├── docs └── resources │ └── logo.png ├── README.md └── model training ├── LinearSVC.py ├── MLP.py ├── LogisticRegression.py ├── CNN.py └── train_data.txt /docs/resources/logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaolei-lab/ISwine/HEAD/docs/resources/logo.png -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ISwine 2 | 3 |

4 | ISwine_logo 5 |

6 | 7 | The Swine Integrated Omics Database([http://iswine.iomics.pro/](http://iswine.iomics.pro/)) 8 | 9 | ## Overview 10 | 11 | [ISwine](http://iswine.iomics.pro/) is an omics knowledgebase for swine that combines genome, transcriptome, quantitative traits and annotation information, and provides an online gene prioritizing platform based on multi-omics data integration by using a machine learning model. In the present version, ISwine houses almost all current published swine genome data, transcriptome data and QTX (Quantitative Trait Loci/Gene/Nucleotide) data which include 825 Re-sequencing individuals, 3,282 RNA-seq samples and 24,238 QTXs. By means of a user-friendly web interface, users can easily browse, search, visualize and prioritizing causal genes. Collectively, ISwine is dedicated to the integration of swine omics data and thus bears great promise to serve as a valuable knowledgebase for worldwide research communities. 12 | 13 | 14 | -------------------------------------------------------------------------------- /model training/LinearSVC.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # python >= 2.7 4 | 5 | import sys 6 | import numpy as np 7 | import pandas as pd 8 | from sklearn.svm import LinearSVC 9 | from sklearn.model_selection import GridSearchCV, train_test_split 10 | from sklearn import preprocessing 11 | from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score 12 | 13 | def main(): 14 | ##Load data 15 | df = pd.read_csv('./train_data.txt', sep='\t', header=None) 16 | df.columns = ['case', 'gene', 'upstream_snp', 'downstream_snp', 'intron_snp', 'synonymous_snp', 'nonsynonymous_snp', 'upstream_indel', 17 | 'downstream_indel', 'intron_indel', 'synonymous_indel', 'nonsynonymous_indel', 'module', 'expression', 'QTN', 'QTL', 'QTG', 'type', 'label'] 18 | selected_columns = ['upstream_snp', 'downstream_snp', 'intron_snp', 'synonymous_snp', 'nonsynonymous_snp', 'upstream_indel', 19 | 'downstream_indel', 'intron_indel', 'synonymous_indel', 'nonsynonymous_indel', 'module', 'expression', 'QTN', 'QTL'] 20 | X = df[selected_columns] 21 | y = df['label'] 22 | print('Positive(N): %d' % df['label'].sum()) 23 | print('Negative(N): %d' % (len(y) - df['label'].sum())) 24 | ##Scale 25 | X = preprocessing.scale(X) 26 | ##Train set and Test set 27 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=8) 28 | ##Grid search 29 | model = LinearSVC() 30 | param_grid = [ 31 | {'penalty': ['l2'], 'C': [10.0, 1.0, 0.1, 0.01], 'loss': ['hinge', 'squared_hinge'],'max_iter':[1000, 10000],'tol':[1e-4, 1e-5]}, 32 | ] 33 | gridsearch = GridSearchCV(model, param_grid, scoring='f1', n_jobs=-1, cv=4) 34 | gridsearch.fit(X_train, y_train) 35 | model = gridsearch.best_estimator_ 36 | ##Print the best parameters 37 | best_parameters = model.get_params() 38 | for param_name in sorted(best_parameters.keys()): 39 | print('\t%s: %r' % (param_name, best_parameters[param_name])) 40 | ##Print the model scores 41 | preds = model.predict(X_test) 42 | print('Accuracy: %.4f' % accuracy_score(y_test, preds)) 43 | print('Precision: %.4f' % precision_score(y_test, preds)) 44 | print('Recall: %.4f' % recall_score(y_test, preds)) 45 | print('F1: %.4f' % f1_score(y_test, preds)) 46 | 47 | if __name__ == '__main__': 48 | try: 49 | main() 50 | except KeyboardInterrupt: 51 | sys.stderr.write('User interrupt me! ;-) See you!\n') 52 | sys.exit(0) 53 | -------------------------------------------------------------------------------- /model training/MLP.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # python >= 2.7 4 | 5 | import sys 6 | import numpy as np 7 | import pandas as pd 8 | from sklearn.neural_network import MLPClassifier 9 | from sklearn.model_selection import GridSearchCV, train_test_split 10 | from sklearn import preprocessing 11 | from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score 12 | 13 | def main(): 14 | ##Load data 15 | df = pd.read_csv('./train_data.txt', sep='\t', header=None) 16 | df.columns = ['case', 'gene', 'upstream_snp', 'downstream_snp', 'intron_snp', 'synonymous_snp', 'nonsynonymous_snp', 'upstream_indel', 17 | 'downstream_indel', 'intron_indel', 'synonymous_indel', 'nonsynonymous_indel', 'module', 'expression', 'QTN', 'QTL', 'QTG', 'type', 'label'] 18 | selected_columns = ['upstream_snp', 'downstream_snp', 'intron_snp', 'synonymous_snp', 'nonsynonymous_snp', 'upstream_indel', 19 | 'downstream_indel', 'intron_indel', 'synonymous_indel', 'nonsynonymous_indel', 'module', 'expression', 'QTN', 'QTL'] 20 | X = df[selected_columns] 21 | y = df['label'] 22 | print('Positive(N): %d' % df['label'].sum()) 23 | print('Negative(N): %d' % (len(y) - df['label'].sum())) 24 | ##Scale 25 | X = preprocessing.scale(X) 26 | ##Train set and Test set 27 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=8) 28 | ##Grid search 29 | model = MLPClassifier(early_stopping=True) 30 | param_grid = [ 31 | {'activation': ['identity', 'logistic','tanh','relu'], 'solver': ['sgd', 'adam'], 'hidden_layer_sizes': [(64,64),(32,32),(16,16)], 'tol': [1e-4,1e-5], 'max_iter': [50, 100, 200],}, 32 | {'activation': ['identity', 'logistic','tanh','relu'], 'solver': ['lbfgs'], 'hidden_layer_sizes': [(8,8),(8,4),(4,4)], 'tol': [1e-4,1e-5], 'max_iter': [10,20, 40, 60],}, 33 | ] 34 | gridsearch = GridSearchCV(model, param_grid, scoring='f1', n_jobs=-1, cv=4) 35 | gridsearch.fit(X_train, y_train) 36 | model = gridsearch.best_estimator_ 37 | ##Print the best parameters 38 | best_parameters = model.get_params() 39 | for param_name in sorted(best_parameters.keys()): 40 | print('\t%s: %r' % (param_name, best_parameters[param_name])) 41 | ##Print the model scores 42 | preds = model.predict(X_test) 43 | print('Accuracy: %.4f' % accuracy_score(y_test, preds)) 44 | print('Precision: %.4f' % precision_score(y_test, preds)) 45 | print('Recall: %.4f' % recall_score(y_test, preds)) 46 | print('F1: %.4f' % f1_score(y_test, preds)) 47 | 48 | if __name__ == '__main__': 49 | try: 50 | main() 51 | except KeyboardInterrupt: 52 | sys.stderr.write('User interrupt me! ;-) See you!\n') 53 | sys.exit(0) 54 | -------------------------------------------------------------------------------- /model training/LogisticRegression.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # python >= 2.7 4 | 5 | import sys 6 | import numpy as np 7 | import pandas as pd 8 | from sklearn.linear_model import LogisticRegression 9 | from sklearn.model_selection import GridSearchCV, train_test_split 10 | from sklearn import preprocessing 11 | from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score 12 | 13 | def main(): 14 | ##Load data 15 | df = pd.read_csv('./train_data.txt', sep='\t', header=None) 16 | df.columns = ['case', 'gene', 'upstream_snp', 'downstream_snp', 'intron_snp', 'synonymous_snp', 'nonsynonymous_snp', 'upstream_indel', 17 | 'downstream_indel', 'intron_indel', 'synonymous_indel', 'nonsynonymous_indel', 'module', 'expression', 'QTN', 'QTL', 'QTG', 'type', 'label'] 18 | selected_columns = ['upstream_snp', 'downstream_snp', 'intron_snp', 'synonymous_snp', 'nonsynonymous_snp', 'upstream_indel', 19 | 'downstream_indel', 'intron_indel', 'synonymous_indel', 'nonsynonymous_indel', 'module', 'expression', 'QTN', 'QTL'] 20 | X = df[selected_columns] 21 | y = df['label'] 22 | print('Positive(N): %d' % df['label'].sum()) 23 | print('Negative(N): %d' % (len(y) - df['label'].sum())) 24 | ##Scale 25 | X = preprocessing.scale(X) 26 | ##Train set and Test set 27 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=8) 28 | ##Grid search 29 | model = LogisticRegression() 30 | param_grid = [ 31 | {'penalty': ['l1'], 'solver': ['liblinear', 'saga'],'max_iter':[100, 1000, 10000],'tol':[1e-4, 1e-5]}, 32 | {'penalty': ['l2'], 'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],'max_iter':[100, 1000, 10000],'tol':[1e-4, 1e-5]}, 33 | {'penalty': ['none'], 'solver': ['newton-cg', 'lbfgs', 'sag', 'saga'],'max_iter':[100, 1000, 10000],'tol':[1e-4, 1e-5]}, 34 | ] 35 | gridsearch = GridSearchCV(model, param_grid, scoring='f1', n_jobs=-1, cv=4) 36 | gridsearch.fit(X_train, y_train) 37 | model = gridsearch.best_estimator_ 38 | ##Print the best parameters 39 | best_parameters = model.get_params() 40 | for param_name in sorted(best_parameters.keys()): 41 | print('\t%s: %r' % (param_name, best_parameters[param_name])) 42 | ##Print the model scores 43 | preds = model.predict(X_test) 44 | print('Accuracy: %.4f' % accuracy_score(y_test, preds)) 45 | print('Precision: %.4f' % precision_score(y_test, preds)) 46 | print('Recall: %.4f' % recall_score(y_test, preds)) 47 | print('F1: %.4f' % f1_score(y_test, preds)) 48 | 49 | if __name__ == '__main__': 50 | try: 51 | main() 52 | except KeyboardInterrupt: 53 | sys.stderr.write('User interrupt me! ;-) See you!\n') 54 | sys.exit(0) 55 | -------------------------------------------------------------------------------- /model training/CNN.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # python >= 2.7 4 | 5 | import numpy as np 6 | import pandas as pd 7 | from sklearn import preprocessing 8 | from keras.utils import to_categorical 9 | from sklearn.model_selection import train_test_split 10 | from keras import layers,models,optimizers,regularizers 11 | import keras.backend as K 12 | 13 | def check_units(y_true, y_pred): 14 | if y_pred.shape[1] != 1: 15 | y_pred = y_pred[:,1:2] 16 | y_true = y_true[:,1:2] 17 | return y_true, y_pred 18 | 19 | def precision(y_true, y_pred): 20 | y_true, y_pred = check_units(y_true, y_pred) 21 | true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) 22 | predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) 23 | precision = true_positives / (predicted_positives + K.epsilon()) 24 | return precision 25 | 26 | def recall(y_true, y_pred): 27 | y_true, y_pred = check_units(y_true, y_pred) 28 | true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) 29 | possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) 30 | recall = true_positives / (possible_positives + K.epsilon()) 31 | return recall 32 | 33 | def f1(y_true, y_pred): 34 | def recall(y_true, y_pred): 35 | true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) 36 | possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) 37 | recall = true_positives / (possible_positives + K.epsilon()) 38 | return recall 39 | 40 | def precision(y_true, y_pred): 41 | true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) 42 | predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) 43 | precision = true_positives / (predicted_positives + K.epsilon()) 44 | return precision 45 | y_true, y_pred = check_units(y_true, y_pred) 46 | precision = precision(y_true, y_pred) 47 | recall = recall(y_true, y_pred) 48 | return 2*((precision*recall)/(precision+recall+K.epsilon())) 49 | 50 | def createModel(): 51 | model = models.Sequential() 52 | model.add(layers.Conv1D(filters=32, kernel_size=3, strides=1,activation='relu',input_shape=(14,1),kernel_regularizer=regularizers.l1(0.001))) 53 | model.add(layers.MaxPooling1D(pool_size=2, strides=2, padding='same')) 54 | 55 | model.add(layers.Conv1D(filters=64, kernel_size=3, strides=1, padding='same',activation='relu',kernel_regularizer=regularizers.l1(0.001))) 56 | model.add(layers.MaxPooling1D(pool_size=2, strides=2, padding='same')) 57 | 58 | model.add(layers.Conv1D(filters=128, kernel_size=3, strides=1, padding='same',activation='relu',kernel_regularizer=regularizers.l1(0.001))) 59 | model.add(layers.MaxPooling1D(pool_size=2, strides=2, padding='same')) 60 | 61 | model.add(layers.Conv1D(filters=256, kernel_size=3, strides=1, padding='same',activation='relu',kernel_regularizer=regularizers.l1(0.001))) 62 | model.add(layers.MaxPooling1D(pool_size=2, strides=2, padding='same')) 63 | 64 | model.add(layers.Flatten()) 65 | model.add(layers.Dense(1024,activation='relu')) 66 | model.add(layers.Dropout(0.2)) 67 | model.add(layers.Dense(512,activation='relu')) 68 | model.add(layers.Dropout(0.2)) 69 | model.add(layers.Dense(2, activation='softmax')) 70 | 71 | model.compile(optimizer=optimizers.RMSprop(lr=1e-4), loss='binary_crossentropy', metrics=['acc', precision,recall, f1]) 72 | return model 73 | 74 | def main(): 75 | ##Load data 76 | df = pd.read_csv('./train_data.txt', sep='\t', header=None) 77 | df.columns = ['case', 'gene', 'upstream_snp', 'downstream_snp', 'intron_snp', 'synonymous_snp', 'nonsynonymous_snp', 'upstream_indel', 78 | 'downstream_indel', 'intron_indel', 'synonymous_indel', 'nonsynonymous_indel', 'module', 'expression', 'QTN', 'QTL', 'QTG', 'type', 'label'] 79 | selected_columns = ['upstream_snp', 'downstream_snp', 'intron_snp', 'synonymous_snp', 'nonsynonymous_snp', 'upstream_indel', 80 | 'downstream_indel', 'intron_indel', 'synonymous_indel', 'nonsynonymous_indel', 'module', 'expression', 'QTN', 'QTL'] 81 | X = df[selected_columns].astype(np.float64) 82 | y = np.array(df[['label']]).reshape((-1)) 83 | print('Positive(N): %d' % df['label'].sum()) 84 | print('Negative(N): %d' % (len(y) - df['label'].sum())) 85 | ##Scale 86 | X = preprocessing.scale(X) 87 | X = np.reshape(X, [len(X), 14, 1]) 88 | y = to_categorical(y) 89 | ##Train set and Test set 90 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=8) 91 | ## Create model 92 | model = createModel() 93 | history = model.fit(X_train, y_train, batch_size=10, epochs=60, verbose=0) 94 | loss,accuracy,precision,recall,f1 = model.evaluate(X_test, y_test , verbose=0) 95 | print("Loss:"+str(loss)) 96 | print("Accuracy:"+str(accuracy)) 97 | print("Precision:"+str(precision)) 98 | print("Recall:"+str(recall)) 99 | print("F1:"+str(f1)) 100 | 101 | if __name__ == '__main__': 102 | try: 103 | main() 104 | except KeyboardInterrupt: 105 | sys.stderr.write('User interrupt me! ;-) See you!\n') 106 | sys.exit(0) 107 | -------------------------------------------------------------------------------- /model training/train_data.txt: -------------------------------------------------------------------------------- 1 | case1 ENSSSCG00000029037 61 28 0 11 45 0 4 0 5 0 0.021941076 14.79 0 0 2 Q1 1 2 | case2 ENSSSCG00000021585 56 67 578 18 177 6 1 34 5 0 0.051473654 6.19 0 0 1 Q1 1 3 | case3 ENSSSCG00000016628 28 61 2615 23 5 0 2 148 1 0 0.11151874 13.03 0 0 1 Q1 1 4 | case4 ENSSSCG00000005109 52 34 2232 74 55 3 5 110 3 0 0.021941076 0.33 1 0 4 Q1 1 5 | case5 ENSSSCG00000003428 60 70 487 67 128 1 4 17 2 0 0.088807365 16.2 2 0 2 Q1 1 6 | case6 ENSSSCG00000003088 28 22 36 35 43 11 2 3 4 1 0.041385211 1138.49 1 0 1 Q1 1 7 | case7 ENSSSCG00000036883 29 37 179 7 77 3 2 12 4 0 0.223590032 57.11 0 0 0 Q2 1 8 | case8 ENSSSCG00000034973 101 64 1009 15 167 3 0 61 6 0 0.179009118 303.27 0 0 3 Q2 1 9 | case9 ENSSSCG00000028512 43 33 645 48 71 4 4 54 3 0 0.156956278 82 6 0 0 Q2 1 10 | case10 ENSSSCG00000026951 26 71 714 18 16 1 1 36 1 0 0.538342052 602.26 12 1 4 Q2 1 11 | case11 ENSSSCG00000025691 31 44 132 22 24 1 2 10 4 0 0.121567487 64.27 0 0 4 Q2 1 12 | case12 ENSSSCG00000025618 37 58 206 87 73 1 3 16 7 0 0.538342052 446.43 12 1 1 Q2 1 13 | case13 ENSSSCG00000025188 86 75 4053 35 243 5 3 211 6 0 0.053102586 0.18 1 2 0 Q2 1 14 | case14 ENSSSCG00000017913 78 73 53 51 88 6 2 2 5 0 0.405685735 76.59 0 0 2 Q2 1 15 | case15 ENSSSCG00000012853 26 66 25 34 47 3 0 2 3 0 0.538342052 222.69 0 6 8 Q2 1 16 | case16 ENSSSCG00000012257 44 25 1385 11 26 2 2 132 0 0 0.547226335 149.55 2 0 0 Q2 1 17 | case17 ENSSSCG00000012077 126 97 1141 69 51 7 3 62 5 1 0.538342052 739.73 1 0 3 Q2 1 18 | case18 ENSSSCG00000006126 28 49 595 37 42 1 0 66 4 0 0.168881583 18.53 0 0 0 Q2 1 19 | case19 ENSSSCG00000005109 52 34 2232 74 55 3 5 110 3 0 0.053102586 0 0 0 0 Q2 1 20 | case20 ENSSSCG00000002475 69 143 949 24 53 7 4 54 2 0 0.168881583 0 0 0 0 Q2 1 21 | case21 ENSSSCG00000001463 59 63 90 41 25 2 4 3 3 0 0.538342052 252.82 12 1 4 Q2 1 22 | case22 ENSSSCG00000001409 39 43 146 277 112 5 3 19 9 3 0.218178103 160.01 13 2 2 Q2 1 23 | case23 ENSSSCG00000039169 65 44 50 6 28 2 3 1 2 0 0.405685735 1.58 0 0 1 Q3 1 24 | case24 ENSSSCG00000038644 30 35 20 2 18 3 6 1 5 0 0.275318679 5.63 0 0 2 Q3 1 25 | case25 ENSSSCG00000038627 55 56 7645 59 31 3 4 631 1 0 0.405685735 2.26 0 0 1 Q3 1 26 | case26 ENSSSCG00000034973 101 64 1009 15 167 3 0 61 6 0 0.179009118 303.27 0 0 3 Q3 1 27 | case27 ENSSSCG00000030585 35 29 12 8 0 3 5 1 0 0 0.275318679 11.12 0 3 0 Q3 1 28 | case28 ENSSSCG00000026605 92 124 2015 22 46 0 2 105 3 0 0.11253525 16.36 1 2 1 Q3 1 29 | case29 ENSSSCG00000025691 31 44 132 22 24 1 2 10 4 0 0.121567487 64.27 0 0 4 Q3 1 30 | case30 ENSSSCG00000025618 37 58 206 87 73 1 3 16 7 0 0.538342052 446.43 12 1 1 Q3 1 31 | case31 ENSSSCG00000024252 90 85 172 4 126 7 4 11 3 0 0.168881583 0 0 0 1 Q3 1 32 | case32 ENSSSCG00000020924 73 51 0 16 16 3 2 0 1 0 0.170447367 0 1 0 1 Q3 1 33 | case33 ENSSSCG00000017913 78 73 53 51 88 6 2 2 5 0 0.405685735 76.59 0 0 2 Q3 1 34 | case34 ENSSSCG00000017251 41 69 24 57 112 2 4 1 16 0 0.168456268 9.18 0 0 3 Q3 1 35 | case35 ENSSSCG00000016856 36 56 2133 17 36 1 10 167 0 0 0.168881583 77.9 0 0 0 Q3 1 36 | case36 ENSSSCG00000015733 58 75 12741 143 107 7 7 657 6 0 0.218178103 2.06 0 0 1 Q3 1 37 | case37 ENSSSCG00000014985 51 69 1423 42 36 6 4 173 1 0 0.405685735 123.62 0 1 2 Q3 1 38 | case38 ENSSSCG00000014213 50 42 5125 14 9 4 5 484 0 1 0.168881583 0.39 0 0 1 Q3 1 39 | case39 ENSSSCG00000012853 26 66 25 34 47 3 0 2 3 0 0.538342052 222.69 0 6 8 Q3 1 40 | case40 ENSSSCG00000012077 126 97 1141 69 51 7 3 62 5 1 0.538342052 739.73 1 0 3 Q3 1 41 | case41 ENSSSCG00000011862 35 76 899 11 31 7 6 66 0 0 0.547226335 535.27 38 2 2 Q3 1 42 | case42 ENSSSCG00000011851 48 37 1893 15 96 2 2 84 6 0 0.405685735 6.5 22 2 1 Q3 1 43 | case43 ENSSSCG00000010316 22 67 3702 145 76 0 0 229 7 2 0.131299104 25.98 0 0 1 Q3 1 44 | case44 ENSSSCG00000006830 55 35 1852 31 17 7 3 117 2 0 0.218178103 46.41 0 0 1 Q3 1 45 | case45 ENSSSCG00000004886 32 60 3425 32 64 1 2 239 9 0 0.131299104 0.25 0 0 1 Q3 1 46 | case46 ENSSSCG00000004347 60 48 1481 40 43 3 6 96 1 0 0.053102586 18.51 0 4 1 Q3 1 47 | case47 ENSSSCG00000004001 24 20 6115 6 14 1 4 328 0 0 0.168881583 0.28 0 0 1 Q3 1 48 | case48 ENSSSCG00000003433 53 57 1256 150 103 4 2 50 3 0 0.218178103 29.55 1 1 6 Q3 1 49 | case49 ENSSSCG00000003402 39 64 453 70 16 6 4 33 0 0 0.701202268 545.02 11 0 1 Q3 1 50 | case50 ENSSSCG00000002873 73 48 864 59 47 1 7 47 3 0 0.240417016 337.67 0 0 2 Q3 1 51 | case51 ENSSSCG00000035805 32 88 134 8 56 3 3 13 3 0 0.020288533 7.44 0 1 2 Q4 1 52 | case52 ENSSSCG00000020924 73 51 0 16 16 3 2 0 1 0 0.427059759 2.77 0 0 1 Q4 1 53 | case53 ENSSSCG00000014985 51 69 1423 42 36 6 4 173 1 0 0.117014672 3.61 0 0 0 Q4 1 54 | case54 ENSSSCG00000006754 85 59 817 26 54 4 11 33 4 0 0.025416065 1.42 1 0 2 Q4 1 55 | case55 ENSSSCG00000005193 56 55 452 16 53 2 0 27 1 0 0.005461422 2.52 0 0 4 Q4 1 56 | case56 ENSSSCG00000002361 46 77 178 28 38 5 3 14 3 0 0.069611073 1 2 1 8 Q4 1 57 | case57 ENSSSCG00000040961 28 34 35 7 41 6 2 2 4 0 0.11253629 1.42 0 0 2 Q5 1 58 | case58 ENSSSCG00000040793 61 39 237 56 160 3 3 17 6 0 0.11253629 187.53 6 18 2 Q5 1 59 | case59 ENSSSCG00000040681 40 43 126 13 24 2 6 10 0 0 0.682512202 11212.17 2 1 0 Q5 1 60 | case60 ENSSSCG00000040464 64 102 687 6 127 1 1 28 6 0 0.682512202 64.44 1 0 1 Q5 1 61 | case61 ENSSSCG00000038918 141 78 155 87 208 6 5 7 3 0 0.073558183 174.38 2 5 0 Q5 1 62 | case62 ENSSSCG00000038500 29 101 109 37 191 3 1 4 16 0 0.069200119 19.38 0 2 5 Q5 1 63 | case63 ENSSSCG00000037910 46 88 48 39 87 8 5 6 8 0 0.11253629 45.23 0 0 5 Q5 1 64 | case64 ENSSSCG00000036883 29 37 179 7 77 3 2 12 4 0 0.682512202 886.19 0 6 4 Q5 1 65 | case65 ENSSSCG00000036236 62 111 4876 7 138 1 4 449 8 0 0.682512202 771.17 20 2 9 Q5 1 66 | case66 ENSSSCG00000035949 77 66 13691 45 33 3 4 1051 2 0 0.042472743 33.69 6 0 2 Q5 1 67 | case67 ENSSSCG00000035293 80 68 507 23 84 0 6 33 12 0 0.11253629 234.86 6 19 1 Q5 1 68 | case68 ENSSSCG00000034689 89 77 86 7 70 3 3 1 3 1 0.682512202 159.58 0 0 0 Q5 1 69 | case69 ENSSSCG00000034610 27 68 3897 1 41 3 5 226 6 0 0.11253629 0.28 0 0 6 Q5 1 70 | case70 ENSSSCG00000034212 84 83 42 46 117 3 4 4 11 0 0.018485991 0 0 1 2 Q5 1 71 | case71 ENSSSCG00000034102 0 52 259 12 4 0 3 15 0 0 0.682512202 5524.61 5 1 1 Q5 1 72 | case72 ENSSSCG00000034035 65 80 88 14 60 4 2 10 5 1 0.046044683 9.48 0 0 0 Q5 1 73 | case73 ENSSSCG00000033626 44 72 404 87 101 6 5 19 5 2 0.01640412 56.78 0 0 3 Q5 1 74 | case74 ENSSSCG00000032782 42 62 181 15 126 5 5 10 10 0 0.073558183 44.08 0 3 0 Q5 1 75 | case75 ENSSSCG00000032242 30 45 138 17 68 0 2 4 8 0 0.032314731 22.55 17 3 6 Q5 1 76 | case76 ENSSSCG00000031100 112 95 0 15 23 3 3 2 0 0 0.016361723 0 0 1 1 Q5 1 77 | case77 ENSSSCG00000030707 104 99 761 26 66 1 2 50 3 0 0.069200119 45.94 0 1 1 Q5 1 78 | case78 ENSSSCG00000029944 32 64 173 157 106 2 5 21 9 0 0.682512202 2368.42 6 1 1 Q5 1 79 | case79 ENSSSCG00000029637 84 92 3041 103 18 0 5 200 0 1 0.032314731 42.61 1 0 3 Q5 1 80 | case80 ENSSSCG00000028322 53 62 59 34 166 4 1 3 21 0 0.11253629 72.68 0 0 1 Q5 1 81 | case81 ENSSSCG00000027898 36 80 4634 59 93 7 10 287 5 0 0.042472743 23.04 0 0 1 Q5 1 82 | case82 ENSSSCG00000027609 35 50 968 8 9 2 8 74 2 0 0.021879706 5.41 1 0 2 Q5 1 83 | case83 ENSSSCG00000026330 95 96 363 3 53 7 7 16 4 0 0.036579826 0.13 0 0 3 Q5 1 84 | case84 ENSSSCG00000025777 86 67 9873 79 46 3 5 506 2 0 0.014622318 2.68 0 1 1 Q5 1 85 | case85 ENSSSCG00000025698 57 75 140 52 26 1 6 4 1 0 0.11253629 59.51 1 0 0 Q5 1 86 | case86 ENSSSCG00000025188 86 75 4053 35 243 5 3 211 6 0 0.013043583 0.46 5 29 7 Q5 1 87 | case87 ENSSSCG00000021954 92 78 0 12 34 8 8 0 1 0 0.016361723 0.21 0 1 1 Q5 1 88 | case88 ENSSSCG00000021585 56 67 578 18 177 6 1 34 5 0 0.040252961 0.11 0 0 2 Q5 1 89 | case89 ENSSSCG00000019232 97 95 0 0 0 4 1 0 0 0 0.016361723 0 6 1 1 Q5 1 90 | case90 ENSSSCG00000017694 66 67 8484 206 32 6 5 533 2 0 0.682512202 297.55 0 2 6 Q5 1 91 | case91 ENSSSCG00000017500 101 79 8 36 56 6 5 1 2 0 0.073558183 20.79 0 1 4 Q5 1 92 | case92 ENSSSCG00000016977 38 44 0 1 5 6 4 0 0 0 0.036798356 0.35 3 0 0 Q5 1 93 | case93 ENSSSCG00000016976 29 44 6209 23 72 0 2 336 0 0 0.013043583 27.29 7 0 0 Q5 1 94 | case94 ENSSSCG00000016975 42 47 736 10 70 2 2 56 5 0 0.032314731 8.99 4 0 0 Q5 1 95 | case95 ENSSSCG00000016628 28 61 2615 23 5 0 2 148 1 0 0.258086995 5.39 0 0 2 Q5 1 96 | case96 ENSSSCG00000016200 63 77 670 23 174 11 2 62 19 1 0.073558183 0.38 0 1 1 Q5 1 97 | case97 ENSSSCG00000014834 124 131 368 21 61 2 9 19 5 0 0.073558183 144.04 1 2 2 Q5 1 98 | case98 ENSSSCG00000014811 30 73 423 172 31 5 1 31 2 0 0.032314731 88.59 2 2 1 Q5 1 99 | case99 ENSSSCG00000014170 74 65 4140 46 134 6 4 398 6 0 0.11253629 125 0 0 1 Q5 1 100 | case100 ENSSSCG00000013399 32 56 6090 19 63 6 7 559 9 0 0.031089576 33.17 0 0 3 Q5 1 101 | case101 ENSSSCG00000013392 46 44 938 129 22 7 0 68 1 0 0.032314731 75.24 0 1 3 Q5 1 102 | case102 ENSSSCG00000013375 47 35 22 10 16 3 3 1 1 0 0.007665361 0.15 0 0 1 Q5 1 103 | case103 ENSSSCG00000013366 50 56 318 67 52 3 6 28 2 0 0.073558183 140.91 1 0 4 Q5 1 104 | case104 ENSSSCG00000013072 79 82 1563 62 87 6 11 94 3 0 0.021879706 20 2 5 9 Q5 1 105 | case105 ENSSSCG00000012583 25 62 1285 90 97 4 3 98 6 0 0.04248883 40.05 20 0 2 Q5 1 106 | case106 ENSSSCG00000011996 57 45 378 7 47 8 4 17 4 0 0.018485991 0.1 0 1 4 Q5 1 107 | case107 ENSSSCG00000011579 25 43 2352 108 24 5 2 171 2 0 0.682512202 292.95 0 2 2 Q5 1 108 | case108 ENSSSCG00000011568 60 52 126 4 33 0 1 7 0 0 0.11253629 0.06 0 0 1 Q5 1 109 | case109 ENSSSCG00000010554 40 54 229 47 185 1 3 12 19 0 0.682512202 3645.97 876 29 20 Q5 1 110 | case110 ENSSSCG00000009919 49 48 357 123 121 2 2 28 4 0 0.021879706 0.35 0 1 2 Q5 1 111 | case111 ENSSSCG00000009179 64 64 2267 34 81 3 2 109 7 0 0.021879706 0.28 0 2 6 Q5 1 112 | case112 ENSSSCG00000008603 85 77 1006 31 27 18 3 75 1 0 0.025368076 0.26 0 1 2 Q5 1 113 | case113 ENSSSCG00000007337 47 129 5728 89 27 4 4 411 2 0 0.075103271 45.71 0 3 3 Q5 1 114 | case114 ENSSSCG00000006872 79 93 4292 87 68 12 9 225 4 0 0.031089576 8.03 0 1 2 Q5 1 115 | case115 ENSSSCG00000006355 47 68 35 1 5 2 3 0 0 0 0.021879706 6.74 2 2 1 Q5 1 116 | case116 ENSSSCG00000006153 55 56 110 18 58 1 1 6 5 0 0.11253629 773.81 2 2 4 Q5 1 117 | case117 ENSSSCG00000005494 85 99 3168 259 210 4 0 198 10 0 0.11253629 16.97 0 4 1 Q5 1 118 | case118 ENSSSCG00000005215 49 53 3617 160 155 2 9 327 12 0 0.04248883 7.71 0 0 1 Q5 1 119 | case119 ENSSSCG00000004904 46 56 0 12 26 2 2 0 1 0 0.034428683 0 2 2 11 Q5 1 120 | case120 ENSSSCG00000004454 52 59 5120 44 33 6 3 309 0 0 0.682512202 326.92 5 0 1 Q5 1 121 | case121 ENSSSCG00000004318 44 42 1711 44 12 2 5 132 1 0 0.031089576 16.32 0 0 0 Q5 1 122 | case122 ENSSSCG00000003867 37 89 1049 5 62 3 0 54 3 1 0.032314731 81.49 0 0 0 Q5 1 123 | case123 ENSSSCG00000003805 65 81 7868 80 95 0 2 394 7 0 0.04248883 9.37 12 1 3 Q5 1 124 | case124 ENSSSCG00000003565 40 31 40 5 58 2 1 2 2 0 0.021879706 0.02 0 0 0 Q5 1 125 | case125 ENSSSCG00000003428 60 70 487 67 128 1 4 17 2 0 0.032314731 15.49 1 2 3 Q5 1 126 | case126 ENSSSCG00000003088 28 22 36 35 43 11 2 3 4 1 0.021879706 4334.63 2 0 2 Q5 1 127 | case127 ENSSSCG00000003035 26 27 101 42 74 0 0 31 10 0 0.042472743 26.48 0 5 4 Q5 1 128 | case128 ENSSSCG00000001539 34 62 1389 78 150 2 3 79 10 0 0.032314731 46.43 2 5 0 Q5 1 129 | case129 ENSSSCG00000001478 59 77 4010 7 63 3 5 191 0 0 0.013043583 9.67 1 6 0 Q5 1 130 | case130 ENSSSCG00000001475 71 51 127 87 87 5 1 8 7 1 0.032314731 85.35 1 6 4 Q5 1 131 | case131 ENSSSCG00000001411 34 32 27 2 28 3 0 2 4 0 0.021879706 10.82 2 10 0 Q5 1 132 | case132 ENSSSCG00000001404 51 56 30 28 62 2 1 2 2 0 0.11253629 3.19 1 14 2 Q5 1 133 | case133 ENSSSCG00000000910 26 102 11624 19 27 2 9 641 3 0 0.019914506 37.01 2 3 4 Q5 1 134 | case134 ENSSSCG00000000405 60 25 113 118 48 3 2 12 1 0 0.11253629 443.56 0 2 0 Q5 1 135 | case135 ENSSSCG00000000182 46 49 261 16 92 4 2 23 3 0 0.010465473 2.65 0 0 3 Q5 1 136 | case136 ENSSSCG00000000006 118 84 908 79 14 5 3 51 1 0 0.021879706 20.01 1 0 2 Q5 1 137 | case137 ENSSSCG00000040793 61 39 237 56 160 3 3 17 6 0 0.199148428 187.53 6 18 2 Q6 1 138 | case138 ENSSSCG00000040464 64 102 687 6 127 1 1 28 6 0 0.682512202 64.44 1 0 1 Q6 1 139 | case139 ENSSSCG00000039557 95 51 1094 13 14 7 6 105 2 0 0.764800782 178.26 1 0 0 Q6 1 140 | case140 ENSSSCG00000038918 141 78 155 87 208 6 5 7 3 0 0.667841098 174.38 2 5 0 Q6 1 141 | case141 ENSSSCG00000037910 46 88 48 39 87 8 5 6 8 0 0.199148428 45.23 0 1 5 Q6 1 142 | case142 ENSSSCG00000035805 32 88 134 8 56 3 3 13 3 0 0.19192986 0.73 0 0 2 Q6 1 143 | case143 ENSSSCG00000035293 80 68 507 23 84 0 6 33 12 0 0.199148428 234.86 6 19 1 Q6 1 144 | case144 ENSSSCG00000034610 27 68 3897 1 41 3 5 226 6 0 0.199148428 0.28 0 0 6 Q6 1 145 | case145 ENSSSCG00000029637 84 92 3041 103 18 0 5 200 0 1 0.277335058 42.61 1 0 3 Q6 1 146 | case146 ENSSSCG00000025777 86 67 9873 79 46 3 5 506 2 0 0.122118663 2.68 0 0 1 Q6 1 147 | case147 ENSSSCG00000025698 57 75 140 52 26 1 6 4 1 0 0.199148428 59.51 1 0 2 Q6 1 148 | case148 ENSSSCG00000025188 86 75 4053 35 243 5 3 211 6 0 0.06081669 0.4 5 29 7 Q6 1 149 | case149 ENSSSCG00000023331 30 37 19 2 9 1 3 4 0 0 0.137358718 783.7 0 2 1 Q6 1 150 | case150 ENSSSCG00000017797 55 48 1487 32 91 5 5 103 4 0 0.199148428 6.01 0 0 1 Q6 1 151 | case151 ENSSSCG00000017694 66 67 8484 206 32 6 5 533 2 0 0.682512202 297.55 0 2 6 Q6 1 152 | case152 ENSSSCG00000016047 25 44 48 4 35 5 2 1 2 0 0.096494805 4.95 0 2 2 Q6 1 153 | case153 ENSSSCG00000013399 32 56 6090 19 63 6 7 559 9 0 0.764800782 77.3 0 1 3 Q6 1 154 | case154 ENSSSCG00000013380 55 57 1546 19 37 2 10 153 2 0 0.108645835 19.9 1 0 3 Q6 1 155 | case155 ENSSSCG00000013366 50 56 318 67 52 3 6 28 2 0 0.667841098 6331.06 1 0 4 Q6 1 156 | case156 ENSSSCG00000012257 44 25 1385 11 26 2 2 132 0 0 0.104114455 136.09 0 0 2 Q6 1 157 | case157 ENSSSCG00000011996 57 45 378 7 47 8 4 17 4 0 0.038507618 0.1 0 1 4 Q6 1 158 | case158 ENSSSCG00000009233 72 85 3282 10 59 6 9 163 2 0 0.086066306 18.25 5 0 1 Q6 1 159 | case159 ENSSSCG00000007522 69 87 249 33 25 4 6 13 0 0 0.199148428 342.05 2 1 4 Q6 1 160 | case160 ENSSSCG00000007496 100 103 2383 47 216 2 6 169 8 0 0.241127357 26.72 1 0 2 Q6 1 161 | case161 ENSSSCG00000007494 83 95 454 67 122 12 4 23 4 0 0.113515617 13.01 1 0 3 Q6 1 162 | case162 ENSSSCG00000007000 80 79 4855 943 334 2 9 378 14 0 0.199148428 27.44 0 0 0 Q6 1 163 | case163 ENSSSCG00000006872 79 93 4292 87 68 12 9 225 4 0 0.764800782 289.63 0 1 4 Q6 1 164 | case164 ENSSSCG00000006153 55 56 110 18 58 1 1 6 5 0 0.199148428 773.81 2 1 4 Q6 1 165 | case165 ENSSSCG00000005382 68 36 1476 61 6 1 8 176 0 0 0.277335058 17.55 0 0 3 Q6 1 166 | case166 ENSSSCG00000004904 46 56 0 12 26 2 2 0 1 0 0.090771905 0 2 2 11 Q6 1 167 | case167 ENSSSCG00000004001 24 20 6115 6 14 1 4 328 0 0 0.139946255 3.28 0 6 0 Q6 1 168 | case168 ENSSSCG00000003749 66 48 4866 57 172 1 2 270 14 0 0.06081669 11.76 0 2 3 Q6 1 169 | case169 ENSSSCG00000003402 39 64 453 70 16 6 4 33 0 0 0.241127357 498.11 1 0 0 Q6 1 170 | case170 ENSSSCG00000000910 26 102 11624 19 27 2 9 641 3 0 0.052439611 37.01 2 3 4 Q6 1 171 | case171 ENSSSCG00000000907 48 62 6520 86 110 6 0 352 8 0 0.242052768 5.78 1 6 2 Q6 1 172 | case172 ENSSSCG00000040681 40 43 126 13 24 2 6 10 0 0 0.682512202 9665.59 0 1 3 Q7 1 173 | case173 ENSSSCG00000039103 62 67 345 22 107 2 4 14 8 0 0.682512202 4327.17 1 0 0 Q7 1 174 | case174 ENSSSCG00000038348 86 86 699 37 92 5 7 58 9 0 0.277335058 16.85 0 0 1 Q7 1 175 | case175 ENSSSCG00000037910 46 88 48 39 87 8 5 6 8 0 0.199148428 45.23 0 0 2 Q7 1 176 | case176 ENSSSCG00000036883 29 37 179 7 77 3 2 12 4 0 0.682512202 886.19 2 10 5 Q7 1 177 | case177 ENSSSCG00000036310 36 29 51 33 59 5 2 5 3 0 0.241127357 13.69 0 0 2 Q7 1 178 | case178 ENSSSCG00000035949 77 66 13691 45 33 3 4 1051 2 0 0.120510699 28.46 2 2 3 Q7 1 179 | case179 ENSSSCG00000035863 70 41 792 57 213 6 2 34 11 0 0.199148428 78.68 0 0 0 Q7 1 180 | case180 ENSSSCG00000035686 30 17 430 51 66 1 3 36 6 0 0.667841098 333.32 0 2 2 Q7 1 181 | case181 ENSSSCG00000035293 80 68 507 23 84 0 6 33 12 0 0.199148428 580.72 0 5 1 Q7 1 182 | case182 ENSSSCG00000034942 30 48 94 10 104 6 3 13 6 0 0.141754596 4.53 0 1 1 Q7 1 183 | case183 ENSSSCG00000034708 62 45 387 7 64 3 6 55 5 0 0.199148428 8.95 10 1 4 Q7 1 184 | case184 ENSSSCG00000034610 27 68 3897 1 41 3 5 226 6 0 0.199148428 0.28 0 0 1 Q7 1 185 | case185 ENSSSCG00000033626 44 72 404 87 101 6 5 19 5 2 0.096494805 56.78 0 0 3 Q7 1 186 | case186 ENSSSCG00000033383 83 69 87 15 134 9 2 5 7 0 0.06081669 5.98 0 1 1 Q7 1 187 | case187 ENSSSCG00000033207 24 31 152 3 49 6 6 16 14 0 0.108645835 1.23 3 0 0 Q7 1 188 | case188 ENSSSCG00000030707 104 99 761 26 66 1 2 50 3 0 0.241127357 40.04 1 1 5 Q7 1 189 | case189 ENSSSCG00000029637 84 92 3041 103 18 0 5 200 0 1 0.277335058 42.61 0 0 1 Q7 1 190 | case190 ENSSSCG00000029311 32 62 2307 38 58 3 3 194 1 0 0.764800782 263.01 0 6 1 Q7 1 191 | case191 ENSSSCG00000028431 60 47 2782 58 109 3 2 171 6 0 0.139946255 266.33 0 0 1 Q7 1 192 | case192 ENSSSCG00000028322 53 62 59 34 166 4 1 3 21 0 0.199148428 72.68 0 0 0 Q7 1 193 | case193 ENSSSCG00000027898 36 80 4634 59 93 7 10 287 5 0 0.120510699 43.82 0 0 1 Q7 1 194 | case194 ENSSSCG00000027801 56 104 50 14 18 7 4 0 2 1 0.139946255 16.33 0 0 2 Q7 1 195 | case195 ENSSSCG00000027609 35 50 968 8 9 2 8 74 2 0 0.139946255 5.41 1 0 1 Q7 1 196 | case196 ENSSSCG00000026533 88 69 22 8 115 16 18 5 9 0 0.667841098 74.68 0 0 1 Q7 1 197 | case197 ENSSSCG00000025820 44 70 935 40 60 2 5 50 3 0 0.129634791 46.42 0 2 1 Q7 1 198 | case198 ENSSSCG00000025777 86 67 9873 79 46 3 5 506 2 0 0.122118663 2.4 4 1 0 Q7 1 199 | case199 ENSSSCG00000025698 57 75 140 52 26 1 6 4 1 0 0.199148428 59.51 0 0 0 Q7 1 200 | case200 ENSSSCG00000025284 72 80 617 33 53 10 2 39 0 0 0.667841098 359.16 1 1 1 Q7 1 201 | case201 ENSSSCG00000025188 86 75 4053 35 243 5 3 211 6 0 0.06081669 0.4 0 7 4 Q7 1 202 | case202 ENSSSCG00000023501 79 92 355 21 172 3 7 19 12 0 0.06081669 0.47 0 1 1 Q7 1 203 | case203 ENSSSCG00000023472 13 21 503 19 43 1 5 32 1 1 0.139946255 5.49 0 0 1 Q7 1 204 | case204 ENSSSCG00000022797 40 57 121 42 103 0 3 7 5 0 0.242052768 108.01 0 0 3 Q7 1 205 | case205 ENSSSCG00000022404 73 106 286 59 19 4 7 20 2 1 0.199148428 173.41 1 1 1 Q7 1 206 | case206 ENSSSCG00000019232 97 95 0 0 0 4 1 0 0 0 0.037448263 0.36 6 2 1 Q7 1 207 | case207 ENSSSCG00000018805 74 90 0 0 0 4 4 0 0 0 0.037448263 36.16 6 2 1 Q7 1 208 | case208 ENSSSCG00000017756 43 60 3917 7 27 3 7 360 5 0 0.121872209 7.08 0 2 2 Q7 1 209 | case209 ENSSSCG00000017500 101 79 8 36 56 6 5 1 2 0 0.667841098 3369.66 0 0 0 Q7 1 210 | case210 ENSSSCG00000017313 66 79 2231 15 49 1 4 127 1 0 0.113515617 0.13 0 0 1 Q7 1 211 | case211 ENSSSCG00000016200 63 77 670 23 174 11 2 62 19 1 0.667841098 37.3 30 5 17 Q7 1 212 | case212 ENSSSCG00000015854 59 62 5854 19 39 7 1 325 0 0 0.082666169 5.64 0 1 3 Q7 1 213 | case213 ENSSSCG00000015840 7 35 2472 39 76 2 3 209 5 0 0.06081669 3.3 0 0 0 Q7 1 214 | case214 ENSSSCG00000015662 63 80 3500 20 56 1 7 234 5 0 0.199148428 1199.9 0 0 1 Q7 1 215 | case215 ENSSSCG00000015639 94 89 151 38 177 3 7 7 5 1 0.038507618 0 0 1 1 Q7 1 216 | case216 ENSSSCG00000015268 24 31 1174 26 22 2 0 76 1 0 0.139946255 160.41 0 0 3 Q7 1 217 | case217 ENSSSCG00000014401 71 48 4457 132 258 4 5 295 31 2 0.277335058 34.57 5 1 1 Q7 1 218 | case218 ENSSSCG00000014170 74 65 4140 46 134 6 4 398 6 0 0.199148428 125 0 0 18 Q7 1 219 | case219 ENSSSCG00000013618 26 18 38 20 46 3 0 4 5 0 0.199148428 0.15 0 0 3 Q7 1 220 | case220 ENSSSCG00000013392 46 44 938 129 22 7 0 68 1 0 0.277335058 75.24 0 0 0 Q7 1 221 | case221 ENSSSCG00000013375 47 35 22 10 16 3 3 1 1 0 0.422053604 48.46 0 1 4 Q7 1 222 | case222 ENSSSCG00000013366 50 56 318 67 52 3 6 28 2 0 0.667841098 6331.06 1 1 1 Q7 1 223 | case223 ENSSSCG00000013354 83 51 475 23 2 7 9 42 0 0 0.064644796 1747.17 1 0 2 Q7 1 224 | case224 ENSSSCG00000013181 62 78 498 62 145 2 2 24 3 1 0.139946255 655.01 0 1 1 Q7 1 225 | case225 ENSSSCG00000013178 34 48 522 104 81 0 1 32 5 0 0.086066306 31.99 2 1 4 Q7 1 226 | case226 ENSSSCG00000013072 79 82 1563 62 87 6 11 94 3 0 0.139946255 20 2 3 9 Q7 1 227 | case227 ENSSSCG00000012583 25 62 1285 90 97 4 3 98 6 0 0.242052768 40.05 20 0 2 Q7 1 228 | case228 ENSSSCG00000012257 44 25 1385 11 26 2 2 132 0 0 0.104114455 109.79 0 0 2 Q7 1 229 | case229 ENSSSCG00000011996 57 45 378 7 47 8 4 17 4 0 0.038507618 0.1 0 1 1 Q7 1 230 | case230 ENSSSCG00000011911 38 79 1069 16 24 3 0 52 2 0 0.037448263 1.02 0 0 1 Q7 1 231 | case231 ENSSSCG00000011568 60 52 126 4 33 0 1 7 0 0 0.199148428 0 0 0 1 Q7 1 232 | case232 ENSSSCG00000011106 81 92 2029 18 25 4 5 165 2 0 0.086066306 34.12 0 0 1 Q7 1 233 | case233 ENSSSCG00000011018 54 73 842 45 168 1 12 54 9 0 0.06081669 7.7 0 0 0 Q7 1 234 | case234 ENSSSCG00000010780 29 42 230 30 49 0 1 7 2 0 0.139946255 2.52 0 0 0 Q7 1 235 | case235 ENSSSCG00000010554 40 54 229 47 185 1 3 12 19 0 0.682512202 3645.97 876 29 20 Q7 1 236 | case236 ENSSSCG00000009919 49 48 357 123 121 2 2 28 4 0 0.139946255 0.35 0 1 2 Q7 1 237 | case237 ENSSSCG00000009186 102 91 2467 59 83 2 12 134 4 0 0.764800782 40.64 0 1 2 Q7 1 238 | case238 ENSSSCG00000009179 64 64 2267 34 81 3 2 109 7 0 0.139946255 0.28 0 1 6 Q7 1 239 | case239 ENSSSCG00000008214 70 80 127 7 57 2 5 18 3 0 0.166678441 1.08 2 0 2 Q7 1 240 | case240 ENSSSCG00000007799 61 83 94 26 11 1 1 3 2 1 0.667841098 34215.26 0 4 1 Q7 1 241 | case241 ENSSSCG00000007528 87 59 10422 41 64 10 4 430 2 0 0.120510699 0.57 0 0 3 Q7 1 242 | case242 ENSSSCG00000007527 79 83 701 4 63 8 2 29 5 0 0.153968325 0.64 0 0 5 Q7 1 243 | case243 ENSSSCG00000007522 69 87 249 33 25 4 6 13 0 0 0.199148428 342.05 0 0 1 Q7 1 244 | case244 ENSSSCG00000007505 52 92 1352 37 31 2 3 60 0 0 0.086066306 2.09 0 0 1 Q7 1 245 | case245 ENSSSCG00000007496 100 103 2383 47 216 2 6 169 8 0 0.241127357 22.37 1 7 1 Q7 1 246 | case246 ENSSSCG00000007491 100 102 0 19 9 6 3 0 0 0 0.037448263 0 1 7 1 Q7 1 247 | case247 ENSSSCG00000006754 85 59 817 26 54 4 11 33 4 0 0.667841098 271.37 0 0 1 Q7 1 248 | case248 ENSSSCG00000006719 43 53 176 26 86 3 7 17 3 0 0.141754596 12.53 0 2 0 Q7 1 249 | case249 ENSSSCG00000006702 24 39 820 13 73 1 1 52 2 0 0.139946255 5.36 0 1 0 Q7 1 250 | case250 ENSSSCG00000006141 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0.121872209 20.28 0 2 4 Q7 1 273 | case273 ENSSSCG00000000492 67 57 152 12 39 4 6 18 1 0 0.199148428 18.21 0 0 2 Q7 1 274 | case274 ENSSSCG00000000405 60 25 113 118 48 3 2 12 1 0 0.199148428 1261.47 0 0 1 Q7 1 275 | case275 ENSSSCG00000000006 118 84 908 79 14 5 3 51 1 0 0.139946255 21.54 0 0 1 Q7 1 276 | case276 ENSSSCG00000039103 62 67 345 22 107 2 4 14 8 0 0.101995966 23.95 0 0 0 Q8 1 277 | case277 ENSSSCG00000038348 86 86 699 37 92 5 7 58 9 0 0.277335058 15.68 0 0 0 Q8 1 278 | case278 ENSSSCG00000036883 29 37 179 7 77 3 2 12 4 0 0.101995966 485.25 0 0 0 Q8 1 279 | case279 ENSSSCG00000035686 30 17 430 51 66 1 3 36 6 0 0.667841098 333.32 0 0 0 Q8 1 280 | case280 ENSSSCG00000032782 42 62 181 15 126 5 5 10 10 0 0.667841098 101.43 0 0 0 Q8 1 281 | case281 ENSSSCG00000029311 32 62 2307 38 58 3 3 194 1 0 0.764800782 271.79 0 0 0 Q8 1 282 | case282 ENSSSCG00000025188 86 75 4053 35 243 5 3 211 6 0 0.06081669 0.18 0 0 0 Q8 1 283 | case283 ENSSSCG00000018805 74 90 0 0 0 4 4 0 0 0 0.037448263 36.16 0 0 0 Q8 1 284 | case284 ENSSSCG00000017694 66 67 8484 206 32 6 5 533 2 0 0.101995966 5.95 0 0 0 Q8 1 285 | case285 ENSSSCG00000017500 101 79 8 36 56 6 5 1 2 0 0.667841098 3369.66 0 0 0 Q8 1 286 | case286 ENSSSCG00000016200 63 77 670 23 174 11 2 62 19 1 0.667841098 43.31 0 0 4 Q8 1 287 | case287 ENSSSCG00000016047 25 44 48 4 35 5 2 1 2 0 0.096494805 5.29 0 0 0 Q8 1 288 | case288 ENSSSCG00000015840 7 35 2472 39 76 2 3 209 5 0 0.06081669 2.23 0 0 0 Q8 1 289 | case289 ENSSSCG00000014811 30 73 423 172 31 5 1 31 2 0 0.277335058 96.68 0 0 0 Q8 1 290 | case290 ENSSSCG00000013399 32 56 6090 19 63 6 7 559 9 0 0.764800782 77.3 0 0 0 Q8 1 291 | case291 ENSSSCG00000013375 47 35 22 10 16 3 3 1 1 0 0.422053604 48.46 0 0 0 Q8 1 292 | case292 ENSSSCG00000013366 50 56 318 67 52 3 6 28 2 0 0.667841098 6928.41 0 0 0 Q8 1 293 | case293 ENSSSCG00000012583 25 62 1285 90 97 4 3 98 6 0 0.242052768 22.17 0 0 1 Q8 1 294 | case294 ENSSSCG00000012257 44 25 1385 11 26 2 2 132 0 0 0.104114455 14.56 0 0 0 Q8 1 295 | case295 ENSSSCG00000011996 57 45 378 7 47 8 4 17 4 0 0.038507618 0.02 0 0 0 Q8 1 296 | case296 ENSSSCG00000010428 50 53 35 4 28 7 7 3 2 0 0.199148428 0.26 0 2 0 Q8 1 297 | case297 ENSSSCG00000009919 49 48 357 123 121 2 2 28 4 0 0.139946255 0.25 0 0 0 Q8 1 298 | case298 ENSSSCG00000006141 38 26 679 25 36 4 3 47 2 0 0.667841098 1664.69 0 0 0 Q8 1 299 | case299 ENSSSCG00000005494 85 99 3168 259 210 4 0 198 10 0 0.199148428 4.55 0 0 0 Q8 1 300 | case300 ENSSSCG00000005485 106 93 664 19 7 3 2 54 4 1 0.139946255 0 0 1 0 Q8 1 301 | case301 ENSSSCG00000003867 37 89 1049 5 62 3 0 54 3 1 0.277335058 33.3 0 0 0 Q8 1 302 | case302 ENSSSCG00000003749 66 48 4866 57 172 1 2 270 14 0 0.06081669 5.74 0 0 0 Q8 1 303 | case303 ENSSSCG00000003491 49 81 183 47 37 2 3 12 0 0 0.082666169 20.82 0 0 0 Q8 1 304 | case304 ENSSSCG00000003035 26 27 101 42 74 0 0 31 10 0 0.120510699 24.53 0 0 0 Q8 1 305 | case305 ENSSSCG00000002906 62 51 29 9 9 1 4 1 0 0 0.037448263 9.71 0 0 0 Q8 1 306 | case306 ENSSSCG00000002829 51 63 987 128 102 1 2 43 6 0 0.277335058 94.6 0 0 0 Q8 1 307 | case307 ENSSSCG00000002361 46 77 178 28 38 5 3 14 3 0 0.153968325 0 0 0 0 Q8 1 308 | case308 ENSSSCG00000002029 30 59 1126 100 18 2 3 39 0 0 0.064644796 2729.57 13 0 0 Q8 1 309 | case309 ENSSSCG00000001696 37 50 315 31 20 6 3 17 0 0 0.199148428 76.9 0 0 0 Q8 1 310 | case310 ENSSSCG00000000755 44 65 12196 43 8 4 1 907 1 0 0.121872209 6.79 0 1 0 Q8 1 311 | case311 ENSSSCG00000000492 67 57 152 12 39 4 6 18 1 0 0.199148428 2.66 0 0 0 Q8 1 312 | case312 ENSSSCG00000000405 60 25 113 118 48 3 2 12 1 0 0.199148428 1261.47 0 0 0 Q8 1 313 | case313 ENSSSCG00000000373 27 31 118 52 53 0 4 6 4 0 0.143375197 5.05 0 0 0 Q8 1 314 | case314 ENSSSCG00000037910 46 88 48 39 87 8 5 6 8 0 0.294198879 29.57 0 0 1 Q9 1 315 | case315 ENSSSCG00000035949 77 66 13691 45 33 3 4 1051 2 0 0.029584143 32.57 0 0 1 Q9 1 316 | case316 ENSSSCG00000013354 83 51 475 23 2 7 9 42 0 0 0.053291321 0.37 0 0 1 Q9 1 317 | case317 ENSSSCG00000012583 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96 2 3 217 4 0 0.06717569 215.03 0 0 0 Q10 1 329 | case329 ENSSSCG00000024914 68 69 82 71 96 0 2 3 1 0 0.047655493 19.44 0 1 5 Q10 1 330 | case330 ENSSSCG00000023738 141 95 5585 77 19 9 2 292 0 0 0.879872892 412.55 1 0 0 Q10 1 331 | case331 ENSSSCG00000022230 56 80 1689 20 17 5 3 77 4 1 0.312453153 837.42 0 0 4 Q10 1 332 | case332 ENSSSCG00000021355 85 65 288 14 20 12 8 25 4 0 0.047655493 1005.4 0 0 1 Q10 1 333 | case333 ENSSSCG00000017755 98 92 6364 80 36 5 5 335 0 0 0.181056466 126.81 0 0 3 Q10 1 334 | case334 ENSSSCG00000017472 58 71 534 31 155 5 7 32 6 0 0.312453153 306 0 0 3 Q10 1 335 | case335 ENSSSCG00000017232 59 81 534 49 62 10 7 32 3 1 0.097629772 528.28 1 0 3 Q10 1 336 | case336 ENSSSCG00000017231 57 63 108 34 223 8 5 1 13 1 0.097629772 37.16 0 0 8 Q10 1 337 | case337 ENSSSCG00000016831 76 80 7128 53 48 8 4 529 0 0 0.845905319 47.92 1 0 1 Q10 1 338 | case338 ENSSSCG00000016230 65 77 6100 58 35 12 2 347 2 0 0.049157174 114.88 6 1 16 Q10 1 339 | case339 ENSSSCG00000016068 69 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ENSSSCG00000004600 33 54 6206 50 139 1 3 453 5 0 0.250621331 62.39 1 0 2 Q10 1 351 | case351 ENSSSCG00000003608 54 50 607 72 35 4 2 56 0 1 0.075847507 11.61 1 0 5 Q10 1 352 | case352 ENSSSCG00000000590 49 32 1261 13 7 1 1 75 0 0 0.845905319 263.04 1 0 2 Q10 1 353 | case353 ENSSSCG00000000182 46 49 261 16 92 4 2 23 3 0 0.529368313 3.37 0 0 1 Q10 1 354 | case354 ENSSSCG00000040961 28 34 35 7 41 6 2 2 4 0 0.199148428 30.59 0 0 2 Q11 1 355 | case355 ENSSSCG00000040793 61 39 237 56 160 3 3 17 6 0 0.199148428 168.5 6 18 2 Q11 1 356 | case356 ENSSSCG00000040464 64 102 687 6 127 1 1 28 6 0 0.682512202 64.44 1 0 1 Q11 1 357 | case357 ENSSSCG00000038918 141 78 155 87 208 6 5 7 3 0 0.667841098 151.79 2 5 0 Q11 1 358 | case358 ENSSSCG00000037910 46 88 48 39 87 8 5 6 8 0 0.199148428 61.72 0 1 5 Q11 1 359 | case359 ENSSSCG00000036883 29 37 179 7 77 3 2 12 4 0 0.682512202 886.19 0 2 4 Q11 1 360 | case360 ENSSSCG00000035949 77 66 13691 45 33 3 4 1051 2 0 0.696945982 80.22 6 0 2 Q11 1 361 | case361 ENSSSCG00000035293 80 68 507 23 84 0 6 33 12 0 0.199148428 234.86 6 19 1 Q11 1 362 | case362 ENSSSCG00000034610 27 68 3897 1 41 3 5 226 6 0 0.199148428 1.19 11 0 6 Q11 1 363 | case363 ENSSSCG00000033383 83 69 87 15 134 9 2 5 7 0 0.06081669 7.46 7 0 2 Q11 1 364 | case364 ENSSSCG00000030707 104 99 761 26 66 1 2 50 3 0 0.241127357 40.04 0 1 1 Q11 1 365 | case365 ENSSSCG00000029637 84 92 3041 103 18 0 5 200 0 1 0.277335058 56.98 1 0 3 Q11 1 366 | case366 ENSSSCG00000029311 32 62 2307 38 58 3 3 194 1 0 0.764800782 263.01 0 1 0 Q11 1 367 | case367 ENSSSCG00000028322 53 62 59 34 166 4 1 3 21 0 0.199148428 72.68 1 1 1 Q11 1 368 | case368 ENSSSCG00000027898 36 80 4634 59 93 7 10 287 5 0 0.696945982 167.8 0 0 1 Q11 1 369 | case369 ENSSSCG00000027609 35 50 968 8 9 2 8 74 2 0 0.931103039 3670.94 4 0 2 Q11 1 370 | case370 ENSSSCG00000026533 88 69 22 8 115 16 18 5 9 0 0.667841098 74.68 1 0 0 Q11 1 371 | case371 ENSSSCG00000025698 57 75 140 52 26 1 6 4 1 0 0.199148428 113.9 1 0 6 Q11 1 372 | case372 ENSSSCG00000025188 86 75 4053 35 243 5 3 211 6 0 0.06081669 3.96 5 29 7 Q11 1 373 | case373 ENSSSCG00000023331 30 37 19 2 9 1 3 4 0 0 0.137358718 377.21 0 2 1 Q11 1 374 | case374 ENSSSCG00000021122 62 49 2477 13 2 2 3 114 0 0 0.199148428 0.59 0 7 0 Q11 1 375 | case375 ENSSSCG00000017694 66 67 8484 206 32 6 5 533 2 0 0.682512202 119.18 0 2 6 Q11 1 376 | case376 ENSSSCG00000017500 101 79 8 36 56 6 5 1 2 0 0.667841098 2354.25 0 1 4 Q11 1 377 | case377 ENSSSCG00000017313 66 79 2231 15 49 1 4 127 1 0 0.113515617 42.99 0 1 2 Q11 1 378 | case378 ENSSSCG00000016974 46 32 2352 149 140 11 2 138 11 0 0.696945982 430.19 2 0 0 Q11 1 379 | case379 ENSSSCG00000016972 94 92 44 6 30 7 6 3 0 0 0.696945982 86.56 2 0 2 Q11 1 380 | case380 ENSSSCG00000016794 80 88 12154 258 74 2 6 705 1 1 0.277335058 75.59 0 0 0 Q11 1 381 | case381 ENSSSCG00000014811 30 73 423 172 31 5 1 31 2 0 0.277335058 144.24 2 2 1 Q11 1 382 | case382 ENSSSCG00000013618 26 18 38 20 46 3 0 4 5 0 0.199148428 0.46 0 0 0 Q11 1 383 | case383 ENSSSCG00000013392 46 44 938 129 22 7 0 68 1 0 0.277335058 108.94 0 1 3 Q11 1 384 | case384 ENSSSCG00000013072 79 82 1563 62 87 6 11 94 3 0 0.931103039 162.91 1 5 0 Q11 1 385 | case385 ENSSSCG00000012593 20 43 3806 7 35 3 2 273 1 0 0.696945982 10.17 0 0 1 Q11 1 386 | case386 ENSSSCG00000012583 25 62 1285 90 97 4 3 98 6 0 0.242052768 114.24 0 0 2 Q11 1 387 | case387 ENSSSCG00000012257 44 25 1385 11 26 2 2 132 0 0 0.104114455 109.79 0 0 2 Q11 1 388 | case388 ENSSSCG00000011911 38 79 1069 16 24 3 0 52 2 0 0.037448263 1.02 0 0 1 Q11 1 389 | case389 ENSSSCG00000011579 25 43 2352 108 24 5 2 171 2 0 0.682512202 214.7 1 2 2 Q11 1 390 | case390 ENSSSCG00000011568 60 52 126 4 33 0 1 7 0 0 0.199148428 0 0 0 1 Q11 1 391 | case391 ENSSSCG00000010638 34 48 6107 67 22 4 3 464 6 0 0.277335058 33.26 0 2 0 Q11 1 392 | case392 ENSSSCG00000010554 40 54 229 47 185 1 3 12 19 0 0.682512202 3645.97 0 2 0 Q11 1 393 | case393 ENSSSCG00000009919 49 48 357 123 121 2 2 28 4 0 0.931103039 9.9 0 1 2 Q11 1 394 | case394 ENSSSCG00000008603 85 77 1006 31 27 18 3 75 1 0 0.112791486 0.26 0 1 2 Q11 1 395 | case395 ENSSSCG00000007522 69 87 249 33 25 4 6 13 0 0 0.199148428 342.05 2 1 4 Q11 1 396 | case396 ENSSSCG00000007496 100 103 2383 47 216 2 6 169 8 0 0.241127357 25.03 1 0 2 Q11 1 397 | case397 ENSSSCG00000007494 83 95 454 67 122 12 4 23 4 0 0.113515617 19.18 1 0 3 Q11 1 398 | case398 ENSSSCG00000007491 100 102 0 19 9 6 3 0 0 0 0.037448263 0.03 1 0 0 Q11 1 399 | case399 ENSSSCG00000006872 79 93 4292 87 68 12 9 225 4 0 0.764800782 289.63 0 1 4 Q11 1 400 | case400 ENSSSCG00000006216 52 49 1083 18 34 0 4 34 0 0 0.11705168 30.67 5 1 0 Q11 1 401 | case401 ENSSSCG00000006153 55 56 110 18 58 1 1 6 5 0 0.199148428 743.11 2 1 4 Q11 1 402 | case402 ENSSSCG00000006141 38 26 679 25 36 4 3 47 2 0 0.667841098 1536.92 0 0 0 Q11 1 403 | case403 ENSSSCG00000005589 53 44 6067 12 5 3 3 403 0 0 0.136523865 2.52 0 11 0 Q11 1 404 | case404 ENSSSCG00000005494 85 99 3168 259 210 4 0 198 10 0 0.199148428 201.12 1 4 1 Q11 1 405 | case405 ENSSSCG00000005215 49 53 3617 160 155 2 9 327 12 0 0.242052768 8.09 0 0 1 Q11 1 406 | case406 ENSSSCG00000005087 25 20 46 11 21 1 2 2 2 0 0.764800782 55.34 0 1 1 Q11 1 407 | case407 ENSSSCG00000004904 46 56 0 12 26 2 2 0 1 0 0.224051515 4.22 2 2 11 Q11 1 408 | case408 ENSSSCG00000004318 44 42 1711 44 12 2 5 132 1 0 0.764800782 48.55 0 0 1 Q11 1 409 | case409 ENSSSCG00000004172 72 45 2467 25 85 5 3 180 6 0 0.108645835 5.23 0 0 1 Q11 1 410 | case410 ENSSSCG00000003867 37 89 1049 5 62 3 0 54 3 1 0.277335058 114.77 0 0 1 Q11 1 411 | case411 ENSSSCG00000003565 40 31 40 5 58 2 1 2 2 0 0.931103039 15.24 0 0 1 Q11 1 412 | case412 ENSSSCG00000003017 21 31 144 54 50 6 4 18 0 0 0.241127357 61.52 0 5 0 Q11 1 413 | case413 ENSSSCG00000002368 72 68 4177 155 152 0 8 179 3 0 0.199148428 306.04 1 0 0 Q11 1 414 | case414 ENSSSCG00000002361 46 77 178 28 38 5 3 14 3 0 0.155795023 0 1 0 0 Q11 1 415 | case415 ENSSSCG00000001696 37 50 315 31 20 6 3 17 0 0 0.199148428 55.15 5 13 0 Q11 1 416 | case416 ENSSSCG00000001539 34 62 1389 78 150 2 3 79 10 0 0.277335058 46.43 17 3 8 Q11 1 417 | case417 ENSSSCG00000001478 59 77 4010 7 63 3 5 191 0 0 0.06081669 9.67 1 6 0 Q11 1 418 | case418 ENSSSCG00000001404 51 56 30 28 62 2 1 2 2 0 0.199148428 0.64 3 14 2 Q11 1 419 | case419 ENSSSCG00000000907 48 62 6520 86 110 6 0 352 8 0 0.242052768 36.67 1 6 2 Q11 1 420 | case420 ENSSSCG00000000755 44 65 12196 43 8 4 1 907 1 0 0.136523865 38.72 1 1 0 Q11 1 421 | case421 ENSSSCG00000000405 60 25 113 118 48 3 2 12 1 0 0.199148428 466.1 0 2 0 Q11 1 422 | case422 ENSSSCG00000015564 37 39 2 0 7 2 4 0 0 0 0.011708301 0 0 0 0 Y 0 423 | case423 ENSSSCG00000033539 56 40 335 36 40 10 2 26 3 1 0.312453153 3.17 0 0 0 Y 0 424 | case424 ENSSSCG00000010256 48 89 6227 10 14 3 7 380 2 0 0.041518482 7.69 0 0 0 Y 0 425 | case425 ENSSSCG00000035709 65 58 0 0 13 6 4 0 0 0 0.015442639 0 0 0 0 Y 0 426 | case426 ENSSSCG00000030029 101 95 251 14 48 15 4 21 0 0 0.015442639 0 0 0 0 Y 0 427 | case427 ENSSSCG00000013375 47 35 22 10 16 3 3 1 1 0 0.029899698 0 0 0 0 Y 0 428 | case428 ENSSSCG00000014366 111 54 138 44 143 0 1 8 3 0 0.696945982 13.42 1 0 0 Y 0 429 | case429 ENSSSCG00000039935 65 50 264 15 60 7 2 12 8 0 0.088807365 26.55 0 0 0 Y 0 430 | case430 ENSSSCG00000038276 39 37 2188 0 43 1 0 141 1 0 0.020963881 0 0 0 0 Y 0 431 | case431 ENSSSCG00000034658 37 71 17 1 4 4 2 0 0 0 0.015442639 0 0 0 0 Y 0 432 | case442 ENSSSCG00000038966 81 52 664 70 108 5 7 45 7 0 0.312453153 81.18 0 0 0 Y 0 433 | case443 ENSSSCG00000032738 35 46 5 4 7 7 2 0 1 0 0.018166314 0 0 0 0 Y 0 434 | case444 ENSSSCG00000010260 44 37 404 23 54 4 3 35 5 0 0.088807365 119.96 0 0 0 Y 0 435 | case445 ENSSSCG00000029186 34 89 829 85 11 5 2 32 2 0 0.696945982 234.38 0 0 0 Y 0 436 | case446 ENSSSCG00000029799 53 89 433 17 107 4 4 24 4 0 0.088807365 12.63 0 0 0 Y 0 437 | case447 ENSSSCG00000035329 24 35 0 1 1 0 2 0 0 0 0.011555695 0 0 0 0 Y 0 438 | case448 ENSSSCG00000036127 67 76 0 1 5 2 3 0 0 0 0.055857788 8.77 0 0 0 Y 0 439 | case449 ENSSSCG00000009445 49 101 28 40 20 4 7 2 3 1 0.696945982 22.75 0 0 0 Y 0 440 | case450 ENSSSCG00000031991 64 93 335 5 15 3 3 14 0 0 0.025936296 56.94 0 0 0 Y 0 441 | case451 ENSSSCG00000011916 41 48 1559 70 102 2 4 106 8 0 0.696945982 159.72 0 0 0 Y 0 442 | case452 ENSSSCG00000036785 92 92 2531 19 56 11 6 216 4 0 0.021941076 35.4 0 0 0 Y 0 443 | case453 ENSSSCG00000034428 113 105 0 14 25 5 5 0 2 1 0.011555695 0 0 0 0 Y 0 444 | case466 ENSSSCG00000007675 27 38 1752 135 54 2 5 90 3 0 0.11151874 15.98 0 0 0 Y 0 445 | case467 ENSSSCG00000027989 65 65 135 18 11 2 0 2 0 0 0.020199581 1.5 0 0 0 Y 0 446 | case468 ENSSSCG00000031643 48 42 0 6 3 3 0 0 2 0 0.017300273 0 0 0 0 Y 0 447 | case469 ENSSSCG00000011454 21 41 2254 19 39 2 2 157 1 0 0.091980702 17.35 0 0 0 Y 0 448 | case470 ENSSSCG00000006460 73 66 176 9 16 5 5 5 0 0 0.124994749 1.62 0 2 0 Y 0 449 | case471 ENSSSCG00000035041 126 88 0 20 20 4 7 0 0 0 0.136672409 0.61 0 0 0 Y 0 450 | case472 ENSSSCG00000006084 56 65 1413 47 210 1 4 95 5 0 0.218178103 79.29 1 0 0 Y 0 451 | case473 ENSSSCG00000029098 76 101 0 0 2 4 1 0 1 0 0.036882052 0 0 0 0 Y 0 452 | case474 ENSSSCG00000038112 82 109 39 11 165 5 12 1 7 0 0.121567487 2.76 0 0 0 Y 0 453 | case475 ENSSSCG00000021141 97 131 0 18 25 8 8 0 0 0 0.036882052 0 35 1 0 Y 0 454 | case476 ENSSSCG00000024770 67 49 1840 81 117 11 5 100 4 0 0.121567487 48.08 0 1 0 Y 0 455 | case477 ENSSSCG00000035207 35 39 164 14 20 2 2 7 2 0 0.129349507 0.1 0 0 0 Y 0 456 | case478 ENSSSCG00000040850 39 47 69 14 74 1 3 5 5 0 0.170447367 1.34 0 0 0 Y 0 457 | case479 ENSSSCG00000036482 83 134 550 25 222 2 8 41 5 0 0.218178103 15.12 1 0 0 Y 0 458 | case480 ENSSSCG00000014108 41 84 897 23 56 0 6 42 4 0 0.168881583 0.11 13 0 0 Y 0 459 | case481 ENSSSCG00000033574 88 118 13707 116 111 2 4 1014 6 1 0.053102586 1.17 0 0 0 Y 0 460 | case482 ENSSSCG00000018924 69 60 0 0 3 1 4 0 0 0 0.036882052 0 0 0 0 Y 0 461 | case483 ENSSSCG00000016958 49 41 1907 114 134 2 3 128 9 0 0.053102586 52.38 0 0 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