Multilocus consensus genetic maps (MCGM): Formulation, algorithms, and results

In process of creating genetic maps different labs/research groups obtain overlapping parts of the map. Merging these parts into one integrative map is based on looking for maximum shared marker orders among the maps. Really, not all shared markers of such maps have consensus order that obstructs bu...

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Bibliographic Details
Published in:	Computational biology and chemistry Vol. 30; no. 1; pp. 12 - 20
Main Authors:	Mester, D.I., Ronin, Y.I., Korostishevsky, M.A., Pikus, V.L., Glazman, A.E., Korol, A.B.
Format:	Journal Article
Language:	English
Published:	England Elsevier Ltd 01-02-2006
Subjects:	Algorithms Consensus Sequence Female Humans Male Models, Genetic Multilocus ordering Physical Chromosome Mapping - methods Re-sampling verification Recombination, Genetic Synchronized discrete optimization TSP Unstable neighborhoods TSP Re-sampling verification Unstable neighborhoods Multilocus ordering Synchronized discrete optimization
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Summary:	In process of creating genetic maps different labs/research groups obtain overlapping parts of the map. Merging these parts into one integrative map is based on looking for maximum shared marker orders among the maps. Really, not all shared markers of such maps have consensus order that obstructs building of the integrative maps. In this paper we propose a new approach to build verified multilocus consensus genetic maps in which shared markers always are integrated in stable consensus order. The approach is based on combined analysis of initial mapping data rather than manipulating with previously constructed maps. We show that more effective and reliable solutions may be obtained based on “synchronized ordering” facilitated by cycles of “re-sampling → ordering → removing unstable markers”. The proposed formulation of consensus genetic mapping can be considered as a version of traveling salesperson problem (TSP) that we refer to as synchronized-TSP. From the viewpoint of optimization, synchronized-TSP belongs to discrete constrained optimization problems. Earlier we developed new powerful and fast guided evolution strategy algorithms for some types of discrete constrained optimization. These algorithms were used here as a basis for solving more challenging problems of consensual marker ordering.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1476-9271 1476-928X
DOI:	10.1016/j.compbiolchem.2005.09.007