13,586 research outputs found

    Biodigital publics: personal genomes as digital media artifacts

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    The recent proliferation of personal genomics and direct-to-consumer (DTC) genomics has attracted much attention and publicity. Concern around these developments has mainly focused on issues of biomedical regulation and hinged on questions of how people understand genomic information as biomedical and what meaning they make of it. However, this publicity amplifies genome sequences which are also made as internet texts and, as such, they generate new reading publics. The practices around the generation, circulation and reading of genome scans do not just raise questions about biomedical regulation, they also provide the focus for an exploration of how contemporary public participation in genomics works. These issues around the public features of DTC genomic testing can be pursued through a close examination of the modes of one of the best known providers—23andMe. In fact, genome sequences circulate as digital artefacts and, hence, people are addressed by them. They are read as texts, annotated and written about in browsers, blogs and wikis. This activity also yields content for media coverage which addresses an indefinite public in line with Michael Warner’s conceptualisation of publics. Digital genomic texts promise empowerment, personalisation and community, but this promise may obscure the compliance and proscription associated with these forms. The kinds of interaction here can be compared to those analysed by Andrew Barry. Direct-to-consumer genetics companies are part of a network providing an infrastructure for genomic reading publics and this network can be mapped and examined to demonstrate the ways in which this formation both exacerbates inequalities and offers possibilities for participation in biodigital culture

    Bioinformatics and the politics of innovation in the life sciences: Science and the state in the United Kingdom, China, and India

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    The governments of China, India, and the United Kingdom are unanimous in their belief that bioinformatics should supply the link between basic life sciences research and its translation into health benefits for the population and the economy. Yet at the same time, as ambitious states vying for position in the future global bioeconomy they differ considerably in the strategies adopted in pursuit of this goal. At the heart of these differences lies the interaction between epistemic change within the scientific community itself and the apparatus of the state. Drawing on desk-based research and thirty-two interviews with scientists and policy makers in the three countries, this article analyzes the politics that shape this interaction. From this analysis emerges an understanding of the variable capacities of different kinds of states and political systems to work with science in harnessing the potential of new epistemic territories in global life sciences innovation

    Beyond bench and bedside: disentangling the concept of translational research

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    The label ‘Translational Research’ (TR) has become ever more popular in the biomedical domain in recent years. It is usually presented as an attempt to bridge a supposed gap between knowledge produced at the lab bench and its use at the clinical bedside. This is claimed to help society harvest the benefits of its investments in scientific research. The rhetorical as well as moral force of the label TR obscure, however, that it is actually used in very different ways. In this paper, we analyse the scientific discourse on TR, with the aim to disentangle and critically evaluate the different meanings of the label. We start with a brief reconstruction of the history of the concept. Subsequently, we unravel how the label is actually used in a sample of scientific publications on TR and examine the presuppositions implied by different views of TR. We argue that it is useful to distinguish different views of TR on the basis of three dimensions, related to (1) the construction of the ‘translational gap’; (2) the model of the translational process; and (3) the cause of the perceived translational gap. We conclude that the motive to make society benefit from its investments in biomedical science may be laudable, but that it is doubtful whether the dominant views of TR will contribute to this en

    Indexing large genome collections on a PC

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    Motivation: The availability of thousands of invidual genomes of one species should boost rapid progress in personalized medicine or understanding of the interaction between genotype and phenotype, to name a few applications. A key operation useful in such analyses is aligning sequencing reads against a collection of genomes, which is costly with the use of existing algorithms due to their large memory requirements. Results: We present MuGI, Multiple Genome Index, which reports all occurrences of a given pattern, in exact and approximate matching model, against a collection of thousand(s) genomes. Its unique feature is the small index size fitting in a standard computer with 16--32\,GB, or even 8\,GB, of RAM, for the 1000GP collection of 1092 diploid human genomes. The solution is also fast. For example, the exact matching queries are handled in average time of 39\,μ\mus and with up to 3 mismatches in 373\,μ\mus on the test PC with the index size of 13.4\,GB. For a smaller index, occupying 7.4\,GB in memory, the respective times grow to 76\,μ\mus and 917\,μ\mus. Availability: Software and Suuplementary material: \url{http://sun.aei.polsl.pl/mugi}

    Privacy in the Genomic Era

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    Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward

    Controlling new knowledge: Genomic science, governance and the politics of bioinformatics

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    The rise of bioinformatics is a direct response to the political difficulties faced by genomics in its quest to be a new biomedical innovation, its value being that it acts as the bridge between the promise of genomics and its realization in the form of health benefits. Western scientific elites are able to use their close relationship with the state to control and facilitate the emergence of new domains compatible with the existing distribution of epistemic power – all within the embrace of public trust. The incorporation of bioinformatics as the saviour of genomics had to be integrated with the operation of two key aspects of governance in this field: the definition and ownership of the new knowledge. This was achieved mainly by the development of common standards and by the promotion of the values of communality, open access and the public ownership of data to legitimize and maintain the governance power of publicly funded genomic science. Opposition from industry advocating the private ownership of knowledge has been largely neutered through the institutions supporting the science-state concordat. However, in order for translation into health benefit to occur and public trust to be assured, genomic and clinical data have to be integrated and knowledge ownership agreed across the separate and distinct governance territories of scientist, clinical medicine and society. Tensions abound as science seeks ways of maintaining its control of knowledge production through the negotiation of new forms of governance with the institutions and values of clinicians and patients

    Infectious Disease Ontology

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    Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain
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