Space-Efficient Indexing of Spaced Seeds for Accurate Overlap Computation of Raw Optical Mapping Data

A key problem in processing raw optical mapping data (Rmaps) is finding Rmaps originating from the same genomic region. These sets of related can be used to correct errors Rmap data, and find overlaps between assemble consensus maps. Previous overlap aligners are computationally very expensive do not scale large eukaryotic sets. We present Selkie , an aligner based on a spaced $(\ell,k)$ -mer index which was pioneered error correction tool Elmeri . Here we space efficient version twice as fast prior art while using just quarter memory human set. Moreover, our for filtering candidates computation, whereas only Rmaps. By combining with exhaustive, but highly accurate, algorithm Valouev et al. (2006), maintains or increases accuracy overlapping bacterial dataset being at least four times faster. Furthermore, dataset, up two orders magnitude faster than previous methods.