Mercurial > repos > iuc > b2btools_single_sequence

annotate script.py @ 0:b694a77ca1e8 draft default tip

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planemo upload commit 599e1135baba020195b3f7576449d595bca9af75
author iuc
date Tue, 09 Aug 2022 12:30:52 +0000
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Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
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0
b694a77ca1e8 planemo upload commit 599e1135baba020195b3f7576449d595bca9af75
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1 import json
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2 import optparse
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3 import os.path
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4 import re
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5 import unicodedata
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6
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7 import matplotlib.pyplot as plt
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8 import numpy as np
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9 import pandas as pd
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10 from b2bTools import SingleSeq
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11
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12
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13 def slugify(value):
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14 """
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15 From https://github.com/django/django/blob/master/django/utils/text.py
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16 Convert to ASCII if 'allow_unicode'. Convert spaces or repeated
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17 dashes to single dashes. Remove characters that aren't alphanumerics,
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18 underscores, or hyphens. Convert to lowercase. Also strip leading and
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19 trailing whitespace, dashes, and underscores.
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20 """
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21 value = str(value)
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22 value = (
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23 unicodedata.normalize("NFKD", value)
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24 .encode("ascii", "ignore")
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25 .decode("ascii")
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26 )
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27 value = re.sub(r"[^\w\s-]", "", value.lower())
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28 return re.sub(r"[-\s]+", "-", value).strip("-_")
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29
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30
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31 def check_min_max(predicted_values, former_min, former_max):
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32 seq_max = max(predicted_values)
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33 seq_min = min(predicted_values)
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34 if (
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35 seq_max + 0.1 > former_max
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36 and not np.isnan(seq_max)
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37 and not np.isinf(seq_max)
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38 ):
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39 former_max = seq_max + 0.1
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40 if (
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41 seq_min - 0.1 < former_min
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42 and not np.isnan(seq_min)
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43 and not np.isinf(seq_min)
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44 ):
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45 former_min = seq_min - 0.1
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46 return former_min, former_max
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47
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48
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49 def plot_prediction(pred_name, hlighting_regions, predicted_values, seq_name):
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50 thresholds_dict = {
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51 "backbone": {
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52 "membrane spanning": [1.0, 1.5],
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53 "rigid": [0.8, 1.0],
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54 "context-dependent": [0.69, 0.8],
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55 "flexible": [-1.0, 0.69],
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56 },
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57 "earlyFolding": {
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58 "early folds": [0.169, 2.0],
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59 "late folds": [-1.0, 0.169],
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60 },
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61 "disoMine": {"ordered": [-1.0, 0.5], "disordered": [0.5, 2.0]},
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62 }
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63 ordered_regions_dict = {
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64 "backbone": [
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65 "flexible",
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66 "context-dependent",
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67 "rigid",
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68 "membrane spanning",
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69 ],
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70 "earlyFolding": ["late folds", "early folds"],
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71 "disoMine": ["ordered", "disordered"],
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72 }
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73 colors = ["yellow", "orange", "pink", "red"]
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74 ranges_dict = {
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75 "backbone": [-0.2, 1.2],
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76 "sidechain": [-0.2, 1.2],
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77 "ppII": [-0.2, 1.2],
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78 "earlyFolding": [-0.2, 1.2],
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79 "disoMine": [-0.2, 1.2],
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80 "agmata": [-0.2, 1.2],
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81 "helix": [-1.0, 1.0],
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82 "sheet": [-1.0, 1.0],
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83 "coil": [-1.0, 1.0],
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84 }
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85 fig, ax = plt.subplots(1, 1)
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86 fig.set_figwidth(10)
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87 fig.set_figheight(5)
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88 ax.set_title(pred_name + " " + "prediction")
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89 min_value, max_value = ranges_dict[pred_name]
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90 if seq_name == "all":
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91 max_len = 0
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92 for seq in predicted_values.keys():
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93 predictions = predicted_values[seq]
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94 min_value, max_value = check_min_max(
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95 predictions, min_value, max_value
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96 )
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97 ax.plot(range(len(predictions)), predictions, label=seq)
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98 if len(predictions) > max_len:
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99 max_len = len(predictions)
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100 ax.set_xlim([0, max_len - 1])
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101 else:
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102 predictions = predicted_values
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103 min_value, max_value = check_min_max(predictions, min_value, max_value)
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104 ax.plot(range(len(predictions)), predictions, label=seq_name)
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105 ax.set_xlim([0, len(predictions) - 1])
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106 legend_lines = plt.legend(
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107 bbox_to_anchor=(1.04, 1), loc="upper left", fancybox=True, shadow=True
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108 )
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109 ax.add_artist(legend_lines)
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110 # Define regions
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111 if hlighting_regions:
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112 if pred_name in ordered_regions_dict.keys():
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113 for i, prediction in enumerate(ordered_regions_dict[pred_name]):
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114 lower = thresholds_dict[pred_name][prediction][0]
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115 upper = thresholds_dict[pred_name][prediction][1]
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116 color = colors[i]
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117 ax.axhspan(
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118 lower, upper, alpha=0.3, color=color, label=prediction
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119 )
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120 included_in_regions_legend = list(
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121 reversed(
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122 [
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123 prediction
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124 for prediction in ordered_regions_dict[pred_name]
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125 ]
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126 )
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127 ) # to sort it "from up to low"
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128 # Get handles and labels
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129 handles, labels = plt.gca().get_legend_handles_labels()
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130 handles_dict = {
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131 label: handles[idx] for idx, label in enumerate(labels)
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132 }
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133 # Add legend for regions, if available
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134 region_legend = ax.legend(
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135 [
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136 handles_dict[region]
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137 for region in included_in_regions_legend
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138 ],
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139 [region for region in included_in_regions_legend],
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140 fancybox=True,
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141 shadow=True,
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142 loc="lower left",
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143 bbox_to_anchor=(1.04, 0),
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144 )
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145 ax.add_artist(region_legend)
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146 ax.set_ylim([min_value, max_value])
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147 ax.set_xlabel("residue index")
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148 ax.set_ylabel("prediction values")
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149 ax.grid(axis="y")
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150 plt.savefig(
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151 os.path.join(
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152 options.plot_output,
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153 "{0}_{1}.png".format(slugify(seq_name), pred_name),
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154 ),
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155 bbox_inches="tight",
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156 )
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157 plt.close()
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158
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159
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160 def df_dict_to_dict_of_values(df_dict, predictor):
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161 results_dict = {}
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162 for seq in df_dict.keys():
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163 df = pd.read_csv(df_dict[seq], sep="\t")
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164 results_dict[seq] = df[predictor]
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165 return results_dict
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166
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167
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168 def main(options):
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169 single_seq = SingleSeq(options.input_fasta)
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170 b2b_tools = []
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171 if options.dynamine:
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172 b2b_tools.append("dynamine")
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173 if options.disomine:
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174 b2b_tools.append("disomine")
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175 if options.efoldmine:
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176 b2b_tools.append("efoldmine")
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177 if options.agmata:
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178 b2b_tools.append("agmata")
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179 single_seq.predict(b2b_tools)
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180 predictions = single_seq.get_all_predictions()
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181
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182 def rounder_function(value):
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183 return round(float(value), 3)
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184
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185 rounded_predictions = json.loads(
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186 json.dumps(predictions), parse_float=rounder_function
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187 )
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188 results_json = json.dumps(rounded_predictions, indent=2, sort_keys=True)
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189 with open(options.json_output, "w") as f:
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190 f.write(results_json)
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191 first_sequence_key = next(iter(predictions))
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192 prediction_keys = predictions[first_sequence_key].keys()
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193 # Sort column names
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194 tsv_column_names = list(prediction_keys)
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195 tsv_column_names.remove("seq")
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196 tsv_column_names = ['residue', *sorted(tsv_column_names)]
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197
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198 df_dictionary = {}
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199 for sequence_key, seq_preds in predictions.items():
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200 residues = seq_preds["seq"]
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201 residues_count = len(residues)
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202 sequence_df = pd.DataFrame(
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203 columns=prediction_keys, index=range(residues_count)
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204 )
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205 sequence_df.index.name = "residue_index"
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206 for predictor in prediction_keys:
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207 sequence_df[predictor] = seq_preds[predictor]
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208 sequence_df = sequence_df.rename(columns={"seq": "residue"})
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209 sequence_df = sequence_df.round(decimals=3)
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210 filename = f"{options.output}/{slugify(sequence_key)}.tsv"
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211 df_dictionary[sequence_key] = filename
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212 sequence_df.to_csv(
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213 filename,
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214 header=True,
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215 columns=tsv_column_names,
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216 sep="\t"
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217 )
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218 # Plot each individual plot (compatible with plot all)
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219 if options.plot:
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220 for predictor in prediction_keys:
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221 if predictor != "seq":
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222 plot_prediction(
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223 pred_name=predictor,
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224 hlighting_regions=True,
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225 predicted_values=seq_preds[predictor],
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226 seq_name=sequence_key,
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227 )
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228 # Plot all together (compatible with plot individual)
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229 if options.plot_all:
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230 for predictor in prediction_keys:
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231 if predictor != "seq":
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232 results_dictionary = df_dict_to_dict_of_values(
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233 df_dictionary, predictor
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234 )
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235 plot_prediction(
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236 pred_name=predictor,
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237 hlighting_regions=True,
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238 predicted_values=results_dictionary,
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239 seq_name="all",
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240 )
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241
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242
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243 if __name__ == "__main__":
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244 parser = optparse.OptionParser()
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245 parser.add_option(
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246 "--dynamine",
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247 action="store_true"
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248 )
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249 parser.add_option(
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250 "--disomine",
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251 action="store_true"
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252 )
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253 parser.add_option(
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254 "--efoldmine",
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255 action="store_true"
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256 )
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257 parser.add_option(
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258 "--agmata",
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259 action="store_true"
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260 )
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261 parser.add_option(
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262 "--file",
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263 dest="input_fasta",
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264 type="string"
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265 )
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266 parser.add_option(
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267 "--output",
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268 dest="output",
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269 type="string"
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270 )
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271 parser.add_option(
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272 "--plot-output",
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273 type="string",
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274 dest="plot_output"
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275 )
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276 parser.add_option(
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277 "--json",
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278 dest="json_output",
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279 type="string"
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280 )
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281 parser.add_option(
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282 "--plot",
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283 action="store_true"
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284 )
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285 parser.add_option(
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286 "--plot_all",
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287 action="store_true"
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288 )
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289 parser.add_option(
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290 "--highlight",
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291 action="store_true"
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292 )
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293 try:
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294 options, args = parser.parse_args()
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295 if not (options.dynamine or options.disomine or options.efoldmine or options.agmata):
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296 parser.error('At least one predictor is required')
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297 if not options.input_fasta:
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298 parser.error('Input file not given (--file)')
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299 if not options.output:
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300 parser.error('Output directory not given (--output)')
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301 if (options.plot or options.plot_all) and not options.plot_output:
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302 parser.error('Plot output directory not given (--plot-output)')
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303 if not options.json_output:
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304 parser.error('Json output file not given (--json)')
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305 main(options)
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306 except optparse.OptionError as exc:
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307 raise RuntimeError(f"Invalid arguments: {args}") from exc