Whole-genome sequencing analysis of CNV using low-coverage and paired-end strategies is efficient and outperforms array-based CNV analysis

Zhou, Bo; Ho, Steve S.; Zhang, Xianglong; Pattni, Reenal; Haraksingh, Rajini; Urban, Alexander

doi:10.1136/jmedgenet-2018-105272

Cited by 93 publications

(77 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CNA supersedes that obtained by FISH, both in the number of abnormalities and specific genomic resolution, as expected from a nontargeted vs a targeted approach. Zhou et al demonstrated the superiority of LD-WGS over high-density oligonucleotide arrays in detecting germline CNAs, compared to even the least sensitive of their methods 16. These findings were also robust across multiple patients hence validate our results and demonstrate the benefit of using non-targeted genomic approach in studying the genetic constitution of MM.…”

supporting

confidence: 82%

“…Clearly, there is value of LD-WGS to not only fulfill the current clinical needs in MM, but to open doors for further exploratory potential in disease biology and therapeutics. Zhou et al demonstrated the superiority of LD-WGS over high-density oligonucleotide arrays in detecting germline CNAs, compared to even the least sensitive of their methods 16. The clinical utility of LD-WGS has been reported in other contexts; 7,8 however, to our knowledge, there have been no reports validating its use in hematological malignancies including those whose prognostic determination relies heavily on CNA detection such as MM.The revised International Staging System for risk stratification of MM is based on cytogenetic information obtained from FISH data 2.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Low‐depth sequencing for copy number abnormalities in multiple myeloma supersedes fluorescent in situ hybridization in scope and resolution

et al. 2019

View full text Add to dashboard Cite

Multiple myeloma (MM) is an incurable hematological malignancy that relies on cytogenetic determination of copy number abnormalities (CNAs) for prognosis and management.Low-depth whole genome sequencing (LD-WGS) is a cost-effective alternative to targeted genomics for CNA detection, but its value has yet to be explored in MM. DNA from CD138+ cells from MM patients were sequenced using an Illumina NextSeq at <1x depth (ultralow-depth). Subsampling analysis and window size adjustment were performed for determining sensitivity limits and results compared to fluorescent in-Situ hybridization (FISH). CNA calls made down to 5 million (M) reads were comparable to those at 20 M reads at a window size of 100 kb had a sensitivity and specificity of 93%, 92% and an area under the curve of 0.94. All CNAs detected by FISH on the MM samples were also detected by LD-WGS; the latter detected a further 36 focal CNAs not detected by FISH.Cost per sample of LD-WGS was significantly lower for our organization than FISH testing. LD-WGS for MM is significantly more sensitive than targeted technologies such as FISH in CNA detection and resolution, provides a more cost-effective option for clinical purposes and potential for exploring prognostically relevant and drug discovery targets. K E Y W O R D S copy number abnormalities, fluorescent in-situ hybridization, low-depth WGS, multiple myeloma

show abstract

supporting

confidence: 82%

mentioning

confidence: 99%

Low‐depth sequencing for copy number abnormalities in multiple myeloma supersedes fluorescent in situ hybridization in scope and resolution

et al. 2019

View full text Add to dashboard Cite

show abstract

“…For downstream inference, these data are typically summarized by generating binned coverage profiles of the sequencing output, whereby the genome is uniformly partitioned into moderately sized contiguous regions (e.g., 10-50 kilobases (kb)) and count-based coverage is calculated by the enumerating the overlapping sequencing reads. Evidence of copy-number variants (CNVs) and aneuploidy may be detected from these profiles using standard CNV detection methods for coverage data 5 .…”

Section: Introductionmentioning

confidence: 99%

Coverage profile correction of shallow-depth circulating cell-free DNA sequencing via multi-distance learning

Larson¹,

Larson²,

Na³

et al. 2019

Preprint

View full text Add to dashboard Cite

Shallow-depth whole-genome sequencing (WGS) of circulating cell-free DNA (ccfDNA) is a popular approach for non-invasive genomic screening assays, including liquid biopsy for early detection of invasive tumors as well as non-invasive prenatal screening (NIPS) for common fetal trisomies. In contrast to nuclear DNA WGS, ccfDNA WGS exhibits extensive inter-and intrasample coverage variability that is not fully explained by typical sources of variation in WGS, such as GC content. This variability may inflate false positive and false negative screening rates of copy-number alterations and aneuploidy, particularly if these features are present at a relatively low proportion of total sequenced content. Herein, we propose an empirically-driven coverage correction strategy that leverages prior annotation information in a multi-distance learning context to improve within-sample coverage profile correction. Specifically, we train a weighted k-nearest neighbors-style method on non-pregnant female donor ccfDNA WGS samples, and apply it to NIPS samples to evaluate coverage profile variability reduction. We additionally characterize improvement in the discrimination of positive fetal trisomy cases relative to normal controls, and compare our results against a more traditional regression-based approach to profile coverage correction based on GC content and mappability. Under cross-validation, performance measures indicated benefit to combining the two feature sets relative to either in isolation. We also observed substantial improvement in coverage profile variability reduction in leave-out clinical NIPS samples, with variability reduced by 26.5-53.5% relative to the standard regression-based method as quantified by median absolute deviation. Finally, we observed improvement discrimination for screening positive trisomy cases reducing ccfDNA WGS coverage variability while additionally improving NIPS trisomy screening assay performance. Overall, our results indicate that machine learning approaches can substantially improve ccfDNA WGS coverage profile correction and downstream analyses.

show abstract

“…In such scenarios, statistics-based segmentation [9, 14, 20] and CNV calling lead to either in false segmentation or missing the focal alterations. However, low-coverage NGS data is still more efficient than array-based data for CNV analysis [21].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Discovery of Large-scale and Focal Copy Number Alterations in Low-coverage Cancer Genomes

Khalil

Khyriem

Chattopadhyay

et al. 2019

Preprint

View full text Add to dashboard Cite

AbstractMotivationDetection of copy number alterations (CNA) is critical to understand genetic diversity, genome evolution and pathological conditions such as cancer. Cancer genomes are plagued with widespread multi-level structural aberrations of chromosomes that pose challenges to discover CNAs of different length scales with distinct biological origin and function. Although several tools are available to identify CNAs using read depth (RD) of coverage, they fail to distinguish between large-scale and focal alterations due to inaccurate modeling of the RD signal of cancer genomes. These tools are also affected by RD signal variations, pronounced in low-coverage data, which significantly inflate false detection of change points and inaccurate CNA calling.ResultsWe have developed CNAtra to hierarchically discover and classify ‘large-scale’ and ‘focal’ copy number gain/loss from whole-genome sequencing (WGS) data. CNAtra provides an analytical and visualization framework for CNV profiling using single sequencing sample. CNAtra first utilizes multimodal distribution to estimate the copy number (CN) reference from the complex RD profile of the cancer genome. We utilized Savitzy-Golay filter and Modified Varri segmentation to capture the change points. We then developed a CN state-driven merging algorithm to identify the large segments with distinct copy number. Next, focal alterations were identified in each large segment using coverage-based thresholding to mitigate the adverse effects of signal variations. We tested CNAtra calls using experimentally verified segmental aneuploidies and focal alterations which confirmed CNAtra’s ability to detect and distinguish the two alteration phenomena. We used realistic simulated data for benchmarking the performance of CNAtra against other detection tools where we artificially spiked-in CNAs in the original cancer profiles. We found that CNAtra is superior in terms of precision, recall, and f-measure. CNAtra shows the highest sensitivity of 93% and 97% for detecting focal and large-scale alterations respectively. Visual inspection of CNAs showed that CNAtra is the most robust detection tool for low-coverage cancer data.Availability and implementationCNAtra is an open source software implemented in MATLAB, and is available at https://github.com/AISKhalil/CNAtra

show abstract

Whole-genome sequencing analysis of CNV using low-coverage and paired-end strategies is efficient and outperforms array-based CNV analysis

Abstract: CNV analysis using low-coverage WGS is efficient and outperforms the array-based analysis that is currently used for clinical cytogenetics.

Cited by 93 publications

References 40 publications

Low‐depth sequencing for copy number abnormalities in multiple myeloma supersedes fluorescent in situ hybridization in scope and resolution

Low‐depth sequencing for copy number abnormalities in multiple myeloma supersedes fluorescent in situ hybridization in scope and resolution

Coverage profile correction of shallow-depth circulating cell-free DNA sequencing via multi-distance learning

Hierarchical Discovery of Large-scale and Focal Copy Number Alterations in Low-coverage Cancer Genomes

Contact Info

Product

Resources

About