Multilocus consensus genetic maps (MCGM): Formulation, algorithms, and results
Computational Biology and Chemistry
Network-Based Filtering of Unreliable Markers in Genome Mapping
ICMLA '11 Proceedings of the 2011 10th International Conference on Machine Learning and Applications and Workshops - Volume 01
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The process of mapping markers from radiation hybrid mapping (RHM) experiments is equivalent to the traveling salesman problem and, thereby, has combinatorial complexity. As an additional problem, experiments typically result in some unreliable markers that reduce the overall quality of the map. We propose a clustering approach for addressing both problems efficiently by eliminating unreliable markers without the need for mapping the complete set of markers. Traditional approaches for eliminating markers use resampling of the full data set, which has an even higher computational complexity than the original mapping problem. In contrast, the proposed approach uses a divide and conquer strategy to construct framework maps based on clusters that exclude unreliable markers. Clusters are ordered using parallel processing and are then combined to form the complete map. Using an RHM data set of the human genome, we compare the framework maps from our proposed approaches with published physical maps and with the Carthagene tool. Overall, our approach has a very low computational complexity and produces solid framework maps with good chromosome coverage and high agreement with the physical map marker order.