Identification Of Copy Number Variations Research Articles

Abstract P6-08-13: Personalized and cost-effective detection of copy number variants by molecular-barcode next-generation sequencing and long-read nanopore sequencing

Abstract Background: Germline copy number variants (CNV) of hereditary breast and ovarian cancer genes are a known class of clinically significant mutations. However, failure to detect CNV is a known limitation of Sanger sequencing and conventional amplicon next-generation sequencing (NGS). Multiplex ligation-dependent probe amplification (MLPA), while robust for CNV detection, is labor-intensive, costly to scale up and has limited breakpoint resolution at exon level only. We evaluated the clinical utility of molecular-barcode NGS and long-read nanopore sequencing in our genetic testing algorithm, specifically on CNV detection and breakpoint characterization of probands and family members. Methods: Both molecular-barcode NGS (BRCA1, BRCA2, TP53, PTEN, PALB2 and CDH1) and MLPA (BRCA1 and BRCA2) were performed in parallel on 200 high-risk breast and/or ovarian cancer probands and 11 CNV positive controls. CNV calling from MLPA and NGS data was performed by Coffalyser.Net and a novel in-house bioinformatics algorithm BAMClipper-SE, respectively. Nanopore amplicon or whole-genome sequencing was attempted to map the exact breakpoint of CNV controls and any newly identified CNV positive probands (up to May 2019). Personalized breakpoint PCR test for specific CNVs was designed and validated on probands and subsequently applied to corresponding family members for carrier testing. Results: Molecular-barcode NGS was 100% sensitive in detecting all BRCA1/2 CNVs from 11 positive controls. A total of 4 BRCA1/2 CNVs were detected by NGS from the 200 probands and matched the corresponding MLPA results. In addition, a PALB2 CNV (exons 4-6 deletion) was also detected by NGS during the parallel comparison. Up to May 2019, a total of 22 probands with detectable CNV in BRCA1, BRCA2, PALB2 or CHEK2 were accrued. Exact CNV breakpoint at nucleotide resolution was identified from 20 probands by nanopore and Sanger sequencing (BRCA1 n=12, BRCA2 n=5, PALB2 n=2 and CHEK2 n=1). High-resolution breakpoint mapping was applicable to inform variant pathogenicity and to enable recurrent CNV identification. Personalized breakpoint PCR designed for the 20 probands were applied to 42 family members, of whom 16 were CNV carriers and 26 were non-carriers. Breakpoint PCR results and any available MLPA results of these family members were 100% concordant. Conclusion: Molecular-barcode NGS allows a streamlined workflow to detect both sequence mutations and CNVs. It proves to be a reliable and cost-effective replacement of BRCA1/2 MLPA in genetic testing algorithm of new probands. Simultaneously the NGS workflow expands CNV detection coverage to additional genes (e.g. PALB2) without costly scale-up of MLPA gene panels. Nanopore sequencing is a practical alternative to MLPA in orthogonal validation of newly detected CNV. The high-resolution breakpoint mapping transforms CNV carrier testing to a personalized, simple and cost-effective breakpoint PCR. Citation Format: Ava Kwong, Chun H Au, Dona N Ho, Elaine YL Wong, Yvonne Chung, Fian BF Law, Cecilia YS Ho, Jiawei Chen, Isabella W Cheuk, Vivian Y Shin, Tsun L Chan, Hextan YS Ngan, Edmond SK Ma. Personalized and cost-effective detection of copy number variants by molecular-barcode next-generation sequencing and long-read nanopore sequencing [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P6-08-13.

"It's probably nothing, but…" Couples' experiences of pregnancy following an uncertain prenatal genetic result.

A common concern regarding the introduction of chromosomal microarray in prenatal testing is the concomitant identification of an uncertain copy number variant (CNV) where significance and clinical implication for the unborn child can be difficult or impossible to predict. Following the identification of an uncertain CNV, prospective parents may decide to continue the pregnancy. The aim of this study was to explore how prospective parents manage uncertainty and experience pregnancy in light of an uncertain CNV result. Qualitative interviews with 16 women and 10 partners who had received a prenatally diagnosed, uncertain CNV. Participants were recruited from the Aarhus University Hospital, Denmark and most were interviewed in their homes 1-14weeks after birth. Data were analyzed using thematic analysis. Following the CNV diagnosis, some couples focused on the severe syndromes ruled out by the result, whereas others were more concerned with the new potential risks, for example, learning disabilities. Most couples did not remember the actual diagnosis, but all described a number of attention points generated by the CNV result. During pregnancy, the couples used various strategies to limit worry and enjoy their pregnancy, such as limiting information seeking, reducing talk of the CNV, and deferring thoughts of potential consequences. Furthermore, ultrasound was considered a valuable resource for reducing worry as it provided reassurance about the development of the baby. Inherited CNVs caused relief on one hand, but also feelings of responsibility for the child's potential challenges. After birth, worry decreased considerably, but all couples paid some extra attention to the child's development, while also being alert to the risk of wrongfully interpreting the child's development in terms of the CNV. Eleven couples expressed satisfaction with knowing about the child's CNV, whereas five couples would rather not have known. The results indicate that health professionals should be mindful of terminology, remember to point out what has been ruled out by the CNV result, and discuss potential coping strategies with the couple. Furthermore, these couples may have a higher need for ultrasound during pregnancy to help reduce worry. More research is needed on the families' long-term coping.

Genome-wide patterns of copy number variation in case-control mastitis study of Polish HF cows using high-density Illumina BovineSNP50 v3 BeadChip array

Background : The mastitis is one of the most important diseases affecting losses of milk yield and functional longevity resulting in economic losses in dairy farms. The cow's inflammation of the mammary gland is a consequence of complex processes in the innate immune system, activation of several cells and accompanying factors that lead to the elimination of microorganism invaders. The study aimed to investigate genetic background by the genome-wide association study (GWAS) for identified copy number variations (CNVs) in one of the most important economically disorders – the mastitis in dairy cattle. Methods : We investigated 682 Polish-HF cows previously genotyped and analyzed using the following steps: i) identification of CNVs across all the cows based on ARS-UCD1.2 genome map, ii) to perform the GWAS with the utilization of logistic regression models, iii) functional analysis for identified CNVs to indicate putative candidate genes. The cow's genotypes were deposited in the Polish cattle SNP genotypes database (cSNP-db). The phenotype database information covered the veterinary history of mastitis was collected from four experimental farms belonging to the National Institute of Animal Production, Balice, Poland. Results: Study identified 326 CNVs of which only 28 CNVs were significant with p-value <0.05. The most significant CNVs were identified for BTA17, BTA22, and BTA25, respectively. However, the most interesting from the functional point of view of previously reported CNVs overlapped with genes associated with mastitis were loaded on BTA03 ( MSH4 ), BTA18 ( FOXC2, FOXL1, MTHFSD ) and BTA27 ( TLR3 ), respectively. Conclusions: The study clearly concluded that the identification of candidate genes associated with mastitis using of CNVs is feasible. Therefore, one can apply this GWAS approach between the common CNVs and mastitis as a case-control experiment. Our study identified few genes like MSH4, MTHFSD, FOXC2, FOXL1, and TLR3 which were overlapped with tree CNVs to functionally assess and might be considered as the candidate genes for mastitis in Polish HF cattle population. Moreover, obtained results supported by the different genome and transcriptome analysis could lead to a better understood of the genetic background of mastitis.

CaSpER identifies and visualizes CNV events by integrative analysis of single-cell or bulk RNA-sequencing data

RNA sequencing experiments generate large amounts of information about expression levels of genes. Although they are mainly used for quantifying expression levels, they contain much more biologically important information such as copy number variants (CNVs). Here, we present CaSpER, a signal processing approach for identification, visualization, and integrative analysis of focal and large-scale CNV events in multiscale resolution using either bulk or single-cell RNA sequencing data. CaSpER integrates the multiscale smoothing of expression signal and allelic shift signals for CNV calling. The allelic shift signal measures the loss-of-heterozygosity (LOH) which is valuable for CNV identification. CaSpER employs an efficient methodology for the generation of a genome-wide B-allele frequency (BAF) signal profile from the reads and utilizes it for correction of CNVs calls. CaSpER increases the utility of RNA-sequencing datasets and complements other tools for complete characterization and visualization of the genomic and transcriptomic landscape of single cell and bulk RNA sequencing data.

Characterization of two novel Alu element-mediated α-globin gene cluster deletions causing α0-thalassemia by targeted next-generation sequencing.

α-thalassemia is an inherited blood disorder commonly caused by deletions or point mutations involving one or both α-globin genes. Recent studies shed new light on the critical role of upstream enhancers multi-species conserved sequences (MCSs) in the ordered regulation of α-globin gene expression. Herein, we reported two unrelated probands with deletions in α-globin genes and MCSs, respectively. The proband from Family A is a compound heterozygote carrying a known α+ mutation (-α3.7) and a novel 60.2kb deletion causing the absence of both α-globin genes. The proband from Family B, on the other hand, is a compound heterozygote with a known α0 mutation (--SEA) and a novel deletion involving only upstream regulatory elements MCS-R1, R2 and R3, while the α-globin genes remain intact. Notably, both these two patients suffered varied extent of anemia, indicating that the loss of enhancer elements could equally lead to reduced synthesis of α-globin. Upon these observations, we then confirmed the exact breakpoints of these two novel deletions using a targeted next-generation sequencing (NGS) previously established by our group, which may enable further elucidation of the rearrangement mechanisms on these deletions and functional dissection of MCSs. Taken together, our study reports a reliable NGS-based molecular screening approach for accurate identification of copy number variations (CNVs) in the α-globin cluster and the genetic diagnosis of these two probands may help to extend the spectrum of α-thalassemia mutations in Chinese population.

DpGMM: A Dirichlet Process Gaussian Mixture Model for Copy Number Variation Detection in Low-Coverage Whole-Genome Sequencing Data

Comprehensive identification and cataloging of copy number variation (CNVs) are essential to providing a complete view of human genetic variation and to finding diseased genes. Due to the large-scale sequencing and cost control whole-genome sequencing (WGS) data, low-coverage data is favorably disposed towards CNV identification. However, such low-coverage data is sensitive to noise and sequencing biases, which results in low resolution of CNV detection in past experimental designs for WGS datasets. In this paper, we present a control-free Dirichlet process Gaussian mixture model (dpGMM) based approach, to analyze the read depth (RD) of low-coverage WGS datasets for CNV discovery. First, noise and biases of the RD signals are corrected through the preprocessing step of dpGMM. Then we assume that RD signals across genomic regions follow a Gaussian mixture model (GMM) in which each Gaussian distribution is followed by a copy number state. Without requiring the number of Gaussian distributions, dpGMM builds a Dirichlet process (DP) GMM for RD signals and further uses a DP prior to infer the number of Gaussian models. After that, we apply dpGMM to simulation datasets with different coverages and individual datasets, and compare ours to three widely used RD-based pipelines, CNVnator, GROM-RD, and BIC-seq2. Simulation results demonstrate that our approach, dpGMM, has a high F1 score in both low- and high- coverage sequences. Also, the number of overlaps between CNVs detected in real data by ours and the standard benchmark is twice as much as that detected by other tools such as CNVnator and GROM-RD.

Whole Exome and Transcriptome Sequencing in 1042 Cases Reveals Distinct Clinically Relevant Genetic Subgroups of Follicular Lymphoma

Koff: Burroughs Wellcome Fund: Research Funding; V Foundation: Research Funding; Lymphoma Research Foundation: Research Funding; American Association for Cancer Research: Research Funding. Leppä:Roche: Honoraria, Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bayer: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen-Cilag: Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees. Gang:ROCHE: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees. Hsi:Abbvie: Research Funding; Eli Lilly: Research Funding; Cleveland Clinic&Abbvie Biotherapeutics Inc: Patents & Royalties: US8,603,477 B2; Jazz: Consultancy. Flowers:AbbVie: Consultancy, Research Funding; Denovo Biopharma: Consultancy; BeiGene: Consultancy, Research Funding; Burroughs Wellcome Fund: Research Funding; Eastern Cooperative Oncology Group: Research Funding; National Cancer Institute: Research Funding; V Foundation: Research Funding; Optimum Rx: Consultancy; Millenium/Takeda: Research Funding; TG Therapeutics: Research Funding; Gilead: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Karyopharm: Consultancy; AstraZeneca: Consultancy; Pharmacyclics/Janssen: Consultancy, Research Funding; Spectrum: Consultancy; Bayer: Consultancy; Acerta: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding. Neff:Enzyvant: Consultancy; EUSA Pharma: Honoraria, Membership on an entity's Board of Directors or advisory committees. Fedoriw:Alexion Pharmaceuticals: Other: Consultant and Speaker. Reddy:Genentech: Research Funding; BMS: Consultancy, Research Funding; Celgene: Consultancy; KITE Pharma: Consultancy; Abbvie: Consultancy. Mason:Sysmex: Honoraria. Behdad:Loxo-Bayer: Membership on an entity's Board of Directors or advisory committees; Thermo Fisher: Membership on an entity's Board of Directors or advisory committees; Pfizer: Other: Speaker. Burton:Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel; Celgene: Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees. Dave:Data Driven Bioscience: Equity Ownership.

Partial LPL deletions: rare copy-number variants contributing towards severe hypertriglyceridemia

Severe hypertriglyceridemia (HTG) is a relatively common form of dyslipidemia with a complex pathophysiology and serious health complications. HTG can develop in the presence of rare genetic factors disrupting genes involved in the triglyceride (TG) metabolic pathway, including large-scale copy-number variants (CNVs). Improvements in next-generation sequencing technologies and bioinformatic analyses have better allowed assessment of CNVs as possible causes of or contributors to severe HTG. We screened targeted sequencing data of 632 patients with severe HTG and identified partial deletions of the LPL gene, encoding the central enzyme involved in the metabolism of TG-rich lipoproteins, in four individuals (0.63%). We confirmed the genomic breakpoints in each patient with Sanger sequencing. Three patients carried an identical heterozygous deletion spanning the 5' untranslated region (UTR) to LPL exon 2, and one patient carried a heterozygous deletion spanning the 5'UTR to LPL exon 1. All four heterozygous CNV carriers were determined to have multifactorial severe HTG. The predicted null nature of our identified LPL deletions may contribute to relatively higher TG levels and a more severe clinical phenotype than other forms of genetic variation associated with the disease, particularly in the polygenic state. The identification of novel CNVs in patients with severe HTG suggests that methods for CNV detection should be included in the diagnostic workup and molecular genetic evaluation of patients with high TG levels.

Identification of Copy Number Variation in Domestic Chicken Using Whole-Genome Sequencing Reveals Evidence of Selection in the Genome.

Simple SummaryChickens have been bred for meat and egg production as a source of animal protein. With the increase of productivity as the main purpose of domestication, factors such as metabolism and immunity were boosted, which are detectable signs of selection on the genome. This study focused on copy number variation (CNV) to find evidence of domestication on the genome. CNV was detected from whole-genome sequencing of 65 chickens including Red Jungle Fowl, broilers, and layers. After that, CNV region, the overlapping region of CNV between individuals, was made to identify which genomic regions showed copy number differentiation. The 663 domesticated-specific CNV regions were associated with various functions such as metabolism and organ development. Also, by performing population differentiation analyses such as clustering analysis and ANOVA test, we found that there are a lot of genomic regions with different copy number patterns between broilers and layers. This result indicates that different genetic variations can be found, depending on the purpose of artificial selection and provides considerations for future animal breeding.Copy number variation (CNV) has great significance both functionally and evolutionally. Various CNV studies are in progress to find the cause of human disease and to understand the population structure of livestock. Recent advances in next-generation sequencing (NGS) technology have made CNV detection more reliable and accurate at whole-genome level. However, there is a lack of CNV studies on chickens using NGS. Therefore, we obtained whole-genome sequencing data of 65 chickens including Red Jungle Fowl, Cornish (broiler), Rhode Island Red (hybrid), and White Leghorn (layer) from the public databases for CNV region (CNVR) detection. Using CNVnator, a read-depth based software, a total of 663 domesticated-specific CNVRs were identified across autosomes. Gene ontology analysis of genes annotated in CNVRs showed that mainly enriched terms involved in organ development, metabolism, and immune regulation. Population analysis revealed that CN and RIR are closer to each other than WL, and many genes (LOC772271, OR52R1, RD3, ADH6, TLR2B, PRSS2, TPK1, POPDC3, etc.) with different copy numbers between breeds found. In conclusion, this study has helped to understand the genetic characteristics of domestic chickens at CNV level, which may provide useful information for the development of breeding systems in chickens.

Rapid screening of copy number variations in STRC by droplet digital PCR in patients with mild-to-moderate hearing loss

Copy number variations (CNVs) are commonly reported in STRC, the causal gene for DFNB16. Various techniques are used clinically for CNV detection, and droplet digital PCR (ddPCR) provides highly precise absolute quantification of DNA copy number. We aimed to validate the feasibility and efficiency of ddPCR in combination with long-range PCR (LR-PCR) in identifying CNVs and mutations in STRC. Additionally, we determined the frequency of CNVs and mutations in STRC in Japanese patients with mild-to-moderate hearing loss. We evaluated 84 unrelated Japanese patients with mild-to-moderate bilateral idiopathic or autosomal recessive nonsyndromic sensorineural hearing loss. The ratio of STRC copy number to the copy number of the internal control RPP30 ranged from 0.949 to 1.009 (0.989 ± 0.017) in 77 patients; it ranged from 0.484 to 0.538 (0.509 ± 0.024) in five patients and was 0.000 in two patients, indicating heterozygous and homozygous deletions, respectively. The copy number deletion prevalence rates were 7.7% and 0.9% in the patients and healthy controls, respectively. In combination with LR-PCR, ddPCR revealed that at least three patients (3.6%) had STRC-related hearing loss. Detecting STRC CNVs by ddPCR was rapid, precise, and cost-effective and facilitated the identification of STRC CNVs.

Copy number variant and runs of homozygosity detection by microarrays enabled more precise molecular diagnoses in 11,020 clinical exome cases

BackgroundExome sequencing (ES) has been successfully applied in clinical detection of single nucleotide variants (SNVs) and small indels. However, identification of copy number variants (CNVs) using ES data remains challenging. The purpose of this study is to understand the contribution of CNVs and copy neutral runs of homozygosity (ROH) in molecular diagnosis of patients referred for ES.MethodsIn a cohort of 11,020 consecutive ES patients, an Illumina SNP array analysis interrogating mostly coding SNPs was performed as a quality control (QC) measurement and for CNV/ROH detection. Among these patients, clinical chromosomal microarray analysis (CMA) was performed at Baylor Genetics (BG) on 3229 patients, either before, concurrently, or after ES. We retrospectively analyzed the findings from CMA and the QC array.ResultsThe QC array can detect ~ 70% of pathogenic/likely pathogenic CNVs (PCNVs) detectable by CMA. Out of the 11,020 ES cases, the QC array identified PCNVs in 327 patients and uniparental disomy (UPD) disorder-related ROH in 10 patients. The overall PCNV/UPD detection rate was 5.9% in the 3229 ES patients who also had CMA at BG; PCNV/UPD detection rate was higher in concurrent ES and CMA than in ES with prior CMA (7.2% vs 4.6%). The PCNVs/UPD contributed to the molecular diagnoses in 17.4% (189/1089) of molecularly diagnosed ES cases with CMA and were estimated to contribute in 10.6% of all molecularly diagnosed ES cases. Dual diagnoses with both PCNVs and SNVs were detected in 38 patients. PCNVs affecting single recessive disorder genes in a compound heterozygous state with SNVs were detected in 4 patients, and homozygous deletions (mostly exonic deletions) were detected in 17 patients. A higher PCNV detection rate was observed for patients with syndromic phenotypes and/or cardiovascular abnormalities.ConclusionsOur clinical genomics study demonstrates that detection of PCNV/UPD through the QC array or CMA increases ES diagnostic rate, provides more precise molecular diagnosis for dominant as well as recessive traits, and enables more complete genetic diagnoses in patients with dual or multiple molecular diagnoses. Concurrent ES and CMA using an array with exonic coverage for disease genes enables most effective detection of both CNVs and SNVs and therefore is recommended especially in time-sensitive clinical situations.

Copy-number variants in clinical genome sequencing: deployment and interpretation for rare and undiagnosed disease

PurposeCurrent diagnostic testing for genetic disorders involves serial use of specialized assays spanning multiple technologies. In principle, genome sequencing (GS) can detect all genomic pathogenic variant types on a single platform. Here we evaluate copy-number variant (CNV) calling as part of a clinically accredited GS test. MethodsWe performed analytical validation of CNV calling on 17 reference samples, compared the sensitivity of GS-based variants with those from a clinical microarray, and set a bound on precision using orthogonal technologies. We developed a protocol for family-based analysis of GS-based CNV calls, and deployed this across a clinical cohort of 79 rare and undiagnosed cases. ResultsWe found that CNV calls from GS are at least as sensitive as those from microarrays, while only creating a modest increase in the number of variants interpreted (~10 CNVs per case). We identified clinically significant CNVs in 15% of the first 79 cases analyzed, all of which were confirmed by an orthogonal approach. The pipeline also enabled discovery of a uniparental disomy (UPD) and a 50% mosaic trisomy 14. Directed analysis of select CNVs enabled breakpoint level resolution of genomic rearrangements and phasing of de novo CNVs. ConclusionRobust identification of CNVs by GS is possible within a clinical testing environment.

Identification of copy number variations using high density whole-genome SNP markers in Chinese Dongxiang spotted pigs.

ObjectiveCopy number variations (CNVs) are a major source of genetic diversity complementary to single nucleotide polymorphism (SNP) in animals. The aim of the study was to perform a comprehensive genomic analysis of CNVs based on high density whole-genome SNP markers in Chinese Dongxiang spotted pigs.MethodsWe used customized Affymetrix Axiom Pig1.4M array plates containing 1.4 million SNPs and the PennCNV algorithm to identify porcine CNVs on autosomes in Chinese Dongxiang spotted pigs. Then, the next generation sequence data was used to confirm the detected CNVs. Next, functional analysis was performed for gene contents in copy number variation regions (CNVRs). In addition, we compared the identified CNVRs with those reported ones and quantitative trait loci (QTL) in the pig QTL database.ResultsWe identified 871 putative CNVs belonging to 2,221 CNVRs on 17 autosomes. We further discarded CNVRs that were detected only in one individual, leaving us 166 CNVRs in total. The 166 CNVRs ranged from 2.89 kb to 617.53 kb with a mean value of 93.65 kb and a genome coverage of 15.55 Mb, corresponding to 0.58% of the pig genome. A total of 119 (71.69%) of the identified CNVRs were confirmed by next generation sequence data. Moreover, functional annotation showed that these CNVRs are involved in a variety of molecular functions. More than half (56.63%) of the CNVRs (n = 94) have been reported in previous studies, while 72 CNVRs are reported for the first time. In addition, 162 (97.59%) CNVRs were found to overlap with 2,765 previously reported QTLs affecting 378 phenotypic traits.ConclusionThe findings improve the catalog of pig CNVs and provide insights and novel molecular markers for further genetic analyses of Chinese indigenous pigs.

BagGMM: Calling copy number variation by bagging multiple Gaussian mixture models from tumor and matched normal next-generation sequencing data

Copy number variations (CNVs) contribute significantly to human genomic variability, some of which lead to diseases. However, effective detection of CNVs from whole genome next generation sequencing data (NGS) remains challenging. Here, we present BagGMM, a new method to call CNVs using tumor-normal matched samples from NGS data. BagGMM extracts read depth ratios of tumor samples to normal samples, divides the genomic sequences into segments by sliding windows to count the average coverage ratio of each segment, filters candidate deletions and duplications based on a coarse criterion of coverage ratio, and then builds Gaussian Mixture Model (GMM) for remaining ratios to identify the remaining ambiguous copy number states after filtration. Bagging multiple GMMs makes false positive calls descent instead of using one GMM, thus enhancing the detection power of BagGMM. Considering the computation speed of GMMs and false positive calls, we employ a segmentation procedure “large window and then small windows”, which is also helpful to determine boundary of CNV regions. We apply BagGMM to three simulation datasets and two groups of human whole genome sequencing (WGS) data for breast cancer patients and ovarian cancer patients to identify CNVs, respectively. All performed experiments demonstrate that BagGMM has the capability of robustly identification of CNVs with different sizes and states. The performance of this tool is compared to four peer existing CNV detection methods. BagGMM shows a significant improvement in both sensitivity and specificity for detecting both copy number gains and losses.

Concurrent Single-Cell RNA and Targeted DNA Sequencing on an Automated Platform for Comeasurement of Genomic and Transcriptomic Signatures.

The comeasurement of both genomic and transcriptomic signatures in single cells is of fundamental importance to accurately assess how the genetic information correlates with the transcriptomic phenotype. However, existing technologies have low throughput and laborious work flows. We developed a new method for concurrent sequencing of the transcriptome and targeted genomic regions (CORTAD-seq) within the same single cell on an automated microfluidic platform. The method was compatible with the downstream library preparation, allowing easy integration into existing next-generation sequencing work flows. We incorporated a single-cell bioinformatics pipeline for transcriptome and mutation analysis. As proof of principle, we applied CORTAD-seq to lung cancer cell lines to dissect the cellular consequences of mutations that result in resistance to targeted therapy. We obtained a mean detection of 6000 expressed genes and an exonic rate of 50%. The targeted DNA-sequencing data achieved a 97.8% detection rate for mutations and allowed for the identification of copy number variations and haplotype construction. We detected expression signatures of tyrosine kinase inhibitor (TKI) resistance, epidermal growth factor receptor (EGFR) amplification, and expansion of the T790M mutation among resistant cells. We also identified characteristics for TKI resistance that were independent of EGFR T790M, indicating that other alterations are required for resistance in this context. CORTAD-seq allows assessment of the interconnection between genetic and transcriptomic changes in single cells. It is operated on an automated, commercially available single-cell isolation platform, making its implementation straightforward.

GPA: A Microbial Genetic Polymorphisms Assignments Tool in Metagenomic Analysis by Bayesian Estimation

Identifying antimicrobial resistant (AMR) bacteria in metagenomics samples is essential for public health and food safety. Next-generation sequencing (NGS) technology has provided a powerful tool in identifying the genetic variation and constructing the correlations between genotype and phenotype in humans and other species. However, for complex bacterial samples, there lacks a powerful bioinformatic tool to identify genetic polymorphisms or copy number variations (CNVs) for given genes. Here we provide a Bayesian framework for genotype estimation for mixtures of multiple bacteria, named as Genetic Polymorphisms Assignments (GPA). Simulation results showed that GPA has reduced the false discovery rate (FDR) and mean absolute error (MAE) in CNV and single nucleotide variant (SNV) identification. This framework was validated by whole-genome sequencing and Pool-seq data from Klebsiella pneumoniae with multiple bacteria mixture models, and showed the high accuracy in the allele fraction detections of CNVs and SNVs in AMR genes between two populations. The quantitative study on the changes of AMR genes fraction between two samples showed a good consistency with the AMR pattern observed in the individual strains. Also, the framework together with the genome annotation and population comparison tools has been integrated into an application, which could provide a complete solution for AMR gene identification and quantification in unculturable clinical samples. The GPA package is available at https://github.com/IID-DTH/GPA-package.

Robust Recurrent CNV Detection in the Presence of Inter-Subject Variability.

The study of recurrent copy number variations (CNVs) plays an important role in understanding the onset and evolution of complex diseases such as cancer. Array-based comparative genomic hybridization (aCGH) is a widely used microarray based technology for identifying CNVs. However, due to high noise levels and inter-sample variability, detecting recurrent CNVs from aCGH data remains a challenging topic. This paper proposes a novel method for identification of the recurrent CNVs. In the proposed method, the noisy aCGH data is modeled as the superposition of three matrices: a full-rank matrix of weighted piece-wise generating signals accounting for the clean aCGH data, a Gaussian noise matrix to model the inherent experimentation errors and other sources of error, and a sparse matrix to capture the sparse inter-sample (sample-specific) variations. We demonstrated the ability of our method to separate accurately recurrent CNVs from sample-specific variations and noise in both simulated (artificial) data and real data. The proposed method produced more accurate results than current state-of-the-art methods used in recurrent CNV detection and exhibited robustness to noise and sample-specific variations.

Noise cancellation using total variation for copy number variation detection

BackgroundDue to recent advances in sequencing technologies, sequence-based analysis has been widely applied to detecting copy number variations (CNVs). There are several techniques for identifying CNVs using next generation sequencing (NGS) data, however methods employing depth of coverage or read depth (RD) have recently become a main technique to identify CNVs. The main assumption of the RD-based CNV detection methods is that the readcount value at a specific genomic location is correlated with the copy number at that location. However, readcount data’s noise and biases distort the association between the readcounts and copy numbers. For more accurate CNV identification, these biases and noise need to be mitigated. In this work, to detect CNVs more precisely and efficiently we propose a novel denoising method based on the total variation approach and the Taut String algorithm.ResultsTo investigate the performance of the proposed denoising method, we computed sensitivities, false discovery rates and specificities of CNV detection when employing denoising, using both simulated and real data. We also compared the performance of the proposed denoising method, Taut String, with that of the commonly used approaches such as moving average (MA) and discrete wavelet transforms (DWT) in terms of sensitivity of detecting true CNVs and time complexity. The results show that Taut String works better than DWT and MA and has a better power to identify very narrow CNVs. The ability of Taut String denoising in preserving CNV segments’ breakpoints and narrow CNVs increases the detection accuracy of segmentation algorithms, resulting in higher sensitivities and lower false discovery rates.ConclusionsIn this study, we proposed a new denoising method for sequence-based CNV detection based on a signal processing technique. Existing CNV detection algorithms identify many false CNV segments and fail in detecting short CNV segments due to noise and biases. Employing an effective and efficient denoising method can significantly enhance the detection accuracy of the CNV segmentation algorithms. Advanced denoising methods from the signal processing field can be employed to implement such algorithms. We showed that non-linear denoising methods that consider sparsity and piecewise constant characteristics of CNV data result in better performance in CNV detection.

A Projection Neural Network for Identifying Copy Number Variants.

The identification of copy number variations (CNVs) helps the diagnosis of many diseases. One major hurdle in the path of CNVs discovery is that the boundaries of normal and aberrant regions cannot be distinguished from the raw data, since various types of noise contaminate them. To tackle this challenge, the total variation regularization is mostly used in the optimization problems to approximate the noise-free data from corrupted observations. The minimization using such regularization is challenging to deal with since it is non-differentiable. In this paper, we propose a projection neural network to solve the non-smooth problem. The proposed neural network has a simple one-layer structure and is theoretically assured to have the global exponential convergence to the solution of the total variation-regularized problem. The experiments on several real and simulated datasets illustrate the reasonable performance of the proposed neural network and show that its performance is comparable with those of more sophisticated algorithms.

Using XCAVATOR and EXCAVATOR2 to Identify CNVs from WGS, WES, and TS Data.

Copy Number Variants (CNVs) are structural rearrangements contributing to phenotypic variation but also associated with many disease states. In recent years, the identification of CNVs from high-throughput sequencing experiments has become a common practice for both research and clinical purposes. Several computational methods have been developed so far. In this unit, we describe and give instructions on how to run two read count-based tools, XCAVATOR and EXCAVATOR2, which are tailored for the detection of both germline and somatic CNVs from different sequencing experiments (whole-genome, whole-exome, and targeted) in various disease contexts and population genetic studies. © 2018 by John Wiley & Sons, Inc.