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Core Hunter II: fast core subset selection based on multiple genetic diversity measures using Mixed Replica search.
Herman De Beukelaer1, Petr Smýkal, Guy F Davenport
1Department of Applied Mathematics and Computer Science, Faculty of Sciences, Ghent University, Krijgslaan 281, S9, 9000 Gent, Belgium. herman.debeukelaer@ugent.be
BMC Bioinformatics
|November 24, 2012
Summary
Gene banks can improve genetic diversity sampling with Core Hunter II. The new Mixed Replica algorithm enhances core set diversity and reduces computation time compared to older methods.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Gene bank managers face challenges in creating representative core subsets from large genetic resources.
- The Core Hunter program aids in generating these subsets using genetic distance and diversity measures.
- Investigating alternative distance measures and algorithms is crucial for optimizing core set creation.
Purpose of the Study:
- To evaluate the impact of minimum distance measures versus mean distance measures in Core Hunter.
- To compare the performance of the original Core Hunter algorithm (REMC) with other heuristics.
- To introduce and assess a new algorithm, Mixed Replica search (MixRep), for improved core set generation.
Main Methods:
- Core Hunter program utilizing distance and diversity indices.
- Comparison of Replica Exchange Monte Carlo (REMC) with simpler heuristic algorithms.
- Implementation and testing of the novel Mixed Replica search (MixRep) algorithm.
Main Results:
- Minimum distance measures ensure greater genetic distance between selected accessions.
- Simpler heuristics offer faster computation but may yield suboptimal diversity, especially with minimum distance measures.
- Mixed Replica search (MixRep) achieved comparable or superior diversity with reduced computation time compared to REMC and other heuristics.
Conclusions:
- The original REMC algorithm performs well but can be surpassed by simpler methods in speed.
- The Mixed Replica algorithm offers significant improvements in both diversity and efficiency.
- Minimum distance measures are recommended for objective functions when maximizing genetic distance is key.
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