Fine Tuning Sparsity Penalties to Improve Structural Variant Detection Genomic variation shared by members of the same species that are longer than a single nucleotide are commonly called structural variants (SVs). Though relatively rare, they represent an increasingly important class of variation as SVs have been associated with diseases and susceptibility to some types of cancer. Computational approaches for detecting SVs often involve parameters that describe certain relevant biological phenomena. In our work, such parameters relate the incidence of inherited and novel SVs to probabilistic models of observing these SVs. In the work presented here, we investigate the sensitivity of our computational framework to these parameters. In particular, we demonstrate the robustness of our method by identifying a wide range of parameter values that lead to highaccuracy SV predictions in simulated data.

Benchmarking of Structural Variant Detection Tools for Microorganisms A variety of structural variant (SV) detection tools exist and they are generally tested on the human genome to assess performance. However, these tests may be inapplicable to the genomes of microorganisms. The focus of this paper is a discussion of benchmarks that were run on microbial genomes. Representative tools from a variety of methods for SV detection, such as: readpair, readdepth or readcount, splitread, and de novo assembly were selected to determine the best approach for detecting SVs in microbial genomes. The tools are tested on simulated data that includes deletion, duplication, insertion, and inversion SVs. Experimental results show that when execution time, precision, recall, and coverage of the reads are considered, the most accurate results come from tools using the readpair method mixed with either the splitread or readcount method, specifically, LUMPY 1 and SVDetect 2 performed best.