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Candidate genes and their corresponding translated proteins were further characterized using the following bioinformatics tools: primary structure similarity relations were determined using ClustalW 1.8 [ 108], structural motif predictions were determined using Prosite [ 109] and peptide domain predictions were determined using ProDom [ 110].

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Uncertainty of the predictions was determined using standard error calculations on the two modeling parameters for water flow decline rates.

Bias in model predictions was determined using the mean prediction error and the slopes from linear regression of the held-out values against the observations.

Bias in model predictions was determined using the normalized mean bias factor (NMBF) [Shaocai Yu, personal communication] and the slope from major-axis linear regression [ 46] of the natural-log transformed left-out observations against the natural-log transformed model predictions.

The gene level performance (sensitivity (Sn) and specificity (Sp)) of the Twinscan and EuGene predictions was determined using as a reference set the longest experimentally verified open reading frame from each of the 378 genes for which we recovered full length sequence, comparing these with only those intergenic predictions which overlapped this set.

Gene sets for class prediction were determined using BRB-ArrayTools Class Predictions, which provide various options for classifier prediction and cross-validation.

Within the Han Chinese population, the most appropriate flanking region for each HLA gene for prediction was determined using the parsimonious rule.

Imperial equations and prediction models were determined using regression analysis and neural networking, respectively.

The rates of genetic gain were determined using the prediction error variance covariance matrix of the traits included into the best linear prediction of breeding values.

Phosphorylation and ubiquitination sites on the AAV-DJ capsid were determined using online prediction tools [ 14, 15].

Signal peptides were determined using Signal-P prediction (http://www.cbs.dtu.dk/services/SignalP/) and the corresponding encoding DNA sequences were removed prior to primer design.

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