A bayesian analysis for identifying DNA copy number variations using a compound poisson process.
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ABSTRACT:
SUBMITTER: Chen J
PROVIDER: S-EPMC3171362 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND: Some diseases, like tumors, can be related to chromosomal aberrations, leading to changes of DNA copy number. The copy number of an aberrant genome can be represented as a piecewise constant function, since it can exhibit regions of deletions or gains. Instead, in a healthy cell the copy number is two because we inherit one copy of each chromosome from each our parents. Bayesian Piecewise Constant Regression (BPCR) is a Bayesian regression method for data that are noisy observations of a piecewise constant function. The method estimates the unknown segment number, the endpoints of the segments and the value of the segment levels of the underlying piecewise constant function. The Bayesian Regression Curve (BRC) estimates the same data with a smoothing curve. However, in the original formulation, some estimators failed to properly determine the corresponding parameters. For example, the boundary estimator did not take into account the dependency among the boundaries and succeeded in estimating more than one breakpoint at the same position, losing segments. RESULTS: We derived an improved version of the BPCR (called mBPCR) and BRC, changing the segment number estimator and the boundary estimator to enhance the fitting procedure. We also proposed an alternative estimator of the variance of the segment levels, which is useful in case of data with high noise. Using artificial data, we compared the original and the modified version of BPCR and BRC with other regression methods, showing that our improved version of BPCR generally outperformed all the others. Similar results were also observed on real data. CONCLUSION: We propose an improved method for DNA copy number estimation, mBPCR, which performed very well compared to previously published algorithms. In particular, mBPCR was more powerful in the detection of the true position of the breakpoints and of small aberrations in very noisy data. Hence, from a biological point of view, our method can be very useful, for example, to find targets of genomic aberrations in clinical cancer samples.
| S-EPMC2674052 | biostudies-literature
Project description:Human genome contains many variations, often called mutations, which are difficult to detect and have remained a challenge for years. A substantial part of the genome encompasses repeats and when such repeats are in the coding region they may lead to change in the gene expression profile followed by pathological conditions. Structural variants are alterations which change one or more sequence feature in the chromosome such as change in the copy number, rearrangements, and translocations of a sequence and can be balanced or unbalanced. Copy number variants (CNVs) may increase or decrease the copies of a given region and have a pivotal role in the onset of many diseases including cardiovascular disorders. Cardiovascular disorders have a magnitude of well-established risk factors and etiology, but their correlation with CNVs is still being studied. In this article, we have discussed history of CNVs and a summary on the diseases associated with CNVs. To detect such variations, we shed light on the number of techniques introduced so far and their limitations. The lack of studies on cardiovascular diseases to determine the frequency of such variants needs clinical studies with larger cohorts. This review is a compilation of articles suggesting the importance of CNVs in multitude of cardiovascular anomalies. Finally, future perspectives for better understanding of CNVs and cardiovascular disorders have also been discussed.
| S-EPMC6291864 | biostudies-other
Project description:DNA copy number variations (CNVs) have been shown to be associated with cancer development and progression. The detection of these CNVs has the potential to impact the basic knowledge and treatment of many types of cancers, and can play a role in the discovery and development of molecular-based personalized cancer therapies. One of the most common types of high-resolution chromosomal microarrays is array-based comparative genomic hybridization (aCGH) methods that assay DNA CNVs across the whole genomic landscape in a single experiment. In this article we propose methods to use aCGH profiles to predict disease states. We employ a Bayesian classification model and treat disease states as outcome, and aCGH profiles as covariates in order to identify significant regions of the genome associated with disease subclasses. We propose a principled two-stage method where we first make inferences on the underlying copy number states associated with the aCGH emissions based on hidden Markov model (HMM) formulations to account for serial dependencies in neighboring probes. Subsequently, we infer associations with disease outcomes, conditional on the copy number states, using Bayesian linear variable selection procedures. The selected probes and their effects are parameters that are useful for predicting the disease categories of any additional individuals on the basis of their aCGH profiles. Using simulated datasets, we investigate the method's accuracy in detecting disease category. Our methodology is motivated by and applied to a breast cancer dataset consisting of aCGH profiles assayed on patients from multiple disease subtypes.
| S-EPMC4196891 | biostudies-literature
Project description:Copy number variations (CNVs) in the human genome are conventionally detected using high-throughput scanning technologies, such as comparative genomic hybridization and high-density single nucleotide polymorphism (SNP) microarrays, or relatively low-throughput techniques, such as quantitative polymerase chain reaction (PCR). All these approaches are limited in resolution and can at best distinguish a twofold (or 50%) difference in copy number. We have developed a new technology to study copy numbers using a platform known as the digital array, a nanofluidic biochip capable of accurately quantitating genes of interest in DNA samples. We have evaluated the digital array's performance using a model system, to show that this technology is exquisitely sensitive, capable of differentiating as little as a 15% difference in gene copy number (or between 6 and 7 copies of a target gene). We have also analyzed commercial DNA samples for their CYP2D6 copy numbers and confirmed that our results were consistent with those obtained independently using conventional techniques. In a screening experiment with breast cancer and normal DNA samples, the ERBB2 gene was found to be amplified in about 35% of breast cancer samples. The use of the digital array enables accurate measurement of gene copy numbers and is of significant value in CNV studies.
| S-EPMC2566873 | biostudies-literature
Project description:Copy number variation (CNV) of DNA plays an important role in the development of many diseases. However, due to the irregularity and sparsity of the CNVs, studying the association between CNVs and a disease outcome or a trait can be challenging. Up to now, not many methods have been proposed in the literature for this problem. Most of the current researchers reply on an ad hoc two-stage procedure by first identifying CNVs in each individual genome and then performing an association test using these identified CNVs. This potentially leads to information loss and as a result a lower power to identify disease associated CNVs. In this article, we describe a new method that combines the two steps into a single coherent model to identify the common CNV across patients that are associated with certain diseases. We use a double penalty model to capture CNVs' association with both the intensities and the disease trait. We validate its performance in simulated datasets and a data example on platinum resistance and CNV in ovarian cancer genome.
| S-EPMC6663092 | biostudies-literature
Project description:Recent reports indicate that copy number variations (CNVs) within the human genome contribute to nucleotide diversity to a larger extent than single nucleotide polymorphisms (SNPs). In addition, the contribution of CNVs to human disease susceptibility may be greater than previously expected, although a complete understanding of the phenotypic consequences of CNVs is incomplete. We have recently reported a comprehensive view of CNVs among 270 HapMap samples using high-density SNP genotyping arrays and BAC array CGH. In this report, we describe a novel algorithm using Affymetrix GeneChip Human Mapping 500K Early Access (500K EA) arrays that identified 1203 CNVs ranging in size from 960 bp to 3.4 Mb. The algorithm consists of three steps: (1) Intensity pre-processing to improve the resolution between pairwise comparisons by directly estimating the allele-specific affinity as well as to reduce signal noise by incorporating probe and target sequence characteristics via an improved version of the Genomic Imbalance Map (GIM) algorithm; (2) CNV extraction using an adapted SW-ARRAY procedure to automatically and robustly detect candidate CNV regions; and (3) copy number inference in which all pairwise comparisons are summarized to more precisely define CNV boundaries and accurately estimate CNV copy number. Independent testing of a subset of CNVs by quantitative PCR and mass spectrometry demonstrated a >90% verification rate. The use of high-resolution oligonucleotide arrays relative to other methods may allow more precise boundary information to be extracted, thereby enabling a more accurate analysis of the relationship between CNVs and other genomic features.
| S-EPMC1665641 | biostudies-literature
Project description:Copy number variations (CNVs) are genomic structural mutations consisting of abnormal numbers of fragment copies. Next-generation sequencing of read-depth signals mirrors these variants. Some tools used to predict CNVs by depth have been published, but most of these tools can be applied to only a specific data type due to modeling limitations. We develop a tool for copy number variation detection by a Bayesian procedure, i.e., CONY, that adopts a Bayesian hierarchical model and an efficient reversible-jump Markov chain Monte Carlo inference algorithm for whole genome sequencing of read-depth data. CONY can be applied not only to individual samples for estimating the absolute number of copies but also to case-control pairs for detecting patient-specific variations. We evaluate the performance of CONY and compare CONY with competing approaches through simulations and by using experimental data from the 1000 Genomes Project. CONY outperforms the other methods in terms of accuracy in both single-sample and paired-samples analyses. In addition, CONY performs well regardless of whether the data coverage is high or low. CONY is useful for detecting both absolute and relative CNVs from read-depth data sequences. The package is available at https://github.com/weiyuchung/CONY.
| S-EPMC7319969 | biostudies-literature
Project description:MotivationTranscription factors play a crucial role in gene regulation by binding to specific regulatory sequences. The sequence motifs recognized by a transcription factor can be described in terms of position frequency matrices. When scanning a sequence for matches to a position frequency matrix, one needs to determine a cut-off, which then in turn results in a certain number of hits. In this paper we describe how to compute the distribution of match scores and of the number of motif hits, which are the prerequisites to perform motif hit enrichment analysis.ResultsWe put forward an improved compound Poisson model that supports general order-d Markov background models and which computes the number of motif-hits more accurately than earlier models. We compared the accuracy of the improved compound Poisson model with previously proposed models across a range of parameters and motifs, demonstrating the improvement. The importance of the order-d model is supported in a case study using CpG-island sequences.Availability and implementationThe method is available as a Bioconductor package named 'motifcounter' https://bioconductor.org/packages/motifcounter.Contactkopp@molgen.mpg.de.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC5860096 | biostudies-literature
Project description:ContextFine-needle aspiration (FNA) is the best diagnostic tool for preoperative evaluation of thyroid nodules but is often inconclusive as a guide for surgical management.ObjectiveOur hypothesis was that thyroid tumor subtypes may show characteristic DNA copy number variation (CNV) patterns, which may further improve the preoperative classification.DesignOur study cohorts included benign follicular adenomas (FAs), classic papillary thyroid carcinomas (PTCs), and follicular variant PTCs (FVPTCs), the three subtypes most commonly associated with inconclusive preoperative cytopathology.SettingTissue and FNA samples were obtained at an academic tertiary referral center.PatientsCases were identified that underwent partial or complete thyroidectomy for malignant or indeterminate thyroid lesions between 2000 and 2008 and had adequate snap-frozen tissue.InterventionsPairs of tumor tissue and matching normal thyroid tissue-derived DNA were compared using 550K single-nucleotide polymorphism arrays.Main outcome measureStatistically significant differences in CNV patterns between tumor subtypes were identified.ResultsSegmental amplifications in chromosomes (Ch) 7 and 12 were more common in FAs than in PTCs or FVPTCs. Additionally, a subset of FAs and FVPTCs showed deletions in Ch22. We identified the 5 CNV-associated genes best at discriminating between FAs and PTCs/FVPTCs, which correctly classified 90% of cases. These 5 Ch12 genes were validated by quantitative genomic PCR and gene expression array analyses on the same patient cohort. The 5-gene signature was then successfully validated against an independent test cohort of benign and malignant tumor samples. Finally, we performed a feasibility study on matched FA-derived intraoperative FNA samples and were able to correctly identify FAs harboring the Ch12 amplification signature, whereas FAs without amplification showed a normal Ch12 signature.ConclusionsThyroid tumor subtypes possess characteristic genomic profiles that may further our understanding of structural genetic changes in thyroid tumor subtypes and may lead to the development of new diagnostic biomarkers in FNA samples.
| S-EPMC3590464 | biostudies-literature
Project description:Stroke is the third leading cause of death worldwide after heart disease and all forms of cancers. Monogenic disorders, genetic, and environmental risk factors contribute to damaging cerebral blood vessels and, consequently, cause stroke. Developments in genomic research led to the discovery of numerous copy number variants (CNVs) that have been recently identified as a new tool for understanding the genetic basis of many diseases. This review discusses the current understanding of the types of stroke, the existing knowledge on the involvement of specific CNVs in stroke as well as the limitations of the methods used for detecting CNVs like SNP-microarray. To confirm an unequivocally association between CNVs and stroke and extend the current findings, it would be desirable to use another methodology to detect smaller CNVs or CNVs in genomic regions poorly covered by this technique, for instance, CGH-array.
| S-EPMC5110597 | biostudies-literature