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5 results for “telomere measurements”
Code and data archive for Nettle et al. 'Consequences of measurement error in qPCR telomere data: A simulation study'
<p>Code and data for Nettle et al. 'Consequences of measurement error in qPCR telomere data: A simulation study'</p> <p>Updated version of March 2019</p> <p>Main simulation functions are contained in the script ‘simulation.functions.r’. When called, these functions (listed below) return datasets with requested properties containing both the ideal values of the quantities (Cqs, TS, etc.), and their post-error measured values. This allows the user to determine the differences between ideal and measured values, and perform other analyses. All simulation parameter values are user-specifiable. The script ‘paper.results.r’ reproduces all the figures and simulation results from the main paper. 'paper.results.r' also reads in the two .csv files of empirical data (dataset1 and dataset2).</p> <p>Datasets consist of observations from <em>n</em> individuals. The steps common to all of the simulation functions are as follows:</p> <ul> <li>A vector of <em>n </em>true single copy gene abundances, <em>true.dna.scg</em> is defined, drawn from a normal distribution with mean <em>b</em> and standard deviation <em>var.sample.size</em> (<em>b </em>is a constant).</li> <li>A vector of <em>n </em>relative telomere lengths, <em>true.telo.var</em> is defined, drawn from a normal distribution with mean 1 and standard deviation <em>telomere.var.</em></li> <li>Hence, the true abundance of the telomere sequence is defined, as <em>a*true.dna.scg*true.telo.var</em>. Here, <em>a</em> is a scaling constant representing how many copies of the telomeric sequence there are per single copy gene in the average sample.</li> <li>Ideal Cq values for both reactions are defined as <em>f – log<sub>2</sub>(true.dna.scg)</em> and <em>f – log<sub>2</sub>(true.dna.telo),</em> where <em>f</em> is a constant representing the chosen fluorescence threshold.</li> <li>Measurement errors in the Cqs are generated from a normal distribution with mean 0; standard deviations given by <em>error.scg</em> and <em>error.telo</em>; and a correlation between <em>error.scg</em> and <em>error.telo</em> given by <em>error.cor</em>.</li> <li>Hence, measured Cqs are generated, which can be compared to the ideal Cq values.</li> <li>TS ratios are calculated both on the measured Cqs, and the ideal ones.</li> </ul> <p>The following functions are available. Specify desired parameter values in the parenthesis, e.g. <em>generate.one.dataset(n=10000, error.telo=0.1, error.scg=0.1, error.cor=0</em>). Default values in the simulation functions are generally those given in table 1 of the main paper.</p> <ul> <li><em>generate.one.dataset()</em> returns a simple dataset (one telomere measurement per individual) for chosen values of all the variables described in section 1. As well as ideal and measured Cqs, it returns ideal and measured TS ratios. It also returns the difference between the ideal and measured TS ratio, calculated two ways, computed (<em>error.computed</em>), and using equation (11) of online supplement 1 (<em>error.analytic</em>). Both methods produce the same number. This was included as an additional check of correctness of the simulation.</li> <li><em>generate.repeated.measure()</em> returns a dataset where telomere lengths from the same individuals are measured twice, via two independent biological samples, and the true telomere length of each individual is assumed not to have changed at all. The data frame it returns is as for <em>generate.one.dataset()</em>, except that there are two of each variable (e.g. <em>true.ts.1, true.ts.2, measured.ts.1, measured.ts.2</em>, etc.).</li> <li><em>calculate.repeatability()</em> calculates the repeatability of the measured T/S ratio (intra-class correlation coefficient) when <em>generate.repeated.measure()</em> is implemented using the given values for all the parameters. It requires prior installation of R package ‘irr’.</li> <li><em>compare.repeatability()</em> returns the repeatability of the T/S ratio and the repeatability calculated on the raw Cq for the telomere reaction, for the given parameter values.</li> </ul> <p> </p>
Data from: Primers to highly conserved elements optimized for qPCR-based telomere length measurements in vertebrates
<p>Telomere length dynamics are an established biomarker of health and aging in animals. The study of telomeres in numerous species has been facilitated by methods to measure telomere length by real-time quantitative PCR (qPCR). In this method, telomere length is determined by quantifying the amount of telomeric DNA repeats in a sample and normalizing this to the total amount of genomic DNA. This normalization requires the development of genomic reference primers suitable for qPCR, which remains challenging in non-model organism with genomes that have not been sequenced. Here we report reference primers that can be used in qPCR to measure telomere lengths in any vertebrate species. We designed primer pairs to amplify genetic elements that are highly conserved between evolutionarily distant taxa and tested them in species that span the vertebrate tree of life. We report five primer pairs that meet the specificity and reproducibility standards of qPCR. In addition, we demonstrate an approach to choose the best primers for a given species by testing the primers on multiple individuals within a species and then applying an established computational tool. These reference primers can facilitate qPCR-based telomere length measurements in any vertebrate species of ecological or economic interest.</p>
Data and code for 'Telomere length measurement for longitudinal analysis: the role of assay precision'
<p>Data and code for<strong> </strong>'Telomere length measurement for longitudinal analysis: implications of assay precision' by Nettle, Gadalla, Susser, Bateson and Aviv'</p> <p>The R script will run both the numerical simulations and the analysis of the empirical data. The exact results of the simulations will vary slightly from run to run.</p>
Data from: Primers to highly conserved elements optimized for qPCR-based telomere length measurements in vertebrates
Open the record for dataset details and reuse information.
Telomere heterogeneity linked to metabolism and pluripotency state revealed by simultaneous measurement of telomere length and RNA-seq in the same human ES cell
GEO Series GSE98644. Homo sapiens. 121 samples. Type: Expression profiling by high throughput sequencing.
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