Additional file 1: of Pipeline for the identification and classification of ion channels in parasitic flatworms

Table S1. Sequence counts per ion channel family obtained from the KEGG and SwissProt databases and included in the training and test datasets. Table S2. Accession numbers of ion channels selected for support vector machine model training. Table S3. The number of sequences in the testing dataset before and after BLASTp analyses. Table S4. The number of identified test data sequences from humans and C. elegans within each group and divided into known ion channel and non-ion channel datasets. Table S5. Cross-validation, training and testing accuracies of each model. Table S6. Final tables of confusion matrices for the “Classifier” and “Dipeptide” models. Table S7. Summary of flatworm ion channels predicted using the MuSICC identification and classification pipeline with high and medium confidence. Table S8. Complete set of flatworm ion channels predicted using the MuSICC identification and classification pipeline. (XLS 2960 kb)

Table S1. 补充表S1：从KEGG与Swiss-Prot数据库获取并纳入训练集与测试集的各离子通道家族序列计数。
Table S2. 补充表S2：用于支持向量机模型训练的所选离子通道登录号。
Table S3. 补充表S3：BLASTp分析前后测试数据集的序列数量。
Table S4. 补充表S4：各组别中来自人类与秀丽隐杆线虫(C. elegans)的已鉴定测试序列数量，且该类序列被划分为已知离子通道数据集与非离子通道数据集。
Table S5. 补充表S5：各模型的交叉验证、训练与测试准确率。
Table S6. 补充表S6："Classifier"与"Dipeptide"模型的完整混淆矩阵最终汇总表。
Table S7. 补充表S7：采用MuSICC鉴定与分类流程预测得到的高、中置信度扁形动物离子通道汇总表。
Table S8. 补充表S8：采用MuSICC鉴定与分类流程预测得到的全部扁形动物离子通道集合。（XLS格式，2960 KB）