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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/rna_tools/rnaformer commit 81ae3a5a824f820d2ee4212dcacb334b3dabd683
author bgruening
date Sun, 26 Jan 2025 08:45:36 +0000
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50a3903370a6 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/rna_tools/rnaformer commit 81ae3a5a824f820d2ee4212dcacb334b3dabd683
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1 <tool id="infer_rnaformer" name="@EXECUTABLE@" version="@TOOL_VERSION@+galaxy1" profile="22.05">
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2 <description>Predict the secondary structure of an RNA with RNAformer</description>
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3 <macros>
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4 <import>macros.xml</import>
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5 </macros>
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6 <expand macro="requirements">
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7 <requirement type="package" version="1.83">biopython</requirement>
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8 </expand>
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9 <command detect_errors="exit_code"><![CDATA[
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10 mkdir -p './model' &&
50a3903370a6 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/rna_tools/rnaformer commit 81ae3a5a824f820d2ee4212dcacb334b3dabd683
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11 wget -O './model/RNAformer_32M_state_dict_intra_family_finetuned.pth' 'https://ml.informatik.uni-freiburg.de/research-artifacts/RNAformer/models/RNAformer_32M_state_dict_intra_family_finetuned.pth'
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12 &&
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13 wget -O './model/RNAformer_32M_config_intra_family_finetuned.yml' 'https://ml.informatik.uni-freiburg.de/research-artifacts/RNAformer/models/RNAformer_32M_config_intra_family_finetuned.yml'
50a3903370a6 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/rna_tools/rnaformer commit 81ae3a5a824f820d2ee4212dcacb334b3dabd683
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14 &&
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15 python '$script_file' > '$output'
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16 ]]></command>
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17 <configfiles>
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18 <configfile name="script_file"><![CDATA[import RNAformer
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19 import os
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20 import argparse
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21 import torch
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22 import urllib.request
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23 import logging
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24 from collections import defaultdict
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25 import torch.cuda
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26 import loralib as lora
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27 from RNAformer.model.RNAformer import RiboFormer
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28 from RNAformer.utils.configuration import Config
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29
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30 from Bio import SeqIO
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31
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32 import logging
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33 import sys
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34
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35 def is_valid_rna_sequence(sequence):
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36 """Check if the sequence contains only RNA bases."""
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37 valid_bases = {'A', 'C', 'G', 'U', 'N'} # Include 'N' if unknown bases are allowed
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38 return all(base in valid_bases for base in sequence.upper())
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39
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40 config_file_path = 'model/RNAformer_32M_config_intra_family_finetuned.yml'
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41 model_file_path = 'model/RNAformer_32M_state_dict_intra_family_finetuned.pth'
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42
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43 config = Config(config_file=config_file_path)
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44 config.RNAformer.cycling = 6
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45 model = RiboFormer(config.RNAformer)
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46 state_dict_file = model_file_path
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47
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48 #if str($input_type.input_type) == 'True'
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49 fasta_path = '$input_type.fasta_input'
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50 sequences = [str(record.seq) for record in SeqIO.parse(fasta_path, 'fasta')]
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51 #else
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52 sequence_string = "$input_type.rna_input_string"
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53 sequences = [seq.strip() for seq in sequence_string.split(',')]
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54 #end if
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55
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56 for seq in sequences:
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57 if not is_valid_rna_sequence(seq):
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58 print(f"Invalid RNA sequence detected: {seq}. Please ensure only RNA sequences are used as input.", file=sys.stderr)
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59 sys.exit(1)
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60
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61 lora_config = {
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62 "r": config.r,
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63 "lora_alpha": config.lora_alpha,
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64 "lora_dropout": config.lora_dropout,
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65 }
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66
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67 with torch.no_grad():
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68 for name, module in model.named_modules():
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69 if any(replace_key in name for replace_key in config.replace_layer):
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70 parent = model.get_submodule(".".join(name.split(".")[:-1]))
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71 target_name = name.split(".")[-1]
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72 target = model.get_submodule(name)
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73 if isinstance(target, torch.nn.Linear) and "qkv" in name:
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74 new_module = lora.MergedLinear(target.in_features, target.out_features,
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75 bias=target.bias is not None,
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76 enable_lora=[True, True, True], **lora_config)
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77 new_module.weight.copy_(target.weight)
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78 if target.bias is not None:
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79 new_module.bias.copy_(target.bias)
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80 elif isinstance(target, torch.nn.Linear):
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81 new_module = lora.Linear(target.in_features, target.out_features,
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82 bias=target.bias is not None, **lora_config)
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83 new_module.weight.copy_(target.weight)
50a3903370a6 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/rna_tools/rnaformer commit 81ae3a5a824f820d2ee4212dcacb334b3dabd683
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84 if target.bias is not None:
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85 new_module.bias.copy_(target.bias)
50a3903370a6 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/rna_tools/rnaformer commit 81ae3a5a824f820d2ee4212dcacb334b3dabd683
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86
50a3903370a6 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/rna_tools/rnaformer commit 81ae3a5a824f820d2ee4212dcacb334b3dabd683
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87 elif isinstance(target, torch.nn.Conv2d):
50a3903370a6 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/rna_tools/rnaformer commit 81ae3a5a824f820d2ee4212dcacb334b3dabd683
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88 kernel_size = target.kernel_size[0]
50a3903370a6 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/rna_tools/rnaformer commit 81ae3a5a824f820d2ee4212dcacb334b3dabd683
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89 new_module = lora.Conv2d(target.in_channels, target.out_channels, kernel_size,
50a3903370a6 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/rna_tools/rnaformer commit 81ae3a5a824f820d2ee4212dcacb334b3dabd683
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90 padding=(kernel_size - 1) // 2, bias=target.bias is not None,
50a3903370a6 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/rna_tools/rnaformer commit 81ae3a5a824f820d2ee4212dcacb334b3dabd683
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91 **lora_config)
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92
50a3903370a6 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/rna_tools/rnaformer commit 81ae3a5a824f820d2ee4212dcacb334b3dabd683
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93 new_module.conv.weight.copy_(target.weight)
50a3903370a6 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/rna_tools/rnaformer commit 81ae3a5a824f820d2ee4212dcacb334b3dabd683
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94 if target.bias is not None:
50a3903370a6 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/rna_tools/rnaformer commit 81ae3a5a824f820d2ee4212dcacb334b3dabd683
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95 new_module.conv.bias.copy_(target.bias)
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96 setattr(parent, target_name, new_module)
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97
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98 state_dict = torch.load(state_dict_file, map_location=torch.device('cpu'))
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99
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100 model.load_state_dict(state_dict, strict=True)
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101 model_name = state_dict_file.split(".pth")[0]
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102
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103 if torch.cuda.is_available():
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104 model = model.cuda()
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105
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106 # check GPU can do bf16
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107 if torch.cuda.is_bf16_supported():
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108 model = model.bfloat16()
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109 else:
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110 model = model.half()
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111
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112 model.eval()
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113 predicted_structures = []
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114
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115 orig_seq = ""
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116
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117 for sequence in sequences:
50a3903370a6 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/rna_tools/rnaformer commit 81ae3a5a824f820d2ee4212dcacb334b3dabd683
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118 with torch.no_grad():
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119 device = "cpu"
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120
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121 seq_vocab = ['A', 'C', 'G', 'U', 'N']
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122 seq_stoi = dict(zip(seq_vocab, range(len(seq_vocab))))
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123
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124 pdb_sample = 1
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125
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126 length = len(sequence)
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127 src_seq = torch.LongTensor(list(map(seq_stoi.get, sequence)))
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128
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129 orig_seq = sequence
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130
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131 sample = {}
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132 sample['src_seq'] = src_seq.clone()
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133 sample['length'] = torch.LongTensor([length])[0]
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134 sample['pdb_sample'] = torch.LongTensor([pdb_sample])[0]
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135
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136 sequence = sample['src_seq'].unsqueeze(0).to(device)
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137 src_len = torch.LongTensor([sequence.shape[-1]]).to(device)
50a3903370a6 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/rna_tools/rnaformer commit 81ae3a5a824f820d2ee4212dcacb334b3dabd683
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138 pdb_sample = torch.FloatTensor([[1]]).to(device)
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139
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140 logits, pair_mask = model(sequence, src_len, pdb_sample)
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141
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142 pred_mat = torch.sigmoid(logits[0, :, :, -1]) > 0.5
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143
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144 pos_id = torch.where(pred_mat == True)
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145 pos1_id = pos_id[0].cpu().tolist()
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146 pos2_id = pos_id[1].cpu().tolist()
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147 predicted_structure = f"Pairing index 1: {pos1_id} \nPairing index 2: {pos2_id}"
50a3903370a6 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/rna_tools/rnaformer commit 81ae3a5a824f820d2ee4212dcacb334b3dabd683
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148 pairs = [[a, b] for a, b in zip(pos1_id, pos2_id)]
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149
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150 seqlen = len(sample['src_seq'])
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151 dot_bracket =['.'] * seqlen
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152 pk_count = 0
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153 pk_list = []
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154 for i in range(len(pos1_id)):
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155 open_index = pos1_id[i]
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156 close_index = pos2_id[i]
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157
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158 if 0 <= open_index < len(dot_bracket) and 0 <= close_index < len(dot_bracket):
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159 if dot_bracket[open_index] == '.' and dot_bracket[close_index] == '.':
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160 dot_bracket[open_index] = '('
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161 dot_bracket[close_index] = ')'
50a3903370a6 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/rna_tools/rnaformer commit 81ae3a5a824f820d2ee4212dcacb334b3dabd683
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162 else:
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163 ## pseudoknots or multiplets present in structure- cannot represent with dot-bracket
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164 pk_count += 1
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165 pk_list.append(pairs[i])
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166 dot_bracket_str_pred = ''.join(dot_bracket)
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167
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168 print(f"{orig_seq}")
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169 print(f"Base pairs: {str(pairs)}")
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170 print(f"Predicted Structure: {dot_bracket_str_pred}")
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171 if pk_count > 0:
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172 print(f"NOTE: {pk_count} pseudoknots and/or multiplets present in predicted structure excluded from dot-bracket notation: {pk_list}")
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173
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174
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175
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176 ]]></configfile>
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177 </configfiles>
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178 <inputs>
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179 <conditional name="input_type">
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180 <param name="input_type" type="select" label="Input from FASTA file">
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181 <option value="False">Provide a single RNA sequence string as text</option>
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182 <option value="True">Provide a FASTA file</option>
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183 </param>
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184 <when value="False">
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185 <param name="rna_input_string" label="Sequence(s) to fold" type="text" value="GCCCGCAUGGUGAAAUCGGUAAACACAUCGCACUAAUGCGCCGCCUCUGGCUUGCCGGUUCAAGUCCGGCUGCGGGCACCA" help="Enter RNA sequences. Separate multiple RNA sequences by commas.">
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186 <sanitizer>
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187 <valid>
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188 <add value="ACGUacgu,"/>
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189 </valid>
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190 </sanitizer>
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191 </param>
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192 </when>
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193 <when value="True">
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194 <param format="fasta" name="fasta_input" type="data" label="Sequence to fold (FASTA file)"/>
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195 </when>
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196 </conditional>
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197 </inputs>
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198 <outputs>
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199 <data name="output" format="txt" label="output"/>
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200 </outputs>
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201 <tests>
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202 <test>
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203 <param name="input_type" value="False"/>
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204 <param name="rna_input_string" value="GCCCGCAUGGUGAAAUCGGUAAACACAUCGCACUAAUGCGCCGCCUCUGGCUUGCCGGUUCAAGUCCGGCUGCGGGCACCA"/>
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205 <output name="output" file="rna_2d_pred_out.txt"/>
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206 </test>
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207 <test>
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208 <param name="input_type" value="True"/>
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209 <param name="fasta_input" value="fasta_input1.fa"/>
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210 <output name="output" file="rna_2d_pred_FASTA.txt"/>
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211 </test>
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212 </tests>
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213 <help><![CDATA[
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214 **RNAformer**
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215 This tool reads RNA sequences and predicts their secondary structure using RNAformer. Note that unlike conventional methods, RNAformer is capable of predicting all possible secondary structure motifs, including pseudoknots and multiplets. These cannot be represented in dot-bracket notation and thus the output will be partial in these cases, excluding these which will be noted in the output file below the (partial) dot-bracket structure.
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216
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217 **Input format**
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218
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219 RNAformer requires one or more RNA sequences either as a single FASTA file or as plain text.
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220
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221 **Outputs**
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222
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223 - Predicted secondary structure as a text file containing the following:
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224 - RNA input sequence
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225 - Base pairs of predicted secondary structure
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226 - Predicted secondary structure in dot-bracket notation
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227 ]]></help>
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228 <expand macro="citations" />
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229 </tool>