After entering the EntrezGene identifiers into the query box the resulting genes are shown together with gene-related functions, processes, cellular components, diseases, etc. (Figure 1). Out of those 400 genes, 24 are linked to pancreatic cancer, and 248 genes are linked to cancer in general. Most of the genes encode membrane proteins and the most common process for those genes is the transduction of signals. Clicking on one of the concepts shows only the related genes together with highlighted text snippets and hyper-links to relevant articles in PubMed. Thus, within seconds the user gets an overview what the genes in a gene set have in common and can quickly follow the links to the literature to find the relevant publications. (All searches were performed on January 17, 2009.)
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Figure 1. The GoGene web interface showing a result from a search for 400 gene IDs taken from an outcome of a microarray experiment on pancreatic cancer. On the left, all relevant concepts from GO and MeSH are shown. Clicking on a concept shows the related genes on the right. Each gene entry primarily consists of a title, a short summary and a link to a detailed gene page, where all information on a gene is summarized.
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Example 4: searching functional annotations for the uncharacterized human protein C15orf39
For the majority of proteins neither structural nor functional
information is available. One approach to gain insights into
the function of a protein is to compare its sequence to the
sequence of other proteins where the function is already known.
Proteins with similar sequences (e.g. homologs) are likely to
share similar functions. For example, the uncharacterized human
protein C15orf39 (UniProt-ID Q6ZRI6) is listed in UniProt without
any GeneOntology annotation. After searching GoGene with its
sequence, genes that code proteins with similar sequences are
retrieved. When exploring the annotations of those genes by
looking at the GO & MeSH tree, one finds almost 25% of genes
are classified as phosphotransferases. The most frequent molecular
functions are binding and catalytic activity, such as hydrolase
activity. (All searches were performed on January 17, 2009.)
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METHODS
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Gene annotations derived automatically from literature are predictions
that rely on the correct identification of genes and ontology
concepts in text. For gene identification, we applied a context-sensitive
algorithm that achieved an
F-measure of 81% in the last BioCreative
challenge for the task of human gene normalization and has been
further improved to also normalize genes from other model organisms
(
10,
11). Terms of GO and MeSH found in PubMed abstracts are
provided by the GoPubMed webserver (
12). For each term found,
the corresponding text is implicitly mapped to all its ascendant
terms according to the GO or MeSH hierarchy (transitive closure).
For instance, if the abstract is annotated with pancreatic cancer,
then it is also annotated with cancer. All possible gene-term
pairs are assigned an association score based on the point-wise
mutual information:
where
N is the number of articles mentioning any gene and any
term from an ontology branch (e.g. a disease),
ng,t is the number
of articles mentioning the gene and the term,
ng is the number
of articles mentioning the gene and any term from the branch,
and
nt is the number of articles mentioning the term and any
gene. The higher the association score the more likely it is
to observe the gene when the term is present (and vice versa).
An association score of zero means that gene and term occur
independently of one another. A negative score signals an under-representation
of a pair in the literature.
To identify mutations in the literature, we apply our rule based tagger that finds textual descriptions of mutations as well as representations of the form wNm, where w represents a wild-type amino acid, N its position in the sequence of the protein, and m the mutant amino acid. If a mutation and a gene are found together in an abstract, the mutation is associated with the gene if any of its protein sequences contains the wild-type residue at the specified position. This approach has been shown to successfully predict mutations with implications in the stability of membrane proteins (13).
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RESULTS
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To measure completeness of text-mined gene annotations only,
we compared them with gene-disease pairs contained in the OMIM
database of genetic disorders in human and with annotations
provided by the GeneOntology annotation project (GOA), and measured
recall rates of 83% and 75%, respectively. Since GoGene contains
diseases as MeSH concepts, we only considered those diseases
in OMIM that are also contained in MeSH. We successfully mapped
358 MeSH concepts to their respective counterparts in OMIM.
Because OMIM and GOA are incomplete, precision cannot be measured reliably. Thus, we followed a different strategy by comparing text-mined annotations between orthologous and non-orthologous genes. We selected all orthologous pairs from the InParanoid database (14) and from NCBI Homologene. Non-orthologous pairs were selected randomly. To measure the similarity between two annotation sets A and B we compute the Dice coefficient as follows:
Figure 2 shows the distribution
of Dice coefficients for gene-pairs from the species human,
mouse, and yeast. The overlap between non-orthologous pairs
is significantly smaller compared to orthologous pairs with
Dice coefficients mostly between 0 and 0.2 (Mann–Whitney
Test,
= 0.01). As we expected, orthologous genes show a much
higher overlap in annotations. In another approach to assess
the biological consistency of results in GoGene, we looked at
the enrichment in diseases.
Figure 3 shows a ranking of text-mined
disease categories from MeSH that are most often associated
with genes. In comparison, the lower part in
Figure 3 shows
the same ranking for known cell-cycle genes only. Cell-cycle
genes are involved in cell-cycle progression and defects in
the cell-cycle are causative for cancers development. Our predictions
show enrichment in neoplasms and depletion in nutritional and
metabolic diseases.
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Figure 2. The distribution of Dice coefficients (see text for explanation) from pairwise species comparison. As expected, annotation sets of non-orthologous gene pairs show significantly lower Dice coefficients than from orthologous gene pairs.
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Figure 3. Disease associations mined from literature for all genes (top) compared to disease associations for known cell-cycle genes. Cell-cycle genes are enriched in neoplasms and depleted in nutritional and metabolic diseases.
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CONCLUSION
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GoGene helps to quickly find interpretations of results from
high-throughput experiments together with relevant literature
or to simply scan the literature for discussed genes. A sequence
query lets users retrieve genes with similar protein sequences
and explore their GO and MeSH annotations, for example, to find
functional hints for yet uncharacterized genes. We presented
four examples that describe typical scenarios for users of the
GoGene web server, where the relevant information can be found
only a few mouse clicks away.
GO and MeSH are evolving in terms of their vocabulary and structure, which makes it difficult to reflect new changes in a timely manner. We approach this problem by performing updates on a regular basis: once every year for MeSH after each new major release by the National Library of Medicine, and every six months for GO. Automated methods do not reach the high-quality of manual annotation. However, GoGene has a high recall of 75% and orthologous gene pairs can be distinguished from non-orthologous pairs purely based on text-mined annotations. GoGene complements manual annotations by providing more than 4 000 000 associations of genes with GO and MeSH terminology from PubMed, thus supporting interpretation of large gene lists resulting from high-throughput experiments, BLAST or literature searches.
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FUNDING
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We kindly acknowledge funding by the EU project SEALIFE and
the BMBF project ForMaT. Funding to pay the Open Access publication
charges for this article was provided by ForMaT.
Conflict of interest statement. None declared.
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ABSTRACT
INTRODUCTION
