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Keep plagiarism at bay with Copyleaks. Proving Originality. Promoting Integrity. Preventing Plagiarism. Manta, Lumpy, CNVnator, cn. The tool was not run on all 38 samples due to computation time: it re-analyzes the base-level coverages of the control samples in every run, resulting in very long running times for WGS samples.
Furthermore, a total CNVs which were called by two or more tools were mostly short: less than bp. The percentages of CNVs called by 2—8 tools were Each row represents a tool, and a blue field denotes a call of the given CNV. All CNVs from each sample were merged across tools, such that any overlapping calls of either duplications or deletions were combined to one. Blue color denotes that the given CNV was called by the tool.
Column dendrogram shows clustering to the level of 20 clusters to reduce complexity. A positive quality score corresponds to duplications, and negative scores denote deletions. Darker grey coloring indicates that the tool was not run for the sample which contained the CNV. Despite the overlap between longer CNV calls, the majority While All tools performed similarly poorly on WES data Figure 6 with cn. CPU and memory requirements were measured on a core server grade cluster node, for the tools where it was possible to obtain an estimation on the NA sample.
Memory-wise, DELLY and Manta were the other two tools with the lowest needs; the latter also having short computational times, while the former had one of the highest, possibly due to the fact that insertions and deletions were called subsequently and not in parallel. Surprisingly, cn. MOPS also showed low memory requirements on exomes, but the highest in genomes.
Some tools can distribute tasks over nodes, and total RAM usage is reported as total maximum. Computational requirements for creation of GATK model were not measured in this benchmark. Due to the batch caller nature of cn. The results from this study contribute to the growing body of knowledge focused on the evaluation of structural variant characterization tools. Specifically, from the reviewed 50 CNV calling tools, we observed that many of the tools were either not maintained with the last updates applied more than 5 years ago, or not widely used.
We included 11 widely used or newly developed CNV calling tools, which fulfilled our selection criteria, to benchmark their performance on CNV calling. Unlike previous studies focused on either a single sample or limited number of samples [ 9 , 11 , 15 , 16 , 17 ], this study has benchmarked the performance of various SV callers on both WES and WGS data originating from multiple samples and evaluated their overall performance.
It is important to note that because of the probe-dependent and genome-distributed nature of the array technology, not all short CNVs could be captured.
Therefore, the CNV calls classified as false positive FP in this benchmark should be interpreted carefully. Additionally, as CNV detection is a technically challenging task, none of the array-based standards in this study can ultimately be regarded as an absolute truth [ 8 ]. The latter might introduce a bias for these samples in this benchmark, as two of the included tools were used in this evaluation Manta and CNVnator.
Furthermore, the NA sample and its truth CNV set are also popular for testing and optimizing CNV tools, which could potentially explain the possible overfitting we observed e. The number of CNVs called varied more than a fold; consequently, the recall rates for tools calling many CNVs were higher, and no systematic trade-off could be found to improve precision for these tools.
In short, tools calling many CNVs hit the target more often, but high confidence CNVs were not generally showing a higher fraction of recall. CNVs selected for experimental validation with MLPA were selected based on targeted gene panel sequencing, and were, therefore, not biased by CNV calls from tools tested in this analysis. It was, however, striking that tools could be split into two groups: those that were able to recall all six independent CNVs and those that called none.
The GnomAD database [ 14 ] shows how CNV calls can be used clinically, but more research and larger cohort studies are needed for better annotation and inference of causation of CNVs.
Our study shows that more work has to be done on collecting large and well-annotated datasets with CNV detection on several platforms, in order to drive the development of tools with improved precision on CNV calling from NGS data.
The current state of tools for finding CNVs is suited for identifying complex traits in large cohorts, for which we suggest to use the overlap between several tools. The future for NGS-based CNV calling tools is likely to rely on the utilization of a combination of long- and short-read sequencing [ 48 ]. This is particularly true considering the need for CNV annotation that explains causative traits and which will require sequencing of large cohorts with two simultaneous protocols.
Alternatively, future improvements on both price and error rate for long-read sequencing are needed. The ability to leverage additional genetic data, such as RNA-seq, or even static knowledge of genetic sites from associations to epigenetic mechanisms or regulation, may also guide the selection and prioritization process in the near future.
In a clinical setting, production of background panels or databases to filter true common CNVs or common FPs called by each tool can greatly reduce the number of relevant CNVs presented for interpretation data not shown , just like databases like gnomAD SV [ 49 ] can be used to reduce the numbers of common CNVs.
Although beyond the scope of this work, another interesting area that requires further investigation is how different CNV calling tools perform based on various SV sizes and read coverage, both of which are known to affect detection and accuracy of SV calling [ 15 , 16 , 50 ]. In a similar manner, the distributions of SVs in biological regions e.
Lastly, transcriptional regulation of the altered regions requires more investigations, so that the causative effect of CNVs can be elucidated, and potentially be predicted in each case.
Our work has several limitations. First, we benchmarked only a limited set of tools; however, findings are in line with larger studies [ 15 ], relying on single truth sets. Furthermore, the observed potential for overfitting to NA Gold Standard sample by some tools complicated the accurate evaluation of recall and precision with a well-annotated dataset.
Moreover, we used whole genomes and whole exomes for WGS and WES sequencing respectively without any additional filtering on GC content or read mappability. Finally, the main limitation of our work is the lack of well-defined true CNV sets, therefore our analysis using CytoScan HD SNP-array calls vastly underestimates CNV call precision on the in-house data sets, but this caveat should not favor specific tools.
For the best reliability of CNV calling from NGS data, we observed that even if the tools were developed for WES data or allowed it as input, they did not perform well. Furthermore, low precision in all tools leads us to recommend a hypothesis-based approach for finding causative CNVs by NGS in the clinic, and further validation of these candidates by manual inspection, MLPA or array-based approaches.
If multiple samples are available from the same protocol, we suggest using these to filter by commonly called CNVs. MOPS using consensus callers e. All authors discussed the results. All authors have read and agreed to the published version of the manuscript.
Ethical review and approval were waived for this study, since the data sets were fully anonymized. National Center for Biotechnology Information , U. Journal List Cancers Basel v. Cancers Basel. Published online Dec Find articles by Migle Gabrielaite.
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Roukos, Academic Editor. Author information Article notes Copyright and License information Disclaimer. Received Nov 2; Accepted Dec 8. Associated Data Supplementary Materials cancerss Abstract Simple Summary Copy-number variations CNVs have important clinical implications for several diseases and cancers. Abstract Copy-number variations CNVs have important clinical implications for several diseases and cancers.
Open in a separate window. Figure 1. Materials and Methods 2. MOPS cn. Results 3. Figure 2. Table 1 Datasets used in this benchmark study. Figure 3. Figure 4. Figure 5. Figure 6. Figure 7.
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