Originpro 2016

Manufactured by OriginLab

Sourced in United States

OriginPro 2016 is a data analysis and graphing software developed by OriginLab. It provides a comprehensive set of tools for data analysis, visualization, and presentation. The software supports a wide range of data formats and offers features such as curve fitting, signal processing, and statistical analysis.

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Lab products found in correlation

Pclamp 10, by Molecular Devices (5 mentions) Prism 5, by GraphPad (5 mentions) Prism 7, by GraphPad (5 mentions) Matlab, by MathWorks (4 mentions) Prism 6, by GraphPad (4 mentions) Spss 20, by IBM (4 mentions) Prism 8, by GraphPad (4 mentions) Spectrum 100 ftir spectrometer, by PerkinElmer (3 mentions) Originpro, by OriginLab (3 mentions) Prism 9, by GraphPad (3 mentions) Statistix 10, by SAS Institute (3 mentions) Clampfit 10, by OriginLab (2 mentions) Spectrum 10, by PerkinElmer (2 mentions) Igor pro, by Wavemetrics (2 mentions) Spss version 20, by IBM (2 mentions) Spss statistics 23, by IBM (2 mentions) Nmr suite 8, by Chenomx (2 mentions) Statistica 13, by TIBCO Software (2 mentions) Origin pro 2016, by Adobe (2 mentions) Illustrator cs6, by Adobe (2 mentions)

Close spelling variants of this product

OriginPro (1134 citations) OriginPro 2016 64BIT software (5 citations) OriginPro v.9.3 (2016) (4 citations) OriginPro version 2016 (4 citations) OriginPro 2016 version 9.3 (2 citations)

316 protocols using originpro 2016

Statistical Analysis of Experimental Groups

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The statistical tests used for the evaluation of differences between the experimental groups are mentioned in the corresponding Figure Legends. Z-score values were calculated after the logarithmic transformation of the data by subtracting the sample mean values from the individual sample values and then dividing the result to the sample standard deviation. Unsupervised hierarchical cluster analysis was performed using Morpheus software (https://software.broadinstitute.org/morpheus (accessed on 17 January 2022)) with the “furthest neighbor” algorithm and either 1-minus Spearman’s rank correlation or Euclidean distance metrics (mentioned in the corresponding Figure Legends). Correlation analysis was performed in OriginPro 2016 software (OriginLab Corporation, Northampton, MA, USA) using a Spearman’s rank correlation approach. Survival analysis was performed in OriginPro 2016 software (OriginLab Corporation) using either the Kaplan–Meyer estimator model with log-rank test or the Cox proportional hazards model. Graphs were plotted using Prism 8 software (GraphPad Software, San Diego, CA, USA) and OriginPro 2016 software (OriginLab Corporation).

Chesnokov M.S., Yadav A, & Chefetz I. (2022). Optimized Transcriptional Signature for Evaluation of MEK/ERK Pathway Baseline Activity and Long-Term Modulations in Ovarian Cancer. International Journal of Molecular Sciences, 23(21), 13365.

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Ribosome Dissociation Dynamics with Antibiotics

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Post-termination ribosome complex (post-TC), with an empty A site and deacylated tRNA in the P site, was prepared by mixing 70S ribosomes (0.5 μM) with XR7-mRNA (Met-Phe-Leu) (1 μM) and deacylated tRNA^Leu (1 μM) in HEPES–polymix buffer. A factor mix (FM) containing RRF (20 µM), EF-G (10 µM), and IF3 (1 µM) was prepared. AMK (0–20 µM) or KAN (0−100 µM) was added to both post-TC and FM. Both mixes were incubated at 37 °C for 5 min. Equal volumes of post-TC and FM were rapidly mixed in a stopped-flow instrument (μSFM BioLogic) and the splitting of post-TC into subunits was monitored as a decrease in Rayleigh light scattering at 365 nm^{62 (link)}. The rate of post-TC dissociation was estimated by fitting the data with the double exponential equation in Origin Pro 2016. The rates and amplitudes of the fast phases were determined. The fraction of the inhibited ribosomes was estimated by subtraction of the amplitude of the fast phase with AMK from the one without AMK, divided by total amplitude change (without any drug). The half-maximal inhibitory concentration (K_I) was estimated by plotting fraction inhibition against AMK concentration and fitting the data with hyperbolic function using Origin Pro 2016.

Seely S.M., Parajuli N.P., De Tarafder A., Ge X., Sanyal S, & Gagnon M.G. (2023). Molecular basis of the pleiotropic effects by the antibiotic amikacin on the ribosome. Nature Communications, 14, 4666.

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HPLC-based Conversion Analysis of PA-B to PA-A

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HPLC data were exported from the Gilson Unipoint software that operates the machinery to OriginPro2016. Peak areas were calculated in OriginPro2016 using the inbuilt Peak Analyzer tool with the baseline mode set to asymmetric least squares smoothing.
To determine the percentage of conversion of fed PA-B to PA-A in the Fig. 2 dataset, samples were first adjusted for their methanol controls, to account for any inherent production of either compound by the strains. Percentage conversion was calculated as the amount of PA-A produced divided by the total pseudomonic acids produced (PA-A and PA-B).

Connolly J.A., Wilson A., Macioszek M., Song Z., Wang L., Mohammad H.H., Yadav M., di Martino M., Miller C.E., Hothersall J., Haines A.S., Stephens E.R., Crump M.P., Willis C.L., Simpson T.J., Winn P.J, & Thomas C.M. (2019). Defining the genes for the final steps in biosynthesis of the complex polyketide antibiotic mupirocin by Pseudomonas fluorescens NCIMB10586. Scientific Reports, 9, 1542.

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Evaluating Seasonal Effects on Crop Traits

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The pot and field experiment data were subjected to a generalized linear mixed model (GLMM) using ORIGIN PRO 2016 (ORIGINLAB, Northampton, MA) (Supplementary Tables 2 and 3) and statistical analysis using STATISTIX version 8.1. Analyses of the data generated from the repeated pot and field experiments (2010 and 2011) were performed to determine the seasonal effect. Comparisons among mean values of treatments were made by Least Significant Difference (LSD) at p < 0.05⁴⁴. Correlation among different factors was assessed by the linear regression/Pearson correlation coefficient test using ORIGINPRO 2016 (ORIGINLAB, Northampton, MA).

Naz R., Bano A., Nosheen A., Yasmin H., Keyani R., Shah S.T., Anwar Z, & Roberts T.H. (2021). Induction of defense-related enzymes and enhanced disease resistance in maize against Fusarium verticillioides by seed treatment with Jacaranda mimosifolia formulations. Scientific Reports, 11, 59.

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Gaussian Curve Analysis of Vegetation Frequency

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The Gaussian function is also known as the normal distribution function, and many ecological data can be described by it [35 ]. We used the Gaussian function to fit the frequency curve of the FVC to the sample set, because it produced a clearer visual representation of the influence of quadrat size on the sampling result. The Gaussian function can be expressed as:

f (x) = a e^{- \frac{{(x - μ)}^{2}}{2 σ^{2}}}

where a is the peak of the fitted curve, μ is the mean, σ is the standard deviation. R² represents the correlation coefficient of the Gaussian function when fitted to the frequency curve.
First, we used OriginPro 2016 to count the frequency of FVCs and plot the frequency distribution histogram. Next, using the Gaussian function (performed using the analysis tool of OriginPro 2016) to fit the frequency curve of the sample FVCs. Finally, we analyzed the changing characteristics of frequency curves created with different quadrat sizes.

Hao L., Qingdong S., Imin B, & Kasim N. (2020). Methodology for optimizing quadrat size in sparse vegetation surveys: A desert case study from the Tarim Basin. PLoS ONE, 15(8), e0235469.

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Comparative Analysis of Protein Expression

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Data are presented as mean ± standard deviation, (x ± SDc) and the statistical analysis performed with the t-Student’s test at significance level of 5% (OriginPro 2016, 64-bit, Sr-2). Graphics were plotted with Excel 2016, Microsoft Office 365 and OriginPro 2016, 64-bit, Sr-2.

Barros M.P., Zacarías-Garcia J., Rey F., Zacarías L, & Rodrigo M.J. (2023). Antioxidant Interactions between Citrus Fruit Carotenoids and Ascorbic Acid in New Models of Animal Cell Membranes. Antioxidants, 12(9), 1733.

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Normality Verification and Statistical Analysis

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The normal distribution of the data was verified by a Shapiro-Wilk test using the OriginPro 2016 software.
Significant changes were analyzed by Wilcoxon-Mann-Whitney tests using the OriginPro 2016 software or the TigrMev software (version 4.6.2). The fold changes of the metabolic data with the adjusted p-values as well as the log2 fold changes of the transcriptomic data with the adjusted p-values can be found in the supplementary files.

Hofmann J.D., Biedendieck R., Michel A.M., Schomburg D., Jahn D, & Neumann-Schaal M. (2021). Influence of L-lactate and low glucose concentrations on the metabolism and the toxin formation of Clostridioides difficile. PLoS ONE, 16(1), e0244988.

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Multimodal Tissue Analysis for Renal Cancer

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Principal component analysis (PCA), using the non-linear iterative partial least squares algorithm, on the TS-MS and DESI-MSI data was performed using MATLAB (MathWorks, Natick, MA) and in-house routines which were described in previous work (41 (link)). Data were normalized using standard normal variate transforms to correct for baseline shifts and global variation in signal intensities. While data were recorded over the range m/z 200–1000, only m/z 700–1000 was utilized for PCA and subsequent linear discriminant analysis (LDA), as this mass range provides better diagnostic information(35 (link)). LDA on the PCA compressed data was performed in MATLAB to discriminate between healthy renal tissue and RCC (42 (link)). PCA-LDA was used to calculate sensitivity (true positive rate) and specificity (true negative rate) by means of cross validation (using n=5 deletion groups, Table S2). This procedure is described in detail elsewhere (43 (link)). The dispersion in relative ion intensity for a few ions was plotted as box and whisker plots using OriginPro 2016 (OriginLab, Northampton, MA). Unpaired two sample T-tests of unequal variance and Kruskal-Wallis non-parametric tests were performed in OriginPro 2016 using the raw, unnormalized signal intensities (Table S3).

Alfaro C.M., Jarmusch A.K., Pirro V., Kerian K.S., Masterson T.A., Cheng L, & Cooks R.G. (2016). Ambient ionization mass spectrometric analysis of human surgical specimens to distinguish renal cell carcinoma from healthy renal tissue. Analytical and bioanalytical chemistry, 408(20), 5407-5414.

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Comparative Analysis of Molecular Interactions

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Data are presented as mean ± standard deviation, (x ± SD, n ≥ 3) and the statistical analysis performed with the Student’s t test at significance level of 5% (OriginPro 2016, 64-bit, Sr-2). Graphics were plotted with Excel 2016, Microsoft Office 365, and OriginPro 2016, 64-bit, Sr-2.

Mano C.M., Guaratini T., Cardozo K.H., Colepicolo P., Bechara E.J, & Barros M.P. (2018). Astaxanthin Restrains Nitrative-Oxidative Peroxidation in Mitochondrial-Mimetic Liposomes: A Pre-Apoptosis Model. Marine Drugs, 16(4), 126.

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Analyzing Biomass Water Storage Dynamics

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The data were log-transformed to ensure that the residuals were normally distributed. Statistical comparisons of water storage, dry mass proportion, and water content ratio were conducted using SPSS 20.0 software (a two-way ANOVA test: age class and biomass component). Tukey’s multiple comparison procedure was used to assess differences between age classes or biomass components. The differences were considered statistically significant when P < 0.05. Linear regression analyses were used to analyze the relationship between water storage (or water content ratio) and dry mass using Origin Pro 2016. Origin Pro 2016 was also used to display the statistical results.

Zhou L., Saeed S., Sun Y., Zhang B., Luo M., Li Z, & Amir M. (2019). The relationships between water storage and biomass components in two conifer species. PeerJ, 7, e7901.

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