Performance comparison using homozygous variants of various structural variation types. We simulated a genome with SVs by embedding 2,500 deletions, tandem duplications, inversions or translocations in random locations in the human reference genome. We then simulated sequence data from the altered genome with varying levels of sequence coverage. The performance measurements for LUMPY and DELLY were based on paired-end (pe) and split-read (sr) alignments, GASVPro considered pe and read-depth (rd), and Pindel considered sr alignments. (A) Sensitivity for each tool. LUMPY was the most sensitive in most cases, and had a marked improvement at lower coverage. DELLY detected three more translocations than LUMPY at 20X, at the expense of 93 more false positives. (B) The corresponding FDR for each tool. LUMPY’s FDR was low in all but the highest coverage cases. GASVPro and Pindel did not support tandem duplications, but false calls were made in some cases, which resulted in a 100% FDR. (C) The absolute number of false positive calls. LUMPY had a high number of false positives in some cases, but these are counterbalanced by a higher number of true positives (A). (D,E) To determine the impact that sequence alignment strategies had on SV detection accuracy, LUMPY’s sensitivity (D) and FDR (E) are shown when predicting deletions at 5X coverage via different alignment strategies from the simulations in (A-C). BWA-MEM produces both pe and sr alignment signals in a single alignment step, and serves as a basis of comparison to the NOVOALIGN (pe) and YAHA (sr) strategy. BWA-MEM provides better sensitivity than NOVOALIGN when using the pe signal alone, yet YAHA provides better sensitivity than BWA-MEM when using the sr signal alone. Sensitivity and FDR are roughly equivalent with either the BWA-MEM or NOVOALIGN/YAHA strategies when LUMPY integrates both alignment signals.