Linking genes to diseases with a SNPedia-Gene Wiki mashup

<p>Abstract</p> <p>Background</p> <p>A variety of topic-focused wikis are used in the biomedical sciences to enable the mass-collaborative synthesis and distribution of diverse bodies of knowledge. To address complex problems such as defining the relationships between genes and disease, it is important to bring the knowledge from many different domains together. Here we show how advances in wiki technology and natural language processing can be used to automatically assemble ‘meta-wikis’ that present integrated views over the data collaboratively created in multiple source wikis.</p> <p>Results</p> <p>We produced a semantic meta-wiki called the Gene Wiki+ that automatically mirrors and integrates data from the Gene Wiki and SNPedia. The Gene Wiki+, available at (<url>http://genewikiplus.org/</url>), captures 8,047 distinct gene-disease relationships. SNPedia accounts for 4,149 of the gene-disease pairs, the Gene Wiki provides 4,377 and only 479 appear independently in both sources. All of this content is available to query and browse and is provided as linked open data.</p> <p>Conclusions</p> <p>Wikis contain increasing amounts of diverse, biological information useful for elucidating the connections between genes and disease. The Gene Wiki+ shows how wiki technology can be used in concert with natural language processing to provide integrated views over diverse underlying data sources.</p

Selected papers from the 14th Annual Bio-Ontologies Special Interest Group Meeting

Over the 14 years, the Bio-Ontologies SIG at ISMB has provided a forum for discussion of the latest and most innovative research in the bio-ontologies development, its applications to biomedicine and more generally the organisation, presentation and dissemination of knowledge in biomedicine and the life sciences. The seven papers selected for this supplement span a wide range of topics including: web-based querying over multiple ontologies, integration of data from wikis, innovative methods of annotating and mining electronic health records, advances in annotating web documents and biomedical literature, quality control of ontology alignments, and the ontology support for predictive models about toxicity and open access to the toxicity data

PRGdb 2.0 : towards a community-based database model for the analysis of R-genes in plants

The Plant Resistance Genes database (PRGdb; http://prgdb.org) is a comprehensive resource on resistance genes (R-genes), a major class of genes in plant genomes that convey disease resistance against pathogens. Initiated in 2009, the database has grown more than 6-fold to recently include annotation derived from recent plant genome sequencing projects. Release 2.0 currently hosts useful biological information on a set of 112 known and 104 310 putative R-genes present in 233 plant species and conferring resistance to 122 different pathogens. Moreover, the website has been completely redesigned with the implementation of Semantic MediaWiki technologies, which makes our repository freely accessed and easily edited by any scientists. To this purpose, we encourage plant biologist experts to join our annotation effort and share their knowledge on resistance-gene biology with the rest of the scientific community

Improvement of usability in user interfaces for massive data analysis: an empirical study

[EN] Big Data challenges the conventional way of analyzing massive data and creates the need to improve the usability of existing user interfaces (UIs) in order to deal with massive amounts of data. How the UIs facilitate the search for information and helps in the end-user's decision-making depends on developers and designers, who have no guides for producing usable UIs. We have proposed a set of interaction patterns for designing massive data analysis UIs by studying 27 real case studies of massive data analysis. We evaluate if the proposed patterns improve the usability of the massive data analysis UIs in the context of literature search. We conducted two replications of the same controlled experiment, one with 24 undergraduate students experienced in scientific literature search and the other with eight researchers who are experienced in biomedical literature search. The experiment, which was planned as a repeated measures design, compares UIs that have been enhanced with the proposed patterns versus original UIs in terms of three response variables: effectiveness, efficiency, and satisfaction. The outcomes show that the use of interaction patterns in UIs for massive data analysis yields better and more significant effects for the three response variables, enhancing the discovery and visualization of the data. The use of the proposed interaction design patterns improves the usability of the UIs that deal with massive data. The patterns can be considered as guides for helping designers and developers to design usable UIs for massive data analysis web applications.The authors thank the members of the PROS Center Genome group for productive discussions. In addition, it is also important to highlight that the Secretaría Nacional de Educación, Ciencia y Tecnología (SENESCYT) and the Escuela Politécnica Nacional from Ecuador have supported this work. 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DISEÑO Y DESARROLLO DE UN SISTEMA DE INFORMACIÓN GENÓMICA BASADO EN UN MODELO CONCEPTUAL HOLÍSTICO DEL GENOMA HUMANO

Entender el genoma es un desafío de primer nivel, y esto se debe en gran parte a la gran cantidad de información existente en el dominio. Gracias a la aplicación de tecnologías NGS (Next-Generation Sequencing) se han generado enormes cantidades de datos -nuevos-, por lo que es fundamental construir estructuras que permitan organizar, procesar y explorar los datos con el fin de lograr un máximo provecho de la información y mejorar la comprensión del genoma humano. En este estudio se define un marco de trabajo centrado en el uso del Modelado Conceptual como estrategia esencial para la búsqueda de soluciones. En el campo médico este enfoque de desarrollo de software está ganando impulso por su impacto en el trabajo realizado por genetistas, laboratorios clínicos y bioinformáticos. Entender el genoma es un dominio de aplicación muy interesante debido a dos aspectos fundamentales: 1) en primer lugar, por las implicaciones sociológicas que supone plantearse la posibilidad de entender el lenguaje de la vida. 2) y, en segundo lugar, desde una perspectiva más práctica de aplicación en el ámbito clínico, debido a su repercusión en la generación de diagnósticos genómicos, los cuales juegan un papel importante dentro de la Medicina de Precisión. En esta Tesis Doctoral se propone utilizar un Modelo Conceptual del Genoma Humano (MCGH) como base fundamental para la generación de Sistemas de Información Genómicos (GeIS), con el objetivo de facilitar una conceptualización del dominio que permita i) alcanzar un conocimiento preciso del dominio y ii) ser capaces de llegar a una medicina de precisión (personalizada). Es importante resaltar que este Modelo Conceptual debe permanecer en constante crecimiento debido a los nuevos aportes que surgen en la comunidad científica. En este trabajo de investigación se presenta la evolución natural del modelo, así como un ejemplo de extensión del mismo, lo que permite comprobar su extensibilidad conservando su definición inicial. Además, se aplica el uso de una metodología (SILE) sistemática para la obtención de los datos desde los distintos repositorios genómicos, los cuales serán explotados a través herramientas software basadas en modelos conceptuales. Mediante el uso de este Modelo Conceptual holístico del Genoma Humano se busca comprender y mejorar el compromiso ontológico con el dominio -genómico-, y desarrollar Sistemas de Información Genómicos apoyados en Modelo Conceptuales para ayudar a la toma de decisiones en el entorno bioinformático.Understanding the genome is a first level challenge, and this is due in large part to a large amount of information in the domain. Thanks to the application of NGS (Next-Generation Sequencing) technologies, enormous amounts of -new- data have been generated, so it is essential to building structures that allow organizing, processing and exploring the data in order to obtain maximum benefit from the information and improve the understanding of the human genome. In this study we define a framework focused on the use of Conceptual Modeling as an essential strategy for finding solutions. In the medical field, this approach to software development is gaining momentum due to its impact on the work carried out by geneticists, clinical laboratories, and bioinformatics. Understanding the genome is a domain of very interesting application due to two fundamental aspects: 1) firstly, because of the sociological implications of considering the possibility of understanding the language of life. 2) secondly, from a more practical perspective of application in the clinical field, due to its repercussion in the generation of genomic diagnoses, which play an important role within Precision Medicine. In this PhD, it is proposed to use a Conceptual Model of the Human Genome (CMHG) as the fundamental basis for the generation of Genomic Information Systems (GeIS), with the aim of facilitating a conceptualization of the domain that allows i) to achieve a precise knowledge of the domain and ii) be able to increase and improve the adaptation of genomics in personalized medicine. It is important to highlight that this Conceptual Model must remain in constant growth due to the new contributions that arise in the scientific community. In this research work the natural evolution of the model is presented, as well as an example of its extension, which allows verifying its extensibility while preserving its initial definition. In addition, the use of a systematic methodology is applied to obtain the data from the different genomic repositories, which will be exploited through software tools based on conceptual models. Through the use of this Holistic Conceptual Model of the Human Genome, we seek to understand and improve the ontological commitment to the -genomic- domain, and develop GeIS supported in Conceptual Model to help decision making in the bioinformatic environment in order to provide better treatment to the patients.Entendre el genoma és un desafiament de primer nivell, i açò es deu en gran part a la gran quantitat d'informació existent en el domini. Gràcies a l'aplicació de tecnologies NGS (Next-Generation Sequencing) s'han generat enormes quantitats de dades - nous-, per la qual cosa és fonamental construir estructures que permeten organitzar, processar i explorar les dades a fi d'aconseguir un màxim profit de la informació i millorar la comprensió del genoma humà. En este estudi es definix un marc de treball centrat en l'ús del Modelatge Conceptual com a estratègia essencial per a la busca de solucions. En el camp mèdic este enfocament de desenvolupament de programari està guanyant impuls pel seu impacte en el treball realitzat per genetistes, laboratoris clínics i bioinformàtics. Entendre el genoma és un domini d'aplicació molt interessant a causa de dos aspectes fonamentals: 1) en primer lloc, per les implicacions sociològiques que suposa plantejar-se la possibilitat d'entendre el llenguatge de la vida. 2) i, en segon lloc, des d'una perspectiva més pràctica d'aplicació en l'àmbit clínic, a causa de la seua repercussió en la generació de diagnòstics genòmics, els quals juguen un paper important dins de la Medicina de Precisió. En esta Tesi Doctoral es proposa utilitzar un Model Conceptual del Genoma Humà (MCGH) com a base fonamental per a la generació de Sistemes d'Informació Genòmics (GeIS), amb l'objectiu de facilitar una conceptualització del domini que permeta i) aconseguir un coneixement precís del domini i ii) ser capaços d'arribar a una medicina de precisió (personalitzada). És important ressaltar que este Model Conceptual ha de romandre en constant creixement degut a les noves aportacions que sorgixen en la comunitat científica. En este treball d'investigació es presenta l'evolució natural del model, així com un exemple d'extensió del mateix, la qual cosa permet comprovar la seua extensibilitat conservant la seua definició inicial. A més, s'aplica l'ús d'una metodologia (SILE) sistemàtica per a l'obtenció de les dades des dels distints reposadors genòmics, els quals seran explotats a través ferramentes de programari basades en models conceptuals. Per mitjà de l'ús d'este Model Conceptual holístic del Genoma Humà es busca comprendre i millorar el compromís ontològic amb el domini -genòmic-, i desenvolupar Sistemes d'Informació Genòmics recolzats en Model Conceptuals per ajudar a la presa de decisions en l'entorn bioinformàtic.Reyes Román, JF. (2018). DISEÑO Y DESARROLLO DE UN SISTEMA DE INFORMACIÓN GENÓMICA BASADO EN UN MODELO CONCEPTUAL HOLÍSTICO DEL GENOMA HUMANO [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/99565TESI