Dataset Information

Helium: visualization of large scale plant pedigrees.

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.

SUBMITTER: Shaw PD

PROVIDER: S-EPMC4133633 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Helium: visualization of large scale plant pedigrees.

Shaw Paul D PD Graham Martin M Kennedy Jessie J Milne Iain I Marshall David F DF

BMC bioinformatics 20140801

<h4>Background</h4>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 enabl ...[more]

PMID: 25085009

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Project description:BackgroundKinetic models of biochemical systems usually consist of ordinary differential equations that have many unknown parameters. Some of these parameters are often practically unidentifiable, that is, their values cannot be uniquely determined from the available data. Possible causes are lack of influence on the measured outputs, interdependence among parameters, and poor data quality. Uncorrelated parameters can be seen as the key tuning knobs of a predictive model. Therefore, before attempting to perform parameter estimation (model calibration) it is important to characterize the subset(s) of identifiable parameters and their interplay. Once this is achieved, it is still necessary to perform parameter estimation, which poses additional challenges.MethodsWe present a methodology that (i) detects high-order relationships among parameters, and (ii) visualizes the results to facilitate further analysis. We use a collinearity index to quantify the correlation between parameters in a group in a computationally efficient way. Then we apply integer optimization to find the largest groups of uncorrelated parameters. We also use the collinearity index to identify small groups of highly correlated parameters. The results files can be visualized using Cytoscape, showing the identifiable and non-identifiable groups of parameters together with the model structure in the same graph.ResultsOur contributions alleviate the difficulties that appear at different stages of the identifiability analysis and parameter estimation process. We show how to combine global optimization and regularization techniques for calibrating medium and large scale biological models with moderate computation times. Then we evaluate the practical identifiability of the estimated parameters using the proposed methodology. The identifiability analysis techniques are implemented as a MATLAB toolbox called VisId, which is freely available as open source from GitHub ( https://github.com/gabora/visid ).ConclusionsOur approach is geared towards scalability. It enables the practical identifiability analysis of dynamic models of large size, and accelerates their calibration. The visualization tool allows modellers to detect parts that are problematic and need refinement or reformulation, and provides experimentalists with information that can be helpful in the design of new experiments.

Dataset Information

Helium: visualization of large scale plant pedigrees.

Publications

Helium: visualization of large scale plant pedigrees.

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