AMYCNE: Confident copy number assessment using whole genome sequencing data

Eisfeldt, Jesper; Nilsson, Daniel; Andersson‐Assarsson, Johanna C.; Wedell, Anna

doi:10.1371/journal.pone.0189710

Cited by 23 publications

(39 citation statements)

References 21 publications

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“…This is in contrast with data from Falchi and colleagues and other studies using qPCR that showed an approximate normal distribution of AMY1 copy number [10,13,22,25]. Furthermore, the copy numbers of AMY1 and AMY2A were shown to be significantly correlated in European populations by two studies using accurate CNV measurement techniques: the first used PRTs and microsatellite measurements [46], while the second used a novel method, 'automatic modeling functionality for copy number estimation', or 'AMYCNE', which uses whole genome sequencing data and appears to exhibit a similar level of accuracy to ddPCR [57]. The haplotype arrangements of AMY1 and AMY2A CNV are also correlated.…”

Section: Association Between Cnv Within Amy1 and Bmimentioning

confidence: 99%

Human amylase gene copy number variation as a determinant of metabolic state

Elder

Ramsden

Burnett

et al. 2018

Expert Review of Endocrinology & Metabolism

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Humans have multiple genes encoding amylase that are broadly divided into salivary (AMY1) and pancreatic (AMY2) genes. They exhibit some of the greatest copy numbers of any human gene, an expansion possibly driven by increased dietary starch intake. Within the population, amylase gene copy number is highly variable and there is evidence of an inverse association between AMY1 copy number and BMI. Areas covered: We examine the evidence for the link between AMY1 and BMI, its potential mechanisms, and the metabolic effects of salivary and pancreatic amylase, both in the gastrointestinal tract and the blood. Expert commentary: Salivary amylase may influence postprandial 'cephalic phase' insulin release, which improves glucose tolerance, while serum amylase may have insulin-sensitizing properties. This could explain the favorable metabolic status associated with higher AMY1 copy number. The association with BMI is harder to explain and is potentially mediated by increased flux of undigested starch into the ileum, with resultant effects on short-chain fatty acids (SCFAs), changes in gut microbiota and effects on appetite and energy expenditure in those with low copy number. Future research on the role of amylase as a determinant of metabolic health and BMI may lead to novel therapies to target obesity.

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Section: Association Between Cnv Within Amy1 and Bmimentioning

confidence: 99%

Human amylase gene copy number variation as a determinant of metabolic state

Elder

Ramsden

Burnett

et al. 2018

Expert Review of Endocrinology & Metabolism

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“…S6a). Using this simulated data, we benchmarked the performance of CNAtra against five RD-based single-sample CNV detection tools which include ReadDepth [14], CNVnator [9], FREEC [10], CLImAT [16] and AMYCNE [11]. We analyzed the performance of these tools after optimizing their parameters for low-coverage data (see Extended Methods under Supplementary Information).…”

Section: Resultsmentioning

confidence: 99%

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

Khalil

Khyriem

Chattopadhyay

et al. 2019

Preprint

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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

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“…S8a). Using this simulated data, we benchmarked the performance of CNAtra against five RD-based single-sample copy number detection tools which include ReadDepth [20], CNVnator [15], FREEC [16], CLImAT [22] and AMYCNE [17]. We analyzed the performance of these tools after optimizing their parameters for low-coverage data (see Extended Methods under Supplementary Information).…”

Section: Cnatra Is a Superior Tool For Detecting Large-scale And Focamentioning

confidence: 99%

“…Several tools have been presented over the years that utilize a single sequencing sample for copy number detection [15][16][17][18][19][20][21][22]. They are built on different assumptions of the underlying probabilistic distribution and percentage of chromosomal abnormalities.…”

mentioning

confidence: 99%

Hierarchical discovery of large-scale and focal copy number alterations in low-coverage cancer genomes

et al. 2020

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Background: Detection of DNA copy number alterations (CNAs) 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, and distinct biological origins and functions. Although several computational tools are available to identify CNAs using read depth (RD) signal, they fail to distinguish between large-scale and focal alterations due to inaccurate modeling of the RD signal of cancer genomes. Additionally, RD signal is affected by overdispersion-driven biases at low coverage, which significantly inflate false detection of CNA regions. Results: We have developed CNAtra framework to hierarchically discover and classify 'large-scale' and 'focal' copy number gain/loss from a single whole-genome sequencing (WGS) sample. CNAtra first utilizes a multimodal-based distribution to estimate the copy number (CN) reference from the complex RD profile of the cancer genome. We implemented Savitzky-Golay smoothing filter and Modified Varri segmentation to capture the change points of the RD signal. We then developed a CN state-driven merging algorithm to identify the large segments with distinct copy numbers. Next, we identified focal alterations in each large segment using coveragebased thresholding to mitigate the adverse effects of signal variations. Using cancer cell lines and patient datasets, we confirmed CNAtra's ability to detect and distinguish the segmental aneuploidies and focal alterations. We used realistic simulated data for benchmarking the performance of CNAtra against other singlesample detection tools, where we artificially introduced 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 large-scale and focal alterations respectively. Visual inspection of CNAs revealed that CNAtra is the most robust detection tool for low-coverage cancer data.

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AMYCNE: Confident copy number assessment using whole genome sequencing data

Cited by 23 publications

References 21 publications

Human amylase gene copy number variation as a determinant of metabolic state

Human amylase gene copy number variation as a determinant of metabolic state

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

Hierarchical discovery of large-scale and focal copy number alterations in low-coverage cancer genomes

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