A noise-aware method for building radiation hybrid maps

Abstract

The large numbers of markers in high-resolution radiation hybrid (RH) maps, increasingly necessitates the use of data mining techniques for reducing both the computational complexity and the impact of noise of the original data. Traditionally, the RH mapping process has been treated as equivalent to the traveling salesman problem, with the correspondingly high computational complexity. These techniques are also susceptible to noise, and unreliable marker can result in major disruptions of the overall order. In this paper, we propose a new approach that recognizes that the focus on nearest-neighbor distances that characterizes the travelingsalesman model, is no longer appropriate for the large number of markers in modern high-resolution mapping experiments. The proposed approach splits the mapping process into two levels, where the higher level only operates on the most stable markers of the lower level. A divide and conquer strategy, which is applied at the lower level, removes much of the impact of noise. Because of the high density of markers, only the most stable representatives from the lower level are then used at the higher level. The groupings within the lower level are so small that exhaustive search can be used. Markers are then mapped iteratively, while excluding problematic markers. The results for RH mapping dataset of the human genome show that the proposed approach can construct high-resolution maps with high agreement with the physical maps in a comparatively very short time.

Radiation Hybrid Mapping: A Resampling-based Method for Building High-Resolution Maps

“…Several heuristics algorithms can be used to build the maps, in this study, we follow the mapping strategy published in [4,9,10] to build a map for each cluster, using the heuristics algorithms implemented in the Carthagene tool [11]. The following steps present the mapping strategy: 1) Mrkdouble command to identify and merge identical markers by a single marker.…”

“…Although the heuristic approaches are relatively fast, they scale exponentially with the number of markers. Figure 2 shows the typical pattern of mapping different datasets of markers using some heuristic algorithms that have been used to map RH populations in many researches [9][10][11][12].…”

“…Then, all the generated samples are grouped into clusters based on their LOD (logarithms of odds -base 10) scores. The LOD score [14] was developed as a probabilistic measure for linkage, and has been used consistently throughout the RH literature [9][10][11]. 2) Merges all RH samples clusters into consensus clusters, and filters out the unreliable markers.…”