Benchmarking of long-read structural variant callers on a recently released truth set using Oxford Nanopore data

Long-read sequencing (LRS) technologies unveil an unprecedented view into the human genome, with recent studies reporting the detection of up to an astonishing 27k structural variants (SVs) per individual. Along with emerging LRS techniques, new bioinformatics tools designed to identify and classify SVs in long reads are in constant development. To allow validation of (recently developed) SV detection methods, Zook et al. (2020; Nature Biotechnology) published a highly curated SV truth set of the Genome in a Bottle sample NA24385, consisting of 5262 insertions and 4095 deletions covering a total of 2.51 Gbp. Five often-used LRS SV callers (cuteSV, SVIM, sniffles, pbsv, nanovar) are tested against this truth set utilising publicly available ultralong nanopore reads from NA24385, and this at different coverages. All callers are evaluated separately and also an ensemble calling with SURVIVOR is evaluated. CuteSV achieves the best performance both in resource usage and precision and recall scores, while nanovar scores the worst in both categories. Merging of the separate caller sets improves both precision and recall significantly. Additionally, different SURVIVOR merging settings are assessed, with a 1 kbp merging distance resulting in the highest overall precision. Furthermore, all tools are applied in a real-life diagnostic setting, utilizing in-house generated nanopore data of two proband-parent trios. The probands have severe intellectual disability of unknown genetic origin. The forced calling ability of both cuteSV and sniffles is leveraged to detect de novo SVs, yielding ~10 de novo events per trio after additional gene content screening and filtering of the identified SVs. This study provides a concise overview of the performance of five LRS SV callers in comparison with a recently released truth set. It illustrates how different SV callers perform on different SV types and lengths, and highlights the need for additional SV benchmarks, especially covering different variant types and in more complex genomic regions

Benchmarking of long-read structural variant callers using Oxford Nanopore data

As long-read sequencing (LRS) technologies mature, several bioinformatics tools designed to identify structural variants (SVs) have been developed. To allow validation of these tools, Zook et al. published a highly curated SV truth set of Genome in a Bottle sample NA24385, consisting of deletions and insertions. We performed a benchmarking analysis with five LRS SV callers against this set utilising in-house generated Oxford Nanopore reads. SVs are called with cuteSV, SVIM, sniffles, pbsv, and nanovar. The callers are assessed in terms of resource usage, reproducibility, and calling performance. The latter is evaluated with Truvari giving recall and precision statistics on SV detection. We further investigate the influence of read support, sequencing coverage, SV type and length, and integration of call sets with Jasmine. CuteSV achieves overall best performance, while nanovar lags behind in both resource usage and calling statistics. A coverage greater than 20x offers no additional advantage for reliable SV detection, while the recommended read support of one third of the coverage proves to be too stringent. Integration of call sets with Jasmine should include three callers to compete with stand-alone call sets. We propose a minimum coverage of at least 15x for optimal sensitivity and specificity. Read support should be set at one fifth of the coverage to obtain optimised calling performance. CuteSV performs best in both sensitivity and specificity, and resource usage. Further work is however needed to assess results for different SV types and more complex regions

Benchmarking of long-read structural variant callers using in-house generated Oxford Nanopore data

As long-read sequencing (LRS) technologies mature, several bioinformatics tools designed to identify structural variants (SVs) have been developed. To allow validation of these tools, Zook et al.1 published a highly curated SV truth set of Genome in a Bottle sample NA24385, consisting of deletions and insertions. We performed a benchmarking analysis with five LRS SV callers against this set utilising in-house generated Oxford Nanopore reads. SVs are called with cuteSV, SVIM, sniffles, pbsv, and nanovar. The callers are assessed in terms of resource usage, reproducibility, and calling performance. The latter is evaluated with Truvari giving recall and precision statistics on SV detection. We further investigate the influence of read support, sequencing coverage, SV type and length, and integration of call sets with Jasmine. CuteSV achieves overall best performance, while nanovar lags behind in both resource usage and calling statistics. A coverage greater than 20x offers no additional advantage for reliable SV detection, while the recommended read support of one third of the coverage proves to be too stringent. Integration of call sets with Jasmine should include three callers to compete with stand-alone call sets. We propose a minimum coverage of at least 15x for optimal sensitivity and specificity. Read support should be set at one fifth of the coverage to obtain optimised calling performance. CuteSV performs best in both sensitivity and specificity, and resource usage. Further work is however needed to assess results for different SV types and more complex regions

Benchmarking of long-read structural variant callers using Oxford Nanopore data

As long-read sequencing (LRS) technologies mature, several bioinformatics tools designed to identify structural variants (SVs) have been developed. To allow validation of these tools, Zook et al. published a highly curated SV truth set of Genome in a Bottle sample NA24385, consisting of deletions and insertions. We performed a benchmarking analysis with five LRS SV callers against this set utilising in-house generated Oxford Nanopore reads. SVs are called with cuteSV, SVIM, sniffles, pbsv, and nanovar. The callers are assessed in terms of resource usage, reproducibility, and calling performance. The latter is evaluated with Truvari giving recall and precision statistics on SV detection. We further investigate the influence of read support, sequencing coverage, SV type and length, and integration of call sets with Jasmine. CuteSV achieves overall best performance, while nanovar lags behind in both resource usage and calling statistics. A coverage greater than 20x offers no additional advantage for reliable SV detection, while the recommended read support of one third of the coverage proves to be too stringent. Integration of call sets with Jasmine should include three callers to compete with stand-alone call sets. We propose a minimum coverage of at least 15x for optimal sensitivity and specificity. Read support should be set at one fifth of the coverage to obtain optimised calling performance. CuteSV performs best in both sensitivity and specificity, and resource usage. Further work is however needed to assess results for different SV types and more complex regions

Benchmarking of long-read structural variant callers on a recently released truth set using Oxford Nanopore data

Long-read sequencing (LRS) technologies unveil an unprecedented view into the human genome, with recent studies reporting the detection of up to an astonishing 27k structural variants (SVs) per individual. Along with emerging LRS techniques, new bioinformatics tools designed to identify and classify SVs in long reads are in constant development. To allow validation of (recently developed) SV detection methods, Zook et al. (2020; Nature Biotechnology) published a highly curated SV truth set of the Genome in a Bottle sample NA24385, consisting of 5262 insertions and 4095 deletions covering a total of 2.51 Gbp. Five often-used LRS SV callers (cuteSV, SVIM, sniffles, pbsv, nanovar) are tested against this truth set utilising publicly available ultralong nanopore reads from NA24385, and this at different coverages. All callers are evaluated separately and also an ensemble calling with SURVIVOR is evaluated. CuteSV achieves the best performance both in resource usage and precision and recall scores, while nanovar scores the worst in both categories. Merging of the separate caller sets improves both precision and recall significantly. Additionally, different SURVIVOR merging settings are assessed, with a 1 kbp merging distance resulting in the highest overall precision. Furthermore, all tools are applied in a real-life diagnostic setting, utilizing in-house generated nanopore data of two proband-parent trios. The probands have severe intellectual disability of unknown genetic origin. The forced calling ability of both cuteSV and sniffles is leveraged to detect de novo SVs, yielding ~10 de novo events per trio after additional gene content screening and filtering of the identified SVs. This study provides a concise overview of the performance of five LRS SV callers in comparison with a recently released truth set. It illustrates how different SV callers perform on different SV types and lengths, and highlights the need for additional SV benchmarks, especially covering different variant types and in more complex genomic regions

An undifferentiated sarcoma of bone with a round to epithelioid cell phenotype harboring a novel EWSR1-SSX2 fusion identified by RNA-based next-generation sequencing

Due to the increased application of RNA-based next-generation sequencing techniques on bone and soft tissue round cell sarcomas new fusions are frequently found, thereby expanding the molecular landscape of these tumors. In this report, we describe and discuss the finding of an undifferentiated sarcoma of the bone with a round to epithelioid cell phenotype harboring a novel EWSR1-SSX2 fusion. Treatment of this new bone tumor entity according to the Euro Ewing 2012 protocol led to complete pathologic response

Undifferentiated sarcoma of bone with a round to epithelioid cell phenotype harboring a novel EWSR1-SSX2 fusion identified by RNA-based next-generation sequencing

Due to the increased application of RNA-based next-generation sequencing techniques on bone and soft tissue round cell sarcomas new fusions are frequently found, thereby expanding the molecular landscape of these tumors. In this report, we describe and discuss the finding of an undifferentiated sarcoma of the bone with a round to epithelioid cell phenotype harboring a novel EWSR1-SSX2 fusion. Treatment of this new bone tumor entity according to the Euro Ewing 2012 protocol led to complete pathologic response

Application of an Ultrasensitive NGS-Based Blood Test for the Diagnosis of Early-Stage Lung Cancer: Sensitivity, a Hurdle Still Difficult to Overcome.

Diagnosis of lung cancer requires histological examination of a tissue sample, which in turn requires an invasive procedure that cannot always be obtained. Circulating tumor DNA can be reliably detected in blood samples of advanced-stage lung cancer patients and might also be a minimally invasive alternative for early-stage lung cancer detection. We wanted to explore the potential of targeted deep sequencing as a test for the diagnosis of early-stage lung cancer in combination with imaging. Mutation detection on cell-free DNA from pretreatment plasma samples of 51 patients with operable non-small cell lung cancer was performed and results were compared with 12 control patients undergoing surgery for a non-malignant lung lesion. By using a variant allele frequency threshold of 1%, somatic variants were detected in 23.5% of patients with a median variant allele fraction of 3.65%. By using this threshold, we could almost perfectly discriminate early-stage lung cancer patients from controls. Our study results are discussed in the light of those from other studies. Notwithstanding the potential of today's techniques for the use of liquid biopsy-based cell-free DNA analysis, sensitivity of this application for early-stage lung cancer detection is currently limited by a biological background of somatic variants with low variant allele fraction

Shallow whole-genome sequencing : a useful, easy to apply molecular technique for CNA detection on FFPE tumor tissue-a glioma-driven study

Copy number alterations (CNAs) have increasingly become part of the diagnostic algorithm of glial tumors. Alterations such as homozygous deletion of CDKN2A/B, 7 +/ 10 - chromosome copy number changes or EGFR amplification are predictive of a poor prognosis. The codeletion of chromosome arms 1p and 19q, typically associated with oligodendroglioma, implies a more favorable prognosis. Detection of this codeletion by the current diagnostic standard, being fluorescence in situ hybridization (FISH), is sometimes however subject to technical and interpretation problems. In this study, we evaluated CNA detection by shallow whole-genome sequencing (sWGS) as an inexpensive, complementary molecular technique. A cohort of 36 glioma tissue samples, enriched with "difficult" and "ambiguous" cases, was analyzed by sWGS. sWGS results were compared with FISH assays of chromosomes 1p and 19q. In addition, CNAs relevant to glioblastoma diagnosis were explored. In 4/36 samples, EGFR (7p11.2) amplifications and homozygous loss of CDKN2A/B were identified by sWGS. Six out of 8 IDH-wild-type glioblastomas demonstrated a prognostic chromosome 7/chromosome 10 signature. In 11/36 samples, local interstitial and terminal 1p/19q alterations were detected by sWGS, implying that FISH's targeted nature might promote false arm-level extrapolations. In this cohort, differences in overall survival between patients with and without codeletion were better pronounced by the sequencing-based distinction (likelihood ratio of 7.48) in comparison to FISH groupings (likelihood ratio of 0.97 at diagnosis and 1.79 +/- 0.62 at reobservation), suggesting sWGS is more accurate than FISH. We recognized adverse effects of tissue block age on FISH signals. In addition, we show how sWGS reveals relevant aberrations beyond the 1p/19q state, such as EGFR amplification, combined gain of chromosome 7 and loss of chromosome 10, and homozygous loss of CDKN2A/B. The findings presented by this study might stimulate implementation of sWGS as a complementary, easy to apply technique for copy number detection