Mercurial > repos > adrian.diaz > b2btools_single_sequence

annotate singleSeq/script.py @ 0:cacb90cde53e draft

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First version of b2btools for single sequences in Galaxy
author adrian.diaz
date Wed, 06 Jul 2022 11:01:15 +0000
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0
cacb90cde53e First version of b2btools for single sequences in Galaxy
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1 import optparse
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2 import os.path
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3 import unicodedata
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4 import re
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5 import pandas as pd
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6 from b2bTools import SingleSeq
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7 import matplotlib.pyplot as plt
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8
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9
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10 def slugify(value):
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11 """
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12 Taken from https://github.com/django/django/blob/master/django/utils/text.py
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13 Convert to ASCII if 'allow_unicode'. Convert spaces or repeated
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14 dashes to single dashes. Remove characters that aren't alphanumerics,
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15 underscores, or hyphens. Convert to lowercase. Also strip leading and
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16 trailing whitespace, dashes, and underscores.
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17 """
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18 value = str(value)
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19 value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')
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20 value = re.sub(r'[^\w\s-]', '', value.lower())
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21 return re.sub(r'[-\s]+', '-', value).strip('-_')
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22
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23
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24 def plot_prediction(prediction_name, highlighting_regions, predicted_values, seq_name):
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25 thresholds_dict = {'backbone': {'membrane spanning': [1., 1.5],
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26 'rigid': [0.8, 1.],
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27 'context-dependent': [0.69, 0.8],
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28 'flexible': [-1.0, 0.69]},
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29 'earlyFolding': {'early folds': [0.169, 2.], 'late folds': [-1., 0.169]},
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30 'disoMine': {'ordered': [-1., 0.5], 'disordered': [0.5, 2.]},
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31 }
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32 ordered_regions_dict = {'backbone': ['flexible', 'context-dependent', 'rigid', 'membrane spanning'],
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33 'earlyFolding': ['late folds', 'early folds'],
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34 'disoMine': ['ordered', 'disordered'],
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35 }
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36 colors = ['yellow', 'orange', 'pink', 'red']
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37 ranges_dict = {
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38 'backbone': [-0.2, 1.2],
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39 'sidechain': [-0.2, 1.2],
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40 'ppII': [-0.2, 1.2],
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41 'earlyFolding': [-0.2, 1.2],
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42 'disoMine': [-0.2, 1.2],
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43 'agmata': [-0.2, 1.2],
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44 'helix': [-1., 1.],
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45 'sheet': [-1., 1.],
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46 'coil': [-1., 1.],
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47 }
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48 fig, ax = plt.subplots(1, 1)
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49 fig.set_figwidth(10)
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50 fig.set_figheight(5)
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51 ax.set_title(prediction_name + ' ' + 'prediction')
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52 plt.tight_layout(rect=[0, 0, 0.75, 1])
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53 if seq_name == 'all':
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54 max_len = 0
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55 for seq in predicted_values.keys():
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56 predictions = predicted_values[seq]
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57 ax.plot(range(len(predictions)), predictions, label=seq)
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58 if len(predictions)>max_len:
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59 max_len = len(predictions)
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60 ax.set_xlim([0, max_len - 1])
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61 else:
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62 predictions = predicted_values
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63 ax.plot(range(len(predictions)), predictions, label=seq_name)
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64 ax.set_xlim([0, len(predictions) - 1])
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65 legend_lines = plt.legend(bbox_to_anchor=(1.04,1), loc="upper left", fancybox=True, shadow=True)
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66 ax.add_artist(legend_lines)
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67 # Define regions
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68 if highlighting_regions:
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69 if prediction_name in ordered_regions_dict.keys():
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70 for i, prediction in enumerate(ordered_regions_dict[prediction_name]):
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71 lower = thresholds_dict[prediction_name][prediction][0]
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72 upper = thresholds_dict[prediction_name][prediction][1]
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73 color = colors[i]
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74 ax.axhspan(lower, upper, alpha=0.3, color=color, label=prediction)
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75 included_in_regions_legend = list(reversed(
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76 [prediction for prediction in ordered_regions_dict[prediction_name]])) # to sort it "from up to low"
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77 # Get handles and labels
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78 handles, labels = plt.gca().get_legend_handles_labels()
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79 handles_dict = {label: handles[idx] for idx, label in enumerate(labels)}
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80 # Add legend for regions, if available
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81 region_legend = ax.legend([handles_dict[region] for region in included_in_regions_legend],
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82 [region for region in included_in_regions_legend], fancybox=True, shadow=True,
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83 loc='lower left', bbox_to_anchor=(1.04,0))
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84 ax.add_artist(region_legend)
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85 ax.set_ylim(ranges_dict[prediction_name])
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86 ax.set_xlabel('residue index')
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87 ax.set_ylabel('prediction values')
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88 ax.grid(axis='y')
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89 plt.savefig(os.path.join(options.plot_output, "{0}_{1}.png".format(slugify(seq_name), prediction_name)), bbox_inches="tight")
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90 plt.close()
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91
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92 def df_dict_to_dict_of_values(df_dict, predictor):
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93 results_dict = {}
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94 for seq in df_dict.keys():
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95 df = pd.read_csv(df_dict[seq], sep='\t')
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96 results_dict[seq] = df[predictor]
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97 return results_dict
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98
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99 def main(options):
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100 single_seq = SingleSeq(options.input_fasta)
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101 b2b_tools = []
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102 if options.dynamine:
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103 b2b_tools.append('dynamine')
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104 if options.disomine:
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105 b2b_tools.append('disomine')
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106 if options.efoldmine:
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107 b2b_tools.append('efoldmine')
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108 if options.agmata:
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109 b2b_tools.append('agmata')
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110
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111 single_seq.predict(b2b_tools)
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112 predictions = single_seq.get_all_predictions()
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113 results_json = single_seq.get_all_predictions_json('all')
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114 with open(options.json_output, 'w') as f:
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115 f.write(results_json)
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116 first_sequence_key = next(iter(predictions))
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117 prediction_keys = predictions[first_sequence_key].keys()
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118 df_dictionary = {}
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119 for sequence_key, sequence_predictions in predictions.items():
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120 residues = sequence_predictions['seq']
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121 residues_count = len(residues)
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122 sequence_df = pd.DataFrame(columns=prediction_keys, index=range(residues_count))
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123 sequence_df.index.name = 'residue_index'
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124 for predictor in prediction_keys:
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125 sequence_df[predictor] = sequence_predictions[predictor]
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126 sequence_df = sequence_df.rename(columns={"seq": "residue"})
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127 sequence_df = sequence_df.round(decimals=2)
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128 filename = f'{options.output}/{slugify(sequence_key)}.tsv'
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129 df_dictionary[sequence_key] = filename
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130 sequence_df.to_csv(filename, sep="\t")
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131 # Plot each individual plot (compatible with plot all)
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132 if options.plot:
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133 for predictor in prediction_keys:
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134 if predictor != 'seq':
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135 plot_prediction(prediction_name=predictor, highlighting_regions=True,
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136 predicted_values=sequence_predictions[predictor], seq_name=sequence_key)
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137 # Plot all together (compatible with plot individual)
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138 if options.plot_all:
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139 for predictor in prediction_keys:
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140 if predictor != 'seq':
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141 results_dictionary = df_dict_to_dict_of_values(df_dict=df_dictionary, predictor=predictor)
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142 plot_prediction(prediction_name=predictor, highlighting_regions=True,
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143 predicted_values=results_dictionary, seq_name='all')
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144
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145 if __name__ == "__main__":
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146 parser = optparse.OptionParser()
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147 parser.add_option("--dynamine", action="store_true", default=False)
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148 parser.add_option("--disomine", action="store_true", default=False)
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149 parser.add_option("--efoldmine", action="store_true", default=False)
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150 parser.add_option("--agmata", action="store_true", default=False)
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151 parser.add_option("--file", dest="input_fasta", default=False)
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152 parser.add_option("--output", dest="output", default=False)
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153 parser.add_option("--plot-output", dest="plot_output", default=False)
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154
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155 parser.add_option("--json", dest="json_output", default=False)
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156 parser.add_option("--plot", action="store_true", default=False)
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157 parser.add_option("--plot_all", action="store_true", default=False)
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158 parser.add_option("--highlight", action="store_true", default=False)
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159 options, _args = parser.parse_args()
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160 main(options)