A fast and scalable clustering-based approach for constructing reliable radiation hybrid maps

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 RHMdata 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.