Finding Overlapping Rmaps via Gaussian Mixture Model Clustering

Optical mapping is a method for creating high resolution restriction maps of an entire genome. Optical mapping has been largely automated, and first produces single molecule restriction maps, called Rmaps, which are assembled to generate genome wide optical maps. Since the location and orientation of each Rmap is unknown, the first problem in the analysis of this data is finding related Rmaps, i.e., pairs of Rmaps that share the same orientation and have significant overlap in their genomic location. Although heuristics for identifying related Rmaps exist, they all require quantization of the data which leads to a loss in the precision. In this paper, we propose a Gaussian mixture modelling clustering based method, which we refer to as OMO_SCPLOWCLUSTC_SCPLOW, that finds overlapping Rmaps without quantization. Using both simulated and real datasets, we show that OMO_SCPLOWCLUSTC_SCPLOW substantially improves the precision (from 48.3% to 73.3%) over the state-of-the art methods while also reducing CPU time and memory consumption. Further, we integrated OMO_SCPLOWCLUSTC_SCPLOW into the error correction methods (Elmeri and O_SCPLOWCC_SCPLOWOMO_SCPLOWETC_SCPLOW) to demonstrate the increase in the performance of these methods. When OMO_SCPLOWCLUSTC_SCPLOW was combined with O_SCPLOWCC_SCPLOWOMO_SCPLOWETC_SCPLOW to error correct Rmap data generated from human DNA, it was able to error correct close to 3x more Rmaps, and reduced the CPU time by more than 35x. Our software is written in C++ and is publicly available under GNU General Public License at https://github.com/kingufl/OMclust