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The biomedical literature grows at a tremendous rate and PubMed comprises already over 15000000 abstracts. Finding relevant literature is an important and difficult problem. We introduce GoPubMed, a web server which allows users to explore PubMed search results with the Gene Ontology (GO), a hierarchically structured vocabulary for molecular biology. GoPubMed provides the following benefits: first, it gives an overview of the literature abstracts by categorizing abstracts according to the GO and thus allowing users to quickly navigate through the abstracts by category. Second, it automatically shows general ontology terms related to the original query, which often do not even appear directly in the abstract. Third, it enables users to verify its classification because GO terms are highlighted in the abstracts and as each term is labelled with an accuracy percentage. Fourth, exploring PubMed abstracts with GoPubMed is useful as it shows definitions of GO terms without the need for further look up. GoPubMed is online at www.gopubmed.org. Querying is currently limited to 100 papers per query.
The biomedical literature grows at a tremendous pace. PubMed, the main biomedical literature database references over 15000000 abstracts. Owing to this size, simple web-style text search of the literature is often not yielding the best results and a lot of important information remains buried in the masses of text.
Consider the following example: a researcher wants to know the enzymes that are inhibited by levamisole. A keyword search for ‘levamisole inhibitor’ produces well over 100 hits in the PubMed. To find out about specific functions, the researcher has to go through all these papers. He/she is interested in the relevant enzymatic functions. From the first titles it is immediately evident that levamisole inhibits alkaline phosphatase. A less well-known fact is, however, still buried in the abstracts. The abstract ‘The effect of levamisole on energy metabolism in Ehrlich ascites tumour cells in vitro’ with PMID 2947578 is ranked very low (position 89 on February 7, 2005) by PubMed. (Please note that all the examples in this paper depend on PubMed's ranking of search results. Since the literature is growing, PubMed may return different articles for the same query at different time points. This means that GoPubMed may display different papers for the examples in this paper. All queries in this paper were checked on February 8, 2005.) The abstract states that levamisole also inhibits phosphofructokinases. Most readers will miss this statement.
Even if the user would try to reduce the number of papers by filtering out the ones mentioning ‘levamisole inibitor’ (e.g. query PubMed for ‘levamisole inhibitor’ NOT ‘phosphatase’), he/she would miss the less obvious hits like ermphosphofructokinase, if both terms occur in the same abstract. Thus, even advanced PubMed queries with Boolean logic cannot always properly structure the search results.
We propose to improve literature search by using ontologies, which are controlled, hierarchical vocabularies. The ontologies are used to categorize and explore abstracts. Currently, one of the most prominent ontology is the Gene Ontology (GO) (1), which has been designed for the annotation of gene products. It comprises over 19000 terms organized in three sub-ontologies for cellular location, molecular function and biological process.
Gene Ontology was initially created to reflect Drosophila in the Flybase database, but has expanded to encompass many other genomes as well as sequence and structure databases. The hierarchical nature of GO allows one to quickly navigate from a rather abstract to very specific terms. As an example, there are maximally 16 terms from the root of the ontology to the deepest and most refined leave concept in GO.
The main problem that needs to be solved before we can use ontologies for literature exploration is term extraction. Finding ontology terms exactly in the literature is rarely possible, as authors do not write their abstracts with the GO in mind. For example, the excerpt ‘tyrosine phosphorylation of a recently identified STAT’ should match with the GO term ‘tyrosine phosphorylation of STAT protein’.
Therefore, a term extraction algorithm has to be able to drop words from both the abstracts and the GO terms. It should have the ability to rank the importance of a word. For example, the word ‘activity’ occurs in many GO terms and is therefore not so important, e.g. ‘phosphorylation’.
For GoPubMed we have developed a term extraction algorithm that is based on (2). It uses local sequence alignment of words of the abstract and the words of GO terms. We are using a special tokenizer and stemmer for the GO terms. The stemmed words of each term are then aligned against the abstract text taking the information content of each word in the GO term into account.
With term extraction and ontologies in place there is an alternative to classical literature search. GoPubMed submits keywords to PubMed, extracts GO terms from the retrieved abstracts, and presents the induced ontology for browsing. The induced ontology is the minimal subset of GO, which comprises all the GO terms found in the documents. The users actually query PubMed. For an explanation of the user interface consider Figure 1.
To illustrate the power of this approach let us consider the levamisole example again. Consider Figures 1 and and2,2, which show screen-shots of the GoPubMed web server. The user wants to learn which enzymes are inhibited by levamisole. He/she submits ‘levamisole inhibitor’ with GoPubMed. GoPubMed classifies the papers with GO and the user can explore the ontological classification of the papers:
GoPubMed is generally useful to gain an overview of a set of articles and to define a profile for these articles. This feature can be used to quickly get an insight into the topics a researcher is working on. Specifying, for example, the name and the affiliation of a researcher as query to GoPubMed one will be able explore the researcher's interest and focus of research. In particular, the induced GO can serve as a profile representing that researcher. As an example, consider Kai Simons in Dresden. The PubMed query ‘simons dresden’ returns some 20 articles. The induced ontology for these papers indicates that he is working on cell organization and biogenesis (within the process ontology) and in particular on lipid raft formation, a term that is found in 13 papers.
Which term is most obviously related to actin? Many researchers will promptly reply myosin. In GoPubMed such obvious relationships can be identified by exploring the most frequently occurring GO terms. In the case of actin, GoPubMed suggests that some 80 papers mention ‘cellular components’ or any sub-terms, nearly 80 papers ‘cell’ or sub-terms, some 70 ‘intracellular’, 67 ‘cytoplasm’, 57 ‘cytoskeleton’, 50 ‘actin cytoskeleton’ and 9 ‘myosin’. Thus, in only five clicks the user can relate actin and myosin and even underpin this relationship through the statements of associated abstracts, such as PMID 15679101: ‘Syntrophin was also able to inhibit actin-activated myosin ATPase activity’.
After querying with Rab5 the ontology shows among the biological processes the path physiological processes → cellular physiological processes → cell growth and/or maintenance → transport → vesicle-mediated transport → vesicle endocytosis. Selecting the papers mentioning vesicle endocytosis, there are statements such as:
Inspecting the ontology for cellular components there is a path: cell → intracellular → cytoplasm → endosome → early endosome. Associated articles contain, for example, the following statements:
Querying for ‘lipid rafts cell adhesion’, the ontology displays among others the terms molecular function → binding → protein binding → cytoskeletal protein binding with children ‘spectrin binding’ and ‘actin binding’. The link between spectrin/actin and the rafts is supported, for example, by
Querying with osteoclast differentiation bone resorption the ontology shows the path molecular function → signal transducer activity → receptor activity → receptor binding → G-Protein coupled receptor binding → chemokine receptor binding with a descendent chemokine activity. The paper with PMID 15265944 supports this statement. In this study, we examined the effect of MIP-1γ, a C–C chemokine family member, on receptor activator of NF-κB ligand (RANKL)-stimulated osteoclast differentiation, survival and activation.
Which morphogenetic processes can be associated with the matrix metalloprotease, MMP2 and the vascular endothelial growth factor, VEGF? The query ‘MMP2 VEGF’ results in an ontology with the path biological process → development → morphogenesis → organogenesis → blood vessel development → angiogenesis. For the latter, the paper PMID 15389539 provides the following evidence ‘which plays an important role in activation of MMP-2 and VEGF to induce angiogenic process and promotion of inflammation-associated adenoma formation in mice’.
Textpresso (3) is an information retrieval system based on a set of some 30 high-level categories, which can be seen as a very shallow ontology. Parts of the category members are based on the GO. Using these categories, Textpresso can answer queries like ‘Which abstracts mention an allele and a biological process in the title?’ There are four main differences between Textpresso and GoPubMed: First, Textpresso uses only 30 categories for classification, while GoPubMed uses the full GO, not limiting itself to the top concepts. Second, Textpresso returns a list of relevant abstract, while GoPubMed uses the deep ontology as vehicle to navigate through a large result set in a non-sequential order. Third, Textpresso is designed for full papers on Caenorhabditis elegans, while GoPubMed works on all the PubMed abstracts. Fourth, Textpresso tries to find the category terms directly in the text only allowing for some variations in lower/uppercase letters and plural forms. GoPubMed uses an algorithm, which allows for gaps within matches and considers the information content of words, which leads to a more refined term extraction. This is necessary, as most GO terms cannot be found directly in free text.
XplorMed (4) maps PubMed results to the eight main MeSH categories and then extracts topic keywords and their co-occurrences. For the query ‘levamisole inhibitor’, XplorMed returns 22 relevant co-occurring words such as activity, protein, cell, which are, however, very general and do not shed any light on the enzymes inhibited, for example.
Vivisimo is closely related to GoPubMed as it also uses an ontology to explore search results. However, instead of the GO, Vivisimo automatically derives an ontology from the search results. While this ensures that the ontology closely matches the articles, the ontology itself cannot be as well structured as a hand-curated one like GO.
There are numerous other tools, which use the GO to explore data other than literature abstracts. Many of them cater for the annotation of gene expression data and are based on GOA, the Gene Ontology Annotation, which annotates sequences with GO terms. For a comprehensive list of these tools please refer to the GO website www.geneontology.org.
We kindly thank the reviewers for their comments, which helped to improve the paper. We gratefully acknowledge support of the EU project REWERSE (IST-2004-506779). Funding to pay the Open Access publication charges for this article was provided by REWERSE.
Conflict of interest statement. None declared.