For each of type II PKS domain, this table shows the subfamily, biosynthetic function, number of domains in each subfamily,

total number of domains and the average length present in 280 known type II PKSs. Construction of type II PKS domain classifiers Type II PKS domain classifiers were developed for each type II PKS subclass using combination of hidden Ponatinib order Markov Model (HMM) and sequence pairwise alignment based support vector machine (SVM) [19]. The profiled HMM of each type II PKS domain was trained with the sequences of the corresponding domain. HMM calculation was performed using the HMMER software package [20]. For

the construction of SVM classifiers, we used the available software package libSVM [21] to implement SVM on our training datasets. The feature vector for SVM classifier was generated from the scores of pairwise sequence comparison by Smith-Waterman algorithm implemented in SSEARCH from the FASTA software package [22]. The SVM model of each domain subfamily was trained with the sequences LDE225 datasheet of the training dataset. We performed training testing cycles using in-house PERL scripts. We used RBF kernel to train and test our SVM models. The parameter value C and r of kernel function were optimized on the training datasets by cross-validation. The best parameter set was determined when

the product of sensitivity and specificity maximize the prediction accuracy. To evaluate the performance of each domain classifier, the following predictive performance measures were used: Sensitivity (SN) = TP/(TP + FN), Specificity (SP) = TN/(TN + FP), Accuracy (AC) = (TP + TN)/(TP + FP + TN + FN) and Matthews correlation coefficient (MCC) = (TP x TN) – (FN x FP)/√(TP + FN) x (TN + FP) x (TP + FP) x (TN + FN) where TP, TN, FP and FN are true positive, Exoribonuclease true negative, false positive and false negative predictions, respectively. We took type II PKS domain subfamily sequences as the positive set and randomly selected sequences from non-type II PKS domains as the negative set. Depending on the dataset size, 4-fold cross-validation (n ≥ 20) or leave-one-out cross-validation (n < 20) were applied. The average of 10 repeated cross-validation results were used to calculate the performances. Table 2 shows the results of evaluation of type II PKS domain classifiers.