Abstract: Optical Maps (OM) provide reads that are very long, and thus can be used to detect large indels not detectable by the shorter reads provided
by sequence-based technologies such as Illumina and PacBio. Two existing tools for detecting large indels from OM data are BioNano Solve and OMSV. However, these two
tools may miss indels with weak signals. We propose a local-assembly based approach, OMIndel, to detect large indels with OM data. The results of applying OMIndel to empirical data demonstrate
that it is able to detect indels with weak signal. Furthermore, compared with the other two OM-based methods, OMIndel has a lower false discovery rate. We
also investigated the indels that can only be detected by OM but not Illumina, PacBio or 10X, and we found that they mostly fall into two categories: complex events or indels on repetitive regions. This implies that adding the OM data to sequence-based technologies can provide significant progress towards a more complete characterization of structural variants (SVs). The algorithm has been implemented in Perl and is publicly available on https://bitbucket.org/xianfan/optmethod.

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