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1.  Finding Our Way through Phenotypes 
Deans, Andrew R. | Lewis, Suzanna E. | Huala, Eva | Anzaldo, Salvatore S. | Ashburner, Michael | Balhoff, James P. | Blackburn, David C. | Blake, Judith A. | Burleigh, J. Gordon | Chanet, Bruno | Cooper, Laurel D. | Courtot, Mélanie | Csösz, Sándor | Cui, Hong | Dahdul, Wasila | Das, Sandip | Dececchi, T. Alexander | Dettai, Agnes | Diogo, Rui | Druzinsky, Robert E. | Dumontier, Michel | Franz, Nico M. | Friedrich, Frank | Gkoutos, George V. | Haendel, Melissa | Harmon, Luke J. | Hayamizu, Terry F. | He, Yongqun | Hines, Heather M. | Ibrahim, Nizar | Jackson, Laura M. | Jaiswal, Pankaj | James-Zorn, Christina | Köhler, Sebastian | Lecointre, Guillaume | Lapp, Hilmar | Lawrence, Carolyn J. | Le Novère, Nicolas | Lundberg, John G. | Macklin, James | Mast, Austin R. | Midford, Peter E. | Mikó, István | Mungall, Christopher J. | Oellrich, Anika | Osumi-Sutherland, David | Parkinson, Helen | Ramírez, Martín J. | Richter, Stefan | Robinson, Peter N. | Ruttenberg, Alan | Schulz, Katja S. | Segerdell, Erik | Seltmann, Katja C. | Sharkey, Michael J. | Smith, Aaron D. | Smith, Barry | Specht, Chelsea D. | Squires, R. Burke | Thacker, Robert W. | Thessen, Anne | Fernandez-Triana, Jose | Vihinen, Mauno | Vize, Peter D. | Vogt, Lars | Wall, Christine E. | Walls, Ramona L. | Westerfeld, Monte | Wharton, Robert A. | Wirkner, Christian S. | Woolley, James B. | Yoder, Matthew J. | Zorn, Aaron M. | Mabee, Paula
PLoS Biology  2015;13(1):e1002033.
Imagine if we could compute across phenotype data as easily as genomic data; this article calls for efforts to realize this vision and discusses the potential benefits.
Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility.
doi:10.1371/journal.pbio.1002033
PMCID: PMC4285398  PMID: 25562316
2.  Standardized description of scientific evidence using the Evidence Ontology (ECO) 
The Evidence Ontology (ECO) is a structured, controlled vocabulary for capturing evidence in biological research. ECO includes diverse terms for categorizing evidence that supports annotation assertions including experimental types, computational methods, author statements and curator inferences. Using ECO, annotation assertions can be distinguished according to the evidence they are based on such as those made by curators versus those automatically computed or those made via high-throughput data review versus single test experiments. Originally created for capturing evidence associated with Gene Ontology annotations, ECO is now used in other capacities by many additional annotation resources including UniProt, Mouse Genome Informatics, Saccharomyces Genome Database, PomBase, the Protein Information Resource and others. Information on the development and use of ECO can be found at http://evidenceontology.org. The ontology is freely available under Creative Commons license (CC BY-SA 3.0), and can be downloaded in both Open Biological Ontologies and Web Ontology Language formats at http://code.google.com/p/evidenceontology. Also at this site is a tracker for user submission of term requests and questions. ECO remains under active development in response to user-requested terms and in collaborations with other ontologies and database resources.
Database URL: Evidence Ontology Web site: http://evidenceontology.org
doi:10.1093/database/bau075
PMCID: PMC4105709  PMID: 25052702
3.  On the Use of Gene Ontology Annotations to Assess Functional Similarity among Orthologs and Paralogs: A Short Report 
PLoS Computational Biology  2012;8(2):e1002386.
A recent paper (Nehrt et al., PLoS Comput. Biol. 7:e1002073, 2011) has proposed a metric for the “functional similarity” between two genes that uses only the Gene Ontology (GO) annotations directly derived from published experimental results. Applying this metric, the authors concluded that paralogous genes within the mouse genome or the human genome are more functionally similar on average than orthologous genes between these genomes, an unexpected result with broad implications if true. We suggest, based on both theoretical and empirical considerations, that this proposed metric should not be interpreted as a functional similarity, and therefore cannot be used to support any conclusions about the “ortholog conjecture” (or, more properly, the “ortholog functional conservation hypothesis”). First, we reexamine the case studies presented by Nehrt et al. as examples of orthologs with divergent functions, and come to a very different conclusion: they actually exemplify how GO annotations for orthologous genes provide complementary information about conserved biological functions. We then show that there is a global ascertainment bias in the experiment-based GO annotations for human and mouse genes: particular types of experiments tend to be performed in different model organisms. We conclude that the reported statistical differences in annotations between pairs of orthologous genes do not reflect differences in biological function, but rather complementarity in experimental approaches. Our results underscore two general considerations for researchers proposing novel types of analysis based on the GO: 1) that GO annotations are often incomplete, potentially in a biased manner, and subject to an “open world assumption” (absence of an annotation does not imply absence of a function), and 2) that conclusions drawn from a novel, large-scale GO analysis should whenever possible be supported by careful, in-depth examination of examples, to help ensure the conclusions have a justifiable biological basis.
Author Summary
Understanding gene function—how individual genes contribute to the biology of an organism at the molecular, cellular and organism levels—is one of the primary aims of biomedical research. It has been a longstanding tenet of model organism research that experimental knowledge obtained in one organism is often applicable to other organisms, particularly if the organisms share the relevant genes because they inherited them from their common ancestor. Nevertheless this tenet is, like any hypothesis, not beyond question. A recent paper has termed this hypothesis a “conjecture,” and performed a statistical analysis, the results of which were interpreted as evidence against the hypothesis. This statistical analysis relied on a computational representation of gene function, the Gene Ontology (GO). As representatives of the international consortium that produces the GO, we show how the apparent evidence against the “ortholog conjecture” can be better explained as an artifact of how molecular biology knowledge is accumulated. In short, a complementarity between knowledge obtained in mouse and human experimental systems was incorrectly interpreted as a disagreement. We discuss the proper interpretation of GO annotations and potential sources of bias, with an eye toward enhancing the informed use of the GO by the scientific community.
doi:10.1371/journal.pcbi.1002386
PMCID: PMC3280971  PMID: 22359495
4.  Towards BioDBcore: a community-defined information specification for biological databases 
The present article proposes the adoption of a community-defined, uniform, generic description of the core attributes of biological databases, BioDBCore. The goals of these attributes are to provide a general overview of the database landscape, to encourage consistency and interoperability between resources; and to promote the use of semantic and syntactic standards. BioDBCore will make it easier for users to evaluate the scope and relevance of available resources. This new resource will increase the collective impact of the information present in biological databases.
doi:10.1093/database/baq027
PMCID: PMC3017395  PMID: 21205783
5.  Towards BioDBcore: a community-defined information specification for biological databases 
Nucleic Acids Research  2010;39(Database issue):D7-D10.
The present article proposes the adoption of a community-defined, uniform, generic description of the core attributes of biological databases, BioDBCore. The goals of these attributes are to provide a general overview of the database landscape, to encourage consistency and interoperability between resources and to promote the use of semantic and syntactic standards. BioDBCore will make it easier for users to evaluate the scope and relevance of available resources. This new resource will increase the collective impact of the information present in biological databases.
doi:10.1093/nar/gkq1173
PMCID: PMC3013734  PMID: 21097465

Results 1-5 (5)