7 research outputs found

    Fast and Accurate Construction of Ultra-Dense Consensus Genetic Maps Using Evolution Strategy Optimization

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    <div><p>Our aim was to develop a fast and accurate algorithm for constructing consensus genetic maps for chip-based SNP genotyping data with a high proportion of shared markers between mapping populations. Chip-based genotyping of SNP markers allows producing high-density genetic maps with a relatively standardized set of marker loci for different mapping populations. The availability of a standard high-throughput mapping platform simplifies consensus analysis by ignoring unique markers at the stage of consensus mapping thereby reducing mathematical complicity of the problem and in turn analyzing bigger size mapping data using global optimization criteria instead of local ones. Our three-phase analytical scheme includes automatic selection of ~100-300 of the most informative (resolvable by recombination) markers per linkage group, building a stable skeletal marker order for each data set and its verification using jackknife re-sampling, and consensus mapping analysis based on global optimization criterion. A novel Evolution Strategy optimization algorithm with a global optimization criterion presented in this paper is able to generate high quality, ultra-dense consensus maps, with many thousands of markers per genome. This algorithm utilizes "potentially good orders" in the initial solution and in the new mutation procedures that generate trial solutions, enabling to obtain a consensus order in reasonable time. The developed algorithm, tested on a wide range of simulated data and real world data (<i>Arabidopsis</i>), outperformed two tested state-of-the-art algorithms by mapping accuracy and computation time.</p></div

    Main features and field of application of the two optimization methods (Globalheuristic and Local exact) for solving multilocus consensus mapping problems.

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    <p>Main features and field of application of the two optimization methods (Globalheuristic and Local exact) for solving multilocus consensus mapping problems.</p

    Comparative effectiveness of the initial solutions and thelocal search procedures on the simulated problems.

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    <p>The utilization of the initial solution step (column 4) and the local search (column 5) considerably reduces the computation time on the test problem.</p><p><sup>1</sup> The three mutation procedures are working.</p><p><sup>2</sup> The Initial solution used.</p><p><sup>3</sup> The local search used.</p><p>Comparative effectiveness of the initial solutions and thelocal search procedures on the simulated problems.</p

    Representing a set of two individual maps as a directed acyclic graph (DAG).

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    <p>Two single maps (map1 and map2) are joined as a DAG. The joint map contains two conflicted regions.</p

    Testing the proposed algorithm on datasets of Group 1.

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    <p>Each dataset of Group 1 contains five subsets of shared markers scored without errors,with different distribution of recombination rates and interference values along the chromosome.</p><p>In the table, NS is the sum of lengths of the non-synchronized maps, FRS is the sum of lengths of the initial (random) consensus solution, LCM is the sum of lengths of the optimal consensus maps, and <i>K</i><sub><i>r</i></sub> is the coefficient of recovery of the simulated marker order.</p><p>Testing the proposed algorithm on datasets of Group 1.</p
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