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118 results for “qPCR”
RT-qPCR dCT values of TLRs, NLRs and cytokines
<p>RT-qPCR dCt values of macrophages incubated with three <em>Lb. plantarum</em> strains. Values are normalized to the expression levels of ß-actin; of NLRs (<em>Nod1</em> and <em>Nod2</em>), TLRs (<em>Tlr2</em>, <em>Tlr4</em>, <em>Tlr5</em>, and <em>Tlr9</em>) and cytokines (<em>Ifn-</em><em>g</em>, <em>Il-10</em>, <em>Il</em>-6 and <em>Tnf-</em><em>a</em>)</p>
qPCR raw datasets on the assessments of cover crop monocultures and mixtures improving the rhizosphere bacterial abundance and functionality through rerooting
<p>Quantitative PCR (qPCR) raw data includes the source data that corresponds to the the counts of 16S rRNA gene copies per gram of soil for different variations, as discussed in the research article - "Cover crop monocultures and mixtures improve the rhizosphere bacterial abundance and functionality through rerooting". The copy number of the 16S rRNA gene per gram of soil was quantified by SYBR® Green-based qPCR using a 7500 Fast Real-Time PCR System (Applied Biosystems™, Thermo Fisher Scientific, Waltham, MA, USA). Aliquots of the same DNA extract utilized in amplicon sequencing were used in qPCR. Dilutions of template DNA were used to compensate for the effect of PCR inhibitors in the samples. Each sample was analyzed in triplicate. A PCR amplicon of the <i>Escherichia coli</i> V3 region was used as standard. Each reaction of 20 µL contained 1 µL of template DNA, the forward primer 341F (Muyzer et al., 1993), the reverse primer 518R (Muyzer et al., 1993), and Luna® Universal qPCR Master Mix (NEB). Reaction conditions were an initial denaturation for 1 min at 95 °C, followed by 40 cycles of denaturation at 95 °C for 15 s and extension at 60 °C for 30 s. The melting curve was recorded in the temperature range of 60 °C to 95 °C. The 16S rRNA gene copy numbers per gram of soil were calculated using the standard curve method and then normalized against the standard (Adelowo et al., 2018). The average efficiency value was 100.77 ± 3.15 %. The absolute copy numbers for each bacterial phylum were calculated by multiplying the qPCR values by the relative abundance values in percent obtained from the 16S rRNA gene sequencing analyses.</p>
Brook trout (Salvelinus fontinalis) cyt b qPCR data from Hidden Lake (Banff National Park, Canada) over two rotenone applications between 2018 and 2020.
Water samples were taken in Hidden Lake at five different time points around two rotenone applications: (i) five weeks prior to the first rotenone application, on July 12 2018; (ii) approximately three weeks after the first application of rotenone, on 7 September 2018; (iii) approximately 10 months after the first rotenone application, on 10 July 2019; and (iv) one year after the final rotenone treatment, on 19 August 2020. For each time point, four pelagic and four littoral water samples were taken from Hidden Lake, as well as 8 to 13 water samples from Hidden Creek and Coral Creek for a total of 16 to 21 samples per time point. Quantitative PCR (qPCR) method was used to produce brook trout (Salvelinus fontinalis) cytochrome b copy number for each sample. The objective of this study was use eDNA to assess the efficacy of invasive brook trout removal using rotenone.
Extensive qPCR analysis reveals altered gene expression in middle ear mucosa from cholesteatoma patients
<p><strong>Abstract</strong></p> <p>The middle ear is a small and hard to reach compartment, limiting the amount of tissue that can be extracted and the possibilities for studying the molecular mechanisms behind diseases like cholesteatoma. In this paper 14 reference gene candidates were evaluated in the middle ear mucosa of cholesteatoma patients and two different control tissues. <em>ACTB</em> and <em>GAPDH</em> were shown to be the optimal genes for the normalisation of target gene expression when investigating middle ear mucosa in multiplex qPCR analysis. Validation of reference genes using <em>c-MYC</em> expression confirmed the suitability of <em>ACTB</em> and <em>GAPDH</em> as reference genes and showed an upregulation of <em>c-MYC</em> in middle ear mucosa during cholesteatoma. The occurrence of participants of the innate immunity, <em>TLR2</em> and <em>TLR4</em>, were analysed in order to compare healthy middle ear mucosa to cholesteatoma. Analysis of <em>TLR2</em> and <em>TLR4</em> showed variable results depending on control tissue used, highlighting the importance of selecting relevant control tissue when investigating causes for disease. It is our belief that a consensus regarding reference genes and control tissue will contribute to the comparability and reproducibility of studies within the field.</p>
qPCR analysis: Arabidopsis AGO1 N-terminal extension acts as an essential hub for PRMT5 interaction and post-translational modifications
<p>Arabidopsis AGO1 N-terminal extension acts as an essential hub for PRMT5 interaction and post-translational modifications' qPCR data following the MIQE guidelines.</p>
Denaturing and dNTPs reagents improve SARS-CoV-2 detection via single and multiplex RT-qPCR
<p>The datas correspond to article entitled: "Denaturing and dNTPs reagents improve SARS-CoV-2 detection via single and multiplex RT-qPCR". </p> <p>The file entitle GISAID have the fasta formats for 107259 genomes from the SARS-CoV-2 GISAID database from January to December 2020. Three documents in plane tex correspond:<br> sequences.fasta contain the original data.<br> sequences_clean.fasta. Corresponds to genomes sequences without nucleotides undeterminateds indicates with "N" in previous document.<br> alignment_clean.fasta. Contain the genomes sequences cleaned alingment. </p> <p>The file entitle GenBank have the data set from 19317 genomes from the SARS-CoV-2 GenBank database from January to October 2020 and the documets have the prrevious order.</p>
eDNAssay: a machine learning tool that accurately predicts qPCR cross-amplification
<p>Environmental DNA (eDNA) sampling is a highly sensitive and cost-effective technique for wildlife monitoring, notably through the use of qPCR assays. However, it can be difficult to ensure assay specificity when many closely related species cooccur. In theory, specificity may be assessed in silico by determining whether assay oligonucleotides have enough base-pair mismatches with nontarget sequences to preclude amplification. However, the mismatch qualities required are poorly understood, making in silico assessments difficult and often necessitating extensive in vitro testing—typically the greatest bottleneck in assay development. Increasing the accuracy of in silico assessments would therefore streamline the assay development process. In this study, we paired 10 qPCR assays with 82 synthetic gene fragments for 530 specificity tests using SYBR Green intercalating dye (n = 262) and TaqMan hydrolysis probes (n = 268). Test results were used to train random forest classifiers to predict amplification. The primer-only model (SYBR Green-based) and full-assay model (TaqMan probe-based) were 99.6% and 100% accurate, respectively, in cross-validation. We further assessed model performance using six independent assays not used in model training. In these tests the primer-only model was 92.4% accurate (n = 119) and the full-assay model was 96.5% accurate (n = 144). The high performance achieved by these models makes it possible for eDNA practitioners to more quickly and confidently develop assays specific to the intended target. Practitioners can access the full-assay model via eDNAssay (https://NationalGenomicsCenter.shinyapps.io/eDNAssay), a user-friendly online tool for predicting qPCR cross-amplification.</p>
Niche partitioning between planktivorous fish in the pelagic Baltic Sea assessed by DNA metabarcoding, qPCR and microscopy: Data and Analyses
<p class="MsoNormal"><span>Marine communities undergo rapid changes because of human-induced ecosystem pressures. The Baltic Sea pelagic food web has experienced several regime shifts during the past century, resulting in a system where competition between planktivorous mesopredators is assumed to be high. While the two clupeids sprat and herring reveal signs of competition, the stickleback population has increased drastically during the past decades. Here, we investigate diet overlap between the three dominating planktivorous fish in the Baltic Sea, utilizing DNA metabarcoding on the <em>18S rRNA</em> gene and the <em>COI </em>gene, targeted qPCR, and microscopy. Our results show niche differentiation between clupeids and stickleback and that rotifers play an important function in niche partitioning of stickleback, as a resource that is not being used, neither by the clupeids nor by other zooplankton. <span>We further show that all the diet assessment methods used in this study are consistent but DNA metabarcoding describes the plankton-fish link at the highest taxonomic resolution. </span>This study suggests that rotifers and other understudied soft-bodied prey may have an important function in the pelagic food web and that the growing population of pelagic stickleback is supported by the unutilized feeding niche offered by the rotifers.</span></p>
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>
Fig. 1 in Species-specific qPCR assays allow for high-resolution population assessment of four species avian schistosome that cause swimmer's itch in recreational lakes
Fig. 1. Abundance of cercariae by sampling site. Water samples were obtained in mid-June and cercariae abundance was determined using the pan-avian schistosomes qPCR.
Fig. 2 in Species-specific qPCR assays allow for high-resolution population assessment of four species avian schistosome that cause swimmer's itch in recreational lakes
Fig. 2. Percent contribution of T. stagnicolae, T. szidati, T. physellae and A. brantae species to each lake. Water samples from different locations and dates were tested using the species-specific qPCR assay and results were pooled by lake to understand the relative contribution overall of each species to each lake. The percent contribution (based on gene copy number) of each species was calculated.
Fig. 3 in Species-specific qPCR assays allow for high-resolution population assessment of four species avian schistosome that cause swimmer's itch in recreational lakes
Fig. 3. Lifecycles of T. stagnicolae, A. brantae, T. szidati, and T. physellae. life cycle summary of the avian schistosome species targeted for species-specific qPCR tests designed in this study.
Figure 3 in Validation of reference genes for quantitative expression analysis by qPCR in various tissues of date mussel (Lithophaga lithophaga)
Figure 3. Pairwise variation (V-value) of candidate reference genes in date mussel (L. lithophaga) using geNorm.
Fig. 3 in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)
Fig. 3. Phylogenetic tree of seven Trypanosome species and subsequent genotypes constructed with sequences of the amplicons generated by the HRMqPCR primers.
Fig. 4 in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)
Fig. 4. Amplification plots, melt curves and standard curves of T. copemani, T. vegrandis G7 and T. noyesi G8 prepared from a plasmid containing trypanosome species.
Fig. 5. A-D in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)
Fig. 5. A-D: Derivative melt curves showing mock mixed infections generated from plasmid clones containing the following DNA: (A) T. noyesi G8 and T. copemani; (B) T. vegrandis G7 and T. copemani; (C) T. vegrandis G7 and T. noyesi G8; (D) T. vegrandis G7, T. noyesi G8 and T. copemani.
Fig. 1 in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)
Fig. 1. Multiple sequence alignment of a portion of the 18S rDNA of seven Trypanosome species and subsequent genotypes used to design the HRM-qPCR assays.
uncropped western blots for analysis of RPN13 ubiquitylation and NRF1 activation by protein aggregates, as well as source data for qPCR plots and flow cytometry gating and FCS files for agDD-GFP in HeLa or HEK cells
<p>This entry contains uncropped blots for Fig 4D and Fig S4C, Fig. 5B, Fig S5 and Fig S6, and the raw FCS files for Flow Cytometry data in doi.org/10.1101/2024.08.30.610524.</p>
Screening for side effects of COVID-19 drug candidates on cardiovascular development -RAW DATA qPCR RESULTS
<p>Raw Data relating to Figures 4 and Supplemental Figures S7 and S8 of the article </p> <p><strong>Screening for side effects of COVID-19 drug candidates on cardiovascular development </strong></p> <p>Alexander Ernst<sup>1#</sup>, Indre Piragyte<sup>1,2#</sup>, Ayisha Marwa MP<sup>1,2</sup>, Ngoc Dung Le<sup>3</sup>, Denis Grandgirard<sup>3</sup>, Stephen L. Leib<sup>3</sup>, Andrew Oates<sup>4</sup>, Nadia Mercader<sup>1,2,5</sup></p> <p> </p> <p> </p> <p> </p> <p><sup>1</sup> Institute of Anatomy, University of Bern, Switzerland</p> <p><sup>2</sup> Department for Biomedical Research DBMR, University of Bern, Switzerland</p> <p><sup>3</sup> Institute for Infectious Diseases, University of Bern, Switzerland</p> <p><sup>4 </sup>School of Life Sciences, École polytechnique fédérale de Lausanne, Switzerland</p> <p><sup>5</sup> Centro Nacional de Investigaciones Cardiovasculares, CNIC, Madrid, Spain</p> <p># shared first-authorship</p>
Tidewater goby and estuarine fish records from seining, qPCR and metabarcoding data for Southern California estuaries in 2023
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