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Copy Number Polymorphism

Copy Number Polymorphism (CNP) refers to the natural variation in the number of copies of a particular gene or genetic region within an individual's genome.
This phenomenon can have significant implications for gene expression, disease susceptibility, and evolutionary adaptation.
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Most cited protocols related to «Copy Number Polymorphism»

TCGAbiolinks is an R package, which is licensed under the General Public License (GPLv3), and is freely available through the Bioconductor repository (21 (link)). By conforming to the strict guidelines for package submission to Bioconductor, we were able to utilize and incorporate existing R/Bioconductor packages and statistics to assist in identifying differentially altered genomic regions defined by mutation, copy number, expression or DNA methylation; to reproduce previous TCGA marker studies; and to integrate data types both within TCGA and across other data types outside of TCGA. TCGAbiolinks consists of functions that can be grouped into three main levels: Data, Analysis and Visualization. More specifically, the package provides multiple methods for the analysis of individual experimental platforms (e.g. differential expression analysis or identifying differentially methylated regions or copy number alterations) and methods for visualization (e.g. survival plots, volcano plots and starburst plots) to facilitate the development of complete analysis pipelines. In addition, TCGAbiolinks offers in-depth integrative analysis of multiple platforms, such as copy number and expression or expression and DNA methylation, as demonstrated and applied in our recent TCGA study of 1122 gliomas (6 ). These functions can be used independently or in combination to provide the user with fully comprehensible analysis pipelines applied to TCGA data. A schematic overview of the package is presented in Figure 2. We will describe each of the three main levels (Data, Analysis and Visualization) below, highlighting the importance and utility of each associated function and subfunction. We will then introduce four tumor case studies, which will help clarify the utility of TCGAbiolinks for the reader. We have also compiled an in-depth vignette, which describes every function in detail. Here, we will summarize the main functions.
Publication 2015
Copy Number Polymorphism DNA Methylation Genome Glioma Mutation Neoplasms
Tumor and normal samples were processed by either of two Biospecimen Core Resources (BCRs), and aliquots of purified nucleic acids were shipped to the genome characterization and sequencing centers (Supplementary Methods). The BCRs provided sample sets in several different batches. To assess any batch effects we examined the mRNA expression, miRNA expression and DNA methylation data sets using a combination of cluster analysis, enhanced principal component analysis, and analysis of variance (Supplementary Methods). Although some differences among batches were detected, we did not correct them computationally because the differences were generally modest and because some of them may reflect biological phenomena (Supplementary Methods).
We used Affymetrix SNP 6.0 microarrays to detect copy-number alterations. A subset of samples was subjected to low pass (2–5X) whole genome sequencing (Illumina HiSeq), in part for detection of SCNA and chromosomal translocations43 (link),44 (link). Gene expression profiles were generated using Agilent microarrays and RNA-Seq. DNA methylation data were obtained using Illumina Infinium (HumanMethylation27) arrays. DNA sequencing of coding regions was performed by exome capture followed by sequencing on the SOLiD or Illumina HiSeq platforms. Details of the analytical methods used are described in Supplementary Methods.
All of the primary sequence files are deposited in dbGap and all other data are deposited at the Data Coordinating Center (DCC) for public access (http://cancergenome.nih.gov/). Data matrices and supporting data can be found at http://tcga-data.nci.nih.gov/docs/publications/coadread_2012/. The data can also be explored via the ISB Regulome Explorer (http://explorer.cancerregulome.org/) and the cBio Cancer Genomics Portal (http://cbioportal.org). Descriptions of the data can be found at https://wiki.nci.nih.gov/x/j5dXAg and in Supplementary Methods.
Publication 2012
Biological Phenomena Chromosomes Copy Number Polymorphism DNA Methylation Exome Genome Malignant Neoplasms Microarray Analysis MicroRNAs Neoplasms Nucleic Acids RNA, Messenger RNA-Seq
We have assembled a collection of strong SNP-phenotype associations from various sources that can be used as potential instruments in Mendelian randomization studies. Instruments are currently restricted to biallelic SNPs but in principle could be extended in future versions to accommodate multi-allelic SNPs or copy number variants (CNVs). The potential instruments generally correspond to the ‘top hits’ from a GWAS, rather than the entire collection of GWAS summary statistics. As such, the traits included here can only be evaluated as potential exposures in a hypothesized exposure-outcome analysis (complete summary data are required when evaluating traits as potential outcomes). All curated instruments are available through the MRInstruments R package (https://github.com/MRCIEU/MRInstruments).
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Publication 2018
Alleles Copy Number Polymorphism Genome-Wide Association Study Phenotype Single Nucleotide Polymorphism
The Long-Range Haplotype (LRH), integrated Haplotype Score (iHS) and Cross Population EHH (XP-EHH) tests detect alleles that have risen to high frequency rapidly enough that long-range association with nearby polymorphisms—the long-range haplotype—has not been eroded by recombination; haplotype length is measured by the EHH8 (link),9 (link). The first two tests detect partial selective sweeps, whereas XP-EHH detects selected alleles that have risen to near fixation in one but not all populations. To evaluate the tests, we simulated genomic data for each HapMap population in a range of demographic scenarios—under neutral evolution and twenty scenarios of positive selection—developing the program Sweep (www.broad.mit.edu/mpg/sweep) for analysis. For our top candidates by the three tests, we tested for haplotype-specific recombination rates and copy-number polymorphisms, possible confounders.
Publication 2007
Alleles Copy Number Polymorphism Evolution, Neutral Genetic Polymorphism Haplotypes HapMap Population Group Recombination, Genetic

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Publication 2015
CDH1 protein, human Copy Number Polymorphism cytidylyl-3'-5'-guanosine DNA Chips DNA Methylation DNA Replication FOXA1 protein, human Genes Genome Head hydrogen sulfite Malignant Neoplasm of Breast Malignant Neoplasms Methylation MicroRNAs Mutation Neoplasms Protein Arrays Proteins RNA-Seq Transcription Initiation Site

Most recents protocols related to «Copy Number Polymorphism»

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Example 2

A tumor single cell and a population of 50 stromal cells, belonging to a disaggregated FFPE section, were digitally sorted by DEPArray™ (Menarini Silicon Biosystems) and whole-genome amplified using the Ampli1™ WGA kit. FIG. 5 shows Low-pass Whole Genome Sequencing results performed by the method disclosed above. The figure shows copy number alterations (CNA) profiles, with gains and losses only for the tumor single cell. These high-quality CNA profiles demonstrate that the presented method is highly resilient to DNA degradation and proved to be a reliable and valuable method for the molecular characterization of tumour heterogeneity in FFPE tissues down to single level.

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Patent 2024
Cells Copy Number Polymorphism DNA Library Genetic Heterogeneity Genome Neoplasms Silicon Stromal Cells Tissues

Example 3

We generated and analyzed a collection of 14 early-passage (passage ≤9) human pES cell lines for the persistence of haploid cells. All cell lines originated from activated oocytes displaying second polar body extrusion and a single pronucleus. We initially utilized chromosome counting by metaphase spreading and G-banding as a method for unambiguous and quantitative discovery of rare haploid nuclei. Among ten individual pES cell lines, a low proportion of haploid metaphases was found exclusively in a single cell line, pES10 (1.3%, Table 1B). We also used viable FACS with Hoechst 33342 staining, aiming to isolate cells with a DNA content corresponding to less than two chromosomal copies (2c) from four additional lines, leading to the successful enrichment of haploid cells from a second cell line, pES12 (Table 2).

Two individual haploid-enriched ES cell lines were established from both pES10 and pES12 (hereafter referred to as h-pES10 and h-pES12) within five to six rounds of 1c-cell FACS enrichment and expansion (FIG. 1C (pES10), FIG. 5A (pES12)). These cell lines were grown in standard culture conditions for over 30 passages while including cells with a normal haploid karyotype (FIG. 1D, FIG. 5B). However, since diploidization occurred at a rate of 3-9% of the cells per day (FIG. 1E), cell sorting at every three to four passages was required for maintenance and analysis of haploid cells. Further, visualization of ploidy in adherent conditions was enabled by DNA fluorescence in situ hybridization (FISH) (FIG. 1F, FIG. 5c) and quantification of centromere protein foci (FIG. 1G, FIG. 5D; FIG. 6). In addition to their intact karyotype, haploid ES cells did not harbor significant copy number variations (CNVs) relative to their unsorted diploid counterparts (FIG. 5E). Importantly, we did not observe common duplications of specific regions in the two cell lines that would result in pseudo-diploidy. Therefore, genome integrity was preserved throughout haploid-cell isolation and maintenance. As expected, single nucleotide polymorphism (SNP) array analysis demonstrated complete homozygosity of diploid pES10 and pES12 cells across all chromosomes.

Both h-pES10 and h-pES12 exhibited classical human pluripotent stem cell features, including typical colony morphology and alkaline phosphatase activity (FIG. 2A, FIG. 2B). Single haploid ES cells expressed various hallmark pluripotency markers (NANOG, OCT4, SOX2, SSEA4 and TRA1-60), as confirmed in essentially pure haploid cultures by centromere foci quantification (>95% haploids) (FIG. 2C, FIG. 7). Notably, selective flow cytometry enabled to validate the expression of two human ES-cell-specific cell surface markers (TRA-1-60 and CLDN618) in single haploid cells (FIG. 2D). Moreover, sorted haploid and diploid ES cells showed highly similar transcriptional and epigenetic signatures of pluripotency genes (FIG. 2E, FIG. 2F). Since the haploid ES cells were derived as parthenotes, they featured distinct transcriptional and epigenetic profiles of maternal imprinting, owing to the absence of paternally-inherited alleles (FIG. 8).

Haploid cells are valuable for loss-of-function genetic screening because phenotypically-selectable mutants can be identified upon disruption of a single allele. To demonstrate the applicability of this principle in haploid human ES cells, we generated a genome-wide mutant library using a piggyBac transposon gene trap system that targets transcriptionally active loci (FIG. 2G, FIG. 8E), and screened for resistance to the purine analog 6-thioguanine (6-TG). Out of six isolated and analyzed 6-TG-resistant colonies, three harbored a gene trap insertion localizing to the nucleoside diphosphate linked moiety X-type motif 5 (NUDT5) autosomal gene (FIG. 2H). NUDT5 disruption was recently confirmed to confer 6-TG resistance in human cells,51 by acting upstream to the production of 5-phospho-D-ribose-1-pyrophosphate (PRPP), which serves as a phosphoribosyl donor in the hypoxanthine phosphoribosyltransferase 1 (HPRT1)-mediated conversion of 6-TG to thioguanosine monophosphate (TGMP) (FIG. 2I). Detection of a loss-of-function phenotype due to an autosomal mutation validates that genetic screening is feasible in haploid human ES cells.

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Patent 2024
Alkaline Phosphatase Alleles Cell Lines Cell Nucleus Cells Cell Separation Centromere Chromosomes Copy Number Polymorphism Diphosphates Diploid Cell Diploidy Embryonic Stem Cells Flow Cytometry Fluorescent in Situ Hybridization Genes Genes, vif Genitalia Genome Genomic Library Haploid Cell HOE 33342 Homo sapiens Homozygote Human Embryonic Stem Cells Hypoxanthine Phosphoribosyltransferase isolation Jumping Genes Karyotype Metaphase Mothers Mutation Nucleosides Oocytes Phenotype Pluripotent Stem Cells Polar Bodies POU5F1 protein, human Proteins purine Ribose Single Nucleotide Polymorphism SOX2 protein, human stage-specific embryonic antigen-4 Tissue Donors Transcription, Genetic
To find associations between TF targeting and promoter methylation status and copy number variation status, we selected 76 melanoma CCLE cell lines and we computed the significance of associations using ANOVA as implemented in the Python package statsmodels v0.13.2 [96 ]. Since we were mostly interested in finding strong associations and prominent regulatory hallmarks of melanoma, we discretized the input data by considering a gene to be amplified if it had more than three copies and to be deleted if both copies are lost. For promoter methylation data, promoters were defined in CCLE as the 1kb region downstream of the gene’s transcriptional start site (TSS). We defined hypermethylated promoter sites as those having methylation status with a z-score greater than three and we defined hypomethylated sites as those having methylation status with a z-score less than negative three; we considered a gene to be amplified if it had evidence of more than three copies in the genome and to be deleted if both copies are lost. We only computed the associations if they had at least three positive instances of the explanatory variable (for example, for a given gene at least three cell lines had a hypomethylation in that gene’s promoter) and corrected for multiple testing using a false discovery rate of less than 25% following the Benjamini-Hochberg procedure [97 ].
In all melanoma cell lines, for each modality (promoter hypomethylation, promoter hypermethylation, gene amplification, and gene deletion) and for each gene, we built an ANOVA model using TF targeting as the response variable across all melanoma cell lines while the status of that gene (either promoter methylation or copy number status) was the explanatory variable. For example, in modeling promoter hypermethylation, we chose positive instances to represent hypermethylated promoters and negative instances for nonmethylated promoters along with an additional factor correcting for the cell lineage. Similarly, for copy number variation analysis, we chose positive instance to represent amplified genes and negative instances for nonamplified genes while correcting for cell lineage. We only computed the associations if they had at least three positive instances of the explanatory variable (for example, promoter hypomethylation in at least three cell lines).
To predict drug response using TF targeting, we conducted a linear regression with elastic net [45 (link)] regularization as implemented in the Python package sklearn v1.1.3 using an equal weight of 0.5 for L1 and L2 penalties using regorafenib cell viability assays in melanoma cell lines as a response variable and the targeting scores of 1,132 TFs (Table S5) as the explanatory variable.
Finally, to model EMT in melanoma, we used MONSTER on two LIONESS networks of melanoma cancer cell lines, one representing a primary tumor (Depmap ID: ACH-000580) as the initial state and the other a metastasis cell line (Depmap ID: ACH-001569) as the end state. We modified the original implementation of MONSTER that implements its own network reconstruction procedure to take any input network, such as LIONESS networks. MONSTER identifies differentially involved TFs in the transition by shuffling the columns of the initial and final state adjacency matrices 1000 times to build a null distribution, which is then used to compute a standardized differential TF involvement score by scaling the obtained scores by those of the null distribution.
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Publication 2023
Biological Assay Cell Lines Cell Survival Copy Number Polymorphism Gene Amplification Gene Deletion Genes Genome Melanoma Methylation Neoplasm Metastasis Neoplasms neuro-oncological ventral antigen 2, human Pharmaceutical Preparations Promoter, Genetic Python Reconstructive Surgical Procedures regorafenib Transcription Initiation Site
To enable further exploration and discovery of biological associations, we built an online resource representing a multi-tiered regulatory network. First, to build a pan-cancer multi-tiered network that connects the genotype to cellular phenotypes, we extended DRAGON networks from modeling pairwise interactions between two biological variables to a multi-omic network that includes more than two node types by sequentially adding a new layer to an initial pairwise DRAGON network. In addition, since DRAGON networks are undirected, we added direction based on our understanding of how biological elements interact with each other. For example, gene expression nodes are upstream of protein level nodes and metabolite nodes. To facilitate browsing and limit exploration to potentially causal associations that best reflect our understanding of how different data types link to one another in cellular biology, our approach was to prune edges between the same node type to build bipartite DRAGON networks between each pair of genomic modalities. In particular, promoter methylation status, copy number variation, histone marks, and miRNA were linked to gene expression in a pairwise fashion. Then, gene expression was linked to protein levels, which in turn was associated with cellular phenotypes represented by metabolite levels, drug sensitivity, and cell fitness following CRISPR gene knockout. To reduce the size of the network to the most relevant positive and negative associations, only the 2000 most positive correlations and the 2000 most negative correlations in each pairwise association in each of the bipartite networks were retained in the final multi-omic network. The CCLE online pan-cancer map was built using Vis.js (v8.5.2) and can be queried for biological associations using user input queries at https://grand.networkmedicine.org/cclemap.
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Publication 2023
Biopharmaceuticals Clustered Regularly Interspaced Short Palindromic Repeats Copy Number Polymorphism Gene Expression Gene Knockout Techniques Genome Genotype Histone Code Hypersensitivity link protein Malignant Neoplasms Methylation MicroRNAs Pharmaceutical Preparations Phenotype Proteins Seizures
The training cohort included 344 patients with NSCLC treated with ICIs (Immunotherapy, MSKCC, Nat Genet 2019) (9 (link)). Validation cohort 1 was integrated from three public cohorts sequenced by the WES, including 56 patients with NSCLC treated with anti-PD-(L)1 therapy (15 (link)), 75 patients with NSCLC treated with PD-1 plus CTLA-4 blockade (LUAD only) (16 (link)), and 69 patients with NSCLC treated with anti-PD-(L)1 monotherapy at Sun Yat-sen University Cancer Center (SYSUCC) (17 (link)). Validation cohort 2 was a pan-cancer cohort including 1181 patients treated with anti-PD-(L)1 therapy (From Samstein cohort, 350 patients with NSCLC, and 130 patients with unknown cancer were excluded) (9 (link)). Validation cohort 3 was also a pan-cancer cohort including 193 patients treated with anti-PD-(L)1 therapy (15 (link)) (From Miao cohort, 56 patients with NSCLC were excluded). Both training and validation cohorts were selected based on the following criteria: (i) patients with no mutation information were excluded; (ii) synonymous mutation, copy number variation, and fusion genes were excluded; (iii) genes were mutated in at least three samples. In addition, data from non-ICI treatment TCGA NSCLC cohorts were used for further exploration, including RNA-seq data downloaded from UCSC Xena (University of California Santa Cruz) (https://xenabrowser.net/datapages/), immune subtype data along with survival data acquired from Thorsson et al. (18 (link)), and mutation data obtained from Ellrott et al. (29 (link)). In addition, six single-cell RNA sequencing data of LUAD patients from Bischoff, P., et al. (30 (link)) were included to reveal the gene expression features in different cell types (30 (link)). In addition, a retrospective southwest hospital clinical (SHC) cohort, with 82 lung cancer patients, was utilized to analyze the correlation between the predictive model and TMB. Of these, 77 were NSCLC, and the remaining were primary lung cancer. Survival data could not be acquired because of the loss of follow-up after surgery. All the samples were collected in the Southwest Hospital, and multiple gene panel target sequencing was conducted. The detailed clinical characteristics of patients in the training cohort, validation cohort 1-3, TCGA cohort, and SHC cohort are summarized in Supplementary Tables 2–7. The detailed mutations data of SHC cohort are listed in Supplementary Table 8.
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Publication 2023
Cells Copy Number Polymorphism Cytotoxic T-Lymphocyte Antigen 4 Gene Expression Genes Genets Immunotherapy Lung Cancer Malignant Neoplasms Multiple Birth Offspring Mutation Neoplasms, Unknown Primary Non-Small Cell Lung Carcinoma Operative Surgical Procedures Patients RNA-Seq Silent Mutation

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More about "Copy Number Polymorphism"

Copy Number Variation (CNV) refers to the natural fluctuations in the number of copies of a particular gene or genomic region within an individual's DNA.
This phenomenon, also known as Copy Number Polymorphism (CNP), can have significant implications for gene expression, disease susceptibility, and evolutionary adaptation.
Understanding and analyzing CNV is crucial in various fields, including genetics, genomics, and biomedical research.
CNV can be studied using a range of advanced technologies, such as the HiSeq 2500, NextSeq 500, HiSeq 2000, and NovaSeq 6000 sequencing platforms, as well as the CytoScan HD array.
These tools enable researchers to detect and quantify the copy number of specific genetic regions with high accuracy and resolution.
Sample preparation and DNA extraction methods, such as the QIAamp DNA Mini Kit and the DNeasy Blood & Tissue Kit, are commonly used to obtain high-quality DNA samples for CNV analysis.
Additionally, the Genome-Wide Human SNP Array 6.0 and TaqMan Copy Number Assays provide valuable tools for targeted CNV assessment.
Analyzing CNV can provide insights into the genetic basis of complex traits, disease susceptibility, and evolutionary adaptations.
Researchers can leverage PubCompare.ai's AI-driven insights to optimize their CNV research by easily identifying relevant protocols from literature, preprints, and patents, while utilizing AI-powered comparisons to select the most suitable protocols and products for their specific needs.
By harnessing the power of these resources, researchers can take their Copy Number Polymorphism research to new heights and make valuable contributions to the field.