Atlas-CNV: a validated approach to call single-exon CNVs in the eMERGESeq gene panel

Chiang, Theodore; Liu, Xiuping; Wu, Tsung Jung; Hu, Jianhong; Sedlazeck, Fritz J.; White, Simon; Schaid, Daniel J.; Andrade, Mariza de; Jarvik, Gail P.; Crosslin, David R.; Stanaway, Ian B.; Carrell, David; Connolly, John J.; Hákonarson, Hákon; Groopman, Emily; Gharavi, Ali G.; Fedotov, Alexander; Bi, Weimin; Leduc, Magalie S.; Murdock, David R.; Jiang, Yunyun; Meng, Linyan; Eng, Christine M.; Wen, Shu; Yang, Yaping; Boerwinkle, Eric; Salerno, William; Gibbs, Richard A.

doi:10.1038/s41436-019-0475-4

Cited by 22 publications

(13 citation statements)

References 24 publications

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“…Also, the availability of nucleotide level coverage information has facilitated prediction of exact breakpoints, especially for partial exonic CNVs. Some tools [ 22 , 25 ] claim to detect CNVs at single exonic level, but it is still challenging to detect partial exonic and mosaic CNVs. Our pipeline has successfully managed to detect such CNVs in routine diagnostics, in addition to exonic CNVs.…”

Section: Discussionmentioning

confidence: 99%

“…In diagnostics labs where sequencing of targeted gene panels is common practice, the main goal is often to detect small (intragenic) disease-associated CNVs in partial, single or a few small exons [ 20 ]. There are a few available tools that claim to be suitable for data from targeted gene panels [ 21 – 25 ], but it is always challenging to detect smaller CNVs, especially partial or single exons or mosaic CNVs, with high sensitivity and specificity consistent with diagnostic standards.…”

Section: Introductionmentioning

confidence: 99%

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Detecting copy number variation in next generation sequencing data from diagnostic gene panels

et al. 2021

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Background Detection of copy number variation (CNV) in genes associated with disease is important in genetic diagnostics, and next generation sequencing (NGS) technology provides data that can be used for CNV detection. However, CNV detection based on NGS data is in general not often used in diagnostic labs as the data analysis is challenging, especially with data from targeted gene panels. Wet lab methods like MLPA (MRC Holland) are widely used, but are expensive, time consuming and have gene-specific limitations. Our aim has been to develop a bioinformatic tool for CNV detection from NGS data in medical genetic diagnostic samples. Results Our computational pipeline for detection of CNVs in NGS data from targeted gene panels utilizes coverage depth of the captured regions and calculates a copy number ratio score for each region. This is computed by comparing the mean coverage of the sample with the mean coverage of the same region in other samples, defined as a pool. The pipeline selects pools for comparison dynamically from previously sequenced samples, using the pool with an average coverage depth that is nearest to the one of the samples. A sliding window-based approach is used to analyze each region, where length of sliding window and sliding distance can be chosen dynamically to increase or decrease the resolution. This helps in detecting CNVs in small or partial exons. With this pipeline we have correctly identified the CNVs in 36 positive control samples, with sensitivity of 100% and specificity of 91%. We have detected whole gene level deletion/duplication, single/multi exonic level deletion/duplication, partial exonic deletion and mosaic deletion. Since its implementation in mid-2018 it has proven its diagnostic value with more than 45 CNV findings in routine tests. Conclusions With this pipeline as part of our diagnostic practices it is now possible to detect partial, single or multi-exonic, and intragenic CNVs in all genes in our target panel. This has helped our diagnostic lab to expand the portfolio of genes where we offer CNV detection, which previously was limited by the availability of MLPA kits.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting copy number variation in next generation sequencing data from diagnostic gene panels

et al. 2021

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“…7 Ten participants had a site-specific variant and an additional consensus returnable variant. Of these ten site-specific variants returned, three were relevant to the indication for testing and seven were non-indication-based findings; 8 14 SERPINA1 and 5 CFTR variants were reported as carrier status.…”

Section: Aggregate Findings and Return Of Resultsmentioning

confidence: 99%

“…6 Like VisCap, Atlas-CNV infers the presence of CNVs from normalized coverage differences to other samples in the same sequencing batch and refines these predictions with a pair of quality control metrics. 8…”

Section: Copy Number Variant (Cnv) Callingmentioning

confidence: 99%

Harmonizing Clinical Sequencing and Interpretation for the eMERGE III Network

Zouk¹,

Venner²,

Lennon³

et al. 2019

The American Journal of Human Genetics

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103

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The eMERGE Consortium* , * The advancement of precision medicine requires new methods to coordinate and deliver genetic data from heterogeneous sources to physicians and patients. The eMERGE III Network enrolled >25,000 participants from biobank and prospective cohorts of predominantly healthy individuals for clinical genetic testing to determine clinically actionable findings. The network developed protocols linking together the 11 participant collection sites and 2 clinical genetic testing laboratories. DNA capture panels targeting 109 genes were used for testing of DNA and sample collection, data generation, interpretation, reporting, delivery, and storage were each harmonized. A compliant and secure network enabled ongoing review and reconciliation of clinical interpretations, while maintaining communication and data sharing between clinicians and investigators. A total of 202 individuals had positive diagnostic findings relevant to the indication for testing and 1,294 had additional/secondary findings of medical significance deemed to be returnable, establishing data return rates for other testing endeavors. This study accomplished integration of structured genomic results into multiple electronic health record (EHR) systems, setting the stage for clinical decision support to enable genomic medicine. Further, the established processes enable different sequencing sites to harmonize technical and interpretive aspects of sequencing tests, a critical achievement toward global standardization of genomic testing. The eMERGE protocols and tools are available for widespread dissemination.

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“…Single-exon homozygous/hemizygous deletion (HD) detection methods, which compare normalized coverage values among samples produced with the same kits already exist (e.g. Atlas-CNV, CoNVaDING, DECoN and HMZDelFinder) (Chiang et al, 2019;Fowler et al, 2016;Gambin et al, 2017;Johansson et al, 2016). While Atlas-CNV and CoNVaDING, as suggested by the authors, can only be used with high coverage sequencing data (e.g.…”

Section: Introductionmentioning

confidence: 99%

VarGenius-HZD allows accurate detection of rare homozygous or hemizygous deletions in targeted sequencing leveraging breadth of coverage

Musacchia

Karali²,

Torella

et al. 2021

Preprint

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Motivation: Homozygous deletions (HDs) may be the cause of rare diseases and cancer and their discovery in targeted sequencing is a challenging task. Different tools have been developed to disentangle HD discovery but a sensitive caller is still lacking. Results: We present VarGenius-HZD, a sensitive and scalable algorithm that leverages breadth-of-coverage for the detection of rare homozygous and hemizygous single-exon deletions (HDs). To assess its effectiveness we detected both real and synthetic rare HDs in fifty exomes from the 1000 Genomes Project obtaining higher sensitivity in comparison with state-of-the-art algorithms which missed at least one event each. We then applied our tool on targeted sequencing data from patients with Inherited Retinal Dystrophies and solved five cases that still lacked a genetic diagnosis. Availability and implementation: We provide VarGenius-HZD either stand-alone or integrated within our recently developed software enabling the automated selection of samples using the internal database. Hence, it could be extremely useful for both diagnostic and research purposes. Our tool is available under GNU General Public License, version 3 at: https://github.com/frankMusacchia/VarGenius-HZD Contact: francesco.musacchia@iit.it Supplementary information is available online.

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Atlas-CNV: a validated approach to call single-exon CNVs in the eMERGESeq gene panel

Cited by 22 publications

References 24 publications

Detecting copy number variation in next generation sequencing data from diagnostic gene panels

Detecting copy number variation in next generation sequencing data from diagnostic gene panels

Harmonizing Clinical Sequencing and Interpretation for the eMERGE III Network

VarGenius-HZD allows accurate detection of rare homozygous or hemizygous deletions in targeted sequencing leveraging breadth of coverage

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