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Accuracy of estimated VAF in simulation. Plotted are the chances (Y axis) of the estimated VAF falling within 10% of the true VAF (X axis). Each data point is estimated from 1000 random samples. Each subplot in the figure contains 4 curves representing the accuracy of VAFs estimated from SNVs (red plus), 1 Kb deletions (green cross), 1 Mb deletions (blue triangle), and inversions/reciprocal-translocations (purple circle). Various types of sequencing data are simulated and results compared: a) short-insert (500 bp) short-read (100 bp) at 5 sequence coverage, b) short insert short read at 30 coverage, c) long insert (3000 bp) short read at 30x coverage and d) short insert short read at 500 coverage.

In summary, this simulation results indicate that our method can accurately estimate VAFs for various types of SVs and can enhance the heterogeneity analysis from either short or long insert data at any coverage.

In terms of accurately estimating VAFs, our approach compared favorably to existing tools. In our simulation, our model could more reliably estimate VAFs than THetA from tumor samples that have multiple clones and a high level of normal contamination. Other approaches such as ABSOLUTE were not directly comparable to our approach, because they were designed to infer tumor purity and ploidy without further characterizing clonal structure or subclonal mutations [7].

We simulated five alterative copies of chromosome 20 (chr20), each containing unique SVs, as represented on the leaf nodes of a phylogeny tree (see Additional file 1: Figure S5). Each of the five clones contains two or four randomly placed non-overlapping 1.5 Mb heterozygous deletions or one-copy tandem duplications. Each clone makes up to a fraction of the total tumor mass. We used wgsim to simulate reads from each chr20 sequences. The corresponding coverages are calculated according to their clonal fraction and the normal contamination rate, which equaled to 0, 0.1, 0.2, 0.3, 0.4 or 0.5 in our simulation. The total coverage was kept at a constant 50X across all conditions. All the deletions and the duplications were simulated as single copy alterations, and therefore the true VAF ranged from 0.05 to 0.3 when the normal contamination rate is 0. When the normal contamination rate is 0.5, the true VAFs ranged from 0.025 to 0.15. We mapped the synthetic reads to the wide-type chr20 reference using bwa-mem [42].

COLO-829 NGS data was downloaded from the European Genome-Phenome Archive (Accession number: EGAD00000000055). CREST and validated call set was from Additional file 3: Table S2 (nmeth.1628-S2) downloaded from [21]. The LOH set was obtained from the Supplementary Table six from [30].

The NGS data for the breast cancer samples were downloaded from the European Genome-Phenome Archive (Accession number: EGAD00001000138). Validated SV set was obtained from Supplementary Table one from [34].

Comparison of BreakDancer with other tools. Structural variants predicted by BreakDancer on the Yoruban (NA18507) sample were compared to sets of variants discovered by alternative approaches14,21. ESP (large structural variants that were found by analyzing discordant fosmid clone-end alignment), DIP (small deletion/insertion polymorphisms found as gaps in the paired alignment between the fosmid end sequences and the reference). The MPSV weighted, MPSV unweighted, Probabilistic, and MoDIL refer to sets of SVs predicted by VariationHunter24 and by MoDIL25 respectively. Call sets for these tools were downloaded from and The dbSNP v129 set refers to indels that are 10 bp or longer in dbSNP version 129. The BGI set refers to 10 bp or longer intra-contig indels produced by Beijing Genome Institute through whole genome de novo assembly on the same sample. The Strict* criteria require the length of the intersection between the validated and the predicted variants to overlap at least 50% of the length of the union of the intervals, or the predicted variants to be entirely encompassed by the fosmid interval. Before the slash sign (/) are the numbers of overlapping variants, after are the number of predictions in the corresponding category.

To facilitate structural variant detection algorithm evaluations, we create a robust simulation framework for somatic structural variants by extending the BAMSurgeon algorithm. We then organize and enable a crowdsourced benchmarking within the ICGC-TCGA DREAM Somatic Mutation Calling Challenge (SMC-DNA). We report here the results of structural variant benchmarking on three different tumors, comprising 204 submissions from 15 teams. In addition to ranking methods, we identify characteristic error profiles of individual algorithms and general trends across them. Surprisingly, we find that ensembles of analysis pipelines do not always outperform the best individual method, indicating a need for new ways to aggregate somatic structural variant detection approaches.

To fill this gap, we created an open challenge-based assessment of somatic SV prediction tools as part of the ICGC-TCGA DREAM Somatic Mutation Calling Challenge (the Challenge). The lack of fully characterized tumor genomes for building gold standard sets of SVs motivated our simulation approach. Specifically, we first extended BAMSurgeon [10], a tool for adding simulated mutations to existing reads, to generate somatic SVs. This approach is advantageous because it permits flexibility with the added mutations while also capturing sequencing technology biases through the use of existing reads. We created and distributed in silico tumors (IS1-IS3), on which 204 submissions were made by 15 teams.

While 15 teams participated in the actual competitive phase of the Challenge, 8 teams have exploited the IS1-3 benchmarking resources since the competition, making 73 submissions to benchmark their methods for pipeline evaluation and development. Evaluations based on the first synthetic tumors, the simplest by design, provide lower bounds on the error rates. As subsequent updates to BAMSurgeon enable the generation of more complex and realistic tumors, the corresponding error rates using these simulations will approach their upper bounds. We hope that journals and developers will begin to plan for benchmarking on these standard datasets, including simulated ones, as a standard part of manuscripts reporting new somatic SV detection algorithms.

In recent years, many software packages for identifying structural variants (SVs) using whole-genome sequencing data have been released. When published, a new method is commonly compared with those already available, but this tends to be selective and incomplete. The lack of comprehensive benchmarking of methods presents challenges for users in selecting methods and for developers in understanding algorithm behaviours and limitations. Here we report the comprehensive evaluation of 10 SV callers, selected following a rigorous process and spanning the breadth of detection approaches, using high-quality reference cell lines, as well as simulations. Due to the nature of available truth sets, our focus is on general-purpose rather than somatic callers. We characterise the impact on performance of event size and type, sequencing characteristics, and genomic context, and analyse the efficacy of ensemble calling and calibration of variant quality scores. Finally, we provide recommendations for both users and methods developers.

Following a careful procedure, we selected 10 general-purpose callers that use different approaches to SV detection and benchmarked these tools. We used simulations to test the behaviour of each method on idealised data across a wide variety of sequencing parameters. Our simulations were designed to be as extensive and efficient as possible, while still avoiding interference between breakpoints calls. To our knowledge, this is the largest SV simulation undertaken and revealed surprising features in some of the callers. These simulations are a powerful tool allowing the evaluation the best-case detection capability across a range of event types and sizes. Knowing the limitations of each caller is critical to ensuring that a caller with poor detection performance on the events of interest is not used. However, simulated data is unrealistic and far too simple for accurate benchmarking, thus we have emphasised the use of reference datasets derived from cell lines with large-scale truth sets or orthogonal validation data. Our general approach is to run each caller using default parameters and visualise results using receiver operating characteristic (ROC)-like plots for each dataset. This allows exploration of the sensitivity and precision trade-off up to the default quality score threshold, and exhaustively on unfiltered calls from those methods that also report all variants. Benchmarking on three datasets identified a few callers that consistently performed well across the multiple reference datasets.

User/Developer recommendation: Do not consider simulation results representative of real-world performance. Simulated results should not be considered representative of performance on real data, but rather an upper bound on actual performance. Simulations are a useful tool for debugging callers, identifying the limits of detection of algorithms on idealised data, and understanding how these limits vary with typical sequencing parameters: read length, sequencing depth, and library fragment size, but they are no substitute for extensive testing on real data. 2ff7e9595c