@article{
 title = {Endogenous indole-3-acetamide levels contribute to the crosstalk between auxin and abscisic acid, and trigger plant stress responses in Arabidopsis},
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 editor = {Napier, Richard},
 journal = {Journal of Experimental Botany},
 number = {2}
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The evolutionary success of plants relies to a large extent on their extraordinary ability to adapt to changes in their environment. These adaptations require that plants balance their growth with their stress responses. Plant hormones are crucial mediators orchestrating the underlying adaptive processes. However, whether and how the growth-related hormone auxin and the stress-related hormones jasmonic acid, salicylic acid, and abscisic acid (ABA) are coordinated remains largely elusive. Here, we analyse the physiological role of AMIDASE 1 (AMI1) in Arabidopsis plant growth and its possible connection to plant adaptations to abiotic stresses. AMI1 contributes to cellular auxin homeostasis by catalysing the conversion of indole-acetamide into the major plant auxin indole-3-acetic acid. Functional impairment of AMI1 increases the plant’s stress status rendering mutant plants more susceptible to abiotic stresses. Transcriptomic analysis of ami1 mutants disclosed the reprogramming of a considerable number of stress-related genes, including jasmonic acid and ABA biosynthesis genes. The ami1 mutants exhibit only moderately repressed growth but an enhanced ABA accumulation, which suggests a role for AMI1 in the crosstalk between auxin and ABA. Altogether, our results suggest that AMI1 is involved in coordinating the trade-off between plant growth and stress responses, balancing auxin and ABA homeostasis.

@article{
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We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.

@article{
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 abstract = {The grammatical structures scholars use to express their assertions are intended to convey various degrees of certainty or speculation. Prior studies have suggested a variety of categorization systems for scholarly certainty; however, these have not been objectively tested for their validity, particularly with respect to representing the interpretation by the reader, rather than the intention of the author. In this study, we use a series of questionnaires to determine how researchers classify various scholarly assertions, using three distinct certainty classification systems. We find that there are three distinct categories of certainty along a spectrum from high to low. We show that these categories can be detected in an automated manner, using a machine learning model, with a cross-validation accuracy of 89.2% relative to an author-annotated corpus, and 82.2% accuracy against a publicly-annotated corpus. This finding provides an opportunity for contextual metadata related to certainty to be captured as a part of text-mining pipelines, which currently miss these subtle linguistic cues. We provide an exemplar machine-accessible representation—a Nanopublication—where certainty category is embedded as metadata in a formal, ontology-based manner within text-mined scholarly assertions.},
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The grammatical structures scholars use to express their assertions are intended to convey various degrees of certainty or speculation. Prior studies have suggested a variety of categorization systems for scholarly certainty; however, these have not been objectively tested for their validity, particularly with respect to representing the interpretation by the reader, rather than the intention of the author. In this study, we use a series of questionnaires to determine how researchers classify various scholarly assertions, using three distinct certainty classification systems. We find that there are three distinct categories of certainty along a spectrum from high to low. We show that these categories can be detected in an automated manner, using a machine learning model, with a cross-validation accuracy of 89.2% relative to an author-annotated corpus, and 82.2% accuracy against a publicly-annotated corpus. This finding provides an opportunity for contextual metadata related to certainty to be captured as a part of text-mining pipelines, which currently miss these subtle linguistic cues. We provide an exemplar machine-accessible representation—a Nanopublication—where certainty category is embedded as metadata in a formal, ontology-based manner within text-mined scholarly assertions.

@article{
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 author = {Jacobsen, Annika and de Miranda Azevedo, Ricardo and Juty, Nick and Batista, Dominique and Coles, Simon and Cornet, Ronald and Courtot, Mélanie and Crosas, Mercè and Dumontier, Michel and Evelo, Chris T. and Goble, Carole and Guizzardi, Giancarlo and Hansen, Karsten Kryger and Hasnain, Ali and Hettne, Kristina and Heringa, Jaap and Hooft, Rob W.W. and Imming, Melanie and Jeffery, Keith G. and Kaliyaperumal, Rajaram and Kersloot, Martijn G. and Kirkpatrick, Christine R. and Kuhn, Tobias and Labastida, Ignasi and Magagna, Barbara and McQuilton, Peter and Meyers, Natalie and Montesanti, Annalisa and van Reisen, Mirjam and Rocca-Serra, Philippe and Pergl, Robert and Sansone, Susanna-Assunta and da Silva Santos, Luiz Olavo Bonino and Schneider, Juliane and Strawn, George and Thompson, Mark and Waagmeester, Andra and Weigel, Tobias and Wilkinson, Mark D. and Willighagen, Egon L. and Wittenburg, Peter and Roos, Marco and Mons, Barend and Schultes, Erik},
 journal = {Data Intelligence},
 number = {1-2}
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The FAIR principles have been widely cited, endorsed and adopted by a broad range of stakeholders since their publication in 2016. By intention, the 15 FAIR guiding principles do not dictate specific technological implementations, but provide guidance for improving Findability, Accessibility, Interoperability and Reusability of digital resources. This has likely contributed to the broad adoption of the FAIR principles, because individual stakeholder communities can implement their own FAIR solutions. However, it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible implementations. Thus, while the FAIR principles are formulated on a high level and may be interpreted and implemented in different ways, for true interoperability we need to support convergence in implementation choices that are widely accessible and (re)-usable. We introduce the concept of FAIR implementation considerations to assist accelerated global participation and convergence towards accessible, robust, widespread and consistent FAIR implementations. Any self-identified stakeholder community may either choose to reuse solutions from existing implementations, or when they spot a gap, accept the challenge to create the needed solution, which, ideally, can be used again by other communities in the future. Here, we provide interpretations and implementation considerations (choices and challenges) for each FAIR principle.

@article{
 title = {Endogenous indole-3-acetamide levels contribute to the crosstalk between auxin and abscisic acid, and trigger plant stress responses in Arabidopsis},
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 editor = {Napier, Richard},
 journal = {Journal of Experimental Botany}
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The evolutionary success of plants relies to a large extent on their extraordinary ability to adapt to changes in their environment. These adaptations require that plants balance their growth with their stress responses. Plant hormones are crucial mediators orchestrating the underlying adaptive processes. However, whether and how the growth-related hormone auxin and the stress-related hormones jasmonic acid, salicylic acid, and abscisic acid (ABA) are coordinated remains largely elusive. Here, we analyse the physiological role of AMIDASE 1 (AMI1) in Arabidopsis plant growth and its possible connection to plant adaptations to abiotic stresses. AMI1 contributes to cellular auxin homeostasis by catalysing the conversion of indole-acetamide into the major plant auxin indole-3-acetic acid. Functional impairment of AMI1 increases the plant’s stress status rendering mutant plants more susceptible to abiotic stresses. Transcriptomic analysis of ami1 mutants disclosed the reprogramming of a considerable number of stress-related genes, including jasmonic acid and ABA biosynthesis genes. The ami1 mutants exhibit only moderately repressed growth but an enhanced ABA accumulation, which suggests a role for AMI1 in the crosstalk between auxin and ABA. Altogether, our results suggest that AMI1 is involved in coordinating the trade-off between plant growth and stress responses, balancing auxin and ABA homeostasis.

@article{
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@article{
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@article{
 title = {Robust and automatic definition of microbiome states},
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 abstract = {Analysis of microbiome dynamics would allow elucidation of patterns within microbial community evolution under a variety of biologically or economically important circumstances; however, this is currently hampered in part by the lack of rigorous, formal, yet generally-applicable approaches to discerning distinct configurations of complex microbial populations. Clustering approaches to define microbiome “community state-types” at a population-scale are widely used, though not yet standardized. Similarly, distinct variations within a state-type are well documented, but there is no rigorous approach to discriminating these more subtle variations in community structure. Finally, intra-individual variations with even fewer differences will likely be found in, for example, longitudinal data, and will correlate with important features such as sickness versus health. We propose an automated, generic, objective, domain-independent, and internally-validating procedure to define statistically distinct microbiome states within datasets containing any degree of phylotypic diversity. Robustness of state identification is objectively established by a combination of diverse techniques for stable cluster verification. To demonstrate the efficacy of our approach in detecting discreet states even in datasets containing highly similar bacterial communities, and to demonstrate the broad applicability of our method, we reuse eight distinct longitudinal microbiome datasets from a variety of ecological niches and species. We also demonstrate our algorithm’s flexibility by providing it distinct taxa subsets as clustering input, demonstrating that it operates on filtered or unfiltered data, and at a range of different taxonomic levels. The final output is a set of robustly defined states which can then be used as general biomarkers for a wide variety of downstream purposes such as association with disease, monitoring response to intervention, or identifying optimally performant populations.},
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Analysis of microbiome dynamics would allow elucidation of patterns within microbial community evolution under a variety of biologically or economically important circumstances; however, this is currently hampered in part by the lack of rigorous, formal, yet generally-applicable approaches to discerning distinct configurations of complex microbial populations. Clustering approaches to define microbiome “community state-types” at a population-scale are widely used, though not yet standardized. Similarly, distinct variations within a state-type are well documented, but there is no rigorous approach to discriminating these more subtle variations in community structure. Finally, intra-individual variations with even fewer differences will likely be found in, for example, longitudinal data, and will correlate with important features such as sickness versus health. We propose an automated, generic, objective, domain-independent, and internally-validating procedure to define statistically distinct microbiome states within datasets containing any degree of phylotypic diversity. Robustness of state identification is objectively established by a combination of diverse techniques for stable cluster verification. To demonstrate the efficacy of our approach in detecting discreet states even in datasets containing highly similar bacterial communities, and to demonstrate the broad applicability of our method, we reuse eight distinct longitudinal microbiome datasets from a variety of ecological niches and species. We also demonstrate our algorithm’s flexibility by providing it distinct taxa subsets as clustering input, demonstrating that it operates on filtered or unfiltered data, and at a range of different taxonomic levels. The final output is a set of robustly defined states which can then be used as general biomarkers for a wide variety of downstream purposes such as association with disease, monitoring response to intervention, or identifying optimally performant populations.

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The grammatical structures scholars use to express their assertions are intended to convey various degrees of certainty or speculation. Prior studies have suggested a variety of categorization systems for scholarly certainty. However, these have not been objectively tested for their validity, particularly with respect to representing the interpretation by the reader, rather than the intention of the author. In this study, we use a series of questionnaires to determine how researchers classify various scholarly assertions, using three distinct certainty classification systems. We find that there are three categories of certainty perceived by readers: one level of high certainty, and two levels of lower certainty that are somewhat less distinct, but nevertheless show a significant degree of inter-annotator agreement. We show that these categories can be detected in an automated manner, using a machine learning model, with a cross-validation accuracy of 89.2% relative to an author-annotated corpus, and 82.2% accuracy against a publicly-annotated corpus. This finding provides an opportunity for contextual metadata related to certainty to be captured as a part of text-mining pipelines, which currently miss these subtle linguistic cues. We provide an exemplar machine-accessible representation - a Nanopublication - where certainty category is embedded as metadata in a formal, ontology-based manner within text-mined scholarly assertions.
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The grammatical structures scholars use to express their assertions are intended to convey various degrees of certainty or speculation. Prior studies have suggested a variety of categorization systems for scholarly certainty. However, these have not been objectively tested for their validity, particularly with respect to representing the interpretation by the reader, rather than the intention of the author. In this study, we use a series of questionnaires to determine how researchers classify various scholarly assertions, using three distinct certainty classification systems. We find that there are three categories of certainty perceived by readers: one level of high certainty, and two levels of lower certainty that are somewhat less distinct, but nevertheless show a significant degree of inter-annotator agreement. We show that these categories can be detected in an automated manner, using a machine learning model, with a cross-validation accuracy of 89.2% relative to an author-annotated corpus, and 82.2% accuracy against a publicly-annotated corpus. This finding provides an opportunity for contextual metadata related to certainty to be captured as a part of text-mining pipelines, which currently miss these subtle linguistic cues. We provide an exemplar machine-accessible representation - a Nanopublication - where certainty category is embedded as metadata in a formal, ontology-based manner within text-mined scholarly assertions.

@article{
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 abstract = {There is a growing acknowledgement in the scientific community of the importance of making experimental data machine findable, accessible, interoperable, and reusable (FAIR). Recognizing that high quality metadata are essential to make datasets FAIR, members of the GO FAIR Initiative and the Research Data Alliance (RDA) have initiated a series of workshops to encourage the creation of Metadata for Machines (M4M), enabling any self-identified stakeholder to define and promote the reuse of standardized, comprehensive machine-actionable metadata. The funders of scientific research recognize that they have an important role to play in ensuring that experimental results are FAIR, and that high quality metadata and careful planning for FAIR data stewardship are central to these goals. We describe the outcome of a recent M4M workshop that has led to a pilot programme involving two national science funders, the Health Research Board of Ireland (HRB) and the Netherlands Organisation for Health Research and Development (ZonMW). These funding organizations will explore new technologies to define at the time that a request for proposals is issued the minimal set of machine-actionable metadata that they would like investigators to use to annotate their datasets, to enable investigators to create such metadata to help make their data FAIR, and to develop data-stewardship plans that ensure that experimental data will be managed appropriately abiding by the FAIR principles. The FAIR Funders design envisions a data-management workflow having seven essential stages, where solution providers are openly invited to participate. The initial pilot programme will launch using existing computer-based tools of those who attended the M4M Workshop.},
 bibtype = {article},
 author = {Wittenburg, P. and Sustkova, H. Pergl and Montesanti, A. and Bloemers, S. M. and de Waard, S. H. and Musen, M. A. and Graybeal, J. B. and Hettne, K. M. and Jacobsen, A. and Pergl, R. and Hooft, R. W. W. and Staiger, C. and van Gelder, C. W. G. and Knijnenburg, S. L. and van Arkel, A. C. and Meerman, B. and Wilkinson, M. D. and Sansone, S-A and Rocca-Serra, P. and McQuilton, P. and Gonzalez-Beltran, A. N. and Aben, G. J. C. and Henning, P. and Alencar, S. and Ribeiro, C. and Silva, C. R. L. and Sayao, L. and Sales, L. and Veiga, V. and Lima, J. and Dib, S. and Xavier, P. and Murtinho, R. and Tendel, J. and Schaap, B. F. and Brouwer, P. M. and Gavai, A. K. and Bouzembrak, Y. and Marvin, H. J. P. and Mons, A. and Kuhn, T. and Gambardella, A. A. and Azevedo, R. de Miranda and Muhonen, V. and van der Naald, M. and Smit, N. W. and Buys, M. J. and de Bruin, T. F. and Schoots, F. and Goodson, H. J. E. and Rzepa, H. S. and Jeffery, K. G. and Shanahan, H. P. and Axton, M. and Tkachenko, V. and Maya, A. D. and Meyers, N. K. and Conlon, M. and Haak, L. L. and Schultes, E. A.},
 journal = {arXiv}
}

There is a growing acknowledgement in the scientific community of the importance of making experimental data machine findable, accessible, interoperable, and reusable (FAIR). Recognizing that high quality metadata are essential to make datasets FAIR, members of the GO FAIR Initiative and the Research Data Alliance (RDA) have initiated a series of workshops to encourage the creation of Metadata for Machines (M4M), enabling any self-identified stakeholder to define and promote the reuse of standardized, comprehensive machine-actionable metadata. The funders of scientific research recognize that they have an important role to play in ensuring that experimental results are FAIR, and that high quality metadata and careful planning for FAIR data stewardship are central to these goals. We describe the outcome of a recent M4M workshop that has led to a pilot programme involving two national science funders, the Health Research Board of Ireland (HRB) and the Netherlands Organisation for Health Research and Development (ZonMW). These funding organizations will explore new technologies to define at the time that a request for proposals is issued the minimal set of machine-actionable metadata that they would like investigators to use to annotate their datasets, to enable investigators to create such metadata to help make their data FAIR, and to develop data-stewardship plans that ensure that experimental data will be managed appropriately abiding by the FAIR principles. The FAIR Funders design envisions a data-management workflow having seven essential stages, where solution providers are openly invited to participate. The initial pilot programme will launch using existing computer-based tools of those who attended the M4M Workshop.

@article{
 title = {Evaluating FAIR Maturity Through a Scalable, Automated, Community-Governed Framework},
 type = {article},
 year = {2019},
 identifiers = {[object Object]},
 websites = {https://www.biorxiv.org/content/early/2019/05/28/649202},
 publisher = {Cold Spring Harbor Laboratory},
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 citation_key = {Wilkinson649202},
 source_type = {article},
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 abstract = {Transparent evaluations of FAIRness are increasingly required by a wide range of stakeholders, from scientists to publishers, funding agencies and policy makers. We propose a scalable, automatable framework to evaluate digital resources that encompasses measurable indicators, open source tools, and participation guidelines, which come together to accommodate domain relevant community-defined FAIR assessments. The components of the framework are: (1) Maturity Indicators - community-authored specifications that delimit a specific automatically-measurable FAIR behavior; (2) Compliance Tests - small Web apps that test digital resources against individual Maturity Indicators; and (3) the Evaluator, a Web application that registers, assembles, and applies community-relevant sets of Compliance Tests against a digital resource, and provides a detailed report about what a machine \textquotedblleftsees\textquotedblright when it visits that resource. We discuss the technical and social considerations of FAIR assessments, and how this translates to our community-driven infrastructure. We then illustrate how the output of the Evaluator tool can serve as a roadmap to assist data stewards to incrementally and realistically improve the FAIRness of their resources.},
 bibtype = {article},
 author = {Wilkinson, Mark D and Dumontier, Michel and Sansone, Susanna-Assunta and da Silva Santos, Luiz Olavo Bonino and Prieto, Mario and Batista, Dominique and McQuilton, Peter and Kuhn, Tobias and Rocca-Serra, Philippe and Crosas, Mercè and Schultes, Erik},
 journal = {bioRxiv}
}

Transparent evaluations of FAIRness are increasingly required by a wide range of stakeholders, from scientists to publishers, funding agencies and policy makers. We propose a scalable, automatable framework to evaluate digital resources that encompasses measurable indicators, open source tools, and participation guidelines, which come together to accommodate domain relevant community-defined FAIR assessments. The components of the framework are: (1) Maturity Indicators - community-authored specifications that delimit a specific automatically-measurable FAIR behavior; (2) Compliance Tests - small Web apps that test digital resources against individual Maturity Indicators; and (3) the Evaluator, a Web application that registers, assembles, and applies community-relevant sets of Compliance Tests against a digital resource, and provides a detailed report about what a machine \textquotedblleftsees\textquotedblright when it visits that resource. We discuss the technical and social considerations of FAIR assessments, and how this translates to our community-driven infrastructure. We then illustrate how the output of the Evaluator tool can serve as a roadmap to assist data stewards to incrementally and realistically improve the FAIRness of their resources.

@article{
 title = {Evaluating FAIR maturity through a scalable, automated, community-governed framework},
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 pages = {174},
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 journal = {Scientific Data},
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}

@article{
 title = {BioHackathon series in 2013 and 2014: improvements of semantic interoperability in life science data and services},
 type = {article},
 year = {2019},
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 pages = {1677},
 volume = {8},
 websites = {https://f1000research.com/articles/8-1677/v1},
 month = {9},
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 abstract = {Publishing databases in the Resource Description Framework (RDF) model is becoming widely accepted to maximize the syntactic and semantic interoperability of open data in life sciences. Here we report advancements made in the 6th and 7th annual BioHackathons which were held in Tokyo and Miyagi respectively. This review consists of two major sections covering: 1) improvement and utilization of RDF data in various domains of the life sciences and 2) meta-data about these RDF data, the resources that store them, and the service quality of SPARQL Protocol and RDF Query Language (SPARQL) endpoints. The first section describes how we developed RDF data, ontologies and tools in genomics, proteomics, metabolomics, glycomics and by literature text mining. The second section describes how we defined descriptions of datasets, the provenance of data, and quality assessment of services and service discovery. By enhancing the harmonization of these two layers of machine-readable data and knowledge, we improve the way community wide resources are developed and published. Moreover, we outline best practices for the future, and prepare ourselves for an exciting and unanticipatable variety of real world applications in coming years.},
 bibtype = {article},
 author = {Katayama, Toshiaki and Kawashima, Shuichi and Micklem, Gos and Kawano, Shin and Kim, Jin-Dong and Kocbek, Simon and Okamoto, Shinobu and Wang, Yue and Wu, Hongyan and Yamaguchi, Atsuko and Yamamoto, Yasunori and Antezana, Erick and Aoki-Kinoshita, Kiyoko F. and Arakawa, Kazuharu and Banno, Masaki and Baran, Joachim and Bolleman, Jerven T. and Bonnal, Raoul J.P. and Bono, Hidemasa and Fernández-Breis, Jesualdo T. and Buels, Robert and Campbell, Matthew P. and Chiba, Hirokazu and Cock, Peter J. A. and Cohen, Kevin B. and Dumontier, Michel and Fujisawa, Takatomo and Fujiwara, Toyofumi and Garcia, Leyla and Gaudet, Pascale and Hattori, Emi and Hoehndorf, Robert and Itaya, Kotone and Ito, Maori and Jamieson, Daniel and Jupp, Simon and Juty, Nick and Kalderimis, Alex and Kato, Fumihiro and Kawaji, Hideya and Kawashima, Takeshi and Kinjo, Akira R. and Komiyama, Yusuke and Kotera, Masaaki and Kushida, Tatsuya and Malone, James and Matsubara, Masaaki and Mizuno, Satoshi and Mizutani, Sayaka and Mori, Hiroshi and Moriya, Yuki and Murakami, Katsuhiko and Nakazato, Takeru and Nishide, Hiroyo and Nishimura, Yosuke and Ogishima, Soichi and Ohta, Tazro and Okuda, Shujiro and Ono, Hiromasa and Perez-Riverol, Yasset and Shinmachi, Daisuke and Splendiani, Andrea and Strozzi, Francesco and Suzuki, Shinya and Takehara, Junichi and Thompson, Mark and Tokimatsu, Toshiaki and Uchiyama, Ikuo and Verspoor, Karin and Wilkinson, Mark D. and Wimalaratne, Sarala and Yamada, Issaku and Yamamoto, Nozomi and Yarimizu, Masayuki and Kawamoto, Shoko and Takagi, Toshihisa},
 journal = {F1000Research}
}

Publishing databases in the Resource Description Framework (RDF) model is becoming widely accepted to maximize the syntactic and semantic interoperability of open data in life sciences. Here we report advancements made in the 6th and 7th annual BioHackathons which were held in Tokyo and Miyagi respectively. This review consists of two major sections covering: 1) improvement and utilization of RDF data in various domains of the life sciences and 2) meta-data about these RDF data, the resources that store them, and the service quality of SPARQL Protocol and RDF Query Language (SPARQL) endpoints. The first section describes how we developed RDF data, ontologies and tools in genomics, proteomics, metabolomics, glycomics and by literature text mining. The second section describes how we defined descriptions of datasets, the provenance of data, and quality assessment of services and service discovery. By enhancing the harmonization of these two layers of machine-readable data and knowledge, we improve the way community wide resources are developed and published. Moreover, we outline best practices for the future, and prepare ourselves for an exciting and unanticipatable variety of real world applications in coming years.

@article{
 title = {Genome-wide analysis of the H3K27me3 epigenome and transcriptome in Brassica rapa},
 type = {article},
 year = {2019},
 identifiers = {[object Object]},
 volume = {8},
 websites = {https://doi.org/10.1093/gigascience/giz147,https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giz147/5652252},
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 abstract = {Genome-wide maps of histone modifications have been obtained for several plant species. However, most studies focus on model systems and do not enforce FAIR data management principles. Here we study the H3K27me3 epigenome and associated transcriptome of Brassica rapa, an important vegetable cultivated worldwide.We performed H3K27me3 chromatin immunoprecipitation followed by high-throughput sequencing and transcriptomic analysis by 3′-end RNA sequencing from B. rapa leaves and inflorescences. To analyze these data we developed a Reproducible Epigenomic Analysis pipeline using Galaxy and Jupyter, packaged into Docker images to facilitate transparency and reuse. We found that H3K27me3 covers roughly one-third of all B. rapa protein-coding genes and its presence correlates with low transcript levels. The comparative analysis between leaves and inflorescences suggested that the expression of various floral regulatory genes during development depends on H3K27me3. To demonstrate the importance of H3K27me3 for B. rapa development, we characterized a mutant line deficient in the H3K27 methyltransferase activity. We found that braA.clf mutant plants presented pleiotropic alterations, e.g., curly leaves due to increased expression and reduced H3K27me3 levels at AGAMOUS-like loci.We characterized the epigenetic mark H3K27me3 at genome-wide levels and provide genetic evidence for its relevance in B. rapa development. Our work reveals the epigenomic landscape of H3K27me3 in B. rapa and provides novel genomics datasets and bioinformatics analytical resources. We anticipate that this work will lead the way to further epigenomic studies in the complex genome of Brassica crops.},
 bibtype = {article},
 author = {Payá-Milans, Miriam and Poza-Viejo, Laura and Martín-Uriz, Patxi San and Lara-Astiaso, David and Wilkinson, Mark D and Crevillén, Pedro},
 journal = {GigaScience},
 number = {12}
}

Genome-wide maps of histone modifications have been obtained for several plant species. However, most studies focus on model systems and do not enforce FAIR data management principles. Here we study the H3K27me3 epigenome and associated transcriptome of Brassica rapa, an important vegetable cultivated worldwide.We performed H3K27me3 chromatin immunoprecipitation followed by high-throughput sequencing and transcriptomic analysis by 3′-end RNA sequencing from B. rapa leaves and inflorescences. To analyze these data we developed a Reproducible Epigenomic Analysis pipeline using Galaxy and Jupyter, packaged into Docker images to facilitate transparency and reuse. We found that H3K27me3 covers roughly one-third of all B. rapa protein-coding genes and its presence correlates with low transcript levels. The comparative analysis between leaves and inflorescences suggested that the expression of various floral regulatory genes during development depends on H3K27me3. To demonstrate the importance of H3K27me3 for B. rapa development, we characterized a mutant line deficient in the H3K27 methyltransferase activity. We found that braA.clf mutant plants presented pleiotropic alterations, e.g., curly leaves due to increased expression and reduced H3K27me3 levels at AGAMOUS-like loci.We characterized the epigenetic mark H3K27me3 at genome-wide levels and provide genetic evidence for its relevance in B. rapa development. Our work reveals the epigenomic landscape of H3K27me3 in B. rapa and provides novel genomics datasets and bioinformatics analytical resources. We anticipate that this work will lead the way to further epigenomic studies in the complex genome of Brassica crops.

@article{
 title = {Conserved Roles of the H3K27ME3 Epigenome on Plant Development},
 type = {article},
 year = {2019},
 pages = {28},
 websites = {http://www.sebiology.org/docs/default-source/event-documents/seb_nucleus-impact_satellite-programme_nov19-digital.pdf},
 publisher = {The Society for Experimental Biology},
 city = {Madrid, 9-11 DECEMBER, 2019},
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 read = {false},
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 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Crevillen2019},
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 abstract = {The study of the epigenome and its relation with the underlying genome sequence has become a central question in Biology nowadays. Genome-wide maps of histone modifications have been obtained for several plant species. However, most studies focus on model systems and do not enforce FAIR data management principles. We have developed a Reproducible Epigenomics Analysis (REA) pipeline combining a Galaxy environment for data analysis and Jupyter notebooks for results visualization composed into Docker. We successfully applied this REA pipeline to study of the H3K27m3 epigenome on leaves and flowers of plant model (Arabidopsis thaliana) and crop (Brassica rapa) species. The comparative analysis between leaves and inflorescences genomic datasets suggested that the expression of various floral regulatory genes during development is controlled by H3K27 methylation. H3K27me3 is a repressive epigenetic mark set by Polycomb protein complexes and counteracted by the histone demethylase activity of specific Jumonji domain proteins. We are also studying the role of these epigenetic factors in A. thaliana and B. rapa. Mutant analyses have shown that these epigenetic factors regulate a wide range of developmental responses including flowering time. The similarities and differences between model and crop systems on the role of H3K27me},
 bibtype = {article},
 author = {Crevillén, Pedro and Poza-Viejo, Laura and Payá-Milans, Miriam and Del Olmo, IVÁN and Wilkinson​, Mark D},
 journal = {IMPACT OF CHROMATIN DOMAINS ON PLANT PHENOTYPES}
}

The study of the epigenome and its relation with the underlying genome sequence has become a central question in Biology nowadays. Genome-wide maps of histone modifications have been obtained for several plant species. However, most studies focus on model systems and do not enforce FAIR data management principles. We have developed a Reproducible Epigenomics Analysis (REA) pipeline combining a Galaxy environment for data analysis and Jupyter notebooks for results visualization composed into Docker. We successfully applied this REA pipeline to study of the H3K27m3 epigenome on leaves and flowers of plant model (Arabidopsis thaliana) and crop (Brassica rapa) species. The comparative analysis between leaves and inflorescences genomic datasets suggested that the expression of various floral regulatory genes during development is controlled by H3K27 methylation. H3K27me3 is a repressive epigenetic mark set by Polycomb protein complexes and counteracted by the histone demethylase activity of specific Jumonji domain proteins. We are also studying the role of these epigenetic factors in A. thaliana and B. rapa. Mutant analyses have shown that these epigenetic factors regulate a wide range of developmental responses including flowering time. The similarities and differences between model and crop systems on the role of H3K27me

@article{
 title = {Erratum: Addendum: The FAIR Guiding Principles for scientific data management and stewardship (Scientific data (2016) 3 (160018))},
 type = {article},
 year = {2019},
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 author = {Wilkinson, M.D. and Dumontier, M. and Jan Aalbersberg, I. and Appleton, G. and Axton, M. and Baak, A. and Blomberg, N. and Boiten, J.-W. and da Silva Santos, L.B. and Bourne, P.E. and Bouwman, J. and Brookes, A.J. and Clark, T. and Crosas, M. and Dillo, I. and Dumon, O. and Edmunds, S. and Evelo, C.T. and Finkers, R. and Gonzalez-Beltran, A. and Gray, A.J.G. and Groth, P. and Goble, C. and Grethe, J.S. and Heringa, J. and Hoen, P.A.C.'. and Hooft, R. and Kuhn, T. and Kok, R. and Kok, J. and Lusher, S.J. and Martone, M.E. and Mons, A. and Packer, A.L. and Persson, B. and Rocca-Serra, P. and Roos, M. and van Schaik, R. and Sansone, S.-A. and Schultes, E. and Sengstag, T. and Slater, T. and Strawn, G. and Swertz, M.A. and Thompson, M. and van der Lei, J. and van Mulligen, E. and Jan Velterop, undefined and Waagmeester, A. and Wittenburg, P. and Wolstencroft, K. and Zhao, J. and Mons, B.},
 journal = {Scientific data},
 number = {1}
}

@article{
 title = {Author Correction: Evaluating FAIR maturity through a scalable, automated, community-governed framework (Scientific Data, (2019), 6, 1, (174), 10.1038/s41597-019-0184-5)},
 type = {article},
 year = {2019},
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 abstract = {The affiliation of Mark Wilkinson and Mario Prieto was incompletely stated in the original publication. It is: Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) – Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Departamento de Biotecnología-Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid (UPM), Madrid, Spain This has now been corrected in the HTML and PDF versions.},
 bibtype = {article},
 author = {Wilkinson, Mark D. and Dumontier, Michel and Sansone, Susanna Assunta and Bonino da Silva Santos, Luiz Olavo and Prieto, Mario and Batista, Dominique and McQuilton, Peter and Kuhn, Tobias and Rocca-Serra, Philippe and Crosas, Mercѐ and Schultes, Erik},
 journal = {Scientific Data},
 number = {1}
}

The affiliation of Mark Wilkinson and Mario Prieto was incompletely stated in the original publication. It is: Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) – Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Departamento de Biotecnología-Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid (UPM), Madrid, Spain This has now been corrected in the HTML and PDF versions.

@inproceedings{
 title = {Enrichment of rare disease patient registries by FAIRification},
 type = {inproceedings},
 year = {2018},
 pages = {POSTER},
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 booktitle = {ECRD 2018}
}

@inproceedings{
 title = {FAIRification of rare disease patient registries},
 type = {inproceedings},
 year = {2018},
 city = {Athens, Greece, April 16-18, 2018},
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 bibtype = {inproceedings},
 author = {Jacobsen, Annika and Thompson, Mark and Sernadela, Pedro and Ehrhart, Friederike and Carta, Claudio and Groenen, Karlijn and Kersloot, Martijn and Kool, Leo Schultze and Mordenti, Marina and Locatelli, Manuela and Sangiorgi, Luca and Schultes, Erik A. and Cornet, Ronald and Wilkinson, Mark D and Taruscio, Domenica and Thompson, Rachel and van Enckevort, David and Evelo, Chris and Roos, Marco},
 booktitle = {Proceedings of the 2018 Annual Partner Meeting of RD Connect}
}

@article{
 title = {A design framework and exemplar metrics for FAIRness},
 type = {article},
 year = {2018},
 identifiers = {[object Object]},
 pages = {180118},
 volume = {5},
 websites = {http://www.nature.com/articles/sdata2018118},
 month = {6},
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@article{
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@article{
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 author = {Wilkinson, Mark D and Dumontier, Michel and Sansone, Susanna-Assunta and Bonino da Silva Santos, Luiz Olavo and Prieto, Mario and Gautier, Julian and McQuilton, Peter and Murphy, Derek and Crosas, Merce and Schultes, Erik},
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With the increased adoption of the FAIR Principles, a wide range of stakeholders, from scientists to publishers, funding agencies and policy makers, are seeking ways to transparently evaluate resource FAIRness. We describe the FAIR Evaluator, a software infrastructure to register and execute tests of compliance with the recently published FAIR Metrics. The Evaluator enables digital resources to be assessed objectively and transparently. We illustrate its application to three widely used generalist repositories - Dataverse, Dryad, and Zenodo - and report their feedback. Evaluations allow communities to select relevant Metric subsets to deliver FAIRness measurements in diverse and specialized applications. Evaluations are executed in a semi-automated manner through Web Forms filled-in by a user, or through a JSON-based API. A comparison of manual vs automated evaluation reveals that automated evaluations are generally stricter, resulting in lower, though more accurate, FAIRness scores. Finally, we highlight the need for enhanced infrastructure such as standards registries, like FAIRsharing, as well as additional community involvement in domain-specific data infrastructure creation.

@article{
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@article{
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The analysis of microbiome dynamics would allow us to elucidate patterns within microbial community evolution; however, microbiome state-transition dynamics have been scarcely studied. This is in part because a necessary first-step in such analyses has not been well-defined: how to deterministically describe a microbiome’s ”state”. Clustering in states have been widely studied, although no standard has been concluded yet. We propose a generic, domain-independent and automatic procedure to determine a reliable set of microbiome sub-states within a specific dataset, and with respect to the conditions of the study. The robustness of sub-state identification is established by the combination of diverse techniques for stable cluster verification. We reuse four distinct longitudinal microbiome datasets to demonstrate the broad applicability of our method, analysing results with different taxa subset allowing to adjust it depending on the application goal, and showing that the methodology provides a set of robust sub-states to examine in downstream studies about dynamics in microbiome.
},
 bibtype = {article},
 author = {García-Jiménez, Beatriz and Wilkinson, Mark D},
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}

The analysis of microbiome dynamics would allow us to elucidate patterns within microbial community evolution; however, microbiome state-transition dynamics have been scarcely studied. This is in part because a necessary first-step in such analyses has not been well-defined: how to deterministically describe a microbiome’s ”state”. Clustering in states have been widely studied, although no standard has been concluded yet. We propose a generic, domain-independent and automatic procedure to determine a reliable set of microbiome sub-states within a specific dataset, and with respect to the conditions of the study. The robustness of sub-state identification is established by the combination of diverse techniques for stable cluster verification. We reuse four distinct longitudinal microbiome datasets to demonstrate the broad applicability of our method, analysing results with different taxa subset allowing to adjust it depending on the application goal, and showing that the methodology provides a set of robust sub-states to examine in downstream studies about dynamics in microbiome.

@article{
 title = {FAIRsharing, a cohesive community approach to the growth in standards, repositories and policies},
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 abstract = {In this modern, data-driven age, governments, funders and publishers expect greater transparency and reuse of research data, as well as greater access to and preservation of the data that supports research findings. Community-developed standards, such as those for the identification and reporting of data, underpin reproducible and reusable research, aid scholarly publishing, and drive both the discovery and evolution of scientific practice. The number of these standardization efforts, driven by large organizations or at the grass root level, has been on the rise since the early 2000s. Thousands of community-developed standards are available (across all disciplines), many of which have been created and/or implemented by several thousand data repositories. Nevertheless, their uptake by the research community, however, has been slow and uneven. This is mainly because investigators lack incentives to follow and adopt standards. The situation is exacerbated if standards are not promptly implemented by databases, repositories and other research tools, or endorsed by infrastructures. Furthermore, the fragmentation of community efforts results in the development of arbitrarily different, incompatible standards. In turn, this leads to standards becoming rapidly obsolete in fast-evolving research areas. As with any other digital object, standards, databases and repositories are dynamic in nature, with a life cycle that encompasses formulation, development and maintenance; their status in this cycle may vary depending on the level of activity of the developing group or community. There is an urgent need for a service that enhances the information available on the evolving constellation of heterogeneous standards, databases and repositories, guides users in the selection of these resources, and that works with developers and maintainers of these resources to foster collaboration and promote harmonization. Such an informative and educational service is vital to reduce the knowledge gap among those involved in producing, managing, serving, curating, preserving, publishing or regulating data. A diverse set of stakeholders-representing academia, industry, funding agencies, standards organizations, infrastructure providers and scholarly publishers, both national and domain-specific as well global and general organizations, have come together as a community, representing the core adopters, advisory board members, and/or key collaborators of the FAIRsharing resource. Here, we introduce its mission and community network. We present an evaluation of the standards landscape, focusing on those for reporting data and metadata - the most diverse and numerous of the standards - and their implementation by databases and repositories. We report on the ongoing challenge to recommend resources, and we discuss the importance of making standards invisible to the end users. We report on the ongoing challenge to recommend resources, and we discuss the importance of making standards invisible to the end users. We present guidelines that highlight the role each stakeholder group must play to maximize the visibility and adoption of standards, databases and repositories.},
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 author = {Sansone, Susanna-Assunta and McQuilton, Peter and Rocca-Serra, Philippe and Gonzalez-Beltran, Alejandra and Izzo, Massimiliano and Lister, Allyson and Thurston, Milo and Batista, Dominique and Granell, Ramon and Adekale, Melanie and Dauga, Delphine and Ganley, Emma and Hodson, Simon and Lawrence, Rebecca and Khodiyar, Varsha and Tenenbaum, Jessica and Axton, J Myles and Ball, Michael and Besson, Sebastien and Bloom, Theodora and Bonazzi, Vivien and Jimenez, Rafael and Carr, David and Chan, Wei Mun and Chung, Caty and Clement-Stoneham, Geraldine and Cousijn, Helena and Dayalan, Saravanan and Dumontier, Michel and Dzale Yeumo, Esther and Edmunds, Scott and Everitt, Nicholas and Fripp, Dom and Goble, Carole and Golebiewski, Martin and Hall, Neil and Hanisch, Robert and Hucka, Michael and Huerta, Michael and Kenall, Amye and Kiley, Robert and Klenk, Juergen and Koureas, Dimitrios and Larkin, Jennie and Lemberger, Thomas and Lynch, Nick and Schriml, Lynn and Ma\textquoterightayan, Avi and MacCallum, Catriona and Mons, Barend and Moore, Josh and Muller, Wolfgang and Murray, Hollydawn and Nobusada, Tomoe and Noesgaard, Daniel and Paxton-Boyd, Jennifer and Orchard, Sandra and Rustici, Gabriella and Schurer, Stephan and Sharples, Kathryn and e Silva, Marina and Stanford, Natalie J and Subirats-Coll, Inmaculada and Swedlow, Jason and Tong, Weida and Wilkinson, Mark and Wise, John and Yilmaz, Pelin},
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In this modern, data-driven age, governments, funders and publishers expect greater transparency and reuse of research data, as well as greater access to and preservation of the data that supports research findings. Community-developed standards, such as those for the identification and reporting of data, underpin reproducible and reusable research, aid scholarly publishing, and drive both the discovery and evolution of scientific practice. The number of these standardization efforts, driven by large organizations or at the grass root level, has been on the rise since the early 2000s. Thousands of community-developed standards are available (across all disciplines), many of which have been created and/or implemented by several thousand data repositories. Nevertheless, their uptake by the research community, however, has been slow and uneven. This is mainly because investigators lack incentives to follow and adopt standards. The situation is exacerbated if standards are not promptly implemented by databases, repositories and other research tools, or endorsed by infrastructures. Furthermore, the fragmentation of community efforts results in the development of arbitrarily different, incompatible standards. In turn, this leads to standards becoming rapidly obsolete in fast-evolving research areas. As with any other digital object, standards, databases and repositories are dynamic in nature, with a life cycle that encompasses formulation, development and maintenance; their status in this cycle may vary depending on the level of activity of the developing group or community. There is an urgent need for a service that enhances the information available on the evolving constellation of heterogeneous standards, databases and repositories, guides users in the selection of these resources, and that works with developers and maintainers of these resources to foster collaboration and promote harmonization. Such an informative and educational service is vital to reduce the knowledge gap among those involved in producing, managing, serving, curating, preserving, publishing or regulating data. A diverse set of stakeholders-representing academia, industry, funding agencies, standards organizations, infrastructure providers and scholarly publishers, both national and domain-specific as well global and general organizations, have come together as a community, representing the core adopters, advisory board members, and/or key collaborators of the FAIRsharing resource. Here, we introduce its mission and community network. We present an evaluation of the standards landscape, focusing on those for reporting data and metadata - the most diverse and numerous of the standards - and their implementation by databases and repositories. We report on the ongoing challenge to recommend resources, and we discuss the importance of making standards invisible to the end users. We report on the ongoing challenge to recommend resources, and we discuss the importance of making standards invisible to the end users. We present guidelines that highlight the role each stakeholder group must play to maximize the visibility and adoption of standards, databases and repositories.

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@article{
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@inproceedings{
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@inproceedings{
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@techreport{
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 author = {Carta, Claudio and Roos, Marco and Jansen, Mascha and Enckevort, David van and Wilkinson, Mark D and Kaliyaperumal, Rajaram and Thompson, Mark and Torreri, Paola and da Silva Santos, Luiz Olavo Bonino and Cornet, Ronald and Kodra, Yllka and Taruscio, Domenica}
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CELEBRATION OF 83 YEARS OF CNMR-ISS, ITALY

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 author = {Enckevort, David van and Claudio, Carta and Thompson, Rachel and Thompson, Mark and Ehrhart, Friederieke and Kaliyaperumal, Rajaram and Sernadela, Pedro and Da Silva Santos, Luiz Olavo Bonino and Jacobsen, Annika and Wilkinson, Mark and Müller, Heimo and Reihs, Robert and Oliviera, Jose Luis and Evelo, Chris and Taruscio, Domenica and 'T Hoen, Peter-Bram and Roos, Marco},
 booktitle = {BioSB2017}
}

@article{
 title = {Interoperability and FAIRness through a novel combination of Web technologies},
 type = {article},
 year = {2017},
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 pages = {e110},
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 abstract = {Data in the life sciences are extremely diverse and are stored in a broad spectrum of repositories ranging from those designed for particular data types (such as KEGG for pathway data or UniProt for protein data) to those that are general-purpose (such as FigShare, Zenodo, Dataverse or EUDAT). These data have widely different levels of sensitivity and security considerations. For example, clinical observations about genetic mutations in patients are highly sensitive, while observations of species diversity are generally not. The lack of uniformity in data models from one repository to another, and in the richness and availability of metadata descriptions, makes integration and analysis of these data a manual, time-consuming task with no scalability. Here we explore a set of resource-oriented Web design patterns for data discovery, accessibility, transformation, and integration that can be implemented by any general- or special-purpose repository as a means to assist users in finding and reusing their data holdings. We show that by using off-the-shelf technologies, interoperability can be achieved atthe level of an individual spreadsheet cell. We note that the behaviours of this architecture compare favourably to the desiderata defined by the FAIR Data Principles, and can therefore represent an exemplar implementation of those principles. The proposed interoperability design patterns may be used to improve discovery and integration of both new and legacy data, maximizing the utility of all scholarly outputs.},
 bibtype = {article},
 author = {Wilkinson, Mark D. and van Mulligen, Erik M. and Ciccarese, Paolo and Schultes, Erik A. and Verborgh, Ruben and Kaliyaperumal, Rajaram and Clark, Tim and Bolleman, Jerven T. and Thompson, Mark and Kelpin, Fleur D.L. and Swertz, Morris A. and Dumontier, Michel and Bonino da Silva Santos, Luiz Olavo and Gray, Alasdair J.G. and Kuzniar, Arnold and Gavai, Anand},
 journal = {PeerJ Computer Science}
}

Data in the life sciences are extremely diverse and are stored in a broad spectrum of repositories ranging from those designed for particular data types (such as KEGG for pathway data or UniProt for protein data) to those that are general-purpose (such as FigShare, Zenodo, Dataverse or EUDAT). These data have widely different levels of sensitivity and security considerations. For example, clinical observations about genetic mutations in patients are highly sensitive, while observations of species diversity are generally not. The lack of uniformity in data models from one repository to another, and in the richness and availability of metadata descriptions, makes integration and analysis of these data a manual, time-consuming task with no scalability. Here we explore a set of resource-oriented Web design patterns for data discovery, accessibility, transformation, and integration that can be implemented by any general- or special-purpose repository as a means to assist users in finding and reusing their data holdings. We show that by using off-the-shelf technologies, interoperability can be achieved atthe level of an individual spreadsheet cell. We note that the behaviours of this architecture compare favourably to the desiderata defined by the FAIR Data Principles, and can therefore represent an exemplar implementation of those principles. The proposed interoperability design patterns may be used to improve discovery and integration of both new and legacy data, maximizing the utility of all scholarly outputs.

@inproceedings{
 title = {Overview of a suite of tools and training material for implementing FAIR data principles},
 type = {inproceedings},
 year = {2017},
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 booktitle = {ELIXIR Webinar}
}

@inproceedings{
 title = {Implementing ELSI 2.0 for international collaborative research: improving access to clinical and genetics data by connecting ELSI policies, FAIR data software solutions, and ELIXIR-AAI},
 type = {inproceedings},
 year = {2017},
 pages = {POSTER},
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 author = {Gainotti, Sabina and Petrini, Carlo and Mascalzoni, Deborah and Floridia, Giovanna and Riva, Luciana and Wilkinson, Mark and Roos, Marco},
 booktitle = {SWAT4HCLS 2017}
}

@inproceedings{
 title = {The FAIRification of data and the potentialities of FAIR resources showed, in practice, at the Rome “Bring Your Own Data” workshop},
 type = {inproceedings},
 year = {2017},
 websites = {http://ceur-ws.org/Vol-2042/paper27.pdf},
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 booktitle = {Proceedings of SWAT4HCLS 2017}
}

@techreport{
 title = {FAIRMetrics/Metrics: Initial set of proposed FAIR Metrics for community discussion},
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@article{
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@inproceedings{
 title = {Overview of a suite of middle-ware services for implementing FAIR data principles},
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 booktitle = {CEUR Workshop Proceedings}
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@inproceedings{
 title = {Bring Your Own Data workshop is an excellent tool to promote the establishment of Findable, Accessible, Interoperable, and Reusable rare disease registries},
 type = {inproceedings},
 year = {2017},
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 author = {Carta, Claudio and Roos, Marco and Jansen, Mascha and Wilkinson, Mark D and Bonino da Silva Santos, Luis and van Enckevort, David and Taruscio, Domenica},
 booktitle = {Procedigs of the European Human Genetics Conference 2017}
}

@article{
 title = {The FAIR Guiding Principles for scientific data management and stewardship},
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 pages = {160018},
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 websites = {http://www.nature.com/articles/sdata201618},
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 author = {Wilkinson, Mark D. and Dumontier, Michel and Aalbersberg, IJsbrand Jan and Appleton, Gabrielle and Axton, Myles and Baak, Arie and Blomberg, Niklas and Boiten, Jan-Willem and da Silva Santos, Luiz Bonino and Bourne, Philip E. and Bouwman, Jildau and Brookes, Anthony J. and Clark, Tim and Crosas, Mercè and Dillo, Ingrid and Dumon, Olivier and Edmunds, Scott and Evelo, Chris T. and Finkers, Richard and Gonzalez-Beltran, Alejandra and Gray, Alasdair J.G. and Groth, Paul and Goble, Carole and Grethe, Jeffrey S. and Heringa, Jaap and ’t Hoen, Peter A.C and Hooft, Rob and Kuhn, Tobias and Kok, Ruben and Kok, Joost and Lusher, Scott J. and Martone, Maryann E. and Mons, Albert and Packer, Abel L. and Persson, Bengt and Rocca-Serra, Philippe and Roos, Marco and van Schaik, Rene and Sansone, Susanna-Assunta and Schultes, Erik and Sengstag, Thierry and Slater, Ted and Strawn, George and Swertz, Morris A. and Thompson, Mark and van der Lei, Johan and van Mulligen, Erik and Velterop, Jan and Waagmeester, Andra and Wittenburg, Peter and Wolstencroft, Katherine and Zhao, Jun and Mons, Barend},
 journal = {Scientific Data}
}

@article{
 title = {Temporal microbiome road-maps guided by perturbations},
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 abstract = {Motivation: There are few tools that allow longitudinal analysis of metagenomic data subjected to distinct perturbations. Methods: This study examines longitudinal metagenomics data modelled as a Markov Decision Process (MDP). Given an external perturbation, the MDP predicts the next microbiome state in a temporal sequence, selected from a finite set of possible microbiome states. Results: We examined three distinct datasets to demonstrate this approach. An MDP created for a vaginal microbiome time series generates a variety of behaviour policies. For example, that moving from a state associated with bacterial vaginosis to a healthier one, requires avoiding perturbations such as lubricant, sex toys, tampons and anal sex. The flexibility of our proposal is verified after applying MDPs to human gut and chick gut microbiomes, taking nutritional intakes, or salmonella and probiotic treatments, respectively, as perturbations. In the latter case, MDPs provided a quantitative explanation for why salmonella vaccine accelerates microbiome maturation in chicks. This novel analytical approach has applications in, for example, medicine where the MDP could suggest the sequence of perturbations (e.g. clinical interventions) to apply to follow the best path from any given starting state, to a desired (healthy) state, avoiding strongly negative states.},
 bibtype = {article},
 author = {García-Jiménez, Beatriz and Wilkinson, Mark},
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}

Motivation: There are few tools that allow longitudinal analysis of metagenomic data subjected to distinct perturbations. Methods: This study examines longitudinal metagenomics data modelled as a Markov Decision Process (MDP). Given an external perturbation, the MDP predicts the next microbiome state in a temporal sequence, selected from a finite set of possible microbiome states. Results: We examined three distinct datasets to demonstrate this approach. An MDP created for a vaginal microbiome time series generates a variety of behaviour policies. For example, that moving from a state associated with bacterial vaginosis to a healthier one, requires avoiding perturbations such as lubricant, sex toys, tampons and anal sex. The flexibility of our proposal is verified after applying MDPs to human gut and chick gut microbiomes, taking nutritional intakes, or salmonella and probiotic treatments, respectively, as perturbations. In the latter case, MDPs provided a quantitative explanation for why salmonella vaccine accelerates microbiome maturation in chicks. This novel analytical approach has applications in, for example, medicine where the MDP could suggest the sequence of perturbations (e.g. clinical interventions) to apply to follow the best path from any given starting state, to a desired (healthy) state, avoiding strongly negative states.

@article{
 title = {Publishing FAIR Data: An Exemplar Methodology Utilizing PHI-Base},
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 year = {2016},
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 volume = {7},
 websites = {http://journal.frontiersin.org/article/10.3389/fpls.2016.00641,http://journal.frontiersin.org/Article/10.3389/fpls.2016.00641/abstract},
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 journal = {Frontiers in Plant Science}
}

@inproceedings{
 title = {MDPbiome: Predicting temporal microbiome dynamics influenced by external perturbations},
 type = {inproceedings},
 year = {2016},
 websites = {http://jbi2016.webs.upv.es/wp-content/uploads/2016/05/JBI2016.AbstractsBooklet-1.pdf},
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 booktitle = {XIII Symposium on Bioinformatics}
}

@inproceedings{
 title = {From hypothesis construction, through experimental execution, to result publication and citation},
 type = {inproceedings},
 year = {2016},
 pages = {14},
 websites = {http://repositorio.upct.es/bitstream/handle/10317/5290/isbn9788460853992.pdf?sequence=4&isAllowed=y},
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 editor = {Francisco de Asis, Artés Hernández and Gutiérrez-Cortines, Marcos Egea and Fernández Hernández, Juan Antonio and Baille, Alain Daniel and Calatrava Leyva, Javier},
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@article{
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@inproceedings{
 title = {Registries of domain-relevant semantic reference models help bootstrap interoperability in domains with fragmented data resources},
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@article{
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@article{
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 abstract = {Unbiased identification of organisms by PCR reactions using universal primers followed by DNA sequencing assumes positive amplification. We used six universal loci spanning 48 plant species and quantified the bias at each step of the identification process from end point PCR to next-generation sequencing. End point amplification was significantly different for single loci and between species. Quantitative PCR revealed that Cq threshold for various loci, even within a single DNA extraction, showed 2,000-fold differences in DNA quantity after amplification. Next-generation sequencing (NGS) experiments in nine species showed significant biases towards species and specific loci using adaptor-specific primers. NGS sequencing bias may be predicted to some extent by the Cq values of qPCR amplification.},
 bibtype = {article},
 author = {Pawluczyk, Marta and Weiss, Julia and Links, Matthew G and Egaña Aranguren, Mikel and Wilkinson, Mark D and Egea-Cortines, Marcos},
 journal = {Analytical and bioanalytical chemistry}
}

Unbiased identification of organisms by PCR reactions using universal primers followed by DNA sequencing assumes positive amplification. We used six universal loci spanning 48 plant species and quantified the bias at each step of the identification process from end point PCR to next-generation sequencing. End point amplification was significantly different for single loci and between species. Quantitative PCR revealed that Cq threshold for various loci, even within a single DNA extraction, showed 2,000-fold differences in DNA quantity after amplification. Next-generation sequencing (NGS) experiments in nine species showed significant biases towards species and specific loci using adaptor-specific primers. NGS sequencing bias may be predicted to some extent by the Cq values of qPCR amplification.

@inbook{
 type = {inbook},
 year = {2015},
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 abstract = {In the creation of diagnostic decision support systems (DDSS) it is crucial to have validated and precise knowledge in order to create accurate systems. Typically, medical experts are the source of this knowledge, but it is not always possible to obtain all the desired information from them. Another valuable source could be medical books or articles describing the diagnosis of diseases managed by the DDSS, but again, it is not easy to extract this information. In this paper we present the results of our research, in which we have used Web scraping and a combination of natural language processing techniques to extract diagnostic criteria from MedlinePlus articles about infectious diseases.},
 bibtype = {inbook},
 author = {Rodríguez-González, Alejandro and Martínez-Romero, Marcos and Costumero, Roberto and Wilkinson, Mark D. and Menasalvas-Ruiz, Ernestina},
 editor = {Overbeek, Ross and Rocha, Miguel P. and Fdez-Riverola, Florentino and Paz, Juan F. De},
 chapter = {Diagnostic Knowledge Extraction from MedlinePlus: An Application for Infectious Diseases},
 title = {9th International Conference on Practical Applications of Computational Biology and Bioinformatics}
}

In the creation of diagnostic decision support systems (DDSS) it is crucial to have validated and precise knowledge in order to create accurate systems. Typically, medical experts are the source of this knowledge, but it is not always possible to obtain all the desired information from them. Another valuable source could be medical books or articles describing the diagnosis of diseases managed by the DDSS, but again, it is not easy to extract this information. In this paper we present the results of our research, in which we have used Web scraping and a combination of natural language processing techniques to extract diagnostic criteria from MedlinePlus articles about infectious diseases.

@misc{
 title = {DataFairPort: The Perl libraries version 0.231},
 type = {misc},
 year = {2015},
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 author = {Wilkinson, Mark and Dumontier, Michel and Durbin, Philip}
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@article{
 title = {Enhanced reproducibility of SADI web service workflows with Galaxy and Docker},
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 pages = {59},
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 author = {Aranguren, Mikel Egaña and Wilkinson, Mark D.},
 journal = {GigaScience},
 number = {1}
}

@book{
 title = {Erratum to: Diagnostic knowledge extraction from MedlinePlus: An application for infectious diseases (9th International Conference on Practical Applications of Computational Biology and Bioinformatics, Advances in Intelligent Systems and Computing 375, 10},
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}

@article{
 title = {Executing SADI services in Galaxy},
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 pages = {42},
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 author = {Egaña Aranguren, Mikel and Rodríguez González, Alejandro and Wilkinson, Mark D.},
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 number = {1}
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@inproceedings{
 title = {Nanopublishing clinical diagnoses: tracking diagnostic knowledge base content and utilization},
 type = {inproceedings},
 year = {2014},
 identifiers = {[object Object]},
 pages = {335-340},
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 bibtype = {inproceedings},
 author = {Rodríguez-González, Alejandro and Martinez-Romero, Marcos and Aranguren, Mikel Egaña and Wilkinson, Mark D.},
 booktitle = {CBMS '14 Proceedings of the 2014 IEEE 27th International Symposium on Computer-Based Medical Systems}
}

@inbook{
 type = {inbook},
 year = {2014},
 identifiers = {[object Object]},
 pages = {371-391},
 websites = {http://link.springer.com/chapter/10.1007/978-3-319-05687-6_15},
 publisher = {Springer International Publishing},
 chapter = {Bioinformatics Tools for Next-Generation RNA Sequencing Analysis},
 id = {8a36acd0-ce7f-3168-b83d-2da0a2c5f4bb},
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 abstract = {The purpose of this chapter is to introduce the reader to some of the most popular bioinformatics tools and resources available for RNA analysis. The introduction of RNA next-generation sequencing led to an explosion in the amount of quantitative transcript sequence data, which necessitated the development of adequate tools to process and make a sense of these rich and complex datasets. A large number of programs, platforms, and databases dedicated to RNA analysis have been produced over the past approximately 20 years; however, like so much other bioinformatics software, only a small portion of them are still available and in-use. As such, we will focus only on those tools and applications still in common use. This chapter is composed of three sections: the description of the general protocols for RNA sequence (generically called RNA-Seq) analyses, an outline of the most common approaches to map polyadenylation sites, and a brief introduction to noncoding RNA (ncRNA) analysis. The first section will describe the composition of steps within a typical RNA-Seq study: the experimental design, the sequencing methods, the data quality control, the read mapping, and the differential expression analysis. The second section will introduce a few recent methods developed to map polyadenylation sites: the experimental protocols (which are variations of RNA-Seq), polyadenylation site databases and prediction programs, and cis-regulatory elements discovery. The third and final section will present several of the ncRNA databases and prediction tools.},
 bibtype = {inbook},
 author = {Marconi, Marco and Rodriguez-Romero, Julio and Sesma, Ane and Wilkinson, Mark D.},
 editor = {Sesma, Ane and von der Haar, T.},
 title = {Fungal RNA biology}
}

The purpose of this chapter is to introduce the reader to some of the most popular bioinformatics tools and resources available for RNA analysis. The introduction of RNA next-generation sequencing led to an explosion in the amount of quantitative transcript sequence data, which necessitated the development of adequate tools to process and make a sense of these rich and complex datasets. A large number of programs, platforms, and databases dedicated to RNA analysis have been produced over the past approximately 20 years; however, like so much other bioinformatics software, only a small portion of them are still available and in-use. As such, we will focus only on those tools and applications still in common use. This chapter is composed of three sections: the description of the general protocols for RNA sequence (generically called RNA-Seq) analyses, an outline of the most common approaches to map polyadenylation sites, and a brief introduction to noncoding RNA (ncRNA) analysis. The first section will describe the composition of steps within a typical RNA-Seq study: the experimental design, the sequencing methods, the data quality control, the read mapping, and the differential expression analysis. The second section will introduce a few recent methods developed to map polyadenylation sites: the experimental protocols (which are variations of RNA-Seq), polyadenylation site databases and prediction programs, and cis-regulatory elements discovery. The third and final section will present several of the ncRNA databases and prediction tools.

@inproceedings{
 title = {The Crop Ontology, a resource for enabling access to breeders'data},
 type = {inproceedings},
 year = {2014},
 pages = {W352},
 websites = {https://pag.confex.com/pag/xxii/webprogram/Paper11073.html},
 city = {San Diego},
 id = {566e1b72-8841-3d10-a14b-65cb74e92a6b},
 created = {2014-07-02T09:11:39.000Z},
 file_attached = {false},
 profile_id = {17c87d5d-2470-32d7-b273-0734a1d9195f},
 last_modified = {2017-03-22T07:45:59.566Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Arnaud2014},
 private_publication = {false},
 abstract = {The Crop Ontology (CO) (www.cropontology.org) is a service of the Integrated Breeding Platform (IBP), Generation Challenge Programme (GCP). CO currently includes reference lists of breeders’traits with measurement methods and scales for 14 crops – with work ongoing to add barley, lentil and sweet potato – as well as environmental and experimental design concepts. The objective is to provide a controlled vocabulary enabling harmonized annotations of evaluation data and metadata. The crop communities of practice, coordinated by the Crop Lead Centers, validate the concepts compiled in the CO trait dictionary template. CO trait concepts are cross-referenced to Plant Ontology (PO) and Trait Ontology (TO). The environmental and trial management concepts will be improved by a group of experts in agronomy and crop modelling. The Integrated Breeding Fieldbook and the Chado schema of the GCP crop databases both use the CO concepts for the capture and annotation of breeders’data. The Programmatic Application Interface (API) of CO enables direct download of the concepts by third-party web sites like: the international cassava database (http://www.cassavabase.org/), the Global Agricultural Trial Repository (Agtrials) (http://agtrials.org/), Eu-Solanaceae database at Wageningen University (https://www.eu-sol.wur.nl/), and the Phenomics Ontology Driven Database (PODD: http://150.229.2.236/podd/about) of the Australian Plant Phenomics Facility (APPF). CO is available in RDF format (Resource Description Framework) using the SKOS vocabulary (Simple Knowledge Organization System) for the purpose of publishing it in the Linked Open Data (LOD) Cloud. CO will be aligned with the AGROVOC thesaurus (FAO;http://www.fao.org/agrovoc) to test the integration of crop data into the mashup site of Agris (http://agris.fao.org/).},
 bibtype = {inproceedings},
 author = {Arnaud, Elizabeth and Matteis, Luca and Laporte, Marie Angelique and Espinosa, Herlin and Hyman, Glenn and Shrestha, Rosemary and Portugal, Arlett and Chibon, Pierre Yves and Devare, Medha and Akintunde, Akinnola and White, Jeffrey W and Wilkinson, Mark and Caracciolo, Caterina and Celli, Fabrizio and Mclaren, Graham},
 booktitle = {In proceeding of: Plant and Animal Genome XXII}
}

The Crop Ontology (CO) (www.cropontology.org) is a service of the Integrated Breeding Platform (IBP), Generation Challenge Programme (GCP). CO currently includes reference lists of breeders’traits with measurement methods and scales for 14 crops – with work ongoing to add barley, lentil and sweet potato – as well as environmental and experimental design concepts. The objective is to provide a controlled vocabulary enabling harmonized annotations of evaluation data and metadata. The crop communities of practice, coordinated by the Crop Lead Centers, validate the concepts compiled in the CO trait dictionary template. CO trait concepts are cross-referenced to Plant Ontology (PO) and Trait Ontology (TO). The environmental and trial management concepts will be improved by a group of experts in agronomy and crop modelling. The Integrated Breeding Fieldbook and the Chado schema of the GCP crop databases both use the CO concepts for the capture and annotation of breeders’data. The Programmatic Application Interface (API) of CO enables direct download of the concepts by third-party web sites like: the international cassava database (http://www.cassavabase.org/), the Global Agricultural Trial Repository (Agtrials) (http://agtrials.org/), Eu-Solanaceae database at Wageningen University (https://www.eu-sol.wur.nl/), and the Phenomics Ontology Driven Database (PODD: http://150.229.2.236/podd/about) of the Australian Plant Phenomics Facility (APPF). CO is available in RDF format (Resource Description Framework) using the SKOS vocabulary (Simple Knowledge Organization System) for the purpose of publishing it in the Linked Open Data (LOD) Cloud. CO will be aligned with the AGROVOC thesaurus (FAO;http://www.fao.org/agrovoc) to test the integration of crop data into the mashup site of Agris (http://agris.fao.org/).

@article{
 title = {The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery.},
 type = {article},
 year = {2014},
 identifiers = {[object Object]},
 pages = {14},
 volume = {5},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/24602174},
 month = {3},
 day = {6},
 id = {2e66e828-3bd8-3772-abc5-ecfaceda8d06},
 created = {2014-07-02T09:11:39.000Z},
 accessed = {2014-03-19},
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 last_modified = {2017-03-22T07:45:59.566Z},
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 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Dumontier2014},
 private_publication = {false},
 abstract = {The Semanticscience Integrated Ontology (SIO) is an ontology to facilitate biomedical knowledge discovery. SIO features a simple upper level comprised of essential types and relations for the rich description of arbitrary (real, hypothesized, virtual, fictional) objects, processes and their attributes. SIO specifies simple design patterns to describe and associate qualities, capabilities, functions, quantities, and informational entities including textual, geometrical, and mathematical entities, and provides specific extensions in the domains of chemistry, biology, biochemistry, and bioinformatics. SIO provides an ontological foundation for the Bio2RDF linked data for the life sciences project and is used for semantic integration and discovery for SADI-based semantic web services. SIO is freely available to all users under a creative commons by attribution license. See website for further information: http://sio.semanticscience.org .},
 bibtype = {article},
 author = {Dumontier, Michel and Baker, Christopher Jo and Baran, Joachim and Callahan, Alison and Chepelev, Leonid and Cruz-Toledo, José and Del Rio, Nicholas R and Duck, Geraint and Furlong, Laura I. and Keath, Nichealla and Klassen, Dana and McCusker, James P. and Queralt-Rosinach, Núria and Samwald, Matthias and Villanueva-Rosales, Natalia and Wilkinson, Mark D and Hoehndorf, Robert},
 journal = {Journal of biomedical semantics},
 number = {1}
}

The Semanticscience Integrated Ontology (SIO) is an ontology to facilitate biomedical knowledge discovery. SIO features a simple upper level comprised of essential types and relations for the rich description of arbitrary (real, hypothesized, virtual, fictional) objects, processes and their attributes. SIO specifies simple design patterns to describe and associate qualities, capabilities, functions, quantities, and informational entities including textual, geometrical, and mathematical entities, and provides specific extensions in the domains of chemistry, biology, biochemistry, and bioinformatics. SIO provides an ontological foundation for the Bio2RDF linked data for the life sciences project and is used for semantic integration and discovery for SADI-based semantic web services. SIO is freely available to all users under a creative commons by attribution license. See website for further information: http://sio.semanticscience.org .

@inproceedings{
 title = {Automatic Annotation of Bioinformatics Workflows with Biomedical Ontologies},
 type = {inproceedings},
 year = {2014},
 identifiers = {[object Object]},
 pages = {464-478},
 volume = {8803},
 websites = {http://arxiv.org/abs/1407.0165,http://link.springer.com/10.1007/978-3-662-45231-8},
 publisher = {Springer Berlin Heidelberg},
 city = {Berlin, Heidelberg},
 series = {Lecture Notes in Computer Science},
 id = {f1ab1039-e253-39df-a88c-d0a192291b6e},
 created = {2014-07-02T09:11:39.000Z},
 accessed = {2014-10-17},
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 last_modified = {2017-03-22T07:45:59.566Z},
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 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Garcia-Jimenez2014},
 folder_uuids = {ec2ec352-fa41-43db-919d-f96649955c40},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {García-Jiménez, Beatriz and Wilkinson, Mark D},
 editor = {Margaria, Tiziana and Steffen, Bernhard},
 booktitle = {Leveraging Applications of Formal Methods, Verification and Validation. Specialized Techniques and Applications - 6th International Symposium, ISoLA 2014, Imperial, Corfu, Greece, October 8-11, 2014, Proceedings, Part II}
}

@article{
 title = {BioHackathon series in 2011 and 2012: penetration of ontology and linked data in life science domains},
 type = {article},
 year = {2014},
 identifiers = {[object Object]},
 pages = {5},
 volume = {5},
 websites = {http://www.jbiomedsem.com/content/5/1/5},
 id = {f01f7c02-6823-3028-bae7-0e021618384c},
 created = {2014-07-02T09:11:39.000Z},
 accessed = {2014-02-05},
 file_attached = {true},
 profile_id = {17c87d5d-2470-32d7-b273-0734a1d9195f},
 last_modified = {2017-03-22T07:45:59.566Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Katayama2014},
 private_publication = {false},
 abstract = {The application of semantic technologies to the integration of biological data and the interoperability of bioinformatics analysis and visualization tools has been the common theme of a series of annual BioHackathons hosted in Japan for the past five years. Here we provide a review of the activities and outcomes from the BioHackathons held in 2011 in Kyoto and 2012 in Toyama. In order to efficiently implement semantic technologies in the life sciences, participants formed various sub-groups and worked on the following topics: Resource Description Framework (RDF) models for specific domains, text mining of the literature, ontology development, essential metadata for biological databases, platforms to enable efficient Semantic Web technology development and interoperability, and the development of applications for Semantic Web data. In this review, we briefly introduce the themes covered by these sub-groups. The observations made, conclusions drawn, and software development projects that emerged from these activities are discussed.},
 bibtype = {article},
 author = {Katayama, Toshiaki and Wilkinson, Mark and Aoki-Kinoshita, Kiyoko and Kawashima, Shuichi and Yamamoto, Yasunori and Yamaguchi, Atsuko and Okamoto, Shinobu and Kawano, Shin and Kim, Jin-Dong and Wang, Yue and Wu, Hongyan and Kano, Yoshinobu and Ono, Hiromasa and Bono, Hidemasa and Kocbek, Simon and Aerts, Jan and Akune, Yukie and Antezana, Erick and Arakawa, Kazuharu and Aranda, Bruno and Baran, Joachim and Bolleman, Jerven and Bonnal, Raoul and Buttigieg, Pier and Campbell, Matthew and Chen, Yi-an and Chiba, Hirokazu and Cock, Peter and Cohen, Kevin and Constantin, Alexandru},
 journal = {Journal of Biomedical Semantics},
 number = {1},
 keywords = {biohackathon}
}

The application of semantic technologies to the integration of biological data and the interoperability of bioinformatics analysis and visualization tools has been the common theme of a series of annual BioHackathons hosted in Japan for the past five years. Here we provide a review of the activities and outcomes from the BioHackathons held in 2011 in Kyoto and 2012 in Toyama. In order to efficiently implement semantic technologies in the life sciences, participants formed various sub-groups and worked on the following topics: Resource Description Framework (RDF) models for specific domains, text mining of the literature, ontology development, essential metadata for biological databases, platforms to enable efficient Semantic Web technology development and interoperability, and the development of applications for Semantic Web data. In this review, we briefly introduce the themes covered by these sub-groups. The observations made, conclusions drawn, and software development projects that emerged from these activities are discussed.

@article{
 title = {Automatic detection and resolution of measurement-unit conflicts in aggregated data},
 type = {article},
 year = {2014},
 identifiers = {[object Object]},
 keywords = {Clinical Research,Measurement-units,Ontologies,Semantic Web Services},
 pages = {S12},
 volume = {7},
 websites = {http://www.biomedcentral.com/1755-8794/7/S1/S12},
 id = {d6595cdd-d461-3d5b-826b-b438ee9c39cd},
 created = {2014-07-02T09:11:39.000Z},
 accessed = {2014-05-08},
 file_attached = {false},
 profile_id = {17c87d5d-2470-32d7-b273-0734a1d9195f},
 last_modified = {2017-03-22T07:45:59.566Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Samadian},
 private_publication = {false},
 abstract = {Motivation: Measurement-unit conflicts are a perennial problem in integrative research domains such as clinical meta-analysis. As multi-national collaborations grow, as new measurement instruments appear, and as Linked Open Data infrastructures become increasingly pervasive, the number of such conflicts will similarly increase. We propose a generic approach to the problem of (a) encoding measurement units in datasets in a machine-readable manner, (b) detecting when a dataset contained mixtures of measurement units, and (c) automatically converting any conflicting units into a desired unit, as defined for a given study. Results: We utilized existing ontologies and standards for scientific data representation, measurement unit definition, and data manipulation to build a simple and flexible Semantic Web Service-based approach to measurement-unit harmonization. A cardiovascular patient cohort in which clinical measurements were recorded in a number of different units (e.g., mmHg and cmHg for blood pressure) was automatically classified into a number of clinical phenotypes, semantically defined using different measurement units. Conclusion: We demonstrate that through a combination of semantic standards and frameworks, unit integration problems can be automatically detected and resolved.},
 bibtype = {article},
 author = {Samadian, Soroush and McManus, Bruce and Wilkinson, Mark},
 journal = {BMC Medical Genomics},
 number = {Suppl 1}
}

Motivation: Measurement-unit conflicts are a perennial problem in integrative research domains such as clinical meta-analysis. As multi-national collaborations grow, as new measurement instruments appear, and as Linked Open Data infrastructures become increasingly pervasive, the number of such conflicts will similarly increase. We propose a generic approach to the problem of (a) encoding measurement units in datasets in a machine-readable manner, (b) detecting when a dataset contained mixtures of measurement units, and (c) automatically converting any conflicting units into a desired unit, as defined for a given study. Results: We utilized existing ontologies and standards for scientific data representation, measurement unit definition, and data manipulation to build a simple and flexible Semantic Web Service-based approach to measurement-unit harmonization. A cardiovascular patient cohort in which clinical measurements were recorded in a number of different units (e.g., mmHg and cmHg for blood pressure) was automatically classified into a number of clinical phenotypes, semantically defined using different measurement units. Conclusion: We demonstrate that through a combination of semantic standards and frameworks, unit integration problems can be automatically detected and resolved.

@article{
 title = {Automatically exposing OpenLifeData via SADI semantic Web Services},
 type = {article},
 year = {2014},
 identifiers = {[object Object]},
 keywords = {Bio2RDF,Galaxy,OpenLifeData,SADI,SHARE,SPARQL,Semantic Web Services,Sentient Knowledge Explorer},
 pages = {46},
 volume = {5},
 websites = {http://www.jbiomedsem.com/content/5/1/46},
 id = {8d17d73b-aeeb-397e-a9f7-c938fd66efbd},
 created = {2014-07-17T12:27:47.000Z},
 accessed = {2014-11-20},
 file_attached = {false},
 profile_id = {17c87d5d-2470-32d7-b273-0734a1d9195f},
 last_modified = {2017-03-22T07:45:59.566Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Gonzalez2014},
 folder_uuids = {ec2ec352-fa41-43db-919d-f96649955c40},
 private_publication = {false},
 abstract = {Background Two distinct trends are emerging with respect to how data is shared, collected, and analyzed within the bioinformatics community. First, Linked Data, exposed as SPARQL endpoints, promises to make data easier to collect and integrate by moving towards the harmonization of data syntax, descriptive vocabularies, and identifiers, as well as providing a standardized mechanism for data access. Second, Web Services, often linked together into workflows, normalize data access and create transparent, reproducible scientific methodologies that can, in principle, be re-used and customized to suit new scientific questions. Constructing queries that traverse semantically-rich Linked Data requires substantial expertise, yet traditional RESTful or SOAP Web Services cannot adequately describe the content of a SPARQL endpoint. We propose that content-driven Semantic Web Services can enable facile discovery of Linked Data, independent of their location. Results We use a well-curated Linked Dataset - OpenLifeData - and utilize its descriptive metadata to automatically configure a series of more than 22,000 Semantic Web Services that expose all of its content via the SADI set of design principles. The OpenLifeData SADI services are discoverable via queries to the SHARE registry and easy to integrate into new or existing bioinformatics workflows and analytical pipelines. We demonstrate the utility of this system through comparison of Web Service-mediated data access with traditional SPARQL, and note that this approach not only simplifies data retrieval, but simultaneously provides protection against resource-intensive queries. Conclusions We show, through a variety of different clients and examples of varying complexity, that data from the myriad OpenLifeData can be recovered without any need for prior-knowledge of the content or structure of the SPARQL endpoints. We also demonstrate that, via clients such as SHARE, the complexity of federated SPARQL queries is dramatically reduced.},
 bibtype = {article},
 author = {González, Alejandro Rodríguez and Callahan, Alison and Cruz-Toledo, José and Garcia, Adrian and Egaña Aranguren, Mikel and Dumontier, Michel and Wilkinson, Mark D},
 journal = {Journal of Biomedical Semantics},
 number = {1}
}

Background Two distinct trends are emerging with respect to how data is shared, collected, and analyzed within the bioinformatics community. First, Linked Data, exposed as SPARQL endpoints, promises to make data easier to collect and integrate by moving towards the harmonization of data syntax, descriptive vocabularies, and identifiers, as well as providing a standardized mechanism for data access. Second, Web Services, often linked together into workflows, normalize data access and create transparent, reproducible scientific methodologies that can, in principle, be re-used and customized to suit new scientific questions. Constructing queries that traverse semantically-rich Linked Data requires substantial expertise, yet traditional RESTful or SOAP Web Services cannot adequately describe the content of a SPARQL endpoint. We propose that content-driven Semantic Web Services can enable facile discovery of Linked Data, independent of their location. Results We use a well-curated Linked Dataset - OpenLifeData - and utilize its descriptive metadata to automatically configure a series of more than 22,000 Semantic Web Services that expose all of its content via the SADI set of design principles. The OpenLifeData SADI services are discoverable via queries to the SHARE registry and easy to integrate into new or existing bioinformatics workflows and analytical pipelines. We demonstrate the utility of this system through comparison of Web Service-mediated data access with traditional SPARQL, and note that this approach not only simplifies data retrieval, but simultaneously provides protection against resource-intensive queries. Conclusions We show, through a variety of different clients and examples of varying complexity, that data from the myriad OpenLifeData can be recovered without any need for prior-knowledge of the content or structure of the SPARQL endpoints. We also demonstrate that, via clients such as SHARE, the complexity of federated SPARQL queries is dramatically reduced.

@article{
 title = {The relationship between host lifespan and pathogen reservoir potential: an analysis in the system Arabidopsis thaliana--cucumber mosaic virus.},
 type = {article},
 year = {2014},
 identifiers = {[object Object]},
 pages = {e1004492},
 volume = {10},
 websites = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4223077&tool=pmcentrez&rendertype=abstract},
 month = {11},
 id = {34bc6727-ebd8-3192-849d-bfccd8739e2f},
 created = {2014-07-29T08:56:57.000Z},
 accessed = {2015-01-09},
 file_attached = {false},
 profile_id = {17c87d5d-2470-32d7-b273-0734a1d9195f},
 last_modified = {2017-03-22T07:45:59.566Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Hily2014a},
 folder_uuids = {ec2ec352-fa41-43db-919d-f96649955c40},
 private_publication = {false},
 abstract = {Identification of the determinants of pathogen reservoir potential is central to understand disease emergence. It has been proposed that host lifespan is one such determinant: short-lived hosts will invest less in costly defenses against pathogens, so that they will be more susceptible to infection, more competent as sources of infection and/or will sustain larger vector populations, thus being effective reservoirs for the infection of long-lived hosts. This hypothesis is sustained by analyses of different hosts of multihost pathogens, but not of different genotypes of the same host species. Here we examined this hypothesis by comparing two genotypes of the plant Arabidopsis thaliana that differ largely both in life-span and in tolerance to its natural pathogen Cucumber mosaic virus (CMV). Experiments with the aphid vector Myzus persicae showed that both genotypes were similarly competent as sources for virus transmission, but the short-lived genotype was more susceptible to infection and was able to sustain larger vector populations. To explore how differences in defense against CMV and its vector relate to reservoir potential, we developed a model that was run for a set of experimentally-determined parameters, and for a realistic range of host plant and vector population densities. Model simulations showed that the less efficient defenses of the short-lived genotype resulted in higher reservoir potential, which in heterogeneous host populations may be balanced by the longer infectious period of the long-lived genotype. This balance was modulated by the demography of both host and vector populations, and by the genetic composition of the host population. Thus, within-species genetic diversity for lifespan and defenses against pathogens will result in polymorphisms for pathogen reservoir potential, which will condition within-population infection dynamics. These results are relevant for a better understanding of host-pathogen co-evolution, and of the dynamics of pathogen emergence.},
 bibtype = {article},
 author = {Hily, Jean Michel and García, Adrián and Moreno, Arancha and Plaza, María and Wilkinson, Mark D and Fereres, Alberto and Fraile, Aurora and García-Arenal, Fernando},
 journal = {PLoS pathogens},
 number = {11}
}

Identification of the determinants of pathogen reservoir potential is central to understand disease emergence. It has been proposed that host lifespan is one such determinant: short-lived hosts will invest less in costly defenses against pathogens, so that they will be more susceptible to infection, more competent as sources of infection and/or will sustain larger vector populations, thus being effective reservoirs for the infection of long-lived hosts. This hypothesis is sustained by analyses of different hosts of multihost pathogens, but not of different genotypes of the same host species. Here we examined this hypothesis by comparing two genotypes of the plant Arabidopsis thaliana that differ largely both in life-span and in tolerance to its natural pathogen Cucumber mosaic virus (CMV). Experiments with the aphid vector Myzus persicae showed that both genotypes were similarly competent as sources for virus transmission, but the short-lived genotype was more susceptible to infection and was able to sustain larger vector populations. To explore how differences in defense against CMV and its vector relate to reservoir potential, we developed a model that was run for a set of experimentally-determined parameters, and for a realistic range of host plant and vector population densities. Model simulations showed that the less efficient defenses of the short-lived genotype resulted in higher reservoir potential, which in heterogeneous host populations may be balanced by the longer infectious period of the long-lived genotype. This balance was modulated by the demography of both host and vector populations, and by the genetic composition of the host population. Thus, within-species genetic diversity for lifespan and defenses against pathogens will result in polymorphisms for pathogen reservoir potential, which will condition within-population infection dynamics. These results are relevant for a better understanding of host-pathogen co-evolution, and of the dynamics of pathogen emergence.

@article{
 title = {Health Web Science},
 type = {article},
 year = {2014},
 identifiers = {[object Object]},
 pages = {269-419},
 volume = {4},
 websites = {http://dx.doi.org/10.1561/1800000019},
 id = {a24fcb08-f3e3-3cb2-aafc-c24dbff0317d},
 created = {2014-10-17T10:05:32.000Z},
 file_attached = {false},
 profile_id = {17c87d5d-2470-32d7-b273-0734a1d9195f},
 last_modified = {2017-03-22T07:45:59.566Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {WEB-019},
 source_type = {article},
 folder_uuids = {ec2ec352-fa41-43db-919d-f96649955c40},
 private_publication = {false},
 bibtype = {article},
 author = {Luciano, Joanne S and Cumming, Grant P and Kahana, Eva and Wilkinson, Mark D and Brooks, Elizabeth H and Jarman, Holly and McGuinness, Deborah L and Levine, Minna S},
 journal = {Foundations and Trends® in Web Science},
 number = {4}
}

@inbook{
 type = {inbook},
 year = {2014},
 websites = {http://ceur-ws.org/Vol-1320/paper_36.pdf},
 publisher = {CEUR Workshop Proceedings},
 city = {Berlin},
 edition = {Volume 132},
 id = {043d46b4-75d6-3318-8fdd-5845f1071a93},
 created = {2014-10-29T07:19:00.000Z},
 file_attached = {false},
 profile_id = {17c87d5d-2470-32d7-b273-0734a1d9195f},
 last_modified = {2017-03-22T07:45:59.566Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Roos2014},
 folder_uuids = {ec2ec352-fa41-43db-919d-f96649955c40},
 private_publication = {false},
 bibtype = {inbook},
 author = {Roos, Marco and Gray, Alasdair J G and Waagmeester, Andra and Thompson, Mark and Kaliyaperumal, Rajaram and Horst, Eelke van der and Mons, Barend and Wilkinson, Mark D},
 editor = {Paschke, Adrian and Burger, Albert and Romano, Paolo and Marshall, M. Scott and Andrea, Splendiani},
 chapter = {Bring Your Own Data workshops: a mechanism to aid data owners to comply with Linked Data best practices},
 title = {Proceedings of Semantic Web Applications and Tools for Life Sciences 2014}
}

@book{
 title = {Automatic annotation of bioinformatics workflows with biomedical ontologies},
 type = {book},
 year = {2014},
 source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
 identifiers = {[object Object]},
 keywords = {Bioinformatics,Ontologies,Scientific workflows,Semantic annotation,Tags,Term extraction,Text mining,Web services},
 volume = {8803},
 id = {6fcf84a2-c4b9-3eb0-8476-a57a62dc7cb6},
 created = {2017-12-03T10:44:25.364Z},
 file_attached = {false},
 profile_id = {17c87d5d-2470-32d7-b273-0734a1d9195f},
 last_modified = {2017-12-12T09:00:54.060Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {© Springer-Verlag Berlin Heidelberg 2014. Legacy scientific workflows, and the services within them, often present scarce and unstructured (i.e. textual) descriptions. This makes it difficult to find, share and reuse them, thus dramatically reducing their value to the community. This paper presents an approach to annotating workflows and their subcomponents with ontology terms, in an attempt to describe these artifacts in a structured way. Despite a dearth of even textual descriptions, we automatically annotated 530 my- Experiment bioinformatics-related workflows, including more than 2600 workflow-associated services, with relevant ontological terms. Quantitative evaluation of the Information Content of these terms suggests that, in cases where annotation was possible at all, the annotation quality was comparable to manually curated bioinformatics resources.},
 bibtype = {book},
 author = {García-Jiménez, B. and Wilkinson, M.D.}
}

© Springer-Verlag Berlin Heidelberg 2014. Legacy scientific workflows, and the services within them, often present scarce and unstructured (i.e. textual) descriptions. This makes it difficult to find, share and reuse them, thus dramatically reducing their value to the community. This paper presents an approach to annotating workflows and their subcomponents with ontology terms, in an attempt to describe these artifacts in a structured way. Despite a dearth of even textual descriptions, we automatically annotated 530 my- Experiment bioinformatics-related workflows, including more than 2600 workflow-associated services, with relevant ontological terms. Quantitative evaluation of the Information Content of these terms suggests that, in cases where annotation was possible at all, the annotation quality was comparable to manually curated bioinformatics resources.

@article{
 title = {Plant Pathogen Interactions Ontology (PPIO)},
 type = {article},
 year = {2013},
 pages = {695-702},
 id = {ff09fc56-2b1a-3679-811a-a24295a2e5da},
 created = {2013-01-21T13:41:57.000Z},
 file_attached = {false},
 profile_id = {17c87d5d-2470-32d7-b273-0734a1d9195f},
 last_modified = {2017-03-22T07:45:59.566Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Iglesias2013},
 folder_uuids = {ec2ec352-fa41-43db-919d-f96649955c40},
 private_publication = {false},
 bibtype = {article},
 author = {Iglesias, Alejandro Rodríguez and Aranguren, Mikel Egaña and Gonzalez, Alejandro Rodríguez and Wilkinson, Mark D.},
 journal = {Proceedings IWBBIO 2013 International Work-Conference on Bioinformatics and Biomedical Engineering}
}

@article{
 title = {The SADI Personal Health Lens: A Web Browser-Based System for Identifying Personally Relevant Drug Interactions},
 type = {article},
 year = {2013},
 identifiers = {[object Object]},
 pages = {e14},
 volume = {2},
 websites = {http://www.researchprotocols.org/2013/1/e14/},
 month = {4},
 day = {5},
 id = {4d66f145-86f5-39d8-a4f0-32de018507e4},
 created = {2013-03-06T10:37:55.000Z},
 accessed = {2013-04-06},
 file_attached = {false},
 profile_id = {17c87d5d-2470-32d7-b273-0734a1d9195f},
 last_modified = {2017-03-22T07:45:59.566Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Vandervalk2013},
 folder_uuids = {ec2ec352-fa41-43db-919d-f96649955c40},
 private_publication = {false},
 abstract = {Background: The Web provides widespread access to vast quantities of health-related information that can improve quality-of-life through better understanding of personal symptoms, medical conditions, and available treatments. Unfortunately, identifying a credible and personally relevant subset of information can be a time-consuming and challenging task for users without a medical background. Objective: The objective of the Personal Health Lens system is to aid users when reading health-related webpages by providing warnings about personally relevant drug interactions. More broadly, we wish to present a prototype for a novel, generalizable approach to facilitating interactions between a patient, their practitioner(s), and the Web. Methods: We utilized a distributed, Semantic Web-based architecture for recognizing personally dangerous drugs consisting of: (1) a private, local triple store of personal health information, (2) Semantic Web services, following the Semantic Automated Discovery and Integration (SADI) design pattern, for text mining and identifying substance interactions, (3) a bookmarklet to trigger analysis of a webpage and annotate it with personalized warnings, and (4) a semantic query that acts as an abstract template of the analytical workflow to be enacted by the system. Results: A prototype implementation of the system is provided in the form of a Java standalone executable JAR file. The JAR file bundles all components of the system: the personal health database, locally-running versions of the SADI services, and a javascript bookmarklet that triggers analysis of a webpage. In addition, the demonstration includes a hypothetical personal health profile, allowing the system to be used immediately without configuration. Usage instructions are provided. Conclusions: The main strength of the Personal Health Lens system is its ability to organize medical information and to present it to the user in a personalized and contextually relevant manner. While this prototype was limited to a single knowledge domain (drug/drug interactions), the proposed architecture is generalizable, and could act as the foundation for much richer personalized-health-Web clients, while importantly providing a novel and personalizable mechanism for clinical experts to inject their expertise into the browsing experience of their patients in the form of customized semantic queries and ontologies.},
 bibtype = {article},
 author = {Vandervalk, Ben and McCarthy, E. Luke and Cruz-Toledo, José and Klein, Artjom and Baker, Christopher J. O. and Dumontier, Michel and Wilkinson, Mark D.},
 journal = {JMIR Research Protocols},
 number = {1}
}

Background: The Web provides widespread access to vast quantities of health-related information that can improve quality-of-life through better understanding of personal symptoms, medical conditions, and available treatments. Unfortunately, identifying a credible and personally relevant subset of information can be a time-consuming and challenging task for users without a medical background. Objective: The objective of the Personal Health Lens system is to aid users when reading health-related webpages by providing warnings about personally relevant drug interactions. More broadly, we wish to present a prototype for a novel, generalizable approach to facilitating interactions between a patient, their practitioner(s), and the Web. Methods: We utilized a distributed, Semantic Web-based architecture for recognizing personally dangerous drugs consisting of: (1) a private, local triple store of personal health information, (2) Semantic Web services, following the Semantic Automated Discovery and Integration (SADI) design pattern, for text mining and identifying substance interactions, (3) a bookmarklet to trigger analysis of a webpage and annotate it with personalized warnings, and (4) a semantic query that acts as an abstract template of the analytical workflow to be enacted by the system. Results: A prototype implementation of the system is provided in the form of a Java standalone executable JAR file. The JAR file bundles all components of the system: the personal health database, locally-running versions of the SADI services, and a javascript bookmarklet that triggers analysis of a webpage. In addition, the demonstration includes a hypothetical personal health profile, allowing the system to be used immediately without configuration. Usage instructions are provided. Conclusions: The main strength of the Personal Health Lens system is its ability to organize medical information and to present it to the user in a personalized and contextually relevant manner. While this prototype was limited to a single knowledge domain (drug/drug interactions), the proposed architecture is generalizable, and could act as the foundation for much richer personalized-health-Web clients, while importantly providing a novel and personalizable mechanism for clinical experts to inject their expertise into the browsing experience of their patients in the form of customized semantic queries and ontologies.

@inproceedings{
 title = {Plant Pathogen Interactions Ontology (PPIO)},
 type = {inproceedings},
 year = {2013},
 pages = {695-702},
 id = {8e0b5cc5-fbe3-3c81-ac4b-7f5a738bdd05},
 created = {2014-07-02T09:11:39.000Z},
 file_attached = {false},
 profile_id = {17c87d5d-2470-32d7-b273-0734a1d9195f},
 last_modified = {2017-03-22T07:45:59.566Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Iglesias2013},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Iglesias, Alejandro Rodríguez and Aranguren, Mikel Egaña and Gonzalez, Alejandro Rodríguez and Wilkinson, Mark D.},
 booktitle = {Proceedings IWBBIO 2013 International Work-Conference on Bioinformatics and Biomedical Engineering}
}

@article{
 title = {The SADI Personal Health Lens: A Web Browser-Based System for Identifying Personally Relevant Drug Interactions},
 type = {article},
 year = {2013},
 identifiers = {[object Object]},
 pages = {e14},
 volume = {2},
 websites = {http://www.researchprotocols.org/2013/1/e14/},
 month = {4},
 day = {5},
 id = {11a86057-6fdf-33e0-a89f-271a5a02a07e},
 created = {2014-07-02T09:11:39.000Z},
 accessed = {2013-04-06},
 file_attached = {false},
 profile_id = {17c87d5d-2470-32d7-b273-0734a1d9195f},
 last_modified = {2017-03-22T07:45:59.566Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Vandervalk2013},
 private_publication = {false},
 bibtype = {article},
 author = {Vandervalk, Ben and McCarthy, E. Luke and Cruz-Toledo, José and Klein, Artjom and Baker, Christopher J. O. and Dumontier, Michel and Wilkinson, Mark D.},
 journal = {JMIR Research Protocols},
 number = {1}
}

@article{
 title = {OPPL-Galaxy, a Galaxy tool for enhancing ontology exploitation as part of bioinformatics workflows.},
 type = {article},
 year = {2013},
 identifiers = {[object Object]},
 pages = {2},
 volume = {4},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/23286517},
 month = {1},
 day = {4},
 id = {af463206-f23e-30d0-8384-53d9b2ff3736},
 created = {2014-07-02T09:11:39.000Z},
 accessed = {2013-01-06},
 file_attached = {false},
 profile_id = {17c87d5d-2470-32d7-b273-0734a1d9195f},
 last_modified = {2017-03-22T07:45:59.566Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {EganaAranguren2013},
 private_publication = {false},
 abstract = {ABSTRACT: BACKGROUND: Biomedical ontologies are key elements for building up the Life Sciences Semantic Web. Reusing and building biomedical ontologies requires flexible and versatile tools to manipulate them efficiently, in particular for enriching their axiomatic content. The Ontology Pre Processor Language (OPPL) is an OWL-based language for automating the changes to be performed in an ontology. OPPL augments the ontologists' toolbox by providing a more efficient, and less error-prone, mechanism for enriching a biomedical ontology than that obtained by a manual treatment. RESULTS: We present OPPL-Galaxy, a wrapper for using OPPL within Galaxy. The functionality delivered by OPPL (i.e. automated ontology manipulation) can be combined with the tools and workflows devised within the Galaxy framework, resulting in an enhancement of OPPL. Use cases are provided in order to demonstrate OPPL-Galaxy's capability for enriching, modifying and querying biomedicalontologies. CONCLUSIONS: Coupling OPPL-Galaxy with other bioinformatics tools of the Galaxy framework results in a system that is more than the sum of its parts. OPPL-Galaxy opens a new dimension of analyses and exploitation of biomedical ontologies, including automated reasoning, paving the way towards advanced biological data analyses.},
 bibtype = {article},
 author = {Egaña Aranguren, Mikel and Fernández-Breis, Jesualdo Tomás and Antezana, Erick and Mungall, Chris and Rodríguez González, Alejandro and Wilkinson, Mark D},
 journal = {Journal of biomedical semantics},
 number = {1}
}

ABSTRACT: BACKGROUND: Biomedical ontologies are key elements for building up the Life Sciences Semantic Web. Reusing and building biomedical ontologies requires flexible and versatile tools to manipulate them efficiently, in particular for enriching their axiomatic content. The Ontology Pre Processor Language (OPPL) is an OWL-based language for automating the changes to be performed in an ontology. OPPL augments the ontologists' toolbox by providing a more efficient, and less error-prone, mechanism for enriching a biomedical ontology than that obtained by a manual treatment. RESULTS: We present OPPL-Galaxy, a wrapper for using OPPL within Galaxy. The functionality delivered by OPPL (i.e. automated ontology manipulation) can be combined with the tools and workflows devised within the Galaxy framework, resulting in an enhancement of OPPL. Use cases are provided in order to demonstrate OPPL-Galaxy's capability for enriching, modifying and querying biomedicalontologies. CONCLUSIONS: Coupling OPPL-Galaxy with other bioinformatics tools of the Galaxy framework results in a system that is more than the sum of its parts. OPPL-Galaxy opens a new dimension of analyses and exploitation of biomedical ontologies, including automated reasoning, paving the way towards advanced biological data analyses.

@article{
 title = {The 3rd DBCLS BioHackathon: improving life science data integration with semantic Web technologies},
 type = {article},
 year = {2013},
 identifiers = {[object Object]},
 pages = {6},
 volume = {4},
 websites = {http://www.jbiomedsem.com/content/4/1/6},
 id = {f73616d0-42da-3a16-bd6c-b6de41efbc9c},
 created = {2014-07-02T09:11:39.000Z},
 accessed = {2013-02-12},
 file_attached = {false},
 profile_id = {17c87d5d-2470-32d7-b273-0734a1d9195f},
 last_modified = {2017-03-22T07:45:59.566Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Katayama2013},
 private_publication = {false},
 bibtype = {article},
 author = {Katayama, Toshiaki and Wilkinson, Mark D. and Micklem, Gos and Kawashima, Shuichi and Yamaguchi, Atsuko and Nakao, Mitsuteru and Yamamoto, Yasunori and Okamoto, Shinobu and Oouchida, Kenta and Chun, Hong-Woo and Aerts, Jan and Afzal, Hammad and Antezana, Erick and Arakawa, Kazuharu and Aranda, Bruno and Belleau, Francois and Bolleman, Jerven and Bonnal, Raoul Jean and Chapman, Brad and Cock, Peter and Eriksson, Tore and Gordon, Paul and Goto, Naohisa and Hayashi, Kazuhiro and Horn, Heiko and Ishiwata, Ryosuke and Kaminuma, Eli and Kasprzyk, Arek and Kawaji, Hideya and Kido, Nobuhiro and Kim, Young Joo and Kinjo, Akira R. and Konishi, Fumikazu and Kwon, Kyung-Hoon and Labarga, Alberto and Lamprecht, Anna-Lena and Lin, Yu and Lindenbaum, Pierre and McCarthy, Luke and Morita, Hideyuki and Murakami, Katsuhiko and Nagao, Koji and Nishida, Kozo and Nishimura, Kunihiro and Nishizawa, Tatsuya and Ogishima, Soichi and Ono, Keiichiro and Oshita, Kazuki and Park, Keun-Joon and Prins, Pjotr and Saito, Taro L. and Samwald, Matthias and Satagopam, Venkata P. and Shigemoto, Yasumasa and Smith, Richard and Splendiani, Andrea and Sugawara, Hideaki and Taylor, James and Vos, Rutger and Withers, David and Yamasaki, Chisato and Zmasek, Christian M. and Kawamoto, Shoko and Okubo, Kosaku and Asai, Kiyoshi and Takagi, Toshihisa},
 journal = {Journal of Biomedical Semantics},
 number = {1}
}

@inbook{
 type = {inbook},
 year = {2013},
 identifiers = {[object Object]},
 keywords = {Personalized Web,Reproducibility,SADI,SHARE,Semantic Web Services,Transparency,Workflow Orchestration},
 pages = {71-74},
 websites = {http://www.thinkmind.org/index.php?view=article&articleid=service_computation_2013_4_20_10017},
 publisher = {Copyright (c) IARIA, 2013},
 city = {Valencia, Spain},
 id = {380be731-07ba-31cf-ad48-52da4bfafd62},
 created = {2014-07-02T09:11:39.000Z},
 file_attached = {false},
 profile_id = {17c87d5d-2470-32d7-b273-0734a1d9195f},
 last_modified = {2017-03-22T07:45:59.566Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Wilkinson2013},
 private_publication = {false},
 abstract = {The emergent domain of Web Science has a number of as-yet unrealized goals. Among these are: to facilitate scientific discourse by supporting the explicit comparison and evaluation of hypotheses; to simplify in silico experiments by providing an ecosystem of expert analytical strategies that can be automatically assembled; to enhance scientific rigor by reducing bias, and improving reproducibility; and to integrate the knowledge gained from the experiment back into the Web. SHARE is a novel orchestration system that automatically chains-together Semantic Automated Discovery and Integration (SADI)–style Semantic Web Services. During development of SHARE, we noted that many requirements of such an end-to-end Web Science research environment were being realized. These include formally-defined, machine-readable, Web-embedded research hypotheses; an explicit, transparent, rigorous, and reproducible research methodology utilizing the most up-to-date data and expert-knowledge from the community; immediate dissemination and re-use of the resulting data and knowledge; and enhanced support for peer-review. This manuscript describes how SHARE is now being tested as a prototype Web Science framework. (Best Paper Award: http://www.iaria.org/conferences2013/AwardsSERVICECOMPUTATION13.html)},
 bibtype = {inbook},
 author = {Wilkinson, Mark D.},
 chapter = {Web Science Studies into Semantic Web Service-Based Research Environments (Awarded "Best Paper")},
 title = {The Fifth International Conferences on Advanced Service Computing, SERVICE COMPUTATION 2013}
}

The emergent domain of Web Science has a number of as-yet unrealized goals. Among these are: to facilitate scientific discourse by supporting the explicit comparison and evaluation of hypotheses; to simplify in silico experiments by providing an ecosystem of expert analytical strategies that can be automatically assembled; to enhance scientific rigor by reducing bias, and improving reproducibility; and to integrate the knowledge gained from the experiment back into the Web. SHARE is a novel orchestration system that automatically chains-together Semantic Automated Discovery and Integration (SADI)–style Semantic Web Services. During development of SHARE, we noted that many requirements of such an end-to-end Web Science research environment were being realized. These include formally-defined, machine-readable, Web-embedded research hypotheses; an explicit, transparent, rigorous, and reproducible research methodology utilizing the most up-to-date data and expert-knowledge from the community; immediate dissemination and re-use of the resulting data and knowledge; and enhanced support for peer-review. This manuscript describes how SHARE is now being tested as a prototype Web Science framework. (Best Paper Award: http://www.iaria.org/conferences2013/AwardsSERVICECOMPUTATION13.html)

@article{
 title = {The emergent discipline of health web science.},
 type = {article},
 year = {2013},
 identifiers = {[object Object]},
 keywords = {Delivery of Health Care,Information Storage and Retrieval,Internet},
 pages = {e166},
 volume = {15},
 websites = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3758025&tool=pmcentrez&rendertype=abstract},
 month = {1},
 id = {e564924d-1419-317f-89d4-ce36d6ef68d6},
 created = {2014-07-11T11:52:32.000Z},
 accessed = {2014-07-11},
 file_attached = {false},
 profile_id = {17c87d5d-2470-32d7-b273-0734a1d9195f},
 last_modified = {2017-03-22T07:45:59.566Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Luciano2013},
 private_publication = {false},
 abstract = {The transformative power of the Internet on all aspects of daily life, including health care, has been widely recognized both in the scientific literature and in public discourse. Viewed through the various lenses of diverse academic disciplines, these transformations reveal opportunities realized, the promise of future advances, and even potential problems created by the penetration of the World Wide Web for both individuals and for society at large. Discussions about the clinical and health research implications of the widespread adoption of information technologies, including the Internet, have been subsumed under the disciplinary label of Medicine 2.0. More recently, however, multi-disciplinary research has emerged that is focused on the achievement and promise of the Web itself, as it relates to healthcare issues. In this paper, we explore and interrogate the contributions of the burgeoning field of Web Science in relation to health maintenance, health care, and health policy. From this, we introduce Health Web Science as a subdiscipline of Web Science, distinct from but overlapping with Medicine 2.0. This paper builds on the presentations and subsequent interdisciplinary dialogue that developed among Web-oriented investigators present at the 2012 Medicine 2.0 Conference in Boston, Massachusetts.},
 bibtype = {article},
 author = {Luciano, Joanne S and Cumming, Grant P and Wilkinson, Mark D and Kahana, Eva},
 journal = {Journal of medical Internet research},
 number = {8}
}

The transformative power of the Internet on all aspects of daily life, including health care, has been widely recognized both in the scientific literature and in public discourse. Viewed through the various lenses of diverse academic disciplines, these transformations reveal opportunities realized, the promise of future advances, and even potential problems created by the penetration of the World Wide Web for both individuals and for society at large. Discussions about the clinical and health research implications of the widespread adoption of information technologies, including the Internet, have been subsumed under the disciplinary label of Medicine 2.0. More recently, however, multi-disciplinary research has emerged that is focused on the achievement and promise of the Web itself, as it relates to healthcare issues. In this paper, we explore and interrogate the contributions of the burgeoning field of Web Science in relation to health maintenance, health care, and health policy. From this, we introduce Health Web Science as a subdiscipline of Web Science, distinct from but overlapping with Medicine 2.0. This paper builds on the presentations and subsequent interdisciplinary dialogue that developed among Web-oriented investigators present at the 2012 Medicine 2.0 Conference in Boston, Massachusetts.

@article{
 title = {Extending and encoding existing biological terminologies and datasets for use in the reasoned semantic web.},
 type = {article},
 year = {2012},
 identifiers = {[object Object]},
 pages = {6},
 volume = {3},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/22818710},
 month = {7},
 day = {20},
 id = {2c11d236-dafd-35e0-b030-ec34f6b2701c},
 created = {2014-07-02T09:11:39.000Z},
 accessed = {2012-07-25},
 file_attached = {false},
 profile_id = {17c87d5d-2470-32d7-b273-0734a1d9195f},
 last_modified = {2017-03-22T07:45:59.566Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Samadian2012},
 private_publication = {false},
 abstract = {ABSTRACT: BACKGROUND: Clinical phenotypes and disease-risk stratification are most often determined through the direct observations of clinicians in conjunction with published standards and guidelines, where the clinical expert is the final arbiter of the patient's classification. While this "human" approach is highly desirable in the context of personalized and optimal patient care, it is problematic in a healthcare research setting because the basis for the patient's classification is not transparent, and likely not reproducible from one clinical expert to another. This sits in opposition to the rigor required to execute, for example, Genome-wide association analyses and other high-throughput studies where a large number of variables are being compared to a complex disease phenotype. Most clinical classification systems and are not structured for automated classification, and similarly, clinical data is generally not represented in a form that lends itself to automated integration and interpretation. Here we apply Semantic Web technologies to the problem of automated, transparent interpretation of clinical data for use in high-throughput research environments, and explore migration-paths for existing data and legacy semantic standards. RESULTS: Using a dataset from a cardiovascular cohort collected two decades ago, we present a migration path - both for the terminologies/classification systems and the data - that enables rich automated clinical classification using well-established standards. This is achieved by establishing a simple and flexible core data model, which is combined with a layered ontological framework utilizing both logical reasoning and analytical algorithms to iteratively "lift" clinical data through increasingly complex layers of interpretation and classification. We compare our automated analysis to that of the clinical expert, and discrepancies are used to refine the ontological models, finally arriving at ontologies that mirror the expert opinion of the individual clinical researcher. Other discrepancies, however, could not be as easily modeled, and we evaluate what information we are lacking that would allow these discrepancies to be resolved in an automated manner. CONCLUSIONS: We demonstrate that the combination of semantically-explicit data, logically rigorous models of clinical guidelines, and publicly-accessible Semantic Web Services, can be used to execute automated, rigorous and reproducible clinical classifications with an accuracy approaching that of an expert. Discrepancies between the manual and automatic approaches reveal, as expected, that clinicians do not always rigorously follow established guidelines for classification; however, we demonstrate that "personalized" ontologies may represent a re-usable and transparent approach to modeling individual clinical expertise, leading to more reproducible science.},
 bibtype = {article},
 author = {Samadian, Soroush and McManus, Bruce and Wilkinson, Mark D},
 journal = {Journal of biomedical semantics},
 number = {1}
}

ABSTRACT: BACKGROUND: Clinical phenotypes and disease-risk stratification are most often determined through the direct observations of clinicians in conjunction with published standards and guidelines, where the clinical expert is the final arbiter of the patient's classification. While this "human" approach is highly desirable in the context of personalized and optimal patient care, it is problema