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      Helium: visualization of large scale plant pedigrees

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      , , , ,
      BMC Bioinformatics
      BioMed Central

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          Abstract

          Background

          Plant breeders use an increasingly diverse range of data types to identify lines with desirable characteristics suitable to be taken forward in plant breeding programmes. There are a number of key morphological and physiological traits, such as disease resistance and yield that need to be maintained and improved upon if a commercial variety is to be successful. Computational tools that provide the ability to integrate and visualize this data with pedigree structure, will enable breeders to make better decisions on the lines that are used in crossings to meet both the demands for increased yield/production and adaptation to climate change.

          Results

          We have used a large and unique set of experimental barley (H. vulgare) data to develop a prototype pedigree visualization system. We then used this prototype to perform a subjective user evaluation with domain experts to guide and direct the development of an interactive pedigree visualization tool called Helium.

          Conclusions

          We show that Helium allows users to easily integrate a number of data types along with large plant pedigrees to offer an integrated environment in which they can explore pedigree data. We have also verified that users were happy with the abstract representation of pedigrees that we have used in our visualization tool.

          Electronic supplementary material

          The online version of this article (doi:10.1186/1471-2105-15-259) contains supplementary material, which is available to authorized users.

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          Most cited references46

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          Climate change and food security.

          Dynamic interactions between and within the biogeophysical and human environments lead to the production, processing, distribution, preparation and consumption of food, resulting in food systems that underpin food security. Food systems encompass food availability (production, distribution and exchange), food access (affordability, allocation and preference) and food utilization (nutritional and societal values and safety), so that food security is, therefore, diminished when food systems are stressed. Such stresses may be induced by a range of factors in addition to climate change and/or other agents of environmental change (e.g. conflict, HIV/AIDS) and may be particularly severe when these factors act in combination. Urbanization and globalization are causing rapid changes to food systems. Climate change may affect food systems in several ways ranging from direct effects on crop production (e.g. changes in rainfall leading to drought or flooding, or warmer or cooler temperatures leading to changes in the length of growing season), to changes in markets, food prices and supply chain infrastructure. The relative importance of climate change for food security differs between regions. For example, in southern Africa, climate is among the most frequently cited drivers of food insecurity because it acts both as an underlying, ongoing issue and as a short-lived shock. The low ability to cope with shocks and to mitigate long-term stresses means that coping strategies that might be available in other regions are unavailable or inappropriate. In other regions, though, such as parts of the Indo-Gangetic Plain of India, other drivers, such as labour issues and the availability and quality of ground water for irrigation, rank higher than the direct effects of climate change as factors influencing food security. Because of the multiple socio-economic and bio-physical factors affecting food systems and hence food security, the capacity to adapt food systems to reduce their vulnerability to climate change is not uniform. Improved systems of food production, food distribution and economic access may all contribute to food systems adapted to cope with climate change, but in adopting such changes it will be important to ensure that they contribute to sustainability. Agriculture is a major contributor of the greenhouse gases methane (CH4) and nitrous oxide (N2O), so that regionally derived policies promoting adapted food systems need to mitigate further climate change.
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            A nested model for visualization design and validation.

            We present a nested model for the visualization design process with four layers: characterize the problem domain, abstract into operations on data types, design visual encoding and interaction techniques, and create algorithms to execute techniques efficiently. The output from a level above is input to the level below, bringing attention to the design challenge that an upstream error inevitably cascades to all downstream levels. This model provides prescriptive guidance for determining appropriate evaluation approaches by identifying threats to validity unique to each level. We call attention to specific steps in the design and evaluation process that are often given short shrift. We also provide three recommendations motivated by this model:authors should distinguish between these levels when claiming contributions at more than one of them, authors should explicitly state upstream assumptions at levels above the focus of a paper, and visualization venues should accept more papers on domain characterization.
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              PEDSTATS: descriptive statistics, graphics and quality assessment for gene mapping data.

              We describe a tool that produces summary statistics and basic quality assessments for gene-mapping data, accommodating either pedigree or case-control datasets. Our tool can also produce graphic output in the PDF format.
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                Author and article information

                Contributors
                paul.shaw@hutton.ac.uk
                m.graham@napier.ac.uk
                j.kennedy@napier.ac.uk
                iain.milne@hutton.ac.uk
                david.marshall@hutton.ac.uk
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                1 August 2014
                1 August 2014
                2014
                : 15
                : 1
                : 259
                Affiliations
                [ ]Information and Computational Sciences, The James Hutton Institute, Invergowrie, Dundee, DD2 5DA UK
                [ ]School of Computing, Edinburgh Napier University, 10 Colinton Road, Edinburgh, EH10 5DT UK
                Article
                6532
                10.1186/1471-2105-15-259
                4133633
                25085009
                257e4744-ed97-4ee6-baa2-c269aefc0b94
                © Shaw et al.; licensee BioMed Central Ltd. 2014

                This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 21 April 2014
                : 10 July 2014
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2014

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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