260,817 research outputs found
Element content and daily intake from dietary supplements (nutraceuticals) based on algae, garlic, yeast fish and krill oils—Should consumers be worried?
The authors would like to thank Agilent Technologies for the loan of the 8800 ICP-QQQ used in this study. Michael Stiboller thanks European Union’s Lifelong Learning Programme ‘Leonardo da Vinci’: “ALUMNI UNI GRAZ MOBILITY PROGRAMME 2013-2015” for financial support of his placement.Peer reviewedPostprin
Profound effect of profiling platform and normalization strategy on detection of differentially expressed microRNAs
Adequate normalization minimizes the effects of systematic technical variations and is a prerequisite for getting meaningful biological changes. However, there is inconsistency about miRNA normalization performances and recommendations. Thus, we investigated the impact of seven different normalization methods (reference gene index, global geometric mean, quantile, invariant selection, loess, loessM, and generalized procrustes analysis) on intra- and inter-platform performance of two distinct and commonly used miRNA profiling platforms. We included data from miRNA profiling analyses derived from a hybridization-based platform (Agilent Technologies) and an RT-qPCR platform (Applied Biosystems). Furthermore, we validated a subset of miRNAs by individual RT-qPCR assays. Our analyses incorporated data from the effect of differentiation and tumor necrosis factor alpha treatment on primary human skeletal muscle cells and a murine skeletal muscle cell line. Distinct normalization methods differed in their impact on (i) standard deviations, (ii) the area under the receiver operating characteristic (ROC) curve, (iii) the similarity of differential expression. Loess, loessM, and quantile analysis were most effective in minimizing standard deviations on the Agilent and TLDA platform. Moreover, loess, loessM, invariant selection and generalized procrustes analysis increased the area under the ROC curve, a measure for the statistical performance of a test. The Jaccard index revealed that inter-platform concordance of differential expression tended to be increased by loess, loessM, quantile, and GPA normalization of AGL and TLDA data as well as RGI normalization of TLDA data. We recommend the application of loess, or loessM, and GPA normalization for miRNA Agilent arrays and qPCR cards as these normalization approaches showed to (i) effectively reduce standard deviations, (ii) increase sensitivity and accuracy of differential miRNA expression detection as well as (iii) increase inter-platform concordance. Results showed the successful adoption of loessM and generalized procrustes analysis to one-color miRNA profiling experiments
Automated Derivatization of Amino Acids with Agilent 1260 Infinity II LC System
Undergraduate
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Pre-processing Agilent microarray data
<p>Abstract</p> <p>Background</p> <p>Pre-processing methods for two-sample long oligonucleotide arrays, specifically the Agilent technology, have not been extensively studied. The goal of this study is to quantify some of the sources of error that affect measurement of expression using Agilent arrays and to compare Agilent's Feature Extraction software with pre-processing methods that have become the standard for normalization of cDNA arrays. These include log transformation followed by loess normalization with or without background subtraction and often a between array scale normalization procedure. The larger goal is to define best study design and pre-processing practices for Agilent arrays, and we offer some suggestions.</p> <p>Results</p> <p>Simple loess normalization without background subtraction produced the lowest variability. However, without background subtraction, fold changes were biased towards zero, particularly at low intensities. ROC analysis of a spike-in experiment showed that differentially expressed genes are most reliably detected when background is not subtracted. Loess normalization and no background subtraction yielded an AUC of 99.7% compared with 88.8% for Agilent processed fold changes. All methods performed well when error was taken into account by t- or z-statistics, AUCs ≥ 99.8%. A substantial proportion of genes showed dye effects, 43% (99%<it>CI </it>: 39%, 47%). However, these effects were generally small regardless of the pre-processing method.</p> <p>Conclusion</p> <p>Simple loess normalization without background subtraction resulted in low variance fold changes that more reliably ranked gene expression than the other methods. While t-statistics and other measures that take variation into account, including Agilent's z-statistic, can also be used to reliably select differentially expressed genes, fold changes are a standard measure of differential expression for exploratory work, cross platform comparison, and biological interpretation and can not be entirely replaced. Although dye effects are small for most genes, many array features are affected. Therefore, an experimental design that incorporates dye swaps or a common reference could be valuable.</p
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Error, reproducibility and sensitivity : a pipeline for data processing of Agilent oligonucleotide expression arrays
Background
Expression microarrays are increasingly used to obtain large scale transcriptomic information on a wide range of biological samples. Nevertheless, there is still much debate on the best ways to process data, to design experiments and analyse the output. Furthermore, many of the more sophisticated mathematical approaches to data analysis in the literature remain inaccessible to much of the biological research community. In this study we examine ways of extracting and analysing a large data set obtained using the Agilent long oligonucleotide transcriptomics platform, applied to a set of human macrophage and dendritic cell samples.
Results
We describe and validate a series of data extraction, transformation and normalisation steps which are implemented via a new R function. Analysis of replicate normalised reference data demonstrate that intrarray variability is small (only around 2% of the mean log signal), while interarray variability from replicate array measurements has a standard deviation (SD) of around 0.5 log2 units ( 6% of mean). The common practise of working with ratios of Cy5/Cy3 signal offers little further improvement in terms of reducing error. Comparison to expression data obtained using Arabidopsis samples demonstrates that the large number of genes in each sample showing a low level of transcription reflect the real complexity of the cellular transcriptome. Multidimensional scaling is used to show that the processed data identifies an underlying structure which reflect some of the key biological variables which define the data set. This structure is robust, allowing reliable comparison of samples collected over a number of years and collected by a variety of operators.
Conclusions
This study outlines a robust and easily implemented pipeline for extracting, transforming normalising and visualising transcriptomic array data from Agilent expression platform. The analysis is used to obtain quantitative estimates of the SD arising from experimental (non biological) intra- and interarray variability, and for a lower threshold for determining whether an individual gene is expressed. The study provides a reliable basis for further more extensive studies of the systems biology of eukaryotic cells
Processing of Agilent microRNA array data
<p>Abstract</p> <p>Background</p> <p>The Agilent microRNA microarray platform interrogates each microRNA with several copies of distinct oligonucleotide probes and integrates the results into a total gene signal (TGS), using a proprietary algorithm that makes use of the background subtracted signal. The TGS can be normalized between arrays, and the Agilent recommendation is either not to normalize or to normalize to the 75<sup>th </sup>percentile signal intensity. The <it>robust multiarray average algorithm </it>(RMA) is an alternative method, originally developed to obtain a summary measure of mRNA Affymetrix gene expression arrays by using a linear model that takes into account the probe affinity effect. The RMA method has been shown to improve the accuracy and precision of expression measurements relative to other competing methods. There is also evidence that it might be preferable to use non-corrected signals for the processing of microRNA data, rather than background-corrected signals. In this study we assess the use of the RMA method to obtain a summarized microRNA signal for the Agilent arrays.</p> <p>Findings</p> <p>We have adapted the RMA method to obtain a processed signal for the Agilent arrays and have compared the RMA summarized signal to the TGS generated with the image analysis software provided by the vendor. We also compared the use of the RMA algorithm with uncorrected and background-corrected signals, and compared quantile normalization with the normalization method recommended by the vendor. The pre-processing methods were compared in terms of their ability to reduce the variability (increase precision) of the signals between biological replicates. Application of the RMA method to non-background corrected signals produced more precise signals than either the RMA-background-corrected signal or the quantile-normalized Agilent TGS. The Agilent TGS normalized to the 75% percentile showed more variation than the other measures.</p> <p>Conclusions</p> <p>Used without background correction, a summarized signal that takes into account the probe effect might provide a more precise estimate of microRNA expression. The variability of quantile normalization was lower compared with the normalization method recommended by the vendor.</p
GUI for impedance measurement automation on Agilent 4294A
Bakalářská práce je zaměřena na vytvoření ovládacího softwaru pro impedanční analyzátor Agilent 4294A. V bakalářské práci je vysvětlen přenos dat přes sběrnici GPIB, základy programovacího prostředí LabView od National Instruments, popis samotného impedančního analyzátoru Agilent 4294A a jeho ovladačů pro LabVIEW. V praktické části je podrobně rozebrán ovládací program, který má za úkol přenos dat z analyzátoru, jejich převod do formátu .xlsx pro Microsoft Excel a vytvoření screenshotu obrazovky ve formátu .png. Jsou zde popsány všechny podprogramy zahrnující nastavování a čtení nastavení analyzátoru. V závěru jsou zhodnoceny případné varianty programu z hlediska časové náročnosti a složitosti.The goal of the bachelor thesis is to develop a control software for Agilent 4294A impedance analyser. Text of the theoretical section explains the dataflow of GPIB interface, basics of National Instruments LabView enviroment and the description of Agilent 4294A and its drivers for LabView. The function of the control software is a transfer of data from analyser, its conversion into Microsoft Excel .xlsx format + receiving screenshot in .png format from the analyser’s display. There are described all of subroutines contains setting and reading analyzers setting in this thesis. The conclusion sums up possible control software variation in regard to its complexity.
Agilent Oscilloscopes
Author Institution: Agilent TechnologiesSlides presented at the 6th Annual Photonic Doppler Velocimetry (PDV) Workshop held at Lawrence Livermore National Laboratory, Livermore, California, November 3-4, 2011
Analysis of Odorous VOCs using TD-GC-MS/FID/PFPD: Development and Applications to Real Samples
This work aims to present the applicability of a gas chromatograph equipped with three detectors for the analysis of odorous mixtures. An Agilent gas chromatograph (mod. 8890), equipped with a mass spectrometer (Agilent 5977B MSD), a Flame Ionization Detector (FID, Agilent) and a Pulsed Flame Photometric Detector (PFPD, OI Analytical mod. 5833) was adopted, obtaining simultaneous acquisition with MS, FID and PFPD detectors. The splitting of the sample into the three detectors was carried out at the end of the chromatographic column, by a capillary flow technology splitter (Agilent Splitter CFT). By using this system, it is, therefore, possible to achieve the specific detection and quantification of organic compounds by FID analysis, sulphur compounds by PFPD and the identification of the compounds by MS analysis, via comparison with mass spectra. Based on the preliminary outcomes obtained,the application of this system in the analysis of odour samples enabled the determination of specific classes, even in traces: by this, the subsequent identification of these compounds during a single chromatographic run is possible. This combination provides significant time and costs savings in the calibration and analysis of chromatographic data
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