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Diversity estimations for both pooled and individual population datasets were calculated in DnaSP, including the number of haplotypes (n), haplotype diversity (h), and nucleotide diversity.

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First, the fold change for Catharanthus WRKY genes from the MPGR dataset was calculated in reference to 0 hour control treatments.

The mean and standard deviation of the accuracies on 30 training and independent test datasets were calculated and shown in Additional file 1.

For each metabolic state, errors caused by KIEs on the three isotopic datasets were calculated and are summarized in Fig.  7.

The correlation between REGγ and every other gene in these datasets were calculated and evaluated statistically.

P-values of the normality of the distribution of the spectra and logarithm of spectra of each time series in the given datasets were calculated by the Kolmogorov-Smirnov test.

The breast and ovarian cancer DE genes (Table 1, Datasets II, III) were calculated in the following way: (i) genes whose average expression differs by at least 2-fold between Control (8 samples) and PTBP1-KD samples (4 samples) were selected for analysis, (ii) then a t-test was applied between the two groups, and p-values were adjusted with Benjamini and Hochberg method (False Discovery Rate; FDR).

For each simulated dataset, parameter values were calculated in DNASP for the original phase-known dataset, and then recalculated after removing unresolved genotypes under each of the two thresholds.

Finally, coverage statistics for each sequence in both datasets was calculated using MosaikCoverage.

The Pearson correlation coefficient between data derived from each probe-set in individual datasets was calculated using BioLayout Express3D and all correlation values written to file.

The extent of agreement between the Affymetrix and Agilent datasets was calculated as the percentage of genes in common out of the possible maximum (Table 1).

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