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Diana J. Rennison

Bio: Diana J. Rennison is an academic researcher from University of Bern. The author has contributed to research in topics: Stickleback & Biology. The author has an hindex of 14, co-authored 25 publications receiving 1956 citations. Previous affiliations of Diana J. Rennison include University of Victoria & University of British Columbia.
Topics: Stickleback, Biology, Population, Medicine, Adaptation

Papers
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Journal ArticleDOI
TL;DR: Emergent trends and gaps in understanding are identified, new approaches to more fully integrate genomics into speciation research are proposed, and an integrative definition of the field of speciation genomics is provided.
Abstract: Speciation is a fundamental evolutionary process, the knowledge of which is crucial for understanding the origins of biodiversity. Genomic approaches are an increasingly important aspect of this research field. We review current understanding of genome-wide effects of accumulating reproductive isolation and of genomic properties that influence the process of speciation. Building on this work, we identify emergent trends and gaps in our understanding, propose new approaches to more fully integrate genomics into speciation research, translate speciation theory into hypotheses that are testable using genomic tools and provide an integrative definition of the field of speciation genomics.

875 citations

Journal ArticleDOI
TL;DR: The results reinforce the notion that, in the long term, research data cannot be reliably preserved by individual researchers, and further demonstrate the urgent need for policies mandating data sharing via public archives.

384 citations

Journal ArticleDOI
TL;DR: It is found that reproducing structure results can be difficult despite the straightforward requirements of the program, and recommendations are made for future use of the software and for reporting structure analyses and results in published works.
Abstract: Reproducibility is the benchmark for results and conclusions drawn from scientific studies, but systematic studies on the reproducibility of scientific results are surprisingly rare. Moreover, many modern statistical methods make use of ‘random walk’ model fitting procedures, and these are inherently stochastic in their output. Does the combination of these statistical procedures and current standards of data archiving and method reporting permit the reproduction of the authors' results? To test this, we reanalysed data sets gathered from papers using the software package structure to identify genetically similar clusters of individuals. We find that reproducing structure results can be difficult despite the straightforward requirements of the program. Our results indicate that 30% of analyses were unable to reproduce the same number of population clusters. To improve this, we make recommendations for future use of the software and for reporting structure analyses and results in published works.

256 citations

Journal ArticleDOI
TL;DR: Evidence regarding the manifestation of (non)parallel evolution in the laboratory, in natural populations, and in applied contexts such as cancer is reviewed.
Abstract: Parallel evolution across replicate populations has provided evolutionary biologists with iconic examples of adaptation. When multiple populations colonize seemingly similar habitats, they may evol...

179 citations

Journal ArticleDOI
TL;DR: It is found that mandated data archiving policies that require the inclusion of a data availability statement in the manuscript improve the odds of finding the data online almost 1000‐fold compared to having no policy.
Abstract: The data underlying scientific papers should be accessible to researchers both now and in the future, but how best can we ensure that these data are available? Here we examine the effectiveness of four approaches to data archiving: no stated archiving policy, recommending (but not requiring) archiving, and two versions of mandating data deposition at acceptance. We control for differences between data types by trying to obtain data from papers that use a single, widespread population genetic analysis, structure. At one extreme, we found that mandated data archiving policies that require the inclusion of a data availability statement in the manuscript improve the odds of finding the data online almost 1000-fold compared to having no policy. However, archiving rates at journals with less stringent policies were only very slightly higher than those with no policy at all. We also assessed the effectiveness of asking for data directly from authors and obtained over half of the requested datasets, albeit with ∼8 d delay and some disagreement with authors. Given the long-term benefits of data accessibility to the academic community, we believe that journal-based mandatory data archiving policies and mandatory data availability statements should be more widely adopted.

141 citations


Cited by
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01 Jan 2016
TL;DR: The modern applied statistics with s is universally compatible with any devices to read, and is available in the digital library an online access to it is set as public so you can download it instantly.
Abstract: Thank you very much for downloading modern applied statistics with s. As you may know, people have search hundreds times for their favorite readings like this modern applied statistics with s, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop. modern applied statistics with s is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the modern applied statistics with s is universally compatible with any devices to read.

5,249 citations

Journal ArticleDOI
TL;DR: Clumpak, available at http://clumpak.tau.ac.il, simplifies the use of model-based analyses of population structure in population genetics and molecular ecology by automating the postprocessing of results of model‐based population structure analyses.
Abstract: The identification of the genetic structure of populations from multilocus genotype data has become a central component of modern population-genetic data analysis. Application of model-based clustering programs often entails a number of steps, in which the user considers different modelling assumptions, compares results across different predetermined values of the number of assumed clusters (a parameter typically denoted K), examines multiple independent runs for each fixed value of K, and distinguishes among runs belonging to substantially distinct clustering solutions. Here, we present CLUMPAK (Cluster Markov Packager Across K), a method that automates the postprocessing of results of model-based population structure analyses. For analysing multiple independent runs at a single K value, CLUMPAK identifies sets of highly similar runs, separating distinct groups of runs that represent distinct modes in the space of possible solutions. This procedure, which generates a consensus solution for each distinct mode, is performed by the use of a Markov clustering algorithm that relies on a similarity matrix between replicate runs, as computed by the software CLUMPP. Next, CLUMPAK identifies an optimal alignment of inferred clusters across different values of K, extending a similar approach implemented for a fixed K in CLUMPP and simplifying the comparison of clustering results across different K values. CLUMPAK incorporates additional features, such as implementations of methods for choosing K and comparing solutions obtained by different programs, models, or data subsets. CLUMPAK, available at http://clumpak.tau.ac.il, simplifies the use of model-based analyses of population structure in population genetics and molecular ecology.

2,252 citations

Journal ArticleDOI
TL;DR: The need for standardization of specimen handling, appropriate normative controls, and isolation and analysis techniques to facilitate comparison of results is emphasized, and it is recognized that continual development and evaluation of techniques will be necessary as new knowledge is amassed.
Abstract: The emergence of publications on extracellular RNA (exRNA) and extracellular vesicles (EV) has highlighted the potential of these molecules and vehicles as biomarkers of disease and therapeutic targets. These findings have created a paradigm shift, most prominently in the field of oncology, prompting expanded interest in the field and dedication of funds for EV research. At the same time, understanding of EV subtypes, biogenesis, cargo and mechanisms of shuttling remains incomplete. The techniques that can be harnessed to address the many gaps in our current knowledge were the subject of a special workshop of the International Society for Extracellular Vesicles (ISEV) in New York City in October 2012. As part of the “ISEV Research Seminar: Analysis and Function of RNA in Extracellular Vesicles (evRNA)”, 6 round-table discussions were held to provide an evidence-based framework for isolation and analysis of EV, purification and analysis of associated RNA molecules, and molecular engineering of EV for therapeutic intervention. This article arises from the discussion of EV isolation and analysis at that meeting. The conclusions of the round table are supplemented with a review of published materials and our experience. Controversies and outstanding questions are identified that may inform future research and funding priorities. While we emphasize the need for standardization of specimen handling, appropriate normative controls, and isolation and analysis techniques to facilitate comparison of results, we also recognize that continual development and evaluation of techniques will be necessary as new knowledge is amassed. On many points, consensus has not yet been achieved and must be built through the reporting of well-controlled experiments. Keywords: extracellular vesicle; exosome; microvesicle; standardization; isolation (Published: 27 May 2013) Citation: Journal of Extracellular Vesicles 2013, 2 : 20360 - http://dx.doi.org/10.3402/jev.v2i0.20360

1,840 citations

Book ChapterDOI
15 Mar 2012

1,516 citations

Journal ArticleDOI
TL;DR: The conceptual and evidence-based foundation provided in this essay is expected to serve as a roadmap for hypothesis-driven, experimentally validated research on holobionts and their hologenomes, thereby catalyzing the continued fusion of biology's subdisciplines.
Abstract: Groundbreaking research on the universality and diversity of microorganisms is now challenging the life sciences to upgrade fundamental theories that once seemed untouchable. To fully appreciate the change that the field is now undergoing, one has to place the epochs and foundational principles of Darwin, Mendel, and the modern synthesis in light of the current advances that are enabling a new vision for the central importance of microbiology. Animals and plants are no longer heralded as autonomous entities but rather as biomolecular networks composed of the host plus its associated microbes, i.e., "holobionts." As such, their collective genomes forge a "hologenome," and models of animal and plant biology that do not account for these intergenomic associations are incomplete. Here, we integrate these concepts into historical and contemporary visions of biology and summarize a predictive and refutable framework for their evaluation. Specifically, we present ten principles that clarify and append what these concepts are and are not, explain how they both support and extend existing theory in the life sciences, and discuss their potential ramifications for the multifaceted approaches of zoology and botany. We anticipate that the conceptual and evidence-based foundation provided in this essay will serve as a roadmap for hypothesis-driven, experimentally validated research on holobionts and their hologenomes, thereby catalyzing the continued fusion of biology's subdisciplines. At a time when symbiotic microbes are recognized as fundamental to all aspects of animal and plant biology, the holobiont and hologenome concepts afford a holistic view of biological complexity that is consistent with the generally reductionist approaches of biology.

817 citations