Evidence map›Paper›PMID 23174036›Full record

ArticleBMC bioinformatics2012

Core Hunter II: fast core subset selection based on multiple genetic diversity measures using Mixed Replica search.

Herman De Beukelaer, Petr Smýkal, Guy F Davenport, Veerle Fack

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Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
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33citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

33 citing papers in PubMed.

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  7. Genetic Diversity Analysis and Core Collection Construction of AncientInternational journal of molecular sciences · 2024
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  16. The MultisitePlant physiology · 2020
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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Herman De BeukelaerDepartment of Applied Mathematics and Computer Science, Faculty of Sciences, Ghent University, Krijgslaan 281, S9, 9000 Gent, Belgium. herman.debeukelaer@ugent.be
Petr Smýkal
Guy F Davenport
Veerle Fack

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSampling core subsets from genetic resources while maintaining as much as possible the genetic diversity of the original collection is an important but computationally complex task for gene bank managers. The Core Hunter computer program was developed as a tool to generate such subsets based on multiple genetic measures, including both distance measures and allelic diversity indices. At first we investigate the effect of minimum (instead of the default mean) distance measures on the performance of Core Hunter. Secondly, we try to gain more insight into the performance of the original Core Hunter search algorithm through comparison with several other heuristics working with several realistic datasets of varying size and allelic composition. Finally, we propose a new algorithm (Mixed Replica search) for Core Hunter II with the aim of improving the diversity of the constructed core sets and their corresponding generation times.

resultsOur results show that the introduction of minimum distance measures leads to core sets in which all accessions are sufficiently distant from each other, which was not always obtained when optimizing mean distance alone. Comparison of the original Core Hunter algorithm, Replica Exchange Monte Carlo (REMC), with simpler heuristics shows that the simpler algorithms often give very good results but with lower runtimes than REMC. However, the performance of the simpler algorithms is slightly worse than REMC under lower sampling intensities and some heuristics clearly struggle with minimum distance measures. In comparison the new advanced Mixed Replica search algorithm (MixRep), which uses heterogeneous replicas, was able to sample core sets with equal or higher diversity scores than REMC and the simpler heuristics, often using less computation time than REMC.

conclusionThe REMC search algorithm used in the original Core Hunter computer program performs well, sometimes leading to slightly better results than some of the simpler methods, although it doesn't always give the best results. By switching to the new Mixed Replica algorithm overall results and runtimes can be significantly improved. Finally we recommend including minimum distance measures in the objective function when looking for core sets in which all accessions are sufficiently distant from each other. Core Hunter II is freely available as an open source project at http://www.corehunter.org.

Indexed as

Genetic VariationSoftwareAlgorithmsAllelesMonte Carlo MethodSequence Analysis, DNA

Identifiers

PMID23174036
PMCPMC3554476

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.