MOTIVATION: Wikipedia is one of the most important channels for the public communication of science and is frequently accessed as an educational resource in computational biology. Joint efforts between the International Society for Computational Biology (ISCB) and the Computational Biology taskforce of WikiProject Molecular Biology (a group of expert Wikipedia editors) have considerably improved computational biology representation on Wikipedia in recent years. However, there is still an urgent need for further improvement in quality, especially when compared to related scientific fields such as genetics and medicine. Facilitating involvement of members from ISCB Communities of Special Interest (COSIs) would improve a vital open education resource in computational biology, additionally allowing COSIs to provide a quality educational resource highly specific to their subfield. RESULTS: We generate a list of around 1500 English Wikipedia articles relating to computational biology and describe the development of a binary COSI-Article matrix, linking COSIs to relevant articles and thereby defining domain-specific open educational resources. Our analysis of the COSI-Article matrix data provides a quantitative assessment of computational biology representation on Wikipedia against other fields and at a COSI-specific level. Furthermore, we conducted similarity analysis and subsequent clustering of COSI-Article data to provide insight into potential relationships between COSIs. Finally, based on our analysis, we suggest courses of action to improve the quality of computational biology representation on Wikipedia.

SUMMARY: The classification of biological samples by means of their respective molecular profiles is a topic of great interest for its potential diagnostic, prognostic and investigational applications. rScudo is an R package for the classification of molecular profiles based on a radically new approach consisting in the analysis of the similarity of rank-based sample-specific signatures. The validity of rScudo unconventional approach has been validated through direct comparison with current methods in the international SBV IMPROVER Diagnostic Signature Challenge. Due to its novelty, there is ample room for conceptual improvements and for exploring additional applications. The rScudo package has been specifically designed to facilitate experimenting with the rank-based signature approach, to test its application to different types of molecular profiles and to simplify direct comparison with existing methods. AVAILABILITY AND IMPLEMENTATION: The package is available as part of the Bioconductor suite at https://bioconductor.org/packages/rScudo.

MOTIVATION: Cell-type-specific surface proteins can be exploited as valuable markers for a range of applications including immunophenotyping live cells, targeted drug delivery and in vivo imaging. Despite their utility and relevance, the unique combination of molecules present at the cell surface are not yet described for most cell types. A significant challenge in analyzing 'omic' discovery datasets is the selection of candidate markers that are most applicable for downstream applications. RESULTS: Here, we developed GenieScore, a prioritization metric that integrates a consensus-based prediction of cell surface localization with user-input data to rank-order candidate cell-type-specific surface markers. In this report, we demonstrate the utility of GenieScore for analyzing human and rodent data from proteomic and transcriptomic experiments in the areas of cancer, stem cell and islet biology. We also demonstrate that permutations of GenieScore, termed IsoGenieScore and OmniGenieScore, can efficiently prioritize co-expressed and intracellular cell-type-specific markers, respectively. AVAILABILITY AND IMPLEMENTATION: Calculation of GenieScores and lookup of SPC scores is made freely accessible via the SurfaceGenie web application: www.cellsurfer.net/surfacegenie. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Genome-wide association studies (GWAS), particularly designed with thousands and thousands of single-nucleotide polymorphisms (SNPs) (big p) genotyped on tens of thousands of subjects (small n), are encountered by a major challenge of p ≪ n. Although the integration of longitudinal information can significantly enhance a GWAS's power to comprehend the genetic architecture of complex traits and diseases, an additional challenge is generated by an autocorrelative process. We have developed several statistical models for addressing these two challenges by implementing dimension reduction methods and longitudinal data analysis. To make these models computationally accessible to applied geneticists, we wrote an R package of computer software, HiGwas, designed to analyze longitudinal GWAS datasets. Functions in the package encompass single SNP analyses, significance-level adjustment, preconditioning and model selection for a high-dimensional set of SNPs. HiGwas provides the estimates of genetic parameters and the confidence intervals of these estimates. We demonstrate the features of HiGwas through real data analysis and vignette document in the package. AVAILABILITY AND IMPLEMENTATION: https://github.com/wzhy2000/higwas. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: We present a new R package admixr, which provides a convenient interface for performing reproducible population genetic analyses (f3, D, f4, f4-ratio, qpWave and qpAdm), as implemented by command-line programs in the ADMIXTOOLS software suite. In a traditional ADMIXTOOLS workflow, the user must first generate a set of text configuration files tailored to each individual analysis, often using a combination of shell scripting and manual text editing. The non-tabular output files then need to be parsed to extract values of interest prior to further analyses. Our package simplifies this process by automating all low-level configuration and parsing steps, making analyses as simple as running a single R command. Furthermore, we provide a set of R functions for processing, filtering and manipulating datasets in the EIGENSTRAT format. By unifying all steps of the workflow under a single R framework, this package enables the automation of analytic pipelines, significantly improving the reproducibility of population genetic studies. AVAILABILITY AND IMPLEMENTATION: The source code of the R package is available under the MIT license. Installation instructions, reference manual and a tutorial can be found on the package website at https://bioinf.eva.mpg.de/admixr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A case was recently brought to the journal’s attention regarding a reviewer who had requested a large number of citations to their own papers as part of their review. After investigation of their most recent reviews, we found that in every review this reviewer requested an average of 35 citations be added, ∼90% of which were to their own papers and the remainder to papers that both cited them extensively and mentioned them by name in the title. The reviewer’s phrasing strongly suggested that inclusion of these citations would influence their recommendation to the editor to accept or reject the paper. The reviewer was unable to provide a satisfactory justification for these requests and Bioinformatics has therefore banned them as a reviewer. Our investigation also suggests that the reviewer has behaved similarly in reviewing for other journals. This case has alerted us to how the peer-review system is vulnerable to unethical behavior, and prompted us to clarify the journal’s policy on when it is appropriate for reviewers to request citations to their own work, and to suggest how some of the current weak points in the peer-review system can be mitigated, so that this behavior can be detected more quickly and efficiently. Peer-reviewers are typically selected based on their expertise in the areas of research associated with newly submitted manuscripts. They are among the most likely to be familiar with prior publications pertinent to the submission, and reviewer feedback on the completeness and accuracy of a manuscript’s reference list is desirable and welcome. It is therefore not unusual that one or a few requested citations may be to the reviewer’s own research. However, reviewers should be aware that a rigorous scientific justification for the inclusion of a new citation must be provided. Since it is easy to provide a tenuous justification for inclusion, as this reviewer often did (e.g. that his papers also involved analysis of sequence data), it should instead be stated why the authors would be remiss or the paper weaker if the citation were not included. Likewise, editors and authors should be aware of the imbalance of power that exists in the review process, and should ensure that any citations added in response to a reviewer comment are relevant and important. Citations have been called the ‘currency’ of science, meaning that they could be considered a quantifiable and objective metric of the impact of a scientist’s research. Specific metrics, such as the H-index, are intended to reflect this and scientists therefore have an incentive to try to improve their H-index. Indeed, the reviewer we caught requesting extensive citation of their work has a webpage that includes prominent mentions of both their high H-index and past awards they received from Thomson Reuters for being a highly cited researcher. When a reviewer agrees to review a paper with the intention of inflating the number of citations to their work, this is a conflict of interest and an unethical manipulation of the peer-review system. One might ask how this reviewer got away with submitting multiple reviews containing coercive requests for citation before being banned. The shortest explanation is that excessive self-citation demands are generally not seen as an ethical problem until a pattern is established, and a decentralized peer-review system is not amenable to detecting patterns. Editors may overlook requests, authors seem reluctant to bring it up explicitly to the editor, reviewer comments are anonymous and scattered across different editors and different journals, and even editors that spot such patterns may not even be aware what options they have, and therefore take the least-energy option of no longer inviting that reviewer. Because accusing someone of unethical conduct is a serious matter, the editors and authors involved are hesitant to do so, particularly if all they have is one instance to base their actions upon. Combined, this creates a system whereby such behavior can persist for a very long time. Even in the rare event that such misbehavior is detected, there is no global solution. Following the guidelines of the Committee on Publication Ethics (COPE), the first step is to contact the reviewer for an explanation. If it is unsatisfactory, then bring the matter to the attention of their immediate supervisor. This second step is only effective in a well-organized academic system, which was not the case here. Despite the actions taken by Bioinformatics, it is likely that this reviewer will continue to review for other journals after this editorial is published. Readers have no doubt noticed that this reviewer is not mentioned by name. This is because we have concluded that we have an obligation to maintain peer-reviewer anonymity, and thus, we can only alert others to the general problem. We debated this last point extensively, and have raised the issue with the COPE as a case report to be discussed. Specific suggestions for reviewers: Motivation. Reviewers should properly motivate their requests for citations and specify how strongly they feel about the addition of references. For example, saying ‘the authors’ review of the field is incomplete, they should add the following references’ is vague, whereas ‘similar studies on the use of X for the purpose of Y were published prior to this one and are needed to alert readers to prior art’ is specific. The less specific a reviewer is regarding motivation, the less weight their request should be given. Moderation. Reviewers should refrain from requesting substantial numbers of references. What is ‘substantial’ will vary by the type of article, with review articles expected to be better in their coverage and short two-page application notes expected to include only the most relevant references. We propose a general rule of thumb to define ‘substantial’ as requesting addition of more than one reference per printed journal page of the paper. In the event the authors’ citations of pertinent prior research is highly incomplete, a reviewer should simply say so and then point them in the right direction with a few citations and let them do the rest. Communication. If a reviewer notices another reviewer has requested excessive or unmerited citations and this has not been commented on by the editor, they should feel free to share their observations and opinions directly with the editor. Reviewers should be cognizant they are also in a position to recognize patterns of abuse from their fellow reviewers. Specific suggestions for journals: Document patterns. Manuscript handling systems should include a checkbox for each reviewer that asks ‘did this reviewer request citation to their own research?’ Editors with a concern about reviewer’s citation requests could then see what percentage of reviews returned contained self-citation requests. Brief but clear guidelines. Instructions should be kept simple and clear. As a result of this case we have updated our reviewer guidelines to state that requests for citations should include ‘a brief, yet specific, rationale as to why their inclusion is merited. This rationale is particularly important if the reviewer requests citation of their own papers.’ Specific suggestions for authors: Voice concerns. Although adding multiple references in response to a reviewer request might seem like an ‘easy’ way to satisfy at least one of the reviewers, each unmerited citation clutters your paper and rewards unethical behavior. Don’t be hesitant to include in your response that you have considered the suggestion and feel they are not necessary. In the event the reviewer responds negatively, you should contact the editor for guidance. This is in accordance with the Ethical Guidelines for Reviewers published by the Committee on Publication Ethics (COPE Council. Ethical guidelines for peer reviewers. September 2017. www.publicationethics.org) Specific suggestions for editors handling papers: Vigilance. The ultimate responsibility in preventing this behavior lies with the editors handling the papers. Careful consideration of the referee reports to detect unethical behavior, including unjustified requests for citations, particularly citations of the reviewer’s own work, is important and all efforts should be made to prevent such requests being made to the authors. Importantly, when a reviewer requests substantial self-citation, this should be reported to the journal so they can investigate whether or not this is part of a pattern, as in the specific case discussed here. Bioinformatics acknowledges that their editorial controls have failed for some time in this particular case, and sincerely apologizes to our authors, referees and readers for not detecting this sooner. This phenomenon of reviewer-coerced citations is not new (Huggett, 2013; Ioannidis, 2015; Resnik et al., 2008; Thombs and Razykov, 2012; Thombs et al., 2015; Wilhite and Fong, 2012), but also not very well explored in terms of how extensive it may be or how it should be dealt with. We hope this editorial will prompt some discussion on the appropriate balance between the need for peer-reviewer anonymity and the need to alert others to potentially unethical behavior once a pattern is established, particularly when it is difficult to detect such patterns. It is possible that eliminating some of the anonymity, either by open peer-review or publishing anonymized peer reviews alongside accepted papers may disincentivize this behavior. Similarly, because highly centralized research resources, such as Publons and ORCID, have been developed, we hope that some ideas or discussion could take place regarding how these or similar centralized resources could be used, responsibly, to help document patterns of ethical concern that are otherwise difficult to detect. Conflict of Interest: none declared.

We present ABAEnrichment, an R package that tests for expression enrichment in specific brain regions at different developmental stages using expression information gathered from multiple regions of the adult and developing human brain, together with ontologically organized structural information about the brain, both provided by the Allen Brain Atlas. We validate ABAEnrichment by successfully recovering the origin of gene sets identified in specific brain cell-types and developmental stages. AVAILABILITY AND IMPLEMENTATION: ABAEnrichment was implemented as an R package and is available under GPL (≥ 2) from the Bioconductor website (http://bioconductor.org/packages/3.3/bioc/html/ABAEnrichment.html). CONTACTS: [email protected], [email protected] or [email protected] information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Pooling multiple samples increases the efficiency and lowers the cost of DNA sequencing. One approach to multiplexing is to use short DNA indices to uniquely identify each sample. After sequencing, reads must be assigned in silico to the sample of origin, a process referred to as demultiplexing. Demultiplexing software typically identifies the sample of origin using a fixed number of mismatches between the read index and a reference index set. This approach may fail or misassign reads when the sequencing quality of the indices is poor. RESULTS: We introduce deML, a maximum likelihood algorithm that demultiplexes Illumina sequences. deML computes the likelihood of an observed index sequence being derived from a specified sample. A quality score which reflects the probability of the assignment being correct is generated for each read. Using these quality scores, even very problematic datasets can be demultiplexed and an error threshold can be set. AVAILABILITY AND IMPLEMENTATION: deML is freely available for use under the GPL (http://bioinf.eva.mpg.de/deml/).

MOTIVATION: The conversion of the raw intensities obtained from next-generation sequencing platforms into nucleotide sequences with well-calibrated quality scores is a critical step in the generation of good sequence data. While recent model-based approaches can yield highly accurate calls, they require a substantial amount of processing time and/or computational resources. We previously introduced Ibis, a fast and accurate basecaller for the Illumina platform. We have continued active development of Ibis to take into account developments in the Illumina technology, as well as to make Ibis fully open source. RESULTS: We introduce here freeIbis, which offers significant improvements in sequence accuracy owing to the use of a novel multiclass support vector machine (SVM) algorithm. Sequence quality scores are now calibrated based on empirically observed scores, thus providing a high correlation to their respective error rates. These improvements result in downstream advantages including improved genotyping accuracy. AVAILABILITY AND IMPLEMENTATION: FreeIbis is freely available for use under the GPL (http://bioinf.eva.mpg.de/freeibis/). It requires a Python interpreter and a C++ compiler. Tailored versions of LIBOCAS and LIBLINEAR are distributed along with the package.

The International Society for Computational Biology is pleased to announce the 2013 ISCB Computational Biology Wikipedia competition. The competition, in which entrants create or improve the content of any Wikipedia article in the field of computational biology, is open to all students and trainees. Further information about the competition can be found here: http://en.wikipedia.org/wiki/Wikipedia:WikiProject_Computational_Biology/ISCB_competition_announcement_2013 The mission of the ISCB is to promote the use of computational biology and to help educate the next generation of computational biologists. The society has numerous activities that help to address these aims, including conferences, training and mentoring initiatives, and an active student council. As the world's largest online encyclopedia, Wikipedia has become an indispensable resource for those seeking information on all scientific and technical topics. The English language version of Wikipedia contains over 4.2 million articles, and Wikipedia is now available in 286 languages. The global rise in smartphone use, which allows access to Wikipedia, means that a large fraction of the world's population can now gain access to the world's knowledge. Wikipedia is the most successful example of crowd-sourcing with about 80,000 active editors updating its content each month. But is Wikipedia a good source of information for computational biology? Certainly, many people are reading the articles. For example, the Bioinformatics article has been visited 1,600 times per day over the last 3 months. Wikipedia contains articles on algorithms, biological databases, software packages, and biographies of eminent computational biologists. The computational biology content ranges from incomplete, a mere “stub” of an article in Wikipedia parlance, to highly detailed Featured Articles. A group of Wikipedia editors have formed the Computational Biology Wikiproject (http://en.wikipedia.org/wiki/Wikipedia:WikiProject_Computational_Biology). This group oversees the computational biology articles and rates them for their importance and their quality. Figure 1 shows the current state of the articles (see also Figure S1). In total, there are over 1,140 articles that have been considered as falling under Computational Biology. There are a small number of articles that have been brought up to the highest levels of quality (Featured Article and Good Article) such as Multiple Sequence Alignment, Genome Wide Association Study, and Folding@home. Figure 1 The computational biology articles rated by quality and importance by the Wikipedia Computational Biology Wikiproject. The 2012 competition began 9th September 2012 (coinciding with the start of the European Conference on Computational Biology) and finished four months later on the 10th January 2013. Each article entered in the competition was reviewed for a difference in article quality between these two dates. In 2012, there were 13 substantive entries into the competition. Six of these articles were shortlisted by members of the ISCB Student Council and then considered by the judging panel. The judging panel considered articles based on the criteria of clarity of the writing, depth of knowledge of the subject, and quality of figures and images used. In one case, it was clear that the article was largely derived from a published review, and was not considered further. For the other entries, the quantity and quality of the contributions were very good, and it was a challenge to rank the articles. After much deliberation, the judging panel selected the following as the winners of the 2012 ISCB Wikipedia competition: 1st prize: James Estevez for improvements to the Genomics Article. 2nd prize: Benjamin Moore for improvements to the European Nucleotide Archive article. 3rd prize: Luis Pedro Coelho for improvements to the Bioimage Analysis article. We are keen to grow the depth and quality of computational biology articles and wish to encourage the widest possible range of students and trainees to take part. We envisage that teachers, tutors, and lecturers could use the competition as an opportunity to train students in literature research on topics of computational biology. This approach to literature review provides the students with a thorough grounding in the subject area of the article. In addition, the collaborative writing environment of Wikipedia encourages critical thinking and improves literature research skills. Furthermore, compared to traditional literature reviews carried out by students, which typically end up unread in a filing cabinet, contributing to Wikipedia means that the students' scholarly contributions will be publicly visible. We hope that the ISCB Wikipedia competition will continue to grow and help improve the quality of Computational Biology information freely available on the Internet. We are interested in improving not just the articles in Wikipedia, but also the associated media, such as images and figures on Wikimedia Commons, and data through Wikidata. We encourage you to get involved by either entering the competition if you are a student or trainee, or getting your own students to participate.

MOTIVATION: Ontologies are used in the annotation and analysis of biological data. As knowledge accumulates, ontologies and annotation undergo constant modifications to reflect this new knowledge. These modifications may influence the results of statistical applications such as functional enrichment analyses that describe experimental data in terms of ontological groupings. Here, we investigate to what degree modifications of the Gene Ontology (GO) impact these statistical analyses for both experimental and simulated data. The analysis is based on new measures for the stability of result sets and considers different ontology and annotation changes. RESULTS: Our results show that past changes in the GO are non-uniformly distributed over different branches of the ontology. Considering the semantic relatedness of significant categories in analysis results allows a more realistic stability assessment for functional enrichment studies. We observe that the results of term-enrichment analyses tend to be surprisingly stable despite changes in ontology and annotation.

The International Society for Computational Biology, ISCB, organizes the largest event in the field of computational biology and bioinformatics, namely the annual international conference on Intelligent Systems for Molecular Biology, the ISMB. This year at ISMB 2012 in Long Beach, ISCB celebrated the 20th anniversary of its flagship meeting. ISCB is a young, lean and efficient society that aspires to make a significant impact with only limited resources. Many constraints make the choice of venues for ISMB a tough challenge. Here, we describe those challenges and invite the contribution of ideas for solutions.

The 2011 International Conference on Bioinformatics (InCoB) conference, which is the annual scientific conference of the Asia-Pacific Bioinformatics Network (APBioNet), is hosted by Kuala Lumpur, Malaysia, is co-organized with the first ISCB-Asia conference of the International Society for Computational Biology (ISCB). InCoB and the sequencing of the human genome are both celebrating their tenth anniversaries and InCoB's goalposts for the next decade, implementing standards in bioinformatics and globally distributed computational networks, will be discussed and adopted at this conference. Of the 49 manuscripts (selected from 104 submissions) accepted to BMC Genomics and BMC Bioinformatics conference supplements, 24 are featured in this issue, covering software tools, genome/proteome analysis, systems biology (networks, pathways, bioimaging) and drug discovery and design.

BACKGROUND: most biomedical ontologies are represented in the OBO Flatfile Format, which is an easy-to-use graph-based ontology language. The semantics of the OBO Flatfile Format 1.2 enforces a strict predetermined interpretation of relationship statements between classes. It does not allow flexible specifications that provide better approximations of the intuitive understanding of the considered relations. If relations cannot be accurately expressed then ontologies built upon them may contain false assertions and hence lead to false inferences. Ontologies in the OBO Foundry must formalize the semantics of relations according to the OBO Relationship Ontology (RO). Therefore, being able to accurately express the intended meaning of relations is of crucial importance. Since the Web Ontology Language (OWL) is an expressive language with a formal semantics, it is suitable to de ne the meaning of relations accurately. RESULTS: we developed a method to provide definition patterns for relations between classes using OWL and describe a novel implementation of the RO based on this method. We implemented our extension in software that converts ontologies in the OBO Flatfile Format to OWL, and also provide a prototype to extract relational patterns from OWL ontologies using automated reasoning. The conversion software is freely available at http://bioonto.de/obo2owl, and can be accessed via a web interface. CONCLUSIONS: explicitly defining relations permits their use in reasoning software and leads to a more flexible and powerful way of representing biomedical ontologies. Using the extended langua0067e and semantics avoids several mistakes commonly made in formalizing biomedical ontologies, and can be used to automatically detect inconsistencies. The use of our method enables the use of graph-based ontologies in OWL, and makes complex OWL ontologies accessible in a graph-based form. Thereby, our method provides the means to gradually move the representation of biomedical ontologies into formal knowledge representation languages that incorporates an explicit semantics. Our method facilitates the use of OWL-based software in the back-end while ontology curators may continue to develop ontologies with an OBO-style front-end.

MOTIVATION: Ontology development and the annotation of biological data using ontologies are time-consuming exercises that currently require input from expert curators. Open, collaborative platforms for biological data annotation enable the wider scientific community to become involved in developing and maintaining such resources. However, this openness raises concerns regarding the quality and correctness of the information added to these knowledge bases. The combination of a collaborative web-based platform with logic-based approaches and Semantic Web technology can be used to address some of these challenges and concerns. RESULTS: We have developed the BOWiki, a web-based system that includes a biological core ontology. The core ontology provides background knowledge about biological types and relations. Against this background, an automated reasoner assesses the consistency of new information added to the knowledge base. The system provides a platform for research communities to integrate information and annotate data collaboratively. AVAILABILITY: The BOWiki and supplementary material is available at http://www.bowiki.net/. The source code is available under the GNU GPL from http://onto.eva.mpg.de/trac/BoWiki.

BACKGROUND: Biological sequences play a major role in molecular and computational biology. They are studied as information-bearing entities that make up DNA, RNA or proteins. The Sequence Ontology, which is part of the OBO Foundry, contains descriptions and definitions of sequences and their properties. Yet the most basic question about sequences remains unanswered: what kind of entity is a biological sequence? An answer to this question benefits formal ontologies that use the notion of biological sequences and analyses in computational biology alike. RESULTS: We provide both an ontological analysis of biological sequences and a formal representation that can be used in knowledge-based applications and other ontologies. We distinguish three distinct kinds of entities that can be referred to as "biological sequence": chains of molecules, syntactic representations such as those in biological databases, and the abstract information-bearing entities. For use in knowledge-based applications and inclusion in biomedical ontologies, we implemented the developed axiom system for use in automated theorem proving. CONCLUSION: Axioms are necessary to achieve the main goal of ontologies: to formally specify the meaning of terms used within a domain. The axiom system for the ontology of biological sequences is the first elaborate axiom system for an OBO Foundry ontology and can serve as starting point for the development of more formal ontologies and ultimately of knowledge-based applications.

UNLABELLED: We present a tool suited for searching for many short nucleotide sequences in large databases, allowing for a predefined number of gaps and mismatches. The commandline-driven program implements a non-deterministic automata matching algorithm on a keyword tree of the search strings. Both queries with and without ambiguity codes can be searched. Search time is short for perfect matches, and retrieval time rises exponentially with the number of edits allowed. AVAILABILITY: The C++ source code for PatMaN is distributed under the GNU General Public License and has been tested on the GNU/Linux operating system. It is available from http://bioinf.eva.mpg.de/patman. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Current efforts within the biomedical ontology community focus on achieving interoperability between various biomedical ontologies that cover a range of diverse domains. Achieving this interoperability will contribute to the creation of a rich knowledge base that can be used for querying, as well as generating and testing novel hypotheses. The OBO Foundry principles, as applied to a number of biomedical ontologies, are designed to facilitate this interoperability. However, semantic extensions are required to meet the OBO Foundry interoperability goals. Inconsistencies may arise when ontologies of properties - mostly phenotype ontologies - are combined with ontologies taking a canonical view of a domain - such as many anatomical ontologies. Currently, there is no support for a correct and consistent integration of such ontologies. RESULTS: We have developed a methodology for accurately representing canonical domain ontologies within the OBO Foundry. This is achieved by adding an extension to the semantics for relationships in the biomedical ontologies that allows for treating canonical information as default. Conclusions drawn from default knowledge may be revoked when additional information becomes available. We show how this extension can be used to achieve interoperability between ontologies, and further allows for the inclusion of more knowledge within them. We apply the formalism to ontologies of mouse anatomy and mammalian phenotypes in order to demonstrate the approach. CONCLUSION: Biomedical ontologies require a new class of relations that can be used in conjunction with default knowledge, thereby extending those currently in use. The inclusion of default knowledge is necessary in order to ensure interoperability between ontologies.