A Java Program for LRE-Based Real-Time qPCR that Enables Large-Scale Absolute Quantification

Background: Linear regression of efficiency (LRE) introduced a new paradigm for real-time qPCR that enables large-scale absolute quantification by eliminating the need for standard curves. Developed through the application of sigmoidal mathematics to SYBR Green I-based assays, target quantity is derived directly from fluorescence readings within the central region of an amplification profile. However, a major challenge of implementing LRE quantification is the labor intensive nature of the analysis. Findings: Utilizing the extensive resources that are available for developing Java-based software, the LRE Analyzer was written using the NetBeans IDE, and is built on top of the modular architecture and windowing system provided by the NetBeans Platform. This fully featured desktop application determines the number of target molecules within a sample with little or no intervention by the user, in addition to providing extensive database capabilities. MS Excel is used to import data, allowing LRE quantification to be conducted with any real-time PCR instrument that provides access to the raw fluorescence readings. An extensive help set also provides an in-depth introduction to LRE, in addition to guidelines on how to implement LRE quantification. Conclusions: The LRE Analyzer provides the automated analysis and data storage capabilities required by large-scale qPCR projects wanting to exploit the many advantages of absolute quantification. Foremost is the universal perspective afforded by absolute quantification, which among other attributes, provides the ability to directly compare quantitative data produced by different assays and/or instruments. Furthermore, absolute quantification has important implications for gene expression profiling in that it provides the foundation for comparing transcript quantities produced by any gene with any other gene, within and between samples

A versatile panel of reference gene assays for the measurement of chicken mRNA by quantitative PCR

Quantitative real-time PCR assays are widely used for the quantification of mRNA within avian experimental samples. Multiple stably-expressed reference genes, selected for the lowest variation in representative samples, can be used to control random technical variation. Reference gene assays must be reliable, have high amplification specificity and efficiency, and not produce signals from contaminating DNA. Whilst recent research papers identify specific genes that are stable in particular tissues and experimental treatments, here we describe a panel of ten avian gene primer and probe sets that can be used to identify suitable reference genes in many experimental contexts. The panel was tested with TaqMan and SYBR Green systems in two experimental scenarios: a tissue collection and virus infection of cultured fibroblasts. GeNorm and NormFinder algorithms were able to select appropriate reference gene sets in each case. We show the effects of using the selected genes on the detection of statistically significant differences in expression. The results are compared with those obtained using 28s ribosomal RNA, the present most widely accepted reference gene in chicken work, identifying circumstances where its use might provide misleading results. Methods for eliminating DNA contamination of RNA reduced, but did not completely remove, detectable DNA. We therefore attached special importance to testing each qPCR assay for absence of signal using DNA template. The assays and analyses developed here provide a useful resource for selecting reference genes for investigations of avian biology

Absolute estimation of initial concentrations of amplicon in a real-time RT-PCR process

<p>Abstract</p> <p>Background</p> <p>Since real time PCR was first developed, several approaches to estimating the initial quantity of template in an RT-PCR reaction have been tried. While initially only the early thermal cycles corresponding to exponential duplication were used, lately there has been an effort to use all of the cycles in a PCR. The efforts have included both fitting empirical sigmoid curves and more elaborate mechanistic models that explore the chemical reactions taking place during each cycle. The more elaborate mechanistic models require many more parameters than can be fit from a single amplification, while the empirical models provide little insight and are difficult to tailor to specific reactants.</p> <p>Results</p> <p>We directly estimate the initial amount of amplicon using a simplified mechanistic model based on chemical reactions in the annealing step of the PCR. The basic model includes the duplication of DNA with the digestion of Taqman probe and the re-annealing between previously synthesized DNA strands of opposite orientation. By modelling the amount of Taqman probe digested and matching that with the observed fluorescence, the conversion factor between the number of fluorescing dye molecules and observed fluorescent emission can be estimated, along with the absolute initial amount of amplicon and the rate parameter for re-annealing. The model is applied to several PCR reactions with known amounts of amplicon and is shown to work reasonably well. An expanded version of the model allows duplication of amplicon without release of fluorescent dye, by adding 1 more parameter to the model. The additional process is helpful in most cases where the initial primer concentration exceeds the initial probe concentration. Software for applying the algorithm to data may be downloaded at <url>http://www.niehs.nih.gov/research/resources/software/pcranalyzer/</url></p> <p>Conclusion</p> <p>We present proof of the principle that a mechanistically based model can be fit to observations from a single PCR amplification. Initial amounts of amplicon are well estimated without using a standard solution. Using the ratio of the predicted initial amounts of amplicon from 2 PCRs is shown to work well even when the absolute amounts of amplicon are underestimated in the individual PCRs.</p

A Mechanistic Model of PCR for Accurate Quantification of Quantitative PCR Data

Background: Quantitative PCR (qPCR) is a workhorse laboratory technique for measuring the concentration of a target DNA sequence with high accuracy over a wide dynamic range. The gold standard method for estimating DNA concentrations via qPCR is quantification cycle (Cq) standard curve quantification, which requires the time- and labor-intensive construction of a Cq standard curve. In theory, the shape of a qPCR data curve can be used to directly quantify DNA concentration by fitting a model to data; however, current empirical model-based quantification methods are not as reliable as Cq standard curve quantification. Principal Findings: We have developed a two-parameter mass action kinetic model of PCR (MAK2) that can be fitted to qPCR data in order to quantify target concentration from a single qPCR assay. To compare the accuracy of MAK2-fitting to other qPCR quantification methods, we have applied quantification methods to qPCR dilution series data generated in three independent laboratories using different target sequences. Quantification accuracy was assessed by analyzing the reliability of concentration predictions for targets at known concentrations. Our results indicate that quantification by MAK2-fitting is as reliable as Cq standard curve quantification for a variety of DNA targets and a wide range of concentrations. Significance: We anticipate that MAK2 quantification will have a profound effect on the way qPCR experiments are designed and analyzed. In particular, MAK2 enables accurate quantification of portable qPCR assays with limited sampl

Dopamine, affordance and active inference.

The role of dopamine in behaviour and decision-making is often cast in terms of reinforcement learning and optimal decision theory. Here, we present an alternative view that frames the physiology of dopamine in terms of Bayes-optimal behaviour. In this account, dopamine controls the precision or salience of (external or internal) cues that engender action. In other words, dopamine balances bottom-up sensory information and top-down prior beliefs when making hierarchical inferences (predictions) about cues that have affordance. In this paper, we focus on the consequences of changing tonic levels of dopamine firing using simulations of cued sequential movements. Crucially, the predictions driving movements are based upon a hierarchical generative model that infers the context in which movements are made. This means that we can confuse agents by changing the context (order) in which cues are presented. These simulations provide a (Bayes-optimal) model of contextual uncertainty and set switching that can be quantified in terms of behavioural and electrophysiological responses. Furthermore, one can simulate dopaminergic lesions (by changing the precision of prediction errors) to produce pathological behaviours that are reminiscent of those seen in neurological disorders such as Parkinson's disease. We use these simulations to demonstrate how a single functional role for dopamine at the synaptic level can manifest in different ways at the behavioural level

Model based analysis of real-time PCR data from DNA binding dye protocols

BACKGROUND: Reverse transcription followed by real-time PCR is widely used for quantification of specific mRNA, and with the use of double-stranded DNA binding dyes it is becoming a standard for microarray data validation. Despite the kinetic information generated by real-time PCR, most popular analysis methods assume constant amplification efficiency among samples, introducing strong biases when amplification efficiencies are not the same. RESULTS: We present here a new mathematical model based on the classic exponential description of the PCR, but modeling amplification efficiency as a sigmoidal function of the product yield. The model was validated with experimental results and used for the development of a new method for real-time PCR data analysis. This model based method for real-time PCR data analysis showed the best accuracy and precision compared with previous methods when used for quantification of in-silico generated and experimental real-time PCR results. Moreover, the method is suitable for the analyses of samples with similar or dissimilar amplification efficiency. CONCLUSION: The presented method showed the best accuracy and precision. Moreover, it does not depend on calibration curves, making it ideal for fully automated high-throughput applications

Five years of searches for point sources of astrophysical neutrinos with the AMANDA-II neutrino telescope

We report the results of a five-year survey of the northern sky to search for point sources of high energy neutrinos. The search was performed on the data collected with the AMANDA-II neutrino telescope in the years 2000 to 2004, with a live time of 1001 days. The sample of selected events consists of 4282 upward going muon tracks with high reconstruction quality and an energy larger than about 100 GeV. We found no indication of point sources of neutrinos and set 90% confidence level flux upper limits for an all-sky search and also for a catalog of 32 selected sources. For the all-sky search, our average (over declination and right ascension) experimentally observed upper limit Phi(0)=(E/1 TeV)(gamma)center dot d Phi/dE to a point source flux of muon and tau neutrino (detected as muons arising from taus) is Phi(nu mu)+nu(0)(mu)+Phi(nu tau)+nu(0)(tau)=11.1x 10(-11) TeV-1 cm(-2) s(-1), in the energy range between 1.6 TeV and 2.5 PeV for a flavor ratio Phi(nu mu)+nu(0)(mu)/Phi(nu tau)+nu(0)(tau)=1 and assuming a spectral index gamma=2. It should be noticed that this is the first time we set upper limits to the flux of muon and tau neutrinos. In previous papers we provided muon neutrino upper limits only neglecting the sensitivity to a signal from tau neutrinos, which improves the limits by 10% to 16%. The value of the average upper limit presented in this work corresponds to twice the limit on the muon neutrino flux Phi(nu mu)+nu(0)(mu)=5.5x10(-11) TeV-1 cm(-2) s(-1). A stacking analysis for preselected active galactic nuclei and a search based on the angular separation of the events were also performed. We report the most stringent flux upper limits to date, including the results of a detailed assessment of systematic uncertainties

Effects of Acute Ethanol Administration on Neocortical Inhibition

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65719/1/j.1530-0277.1986.tb05132.x.pd

A new real-time PCR method to overcome significant quantitative inaccuracy due to slight amplification inhibition

<p>Abstract</p> <p>Background</p> <p>Real-time PCR analysis is a sensitive DNA quantification technique that has recently gained considerable attention in biotechnology, microbiology and molecular diagnostics. Although, the cycle-threshold (<it>Ct</it>) method is the present "gold standard", it is far from being a standard assay. Uniform reaction efficiency among samples is the most important assumption of this method. Nevertheless, some authors have reported that it may not be correct and a slight PCR efficiency decrease of about 4% could result in an error of up to 400% using the <it>Ct </it>method. This reaction efficiency decrease may be caused by inhibiting agents used during nucleic acid extraction or copurified from the biological sample.</p> <p>We propose a new method (<it>Cy</it>_<it>0</it>) that does not require the assumption of equal reaction efficiency between unknowns and standard curve.</p> <p>Results</p> <p>The <it>Cy</it>_{<it>0 </it>}method is based on the fit of Richards' equation to real-time PCR data by nonlinear regression in order to obtain the best fit estimators of reaction parameters. Subsequently, these parameters were used to calculate the <it>Cy</it>_{<it>0 </it>}value that minimizes the dependence of its value on PCR kinetic.</p> <p>The <it>Ct</it>, second derivative (<it>Cp</it>), sigmoidal curve fitting method (<it>SCF</it>) and <it>Cy</it>_{<it>0 </it>}methods were compared using two criteria: precision and accuracy. Our results demonstrated that, in optimal amplification conditions, these four methods are equally precise and accurate. However, when PCR efficiency was slightly decreased, diluting amplification mix quantity or adding a biological inhibitor such as IgG, the <it>SCF</it>, <it>Ct </it>and <it>Cp </it>methods were markedly impaired while the <it>Cy</it>_{<it>0 </it>}method gave significantly more accurate and precise results.</p> <p>Conclusion</p> <p>Our results demonstrate that <it>Cy</it>_{<it>0 </it>}represents a significant improvement over the standard methods for obtaining a reliable and precise nucleic acid quantification even in sub-optimal amplification conditions overcoming the underestimation caused by the presence of some PCR inhibitors.</p

Amplification by PCR Artificially Reduces the Proportion of the Rare Biosphere in Microbial Communities

The microbial world has been shown to hold an unimaginable diversity. The use of rRNA genes and PCR amplification to assess microbial community structure and diversity present biases that need to be analyzed in order to understand the risks involved in those estimates. Herein, we show that PCR amplification of specific sequence targets within a community depends on the fractions that those sequences represent to the total DNA template. Using quantitative, real-time, multiplex PCR and specific Taqman probes, the amplification of 16S rRNA genes from four bacterial species within a laboratory community were monitored. Results indicate that the relative amplification efficiency for each bacterial species is a nonlinear function of the fraction that each of those taxa represent within a community or multispecies DNA template. Consequently, the low-proportion taxa in a community are under-represented during PCR-based surveys and a large number of sequences might need to be processed to detect some of the bacterial taxa within the ‘rare biosphere’. The structure of microbial communities from PCR-based surveys is clearly biased against low abundant taxa which are required to decipher the complete extent of microbial diversity in nature