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Commit
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Add AgroNT-1b model for promoter strength in leaf prediction

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Files changed (8) hide show
  1. README.md +63 -3
  2. config.json +36 -0
  3. esm_config.py +379 -0
  4. model.safetensors +3 -0
  5. modeling_esm.py +1446 -0
  6. special_tokens_map.json +6 -0
  7. tokenizer_config.json +44 -0
  8. vocab.txt +4107 -0
README.md CHANGED
@@ -1,3 +1,63 @@
1
- ---
2
- license: cc-by-nc-sa-4.0
3
- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: cc-by-nc-sa-4.0
3
+ widget:
4
+ - text: AGTCCAGTGGACGACCAGCCACGGCTCCGGTCTGTAGAACCATCGCGGAAACGGCTCGCAAAACTCTAAACAGCGCAAACGATGCGCGCGCCGAAGCAACCCGGCTCTACTTATAAAAACGTCCAACGGTGAGCACCGAGCAGCTACTACTCGTACTCCCCCCACCGATC
5
+ tags:
6
+ - DNA
7
+ - biology
8
+ - genomics
9
+ ---
10
+ # Plant foundation DNA large language models
11
+
12
+ The plant DNA large language models (LLMs) contain a series of foundation models based on different model architectures, which are pre-trained on various plant reference genomes.
13
+ All the models have a comparable model size between 90 MB and 150 MB, BPE tokenizer is used for tokenization and 8000 tokens are included in the vocabulary.
14
+
15
+
16
+ **Developed by:** zhangtaolab
17
+
18
+ ### Model Sources
19
+
20
+ - **Repository:** [Plant DNA LLMs](https://github.com/zhangtaolab/plant_DNA_LLMs)
21
+ - **Manuscript:** [Versatile applications of foundation DNA language models in plant genomes]()
22
+
23
+ ### Architecture
24
+
25
+ The model is trained based on the InstaDeepAI/agro-nucleotide-transformer-1b model.
26
+
27
+ This model is fine-tuned for predicting promoter strength in tobacco leaves system.
28
+
29
+
30
+ ### How to use
31
+
32
+ Install the runtime library first:
33
+ ```bash
34
+ pip install transformers
35
+ ```
36
+
37
+ Here is a simple code for inference:
38
+ ```python
39
+ from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
40
+
41
+ model_name = 'agront-1b-promoter_strength_leaf'
42
+ # load model and tokenizer
43
+ model = AutoModelForSequenceClassification.from_pretrained(f'zhangtaolab/{model_name}', trust_remote_code=True)
44
+ tokenizer = AutoTokenizer.from_pretrained(f'zhangtaolab/{model_name}', trust_remote_code=True)
45
+
46
+ # inference
47
+ sequences = ['TACTCTAATCGTATCAGCTGCACTTGCGTACAGGCTACCGGCGTCCTCAGCCACGTAAGAAAAGGCCCAATAAAGGCCCAACTACAACCAGCGGATATATATACTGGAGCCTGGCGAGATCACCCTAACCCCTCACACTCCCATCCAGCCGCCACCAGGTGCAGAGTGTT',
48
+ 'ATTTCAAAACTAGTTTTCTATAAACGAAAACTTATATTTATTCCGCTTGTTCCGTTTGATCTGCTGATTCGACACCGTTTTAACGTATTTTAAGTAAGTATCAGAAATATTAATGTGAAGATAAAAGAAAATAGAGTAAATGTAAAGGAAAATGCATAAGATTTTGTTGA']
49
+ pipe = pipeline('text-classification', model=model, tokenizer=tokenizer,
50
+ trust_remote_code=True, top_k=None)
51
+ results = pipe(sequences)
52
+ print(results)
53
+
54
+ ```
55
+
56
+
57
+ ### Training data
58
+ We use EsmForSequenceClassification to fine-tune the model.
59
+ Detailed training procedure can be found in our manuscript.
60
+
61
+
62
+ #### Hardware
63
+ Model was trained on a NVIDIA RTX4090 GPU (24 GB).
config.json ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "AgroNT-1b_promoter_strength_leaf",
3
+ "architectures": [
4
+ "EsmForSequenceClassification"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.0,
7
+ "emb_layer_norm_before": false,
8
+ "esmfold_config": null,
9
+ "hidden_dropout_prob": 0.0,
10
+ "hidden_size": 1500,
11
+ "id2label": {
12
+ "0": "Promoter strength in tobacco leaves"
13
+ },
14
+ "initializer_range": 0.02,
15
+ "intermediate_size": 5120,
16
+ "is_folding_model": false,
17
+ "label2id": {
18
+ "Promoter strength in tobacco leaves": 0
19
+ },
20
+ "layer_norm_eps": 1e-12,
21
+ "mask_token_id": 2,
22
+ "max_position_embeddings": 1026,
23
+ "model_type": "esm",
24
+ "num_attention_heads": 20,
25
+ "num_hidden_layers": 40,
26
+ "pad_token_id": 1,
27
+ "position_embedding_type": "absolute",
28
+ "problem_type": "regression",
29
+ "tie_word_embeddings": false,
30
+ "token_dropout": false,
31
+ "torch_dtype": "float32",
32
+ "transformers_version": "4.39.1",
33
+ "use_cache": false,
34
+ "vocab_list": null,
35
+ "vocab_size": 4107
36
+ }
esm_config.py ADDED
@@ -0,0 +1,379 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2022 Meta and The HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+ """ ESM model configuration"""
16
+
17
+ from dataclasses import asdict, dataclass
18
+ from typing import Optional
19
+
20
+ from transformers import PretrainedConfig, logging
21
+
22
+ logger = logging.get_logger(__name__)
23
+
24
+ # TODO Update this
25
+ ESM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
26
+ "facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/resolve/main/config.json",
27
+ # See all ESM models at https://huggingface.co/models?filter=esm
28
+ }
29
+
30
+
31
+ class EsmConfig(PretrainedConfig):
32
+ r"""
33
+ This is the configuration class to store the configuration of a [`ESMModel`]. It is used to instantiate a ESM model
34
+ according to the specified arguments, defining the model architecture. Instantiating a configuration with the
35
+ defaults will yield a similar configuration to that of the ESM
36
+ [facebook/esm-1b](https://huggingface.co/facebook/esm-1b) architecture.
37
+
38
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
39
+ documentation from [`PretrainedConfig`] for more information.
40
+
41
+
42
+ Args:
43
+ vocab_size (`int`, *optional*):
44
+ Vocabulary size of the ESM model. Defines the number of different tokens that can be represented by the
45
+ `inputs_ids` passed when calling [`ESMModel`].
46
+ mask_token_id (`int`, *optional*):
47
+ The index of the mask token in the vocabulary. This must be included in the config because of the
48
+ "mask-dropout" scaling trick, which will scale the inputs depending on the number of masked tokens.
49
+ pad_token_id (`int`, *optional*):
50
+ The index of the padding token in the vocabulary. This must be included in the config because certain parts
51
+ of the ESM code use this instead of the attention mask.
52
+ hidden_size (`int`, *optional*, defaults to 768):
53
+ Dimensionality of the encoder layers and the pooler layer.
54
+ num_hidden_layers (`int`, *optional*, defaults to 12):
55
+ Number of hidden layers in the Transformer encoder.
56
+ num_attention_heads (`int`, *optional*, defaults to 12):
57
+ Number of attention heads for each attention layer in the Transformer encoder.
58
+ intermediate_size (`int`, *optional*, defaults to 3072):
59
+ Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
60
+ hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
61
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
62
+ attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
63
+ The dropout ratio for the attention probabilities.
64
+ max_position_embeddings (`int`, *optional*, defaults to 1026):
65
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
66
+ just in case (e.g., 512 or 1024 or 2048).
67
+ initializer_range (`float`, *optional*, defaults to 0.02):
68
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
69
+ layer_norm_eps (`float`, *optional*, defaults to 1e-12):
70
+ The epsilon used by the layer normalization layers.
71
+ position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
72
+ Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query", "rotary"`.
73
+ For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
74
+ [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
75
+ For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
76
+ with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
77
+ is_decoder (`bool`, *optional*, defaults to `False`):
78
+ Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
79
+ use_cache (`bool`, *optional*, defaults to `True`):
80
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
81
+ relevant if `config.is_decoder=True`.
82
+ emb_layer_norm_before (`bool`, *optional*):
83
+ Whether to apply layer normalization after embeddings but before the main stem of the network.
84
+ token_dropout (`bool`, defaults to `False`):
85
+ When this is enabled, masked tokens are treated as if they had been dropped out by input dropout.
86
+
87
+ Examples:
88
+
89
+ ```python
90
+ >>> from transformers import EsmModel, EsmConfig
91
+
92
+ >>> # Initializing a ESM facebook/esm-1b style configuration >>> configuration = EsmConfig()
93
+
94
+ >>> # Initializing a model from the configuration >>> model = ESMModel(configuration)
95
+
96
+ >>> # Accessing the model configuration >>> configuration = model.config
97
+ ```"""
98
+ model_type = "esm"
99
+
100
+ def __init__(
101
+ self,
102
+ vocab_size=None,
103
+ mask_token_id=None,
104
+ pad_token_id=None,
105
+ hidden_size=768,
106
+ num_hidden_layers=12,
107
+ num_attention_heads=12,
108
+ intermediate_size=3072,
109
+ hidden_dropout_prob=0.1,
110
+ attention_probs_dropout_prob=0.1,
111
+ max_position_embeddings=1026,
112
+ initializer_range=0.02,
113
+ layer_norm_eps=1e-12,
114
+ position_embedding_type="absolute",
115
+ use_cache=True,
116
+ emb_layer_norm_before=None,
117
+ token_dropout=False,
118
+ is_folding_model=False,
119
+ esmfold_config=None,
120
+ vocab_list=None,
121
+ add_bias_fnn=True,
122
+ **kwargs,
123
+ ):
124
+ super().__init__(
125
+ pad_token_id=pad_token_id, mask_token_id=mask_token_id, **kwargs
126
+ )
127
+
128
+ self.vocab_size = vocab_size
129
+ self.hidden_size = hidden_size
130
+ self.num_hidden_layers = num_hidden_layers
131
+ self.num_attention_heads = num_attention_heads
132
+ self.intermediate_size = intermediate_size
133
+ self.hidden_dropout_prob = hidden_dropout_prob
134
+ self.attention_probs_dropout_prob = attention_probs_dropout_prob
135
+ self.max_position_embeddings = max_position_embeddings
136
+ self.initializer_range = initializer_range
137
+ self.layer_norm_eps = layer_norm_eps
138
+ self.position_embedding_type = position_embedding_type
139
+ self.use_cache = use_cache
140
+ self.emb_layer_norm_before = emb_layer_norm_before
141
+ self.token_dropout = token_dropout
142
+ self.is_folding_model = is_folding_model
143
+ # Arguments needed for Dalmatian
144
+ self.add_bias_fnn = add_bias_fnn
145
+ if is_folding_model:
146
+ if esmfold_config is None:
147
+ logger.info(
148
+ "No esmfold_config supplied for folding model, using default values."
149
+ )
150
+ esmfold_config = EsmFoldConfig()
151
+ elif isinstance(esmfold_config, dict):
152
+ esmfold_config = EsmFoldConfig(**esmfold_config)
153
+ self.esmfold_config = esmfold_config
154
+ if vocab_list is None:
155
+ logger.warning(
156
+ "No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!"
157
+ )
158
+ self.vocab_list = get_default_vocab_list()
159
+ else:
160
+ self.vocab_list = vocab_list
161
+ else:
162
+ self.esmfold_config = None
163
+ self.vocab_list = None
164
+ if self.esmfold_config is not None and getattr(
165
+ self.esmfold_config, "use_esm_attn_map", False
166
+ ):
167
+ raise ValueError(
168
+ "The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!"
169
+ )
170
+
171
+ def to_dict(self):
172
+ """
173
+ Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
174
+
175
+ Returns:
176
+ `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
177
+ """
178
+ output = super().to_dict()
179
+ if isinstance(self.esmfold_config, EsmFoldConfig):
180
+ output["esmfold_config"] = self.esmfold_config.to_dict()
181
+ return output
182
+
183
+
184
+ @dataclass
185
+ class EsmFoldConfig:
186
+ esm_type: str = None
187
+ fp16_esm: bool = True
188
+ use_esm_attn_map: bool = False
189
+ esm_ablate_pairwise: bool = False
190
+ esm_ablate_sequence: bool = False
191
+ esm_input_dropout: float = 0
192
+
193
+ embed_aa: bool = True
194
+ bypass_lm: bool = False
195
+
196
+ lddt_head_hid_dim: int = 128
197
+ trunk: "TrunkConfig" = None
198
+
199
+ def __post_init__(self):
200
+ if self.trunk is None:
201
+ self.trunk = TrunkConfig()
202
+ elif isinstance(self.trunk, dict):
203
+ self.trunk = TrunkConfig(**self.trunk)
204
+
205
+ def to_dict(self):
206
+ """
207
+ Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
208
+
209
+ Returns:
210
+ `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
211
+ """
212
+ output = asdict(self)
213
+ output["trunk"] = self.trunk.to_dict()
214
+ return output
215
+
216
+
217
+ @dataclass
218
+ class TrunkConfig:
219
+ num_blocks: int = 48
220
+ sequence_state_dim: int = 1024
221
+ pairwise_state_dim: int = 128
222
+ sequence_head_width: int = 32
223
+ pairwise_head_width: int = 32
224
+ position_bins: int = 32
225
+ dropout: float = 0
226
+ layer_drop: float = 0
227
+ cpu_grad_checkpoint: bool = False
228
+ max_recycles: int = 4
229
+ chunk_size: Optional[int] = 128
230
+ structure_module: "StructureModuleConfig" = None
231
+
232
+ def __post_init__(self):
233
+ if self.structure_module is None:
234
+ self.structure_module = StructureModuleConfig()
235
+ elif isinstance(self.structure_module, dict):
236
+ self.structure_module = StructureModuleConfig(**self.structure_module)
237
+
238
+ if self.max_recycles <= 0:
239
+ raise ValueError(
240
+ f"`max_recycles` should be positive, got {self.max_recycles}."
241
+ )
242
+ if self.sequence_state_dim % self.sequence_state_dim != 0:
243
+ raise ValueError(
244
+ "`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
245
+ f" {self.sequence_state_dim} and {self.sequence_state_dim}."
246
+ )
247
+ if self.pairwise_state_dim % self.pairwise_state_dim != 0:
248
+ raise ValueError(
249
+ "`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
250
+ f" {self.pairwise_state_dim} and {self.pairwise_state_dim}."
251
+ )
252
+
253
+ sequence_num_heads = self.sequence_state_dim // self.sequence_head_width
254
+ pairwise_num_heads = self.pairwise_state_dim // self.pairwise_head_width
255
+
256
+ if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
257
+ raise ValueError(
258
+ "`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
259
+ f" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."
260
+ )
261
+ if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
262
+ raise ValueError(
263
+ "`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
264
+ f" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."
265
+ )
266
+ if self.pairwise_state_dim % 2 != 0:
267
+ raise ValueError(
268
+ f"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."
269
+ )
270
+
271
+ if self.dropout >= 0.4:
272
+ raise ValueError(
273
+ f"`dropout` should not be greater than 0.4, got {self.dropout}."
274
+ )
275
+
276
+ def to_dict(self):
277
+ """
278
+ Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
279
+
280
+ Returns:
281
+ `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
282
+ """
283
+ output = asdict(self)
284
+ output["structure_module"] = self.structure_module.to_dict()
285
+ return output
286
+
287
+
288
+ @dataclass
289
+ class StructureModuleConfig:
290
+ """
291
+ Args:
292
+ sequence_dim:
293
+ Single representation channel dimension
294
+ pairwise_dim:
295
+ Pair representation channel dimension
296
+ ipa_dim:
297
+ IPA hidden channel dimension
298
+ resnet_dim:
299
+ Angle resnet (Alg. 23 lines 11-14) hidden channel dimension
300
+ num_heads_ipa:
301
+ Number of IPA heads
302
+ num_qk_points:
303
+ Number of query/key points to generate during IPA
304
+ num_v_points:
305
+ Number of value points to generate during IPA
306
+ dropout_rate:
307
+ Dropout rate used throughout the layer
308
+ num_blocks:
309
+ Number of structure module blocks
310
+ num_transition_layers:
311
+ Number of layers in the single representation transition (Alg. 23 lines 8-9)
312
+ num_resnet_blocks:
313
+ Number of blocks in the angle resnet
314
+ num_angles:
315
+ Number of angles to generate in the angle resnet
316
+ trans_scale_factor:
317
+ Scale of single representation transition hidden dimension
318
+ epsilon:
319
+ Small number used in angle resnet normalization
320
+ inf:
321
+ Large number used for attention masking
322
+ """
323
+
324
+ sequence_dim: int = 384
325
+ pairwise_dim: int = 128
326
+ ipa_dim: int = 16
327
+ resnet_dim: int = 128
328
+ num_heads_ipa: int = 12
329
+ num_qk_points: int = 4
330
+ num_v_points: int = 8
331
+ dropout_rate: float = 0.1
332
+ num_blocks: int = 8
333
+ num_transition_layers: int = 1
334
+ num_resnet_blocks: int = 2
335
+ num_angles: int = 7
336
+ trans_scale_factor: int = 10
337
+ epsilon: float = 1e-8
338
+ inf: float = 1e5
339
+
340
+ def to_dict(self):
341
+ return asdict(self)
342
+
343
+
344
+ def get_default_vocab_list():
345
+ return (
346
+ "<cls>",
347
+ "<pad>",
348
+ "<eos>",
349
+ "<unk>",
350
+ "L",
351
+ "A",
352
+ "G",
353
+ "V",
354
+ "S",
355
+ "E",
356
+ "R",
357
+ "T",
358
+ "I",
359
+ "D",
360
+ "P",
361
+ "K",
362
+ "Q",
363
+ "N",
364
+ "F",
365
+ "Y",
366
+ "M",
367
+ "H",
368
+ "W",
369
+ "C",
370
+ "X",
371
+ "B",
372
+ "U",
373
+ "Z",
374
+ "O",
375
+ ".",
376
+ "-",
377
+ "<null_1>",
378
+ "<mask>",
379
+ )
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:4afb59ea99e702ec2c5565eef73b9c985d54add6bb46c868794b32aaca425f2b
3
+ size 3940481176
modeling_esm.py ADDED
@@ -0,0 +1,1446 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2022 Meta and The HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+ """ PyTorch ESM model."""
16
+
17
+ import math
18
+ from typing import List, Optional, Tuple, Union
19
+
20
+ import torch
21
+ import torch.utils.checkpoint
22
+ from torch import nn
23
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss, SiLU
24
+ from transformers.file_utils import (
25
+ add_code_sample_docstrings,
26
+ add_start_docstrings,
27
+ add_start_docstrings_to_model_forward,
28
+ )
29
+ from transformers.modeling_outputs import (
30
+ BaseModelOutputWithPastAndCrossAttentions,
31
+ BaseModelOutputWithPoolingAndCrossAttentions,
32
+ MaskedLMOutput,
33
+ SequenceClassifierOutput,
34
+ TokenClassifierOutput,
35
+ )
36
+ from transformers.modeling_utils import (
37
+ PreTrainedModel,
38
+ find_pruneable_heads_and_indices,
39
+ prune_linear_layer,
40
+ )
41
+ from transformers.utils import logging
42
+
43
+ from .esm_config import EsmConfig
44
+
45
+ logger = logging.get_logger(__name__)
46
+
47
+ _CHECKPOINT_FOR_DOC = "facebook/esm2_t6_8M_UR50D"
48
+ _CONFIG_FOR_DOC = "EsmConfig"
49
+
50
+ ESM_PRETRAINED_MODEL_ARCHIVE_LIST = [
51
+ "facebook/esm2_t6_8M_UR50D",
52
+ "facebook/esm2_t12_35M_UR50D",
53
+ # This is not a complete list of all ESM models!
54
+ # See all ESM models at https://huggingface.co/models?filter=esm
55
+ ]
56
+
57
+
58
+ def rotate_half(x):
59
+ x1, x2 = x.chunk(2, dim=-1)
60
+ return torch.cat((-x2, x1), dim=-1)
61
+
62
+
63
+ def apply_rotary_pos_emb(x, cos, sin):
64
+ cos = cos[:, :, : x.shape[-2], :]
65
+ sin = sin[:, :, : x.shape[-2], :]
66
+
67
+ return (x * cos) + (rotate_half(x) * sin)
68
+
69
+
70
+ def gelu(x):
71
+ """
72
+ This is the gelu implementation from the original ESM repo. Using F.gelu yields subtly wrong results.
73
+ """
74
+ return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
75
+
76
+
77
+ def symmetrize(x):
78
+ "Make layer symmetric in final two dimensions, used for contact prediction."
79
+ return x + x.transpose(-1, -2)
80
+
81
+
82
+ def average_product_correct(x):
83
+ "Perform average product correct, used for contact prediction."
84
+ a1 = x.sum(-1, keepdims=True)
85
+ a2 = x.sum(-2, keepdims=True)
86
+ a12 = x.sum((-1, -2), keepdims=True)
87
+
88
+ avg = a1 * a2
89
+ avg.div_(a12) # in-place to reduce memory
90
+ normalized = x - avg
91
+ return normalized
92
+
93
+
94
+ class RotaryEmbedding(torch.nn.Module):
95
+ """
96
+ Rotary position embeddings based on those in
97
+ [RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer). Query and keys are transformed by rotation
98
+ matrices which depend on their relative positions.
99
+ """
100
+
101
+ def __init__(self, dim: int):
102
+ super().__init__()
103
+ # Generate and save the inverse frequency buffer (non trainable)
104
+ inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
105
+ inv_freq = inv_freq
106
+ self.register_buffer("inv_freq", inv_freq)
107
+
108
+ self._seq_len_cached = None
109
+ self._cos_cached = None
110
+ self._sin_cached = None
111
+
112
+ def _update_cos_sin_tables(self, x, seq_dimension=2):
113
+ seq_len = x.shape[seq_dimension]
114
+
115
+ # Reset the tables if the sequence length has changed,
116
+ # or if we're on a new device (possibly due to tracing for instance)
117
+ if seq_len != self._seq_len_cached or self._cos_cached.device != x.device:
118
+ self._seq_len_cached = seq_len
119
+ t = torch.arange(x.shape[seq_dimension], device=x.device).type_as(
120
+ self.inv_freq
121
+ )
122
+ freqs = torch.outer(t, self.inv_freq)
123
+ emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
124
+
125
+ self._cos_cached = emb.cos()[None, None, :, :]
126
+ self._sin_cached = emb.sin()[None, None, :, :]
127
+
128
+ return self._cos_cached, self._sin_cached
129
+
130
+ def forward(
131
+ self, q: torch.Tensor, k: torch.Tensor
132
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
133
+ self._cos_cached, self._sin_cached = self._update_cos_sin_tables(
134
+ k, seq_dimension=-2
135
+ )
136
+
137
+ return (
138
+ apply_rotary_pos_emb(q, self._cos_cached, self._sin_cached),
139
+ apply_rotary_pos_emb(k, self._cos_cached, self._sin_cached),
140
+ )
141
+
142
+
143
+ class EsmContactPredictionHead(nn.Module):
144
+ """Performs symmetrization, apc, and computes a logistic regression on the output features"""
145
+
146
+ def __init__(
147
+ self,
148
+ in_features: int,
149
+ bias=True,
150
+ eos_idx: int = 2,
151
+ ):
152
+ super().__init__()
153
+ self.in_features = in_features
154
+ self.eos_idx = eos_idx
155
+ self.regression = nn.Linear(in_features, 1, bias)
156
+ self.activation = nn.Sigmoid()
157
+
158
+ def forward(self, tokens, attentions):
159
+ # remove eos token attentions
160
+ eos_mask = tokens.ne(self.eos_idx).to(attentions)
161
+ eos_mask = eos_mask.unsqueeze(1) * eos_mask.unsqueeze(2)
162
+ attentions = attentions * eos_mask[:, None, None, :, :]
163
+ attentions = attentions[..., :-1, :-1]
164
+ # remove cls token attentions
165
+ attentions = attentions[..., 1:, 1:]
166
+ batch_size, layers, heads, seqlen, _ = attentions.size()
167
+ attentions = attentions.view(batch_size, layers * heads, seqlen, seqlen)
168
+
169
+ # features: batch x channels x tokens x tokens (symmetric)
170
+ attentions = attentions.to(
171
+ self.regression.weight.device
172
+ ) # attentions always float32, may need to convert to float16
173
+ attentions = average_product_correct(symmetrize(attentions))
174
+ attentions = attentions.permute(0, 2, 3, 1)
175
+ return self.activation(self.regression(attentions).squeeze(3))
176
+
177
+
178
+ class EsmEmbeddings(nn.Module):
179
+ """
180
+ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
181
+ """
182
+
183
+ def __init__(self, config):
184
+ super().__init__()
185
+ self.word_embeddings = nn.Embedding(
186
+ config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id
187
+ )
188
+
189
+ if config.emb_layer_norm_before:
190
+ self.layer_norm = nn.LayerNorm(
191
+ config.hidden_size, eps=config.layer_norm_eps
192
+ )
193
+ else:
194
+ self.layer_norm = None
195
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
196
+ # position_ids (1, len position emb) is contiguous in memory and exported when serialized
197
+ self.position_embedding_type = getattr(
198
+ config, "position_embedding_type", "absolute"
199
+ )
200
+ self.register_buffer(
201
+ "position_ids",
202
+ torch.arange(config.max_position_embeddings).expand((1, -1)),
203
+ persistent=False,
204
+ )
205
+
206
+ self.padding_idx = config.pad_token_id
207
+ self.position_embeddings = nn.Embedding(
208
+ config.max_position_embeddings,
209
+ config.hidden_size,
210
+ padding_idx=self.padding_idx,
211
+ )
212
+ self.token_dropout = config.token_dropout
213
+ self.mask_token_id = config.mask_token_id
214
+
215
+ def forward(
216
+ self,
217
+ input_ids=None,
218
+ attention_mask=None,
219
+ position_ids=None,
220
+ inputs_embeds=None,
221
+ past_key_values_length=0,
222
+ ):
223
+ if position_ids is None:
224
+ if input_ids is not None:
225
+ # Create the position ids from the input token ids. Any padded tokens remain padded.
226
+ position_ids = create_position_ids_from_input_ids(
227
+ input_ids, self.padding_idx, past_key_values_length
228
+ )
229
+ else:
230
+ position_ids = self.create_position_ids_from_inputs_embeds(
231
+ inputs_embeds
232
+ )
233
+
234
+ if inputs_embeds is None:
235
+ inputs_embeds = self.word_embeddings(input_ids)
236
+
237
+ # Note that if we want to support ESM-1 (not 1b!) in future then we need to support an
238
+ # embedding_scale factor here.
239
+ embeddings = inputs_embeds
240
+
241
+ # Matt: ESM has the option to handle masking in MLM in a slightly unusual way. If the token_dropout
242
+ # flag is False then it is handled in the same was as BERT/RoBERTa. If it is set to True, however,
243
+ # masked tokens are treated as if they were selected for input dropout and zeroed out.
244
+ # This "mask-dropout" is compensated for when masked tokens are not present, by scaling embeddings by
245
+ # a factor of (fraction of unmasked tokens during training) / (fraction of unmasked tokens in sample).
246
+ # This is analogous to the way that dropout layers scale down outputs during evaluation when not
247
+ # actually dropping out values (or, equivalently, scale up their un-dropped outputs in training).
248
+ if self.token_dropout:
249
+ embeddings.masked_fill_(
250
+ (input_ids == self.mask_token_id).unsqueeze(-1), 0.0
251
+ )
252
+ mask_ratio_train = (
253
+ 0.15 * 0.8
254
+ ) # Hardcoded as the ratio used in all ESM model training runs
255
+ src_lengths = attention_mask.sum(-1)
256
+ mask_ratio_observed = (input_ids == self.mask_token_id).sum(
257
+ -1
258
+ ).float() / src_lengths
259
+ embeddings = (
260
+ embeddings
261
+ * (1 - mask_ratio_train)
262
+ / (1 - mask_ratio_observed)[:, None, None]
263
+ ).to(embeddings.dtype)
264
+
265
+ if self.position_embedding_type == "absolute":
266
+ position_embeddings = self.position_embeddings(position_ids)
267
+ embeddings += position_embeddings
268
+
269
+ if self.layer_norm is not None:
270
+ embeddings = self.layer_norm(embeddings)
271
+ if attention_mask is not None:
272
+ embeddings = (embeddings * attention_mask.unsqueeze(-1)).to(
273
+ embeddings.dtype
274
+ )
275
+ # Matt: I think this line was copied incorrectly from BERT, disabling it for now.
276
+ # embeddings = self.dropout(embeddings)
277
+ return embeddings
278
+
279
+ def create_position_ids_from_inputs_embeds(self, inputs_embeds):
280
+ """
281
+ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
282
+
283
+ Args:
284
+ inputs_embeds: torch.Tensor
285
+
286
+ Returns: torch.Tensor
287
+ """
288
+ input_shape = inputs_embeds.size()[:-1]
289
+ sequence_length = input_shape[1]
290
+
291
+ position_ids = torch.arange(
292
+ self.padding_idx + 1,
293
+ sequence_length + self.padding_idx + 1,
294
+ dtype=torch.long,
295
+ device=inputs_embeds.device,
296
+ )
297
+ return position_ids.unsqueeze(0).expand(input_shape)
298
+
299
+
300
+ class EsmSelfAttention(nn.Module):
301
+ def __init__(self, config, position_embedding_type=None):
302
+ super().__init__()
303
+ if config.hidden_size % config.num_attention_heads != 0 and not hasattr(
304
+ config, "embedding_size"
305
+ ):
306
+ raise ValueError(
307
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
308
+ f"heads ({config.num_attention_heads})"
309
+ )
310
+
311
+ self.num_attention_heads = config.num_attention_heads
312
+ self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
313
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
314
+
315
+ self.query = nn.Linear(config.hidden_size, self.all_head_size)
316
+ self.key = nn.Linear(config.hidden_size, self.all_head_size)
317
+ self.value = nn.Linear(config.hidden_size, self.all_head_size)
318
+
319
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
320
+ self.position_embedding_type = position_embedding_type or getattr(
321
+ config, "position_embedding_type", "absolute"
322
+ )
323
+ self.rotary_embeddings = None
324
+ if (
325
+ self.position_embedding_type == "relative_key"
326
+ or self.position_embedding_type == "relative_key_query"
327
+ ):
328
+ self.max_position_embeddings = config.max_position_embeddings
329
+ self.distance_embedding = nn.Embedding(
330
+ 2 * config.max_position_embeddings - 1, self.attention_head_size
331
+ )
332
+ elif self.position_embedding_type == "rotary":
333
+ self.rotary_embeddings = RotaryEmbedding(dim=self.attention_head_size)
334
+
335
+ self.is_decoder = config.is_decoder
336
+
337
+ def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
338
+ new_x_shape = x.size()[:-1] + (
339
+ self.num_attention_heads,
340
+ self.attention_head_size,
341
+ )
342
+ x = x.view(new_x_shape)
343
+ return x.permute(0, 2, 1, 3)
344
+
345
+ def forward(
346
+ self,
347
+ hidden_states: torch.Tensor,
348
+ attention_mask: Optional[torch.FloatTensor] = None,
349
+ head_mask: Optional[torch.FloatTensor] = None,
350
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
351
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
352
+ past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
353
+ output_attentions: Optional[bool] = False,
354
+ ) -> Tuple[torch.Tensor]:
355
+ mixed_query_layer = self.query(hidden_states)
356
+
357
+ # If this is instantiated as a cross-attention module, the keys
358
+ # and values come from an encoder; the attention mask needs to be
359
+ # such that the encoder's padding tokens are not attended to.
360
+ is_cross_attention = encoder_hidden_states is not None
361
+
362
+ if is_cross_attention and past_key_value is not None:
363
+ # reuse k,v, cross_attentions
364
+ key_layer = past_key_value[0]
365
+ value_layer = past_key_value[1]
366
+ attention_mask = encoder_attention_mask
367
+ elif is_cross_attention:
368
+ key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
369
+ value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
370
+ attention_mask = encoder_attention_mask
371
+ elif past_key_value is not None:
372
+ key_layer = self.transpose_for_scores(self.key(hidden_states))
373
+ value_layer = self.transpose_for_scores(self.value(hidden_states))
374
+ key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
375
+ value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
376
+ else:
377
+ key_layer = self.transpose_for_scores(self.key(hidden_states))
378
+ value_layer = self.transpose_for_scores(self.value(hidden_states))
379
+
380
+ query_layer = self.transpose_for_scores(mixed_query_layer)
381
+
382
+ # Matt: Our BERT model (which this code was derived from) scales attention logits down by sqrt(head_dim).
383
+ # ESM scales the query down by the same factor instead. Modulo numerical stability these are equivalent,
384
+ # but not when rotary embeddings get involved. Therefore, we scale the query here to match the original
385
+ # ESM code and fix rotary embeddings.
386
+ query_layer = query_layer * self.attention_head_size**-0.5
387
+
388
+ if self.is_decoder:
389
+ # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
390
+ # Further calls to cross_attention layer can then reuse all cross-attention
391
+ # key/value_states (first "if" case)
392
+ # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
393
+ # all previous decoder key/value_states. Further calls to uni-directional self-attention
394
+ # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
395
+ # if encoder bi-directional self-attention `past_key_value` is always `None`
396
+ past_key_value = (key_layer, value_layer)
397
+
398
+ if self.position_embedding_type == "rotary":
399
+ query_layer, key_layer = self.rotary_embeddings(query_layer, key_layer)
400
+
401
+ # Take the dot product between "query" and "key" to get the raw attention scores.
402
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
403
+
404
+ if (
405
+ self.position_embedding_type == "relative_key"
406
+ or self.position_embedding_type == "relative_key_query"
407
+ ):
408
+ seq_length = hidden_states.size()[1]
409
+ position_ids_l = torch.arange(
410
+ seq_length, dtype=torch.long, device=hidden_states.device
411
+ ).view(-1, 1)
412
+ position_ids_r = torch.arange(
413
+ seq_length, dtype=torch.long, device=hidden_states.device
414
+ ).view(1, -1)
415
+ distance = position_ids_l - position_ids_r
416
+ positional_embedding = self.distance_embedding(
417
+ distance + self.max_position_embeddings - 1
418
+ )
419
+ positional_embedding = positional_embedding.to(
420
+ dtype=query_layer.dtype
421
+ ) # fp16 compatibility
422
+
423
+ if self.position_embedding_type == "relative_key":
424
+ relative_position_scores = torch.einsum(
425
+ "bhld,lrd->bhlr", query_layer, positional_embedding
426
+ )
427
+ attention_scores = attention_scores + relative_position_scores
428
+ elif self.position_embedding_type == "relative_key_query":
429
+ relative_position_scores_query = torch.einsum(
430
+ "bhld,lrd->bhlr", query_layer, positional_embedding
431
+ )
432
+ relative_position_scores_key = torch.einsum(
433
+ "bhrd,lrd->bhlr", key_layer, positional_embedding
434
+ )
435
+ attention_scores = (
436
+ attention_scores
437
+ + relative_position_scores_query
438
+ + relative_position_scores_key
439
+ )
440
+
441
+ if attention_mask is not None:
442
+ # Apply the attention mask is (precomputed for all layers in EsmModel forward() function)
443
+ attention_scores = attention_scores + attention_mask
444
+
445
+ # Normalize the attention scores to probabilities.
446
+ attention_probs = nn.functional.softmax(attention_scores, dim=-1)
447
+
448
+ # This is actually dropping out entire tokens to attend to, which might
449
+ # seem a bit unusual, but is taken from the original Transformer paper.
450
+ attention_probs = self.dropout(attention_probs)
451
+
452
+ # Mask heads if we want to
453
+ if head_mask is not None:
454
+ attention_probs = attention_probs * head_mask
455
+
456
+ context_layer = torch.matmul(attention_probs, value_layer)
457
+
458
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
459
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
460
+ context_layer = context_layer.view(new_context_layer_shape)
461
+
462
+ outputs = (
463
+ (context_layer, attention_probs) if output_attentions else (context_layer,)
464
+ )
465
+
466
+ if self.is_decoder:
467
+ outputs = outputs + (past_key_value,)
468
+ return outputs
469
+
470
+
471
+ class EsmSelfOutput(nn.Module):
472
+ def __init__(self, config):
473
+ super().__init__()
474
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
475
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
476
+
477
+ def forward(self, hidden_states, input_tensor):
478
+ hidden_states = self.dense(hidden_states)
479
+ hidden_states = self.dropout(hidden_states)
480
+ hidden_states += input_tensor
481
+ return hidden_states
482
+
483
+
484
+ class EsmAttention(nn.Module):
485
+ def __init__(self, config):
486
+ super().__init__()
487
+ self.self = EsmSelfAttention(config)
488
+ self.output = EsmSelfOutput(config)
489
+ self.pruned_heads = set()
490
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
491
+
492
+ def prune_heads(self, heads):
493
+ if len(heads) == 0:
494
+ return
495
+ heads, index = find_pruneable_heads_and_indices(
496
+ heads,
497
+ self.self.num_attention_heads,
498
+ self.self.attention_head_size,
499
+ self.pruned_heads,
500
+ )
501
+
502
+ # Prune linear layers
503
+ self.self.query = prune_linear_layer(self.self.query, index)
504
+ self.self.key = prune_linear_layer(self.self.key, index)
505
+ self.self.value = prune_linear_layer(self.self.value, index)
506
+ self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
507
+
508
+ # Update hyper params and store pruned heads
509
+ self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
510
+ self.self.all_head_size = (
511
+ self.self.attention_head_size * self.self.num_attention_heads
512
+ )
513
+ self.pruned_heads = self.pruned_heads.union(heads)
514
+
515
+ def forward(
516
+ self,
517
+ hidden_states,
518
+ attention_mask=None,
519
+ head_mask=None,
520
+ encoder_hidden_states=None,
521
+ encoder_attention_mask=None,
522
+ past_key_value=None,
523
+ output_attentions=False,
524
+ ):
525
+ hidden_states_ln = self.LayerNorm(hidden_states)
526
+ self_outputs = self.self(
527
+ hidden_states_ln,
528
+ attention_mask,
529
+ head_mask,
530
+ encoder_hidden_states,
531
+ encoder_attention_mask,
532
+ past_key_value,
533
+ output_attentions,
534
+ )
535
+ attention_output = self.output(self_outputs[0], hidden_states)
536
+ outputs = (attention_output,) + self_outputs[
537
+ 1:
538
+ ] # add attentions if we output them
539
+ return outputs
540
+
541
+
542
+ class EsmIntermediate(nn.Module):
543
+ def __init__(self, config):
544
+ super().__init__()
545
+
546
+ self.dense = nn.Linear(
547
+ config.hidden_size,
548
+ int(config.intermediate_size * 2),
549
+ bias=config.add_bias_fnn,
550
+ )
551
+ self.activation_fn = SiLU()
552
+
553
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
554
+ hidden_states = self.dense(hidden_states)
555
+
556
+ # GLU
557
+ x1, x2 = hidden_states.split(int(hidden_states.size(-1) / 2), -1)
558
+ hidden_states = self.activation_fn(x1) * x2
559
+
560
+ return hidden_states
561
+
562
+
563
+ class EsmOutput(nn.Module):
564
+ def __init__(self, config):
565
+ super().__init__()
566
+ self.dense = nn.Linear(
567
+ config.intermediate_size, config.hidden_size, bias=config.add_bias_fnn
568
+ )
569
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
570
+
571
+ def forward(self, hidden_states, input_tensor):
572
+ hidden_states = self.dense(hidden_states)
573
+ hidden_states = self.dropout(hidden_states)
574
+ hidden_states += input_tensor
575
+ return hidden_states
576
+
577
+
578
+ class EsmLayer(nn.Module):
579
+ def __init__(self, config):
580
+ super().__init__()
581
+ self.chunk_size_feed_forward = config.chunk_size_feed_forward
582
+ self.seq_len_dim = 1
583
+ self.attention = EsmAttention(config)
584
+ self.is_decoder = config.is_decoder
585
+ self.add_cross_attention = config.add_cross_attention
586
+ if self.add_cross_attention:
587
+ if not self.is_decoder:
588
+ raise RuntimeError(
589
+ f"{self} should be used as a decoder model if cross attention is added"
590
+ )
591
+ self.crossattention = EsmAttention(config)
592
+ self.intermediate = EsmIntermediate(config)
593
+ self.output = EsmOutput(config)
594
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
595
+
596
+ def forward(
597
+ self,
598
+ hidden_states,
599
+ attention_mask=None,
600
+ head_mask=None,
601
+ encoder_hidden_states=None,
602
+ encoder_attention_mask=None,
603
+ past_key_value=None,
604
+ output_attentions=False,
605
+ ):
606
+ # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
607
+ self_attn_past_key_value = (
608
+ past_key_value[:2] if past_key_value is not None else None
609
+ )
610
+ self_attention_outputs = self.attention(
611
+ hidden_states,
612
+ attention_mask,
613
+ head_mask,
614
+ output_attentions=output_attentions,
615
+ past_key_value=self_attn_past_key_value,
616
+ )
617
+ attention_output = self_attention_outputs[0]
618
+
619
+ # if decoder, the last output is tuple of self-attn cache
620
+ if self.is_decoder:
621
+ outputs = self_attention_outputs[1:-1]
622
+ present_key_value = self_attention_outputs[-1]
623
+ else:
624
+ outputs = self_attention_outputs[
625
+ 1:
626
+ ] # add self attentions if we output attention weights
627
+
628
+ cross_attn_present_key_value = None
629
+ if self.is_decoder and encoder_hidden_states is not None:
630
+ if not hasattr(self, "crossattention"):
631
+ raise AttributeError(
632
+ f"If `encoder_hidden_states` are passed, {self} has to be instantiated"
633
+ " with cross-attention layers by setting `config.add_cross_attention=True`"
634
+ )
635
+
636
+ # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
637
+ cross_attn_past_key_value = (
638
+ past_key_value[-2:] if past_key_value is not None else None
639
+ )
640
+ cross_attention_outputs = self.crossattention(
641
+ attention_output,
642
+ attention_mask,
643
+ head_mask,
644
+ encoder_hidden_states,
645
+ encoder_attention_mask,
646
+ cross_attn_past_key_value,
647
+ output_attentions,
648
+ )
649
+ attention_output = cross_attention_outputs[0]
650
+ outputs = (
651
+ outputs + cross_attention_outputs[1:-1]
652
+ ) # add cross attentions if we output attention weights
653
+
654
+ # add cross-attn cache to positions 3,4 of present_key_value tuple
655
+ cross_attn_present_key_value = cross_attention_outputs[-1]
656
+ present_key_value = present_key_value + cross_attn_present_key_value
657
+
658
+ layer_output = self.feed_forward_chunk(attention_output)
659
+
660
+ outputs = (layer_output,) + outputs
661
+
662
+ # if decoder, return the attn key/values as the last output
663
+ if self.is_decoder:
664
+ outputs = outputs + (present_key_value,)
665
+ return outputs
666
+
667
+ def feed_forward_chunk(self, attention_output):
668
+ attention_output_ln = self.LayerNorm(attention_output)
669
+ intermediate_output = self.intermediate(attention_output_ln)
670
+ layer_output = self.output(intermediate_output, attention_output)
671
+ return layer_output
672
+
673
+
674
+ class EsmEncoder(nn.Module):
675
+ def __init__(self, config):
676
+ super().__init__()
677
+ self.config = config
678
+ self.layer = nn.ModuleList(
679
+ [EsmLayer(config) for _ in range(config.num_hidden_layers)]
680
+ )
681
+ self.emb_layer_norm_after = nn.LayerNorm(
682
+ config.hidden_size, eps=config.layer_norm_eps
683
+ )
684
+ self.gradient_checkpointing = False
685
+
686
+ def forward(
687
+ self,
688
+ hidden_states,
689
+ attention_mask=None,
690
+ head_mask=None,
691
+ encoder_hidden_states=None,
692
+ encoder_attention_mask=None,
693
+ past_key_values=None,
694
+ use_cache=None,
695
+ output_attentions=False,
696
+ output_hidden_states=False,
697
+ return_dict=True,
698
+ ):
699
+ if self.gradient_checkpointing and self.training:
700
+ if use_cache:
701
+ logger.warning_once(
702
+ "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
703
+ "`use_cache=False`..."
704
+ )
705
+ use_cache = False
706
+ all_hidden_states = () if output_hidden_states else None
707
+ all_self_attentions = () if output_attentions else None
708
+ all_cross_attentions = (
709
+ () if output_attentions and self.config.add_cross_attention else None
710
+ )
711
+
712
+ next_decoder_cache = () if use_cache else None
713
+ for i, layer_module in enumerate(self.layer):
714
+ if output_hidden_states:
715
+ all_hidden_states = all_hidden_states + (hidden_states,)
716
+
717
+ layer_head_mask = head_mask[i] if head_mask is not None else None
718
+ past_key_value = past_key_values[i] if past_key_values is not None else None
719
+
720
+ if self.gradient_checkpointing and self.training:
721
+
722
+ def create_custom_forward(module):
723
+ def custom_forward(*inputs):
724
+ return module(*inputs, past_key_value, output_attentions)
725
+
726
+ return custom_forward
727
+
728
+ layer_outputs = torch.utils.checkpoint.checkpoint(
729
+ create_custom_forward(layer_module),
730
+ hidden_states,
731
+ attention_mask,
732
+ layer_head_mask,
733
+ encoder_hidden_states,
734
+ encoder_attention_mask,
735
+ )
736
+ else:
737
+ layer_outputs = layer_module(
738
+ hidden_states,
739
+ attention_mask,
740
+ layer_head_mask,
741
+ encoder_hidden_states,
742
+ encoder_attention_mask,
743
+ past_key_value,
744
+ output_attentions,
745
+ )
746
+
747
+ hidden_states = layer_outputs[0]
748
+ if use_cache:
749
+ next_decoder_cache += (layer_outputs[-1],)
750
+ if output_attentions:
751
+ all_self_attentions = all_self_attentions + (layer_outputs[1],)
752
+ if self.config.add_cross_attention:
753
+ all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
754
+
755
+ if self.emb_layer_norm_after:
756
+ hidden_states = self.emb_layer_norm_after(hidden_states)
757
+
758
+ if output_hidden_states:
759
+ all_hidden_states = all_hidden_states + (hidden_states,)
760
+
761
+ if not return_dict:
762
+ return tuple(
763
+ v
764
+ for v in [
765
+ hidden_states,
766
+ next_decoder_cache,
767
+ all_hidden_states,
768
+ all_self_attentions,
769
+ all_cross_attentions,
770
+ ]
771
+ if v is not None
772
+ )
773
+ return BaseModelOutputWithPastAndCrossAttentions(
774
+ last_hidden_state=hidden_states,
775
+ past_key_values=next_decoder_cache,
776
+ hidden_states=all_hidden_states,
777
+ attentions=all_self_attentions,
778
+ cross_attentions=all_cross_attentions,
779
+ )
780
+
781
+
782
+ # Copied from transformers.models.bert.modeling_bert.BertPooler
783
+ class EsmPooler(nn.Module):
784
+ def __init__(self, config):
785
+ super().__init__()
786
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
787
+ self.activation = nn.Tanh()
788
+
789
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
790
+ # We "pool" the model by simply taking the hidden state corresponding
791
+ # to the first token.
792
+ first_token_tensor = hidden_states[:, 0]
793
+ pooled_output = self.dense(first_token_tensor)
794
+ pooled_output = self.activation(pooled_output)
795
+ return pooled_output
796
+
797
+
798
+ class EsmPreTrainedModel(PreTrainedModel):
799
+ """
800
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
801
+ models.
802
+ """
803
+
804
+ config_class = EsmConfig
805
+ base_model_prefix = "esm"
806
+ _no_split_modules = ["EsmLayer", "EsmFoldTriangularSelfAttentionBlock"]
807
+
808
+ # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
809
+ def _init_weights(self, module):
810
+ """Initialize the weights"""
811
+ if isinstance(module, nn.Linear):
812
+ # Slightly different from the TF version which uses truncated_normal for initialization
813
+ # cf https://github.com/pytorch/pytorch/pull/5617
814
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
815
+ if module.bias is not None:
816
+ module.bias.data.zero_()
817
+ elif isinstance(module, nn.Embedding):
818
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
819
+ if module.padding_idx is not None:
820
+ module.weight.data[module.padding_idx].zero_()
821
+ elif isinstance(module, nn.LayerNorm):
822
+ module.bias.data.zero_()
823
+ module.weight.data.fill_(1.0)
824
+
825
+
826
+ ESM_START_DOCSTRING = r"""
827
+
828
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
829
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
830
+ etc.)
831
+
832
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
833
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
834
+ and behavior.
835
+
836
+ Parameters:
837
+ config ([`EsmConfig`]): Model configuration class with all the parameters of the
838
+ model. Initializing with a config file does not load the weights associated with the model, only the
839
+ configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
840
+ """
841
+
842
+ ESM_INPUTS_DOCSTRING = r"""
843
+ Args:
844
+ input_ids (`torch.LongTensor` of shape `({0})`):
845
+ Indices of input sequence tokens in the vocabulary.
846
+
847
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
848
+ [`PreTrainedTokenizer.__call__`] for details.
849
+
850
+ [What are input IDs?](../glossary#input-ids)
851
+ attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
852
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
853
+
854
+ - 1 for tokens that are **not masked**,
855
+ - 0 for tokens that are **masked**.
856
+
857
+ [What are attention masks?](../glossary#attention-mask)
858
+ position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
859
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
860
+ config.max_position_embeddings - 1]`.
861
+
862
+ [What are position IDs?](../glossary#position-ids)
863
+ head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
864
+ Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
865
+
866
+ - 1 indicates the head is **not masked**,
867
+ - 0 indicates the head is **masked**.
868
+
869
+ inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
870
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
871
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
872
+ model's internal embedding lookup matrix.
873
+ output_attentions (`bool`, *optional*):
874
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
875
+ tensors for more detail.
876
+ output_hidden_states (`bool`, *optional*):
877
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
878
+ more detail.
879
+ return_dict (`bool`, *optional*):
880
+ Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
881
+ """
882
+
883
+
884
+ @add_start_docstrings(
885
+ "The bare ESM Model transformer outputting raw hidden-states without any specific head on top.",
886
+ ESM_START_DOCSTRING,
887
+ )
888
+ class EsmModel(EsmPreTrainedModel):
889
+ """
890
+
891
+ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
892
+ cross-attention is added between the self-attention layers, following the architecture described in [Attention is
893
+ all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
894
+ Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
895
+
896
+ To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
897
+ to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
898
+ `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
899
+ """
900
+
901
+ supports_gradient_checkpointing = False
902
+
903
+ def __init__(self, config, add_pooling_layer=True):
904
+ super().__init__(config)
905
+ self.config = config
906
+
907
+ self.embeddings = EsmEmbeddings(config)
908
+ self.encoder = EsmEncoder(config)
909
+
910
+ self.pooler = EsmPooler(config) if add_pooling_layer else None
911
+
912
+ self.contact_head = EsmContactPredictionHead(
913
+ in_features=config.num_hidden_layers * config.num_attention_heads, bias=True
914
+ )
915
+
916
+ # Initialize weights and apply final processing
917
+ self.post_init()
918
+
919
+ def _set_gradient_checkpointing(self, module, value=False):
920
+ if isinstance(module, EsmEncoder):
921
+ module.gradient_checkpointing = value
922
+
923
+ def get_input_embeddings(self):
924
+ return self.embeddings.word_embeddings
925
+
926
+ def set_input_embeddings(self, value):
927
+ self.embeddings.word_embeddings = value
928
+
929
+ def _prune_heads(self, heads_to_prune):
930
+ """
931
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
932
+ class PreTrainedModel
933
+ """
934
+ for layer, heads in heads_to_prune.items():
935
+ self.encoder.layer[layer].attention.prune_heads(heads)
936
+
937
+ @add_start_docstrings_to_model_forward(
938
+ ESM_INPUTS_DOCSTRING.format("(batch_size, sequence_length)")
939
+ )
940
+ @add_code_sample_docstrings(
941
+ checkpoint=_CHECKPOINT_FOR_DOC,
942
+ output_type=BaseModelOutputWithPoolingAndCrossAttentions,
943
+ config_class=_CONFIG_FOR_DOC,
944
+ )
945
+ def forward(
946
+ self,
947
+ input_ids: Optional[torch.Tensor] = None,
948
+ attention_mask: Optional[torch.Tensor] = None,
949
+ position_ids: Optional[torch.Tensor] = None,
950
+ head_mask: Optional[torch.Tensor] = None,
951
+ inputs_embeds: Optional[torch.Tensor] = None,
952
+ encoder_hidden_states: Optional[torch.Tensor] = None,
953
+ encoder_attention_mask: Optional[torch.Tensor] = None,
954
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
955
+ use_cache: Optional[bool] = None,
956
+ output_attentions: Optional[bool] = None,
957
+ output_hidden_states: Optional[bool] = None,
958
+ return_dict: Optional[bool] = None,
959
+ ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
960
+ r"""
961
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
962
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
963
+ the model is configured as a decoder.
964
+ encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
965
+ Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
966
+ the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
967
+
968
+ - 1 for tokens that are **not masked**,
969
+ - 0 for tokens that are **masked**.
970
+ past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
971
+ Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
972
+
973
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
974
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
975
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
976
+ use_cache (`bool`, *optional*):
977
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
978
+ `past_key_values`).
979
+ """
980
+ output_attentions = (
981
+ output_attentions
982
+ if output_attentions is not None
983
+ else self.config.output_attentions
984
+ )
985
+ output_hidden_states = (
986
+ output_hidden_states
987
+ if output_hidden_states is not None
988
+ else self.config.output_hidden_states
989
+ )
990
+ return_dict = (
991
+ return_dict if return_dict is not None else self.config.use_return_dict
992
+ )
993
+
994
+ if self.config.is_decoder:
995
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
996
+ else:
997
+ use_cache = False
998
+
999
+ if input_ids is not None and inputs_embeds is not None:
1000
+ raise ValueError(
1001
+ "You cannot specify both input_ids and inputs_embeds at the same time"
1002
+ )
1003
+ elif input_ids is not None:
1004
+ input_shape = input_ids.size()
1005
+ elif inputs_embeds is not None:
1006
+ input_shape = inputs_embeds.size()[:-1]
1007
+ else:
1008
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
1009
+
1010
+ batch_size, seq_length = input_shape
1011
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
1012
+
1013
+ # past_key_values_length
1014
+ past_key_values_length = (
1015
+ past_key_values[0][0].shape[2] if past_key_values is not None else 0
1016
+ )
1017
+
1018
+ if attention_mask is None:
1019
+ attention_mask = torch.ones(
1020
+ ((batch_size, seq_length + past_key_values_length)), device=device
1021
+ )
1022
+
1023
+ # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
1024
+ # ourselves in which case we just need to make it broadcastable to all heads.
1025
+ extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
1026
+ attention_mask, input_shape
1027
+ )
1028
+
1029
+ # If a 2D or 3D attention mask is provided for the cross-attention
1030
+ # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
1031
+ if self.config.is_decoder and encoder_hidden_states is not None:
1032
+ (
1033
+ encoder_batch_size,
1034
+ encoder_sequence_length,
1035
+ _,
1036
+ ) = encoder_hidden_states.size()
1037
+ encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
1038
+ if encoder_attention_mask is None:
1039
+ encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
1040
+ encoder_extended_attention_mask = self.invert_attention_mask(
1041
+ encoder_attention_mask
1042
+ )
1043
+ else:
1044
+ encoder_extended_attention_mask = None
1045
+
1046
+ # Prepare head mask if needed
1047
+ # 1.0 in head_mask indicate we keep the head
1048
+ # attention_probs has shape bsz x n_heads x N x N
1049
+ # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
1050
+ # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
1051
+ head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
1052
+
1053
+ embedding_output = self.embeddings(
1054
+ input_ids=input_ids,
1055
+ position_ids=position_ids,
1056
+ attention_mask=attention_mask,
1057
+ inputs_embeds=inputs_embeds,
1058
+ past_key_values_length=past_key_values_length,
1059
+ )
1060
+ encoder_outputs = self.encoder(
1061
+ embedding_output,
1062
+ attention_mask=extended_attention_mask,
1063
+ head_mask=head_mask,
1064
+ encoder_hidden_states=encoder_hidden_states,
1065
+ encoder_attention_mask=encoder_extended_attention_mask,
1066
+ past_key_values=past_key_values,
1067
+ use_cache=use_cache,
1068
+ output_attentions=output_attentions,
1069
+ output_hidden_states=output_hidden_states,
1070
+ return_dict=return_dict,
1071
+ )
1072
+ sequence_output = encoder_outputs[0]
1073
+ pooled_output = (
1074
+ self.pooler(sequence_output) if self.pooler is not None else None
1075
+ )
1076
+
1077
+ if not return_dict:
1078
+ return (sequence_output, pooled_output) + encoder_outputs[1:]
1079
+
1080
+ return BaseModelOutputWithPoolingAndCrossAttentions(
1081
+ last_hidden_state=sequence_output,
1082
+ pooler_output=pooled_output,
1083
+ past_key_values=encoder_outputs.past_key_values,
1084
+ hidden_states=encoder_outputs.hidden_states,
1085
+ attentions=encoder_outputs.attentions,
1086
+ cross_attentions=encoder_outputs.cross_attentions,
1087
+ )
1088
+
1089
+ def predict_contacts(self, tokens, attention_mask):
1090
+ attns = self(
1091
+ tokens,
1092
+ attention_mask=attention_mask,
1093
+ return_dict=True,
1094
+ output_attentions=True,
1095
+ ).attentions
1096
+ attns = torch.stack(attns, dim=1) # Matches the original model layout
1097
+ # In the original model, attentions for padding tokens are completely zeroed out.
1098
+ # This makes no difference most of the time because the other tokens won't attend to them,
1099
+ # but it does for the contact prediction task, which takes attentions as input,
1100
+ # so we have to mimic that here.
1101
+ attns *= attention_mask.unsqueeze(1).unsqueeze(2).unsqueeze(3)
1102
+ attns *= attention_mask.unsqueeze(1).unsqueeze(2).unsqueeze(4)
1103
+ return self.contact_head(tokens, attns)
1104
+
1105
+
1106
+ @add_start_docstrings(
1107
+ """ESM Model with a `language modeling` head on top.""", ESM_START_DOCSTRING
1108
+ )
1109
+ class EsmForMaskedLM(EsmPreTrainedModel):
1110
+ _tied_weights_keys = ["lm_head.decoder.weight"]
1111
+
1112
+ def __init__(self, config):
1113
+ super().__init__(config)
1114
+
1115
+ if config.is_decoder:
1116
+ logger.warning(
1117
+ "If you want to use `EsmForMaskedLM` make sure `config.is_decoder=False` for "
1118
+ "bi-directional self-attention."
1119
+ )
1120
+
1121
+ self.esm = EsmModel(config, add_pooling_layer=False)
1122
+ self.lm_head = EsmLMHead(config)
1123
+
1124
+ self.init_weights()
1125
+
1126
+ def get_output_embeddings(self):
1127
+ return self.lm_head.decoder
1128
+
1129
+ def set_output_embeddings(self, new_embeddings):
1130
+ self.lm_head.decoder = new_embeddings
1131
+
1132
+ @add_start_docstrings_to_model_forward(
1133
+ ESM_INPUTS_DOCSTRING.format("batch_size, sequence_length")
1134
+ )
1135
+ @add_code_sample_docstrings(
1136
+ checkpoint=_CHECKPOINT_FOR_DOC,
1137
+ output_type=MaskedLMOutput,
1138
+ config_class=_CONFIG_FOR_DOC,
1139
+ mask="<mask>",
1140
+ )
1141
+ def forward(
1142
+ self,
1143
+ input_ids: Optional[torch.LongTensor] = None,
1144
+ attention_mask: Optional[torch.Tensor] = None,
1145
+ position_ids: Optional[torch.LongTensor] = None,
1146
+ head_mask: Optional[torch.Tensor] = None,
1147
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1148
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
1149
+ encoder_attention_mask: Optional[torch.Tensor] = None,
1150
+ labels: Optional[torch.LongTensor] = None,
1151
+ output_attentions: Optional[bool] = None,
1152
+ output_hidden_states: Optional[bool] = None,
1153
+ return_dict: Optional[bool] = None,
1154
+ ) -> Union[Tuple, MaskedLMOutput]:
1155
+ r"""
1156
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1157
+ Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
1158
+ config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
1159
+ loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
1160
+ kwargs (`Dict[str, any]`, optional, defaults to *{}*):
1161
+ Used to hide legacy arguments that have been deprecated.
1162
+ """
1163
+ return_dict = (
1164
+ return_dict if return_dict is not None else self.config.use_return_dict
1165
+ )
1166
+
1167
+ outputs = self.esm(
1168
+ input_ids,
1169
+ attention_mask=attention_mask,
1170
+ position_ids=position_ids,
1171
+ head_mask=head_mask,
1172
+ inputs_embeds=inputs_embeds,
1173
+ encoder_hidden_states=encoder_hidden_states,
1174
+ encoder_attention_mask=encoder_attention_mask,
1175
+ output_attentions=output_attentions,
1176
+ output_hidden_states=output_hidden_states,
1177
+ return_dict=return_dict,
1178
+ )
1179
+ sequence_output = outputs[0]
1180
+ prediction_scores = self.lm_head(sequence_output)
1181
+
1182
+ masked_lm_loss = None
1183
+ if labels is not None:
1184
+ loss_fct = CrossEntropyLoss()
1185
+
1186
+ labels = labels.to(prediction_scores.device)
1187
+ masked_lm_loss = loss_fct(
1188
+ prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)
1189
+ )
1190
+
1191
+ if not return_dict:
1192
+ output = (prediction_scores,) + outputs[2:]
1193
+ return (
1194
+ ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
1195
+ )
1196
+
1197
+ return MaskedLMOutput(
1198
+ loss=masked_lm_loss,
1199
+ logits=prediction_scores,
1200
+ hidden_states=outputs.hidden_states,
1201
+ attentions=outputs.attentions,
1202
+ )
1203
+
1204
+ def predict_contacts(self, tokens, attention_mask):
1205
+ return self.esm.predict_contacts(tokens, attention_mask=attention_mask)
1206
+
1207
+
1208
+ class EsmLMHead(nn.Module):
1209
+ """ESM Head for masked language modeling."""
1210
+
1211
+ def __init__(self, config):
1212
+ super().__init__()
1213
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
1214
+ self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
1215
+
1216
+ self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
1217
+ self.bias = nn.Parameter(torch.zeros(config.vocab_size))
1218
+
1219
+ def forward(self, features, **kwargs):
1220
+ x = self.dense(features)
1221
+ x = gelu(x)
1222
+ x = self.layer_norm(x)
1223
+
1224
+ # project back to size of vocabulary with bias
1225
+ x = self.decoder(x) + self.bias
1226
+ return x
1227
+
1228
+
1229
+ @add_start_docstrings(
1230
+ """
1231
+ ESM Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
1232
+ output) e.g. for GLUE tasks.
1233
+ """,
1234
+ ESM_START_DOCSTRING,
1235
+ )
1236
+ class EsmForSequenceClassification(EsmPreTrainedModel):
1237
+ def __init__(self, config):
1238
+ super().__init__(config)
1239
+ self.num_labels = config.num_labels
1240
+ self.config = config
1241
+
1242
+ self.esm = EsmModel(config, add_pooling_layer=False)
1243
+ self.classifier = EsmClassificationHead(config)
1244
+
1245
+ self.init_weights()
1246
+
1247
+ @add_start_docstrings_to_model_forward(
1248
+ ESM_INPUTS_DOCSTRING.format("batch_size, sequence_length")
1249
+ )
1250
+ @add_code_sample_docstrings(
1251
+ checkpoint=_CHECKPOINT_FOR_DOC,
1252
+ output_type=SequenceClassifierOutput,
1253
+ config_class=_CONFIG_FOR_DOC,
1254
+ )
1255
+ def forward(
1256
+ self,
1257
+ input_ids: Optional[torch.LongTensor] = None,
1258
+ attention_mask: Optional[torch.Tensor] = None,
1259
+ position_ids: Optional[torch.LongTensor] = None,
1260
+ head_mask: Optional[torch.Tensor] = None,
1261
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1262
+ labels: Optional[torch.LongTensor] = None,
1263
+ output_attentions: Optional[bool] = None,
1264
+ output_hidden_states: Optional[bool] = None,
1265
+ return_dict: Optional[bool] = None,
1266
+ ) -> Union[Tuple, SequenceClassifierOutput]:
1267
+ r"""
1268
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1269
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1270
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1271
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1272
+ """
1273
+ return_dict = (
1274
+ return_dict if return_dict is not None else self.config.use_return_dict
1275
+ )
1276
+
1277
+ outputs = self.esm(
1278
+ input_ids,
1279
+ attention_mask=attention_mask,
1280
+ position_ids=position_ids,
1281
+ head_mask=head_mask,
1282
+ inputs_embeds=inputs_embeds,
1283
+ output_attentions=output_attentions,
1284
+ output_hidden_states=output_hidden_states,
1285
+ return_dict=return_dict,
1286
+ )
1287
+ sequence_output = outputs[0]
1288
+ logits = self.classifier(sequence_output)
1289
+
1290
+ loss = None
1291
+ if labels is not None:
1292
+ labels = labels.to(logits.device)
1293
+
1294
+ if self.config.problem_type is None:
1295
+ if self.num_labels == 1:
1296
+ self.config.problem_type = "regression"
1297
+ elif self.num_labels > 1 and (
1298
+ labels.dtype == torch.long or labels.dtype == torch.int
1299
+ ):
1300
+ self.config.problem_type = "single_label_classification"
1301
+ else:
1302
+ self.config.problem_type = "multi_label_classification"
1303
+
1304
+ if self.config.problem_type == "regression":
1305
+ loss_fct = MSELoss()
1306
+ if self.num_labels == 1:
1307
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
1308
+ else:
1309
+ loss = loss_fct(logits, labels)
1310
+ elif self.config.problem_type == "single_label_classification":
1311
+ loss_fct = CrossEntropyLoss()
1312
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
1313
+ elif self.config.problem_type == "multi_label_classification":
1314
+ loss_fct = BCEWithLogitsLoss()
1315
+ loss = loss_fct(logits, labels)
1316
+
1317
+ if not return_dict:
1318
+ output = (logits,) + outputs[2:]
1319
+ return ((loss,) + output) if loss is not None else output
1320
+
1321
+ return SequenceClassifierOutput(
1322
+ loss=loss,
1323
+ logits=logits,
1324
+ hidden_states=outputs.hidden_states,
1325
+ attentions=outputs.attentions,
1326
+ )
1327
+
1328
+
1329
+ @add_start_docstrings(
1330
+ """
1331
+ ESM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
1332
+ Named-Entity-Recognition (NER) tasks.
1333
+ """,
1334
+ ESM_START_DOCSTRING,
1335
+ )
1336
+ class EsmForTokenClassification(EsmPreTrainedModel):
1337
+ def __init__(self, config):
1338
+ super().__init__(config)
1339
+ self.num_labels = config.num_labels
1340
+
1341
+ self.esm = EsmModel(config, add_pooling_layer=False)
1342
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
1343
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
1344
+
1345
+ self.init_weights()
1346
+
1347
+ @add_start_docstrings_to_model_forward(
1348
+ ESM_INPUTS_DOCSTRING.format("batch_size, sequence_length")
1349
+ )
1350
+ @add_code_sample_docstrings(
1351
+ checkpoint=_CHECKPOINT_FOR_DOC,
1352
+ output_type=TokenClassifierOutput,
1353
+ config_class=_CONFIG_FOR_DOC,
1354
+ )
1355
+ def forward(
1356
+ self,
1357
+ input_ids: Optional[torch.LongTensor] = None,
1358
+ attention_mask: Optional[torch.Tensor] = None,
1359
+ position_ids: Optional[torch.LongTensor] = None,
1360
+ head_mask: Optional[torch.Tensor] = None,
1361
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1362
+ labels: Optional[torch.LongTensor] = None,
1363
+ output_attentions: Optional[bool] = None,
1364
+ output_hidden_states: Optional[bool] = None,
1365
+ return_dict: Optional[bool] = None,
1366
+ ) -> Union[Tuple, TokenClassifierOutput]:
1367
+ r"""
1368
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1369
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
1370
+ """
1371
+ return_dict = (
1372
+ return_dict if return_dict is not None else self.config.use_return_dict
1373
+ )
1374
+
1375
+ outputs = self.esm(
1376
+ input_ids,
1377
+ attention_mask=attention_mask,
1378
+ position_ids=position_ids,
1379
+ head_mask=head_mask,
1380
+ inputs_embeds=inputs_embeds,
1381
+ output_attentions=output_attentions,
1382
+ output_hidden_states=output_hidden_states,
1383
+ return_dict=return_dict,
1384
+ )
1385
+
1386
+ sequence_output = outputs[0]
1387
+
1388
+ sequence_output = self.dropout(sequence_output)
1389
+ logits = self.classifier(sequence_output)
1390
+
1391
+ loss = None
1392
+ if labels is not None:
1393
+ loss_fct = CrossEntropyLoss()
1394
+
1395
+ labels = labels.to(logits.device)
1396
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
1397
+
1398
+ if not return_dict:
1399
+ output = (logits,) + outputs[2:]
1400
+ return ((loss,) + output) if loss is not None else output
1401
+
1402
+ return TokenClassifierOutput(
1403
+ loss=loss,
1404
+ logits=logits,
1405
+ hidden_states=outputs.hidden_states,
1406
+ attentions=outputs.attentions,
1407
+ )
1408
+
1409
+
1410
+ class EsmClassificationHead(nn.Module):
1411
+ """Head for sentence-level classification tasks."""
1412
+
1413
+ def __init__(self, config):
1414
+ super().__init__()
1415
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
1416
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
1417
+ self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
1418
+
1419
+ def forward(self, features, **kwargs):
1420
+ x = features[:, 0, :] # take <s> token (equiv. to [CLS])
1421
+ x = self.dropout(x)
1422
+ x = self.dense(x)
1423
+ x = torch.tanh(x)
1424
+ x = self.dropout(x)
1425
+ x = self.out_proj(x)
1426
+ return x
1427
+
1428
+
1429
+ def create_position_ids_from_input_ids(
1430
+ input_ids, padding_idx, past_key_values_length=0
1431
+ ):
1432
+ """
1433
+ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
1434
+ are ignored. This is modified from fairseq's `utils.make_positions`.
1435
+
1436
+ Args:
1437
+ x: torch.Tensor x:
1438
+
1439
+ Returns: torch.Tensor
1440
+ """
1441
+ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
1442
+ mask = input_ids.ne(padding_idx).int()
1443
+ incremental_indices = (
1444
+ torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length
1445
+ ) * mask
1446
+ return incremental_indices.long() + padding_idx
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+ ATTCGA
370
+ ATTCGT
371
+ ATTCGC
372
+ ATTCGG
373
+ ATTGAA
374
+ ATTGAT
375
+ ATTGAC
376
+ ATTGAG
377
+ ATTGTA
378
+ ATTGTT
379
+ ATTGTC
380
+ ATTGTG
381
+ ATTGCA
382
+ ATTGCT
383
+ ATTGCC
384
+ ATTGCG
385
+ ATTGGA
386
+ ATTGGT
387
+ ATTGGC
388
+ ATTGGG
389
+ ATCAAA
390
+ ATCAAT
391
+ ATCAAC
392
+ ATCAAG
393
+ ATCATA
394
+ ATCATT
395
+ ATCATC
396
+ ATCATG
397
+ ATCACA
398
+ ATCACT
399
+ ATCACC
400
+ ATCACG
401
+ ATCAGA
402
+ ATCAGT
403
+ ATCAGC
404
+ ATCAGG
405
+ ATCTAA
406
+ ATCTAT
407
+ ATCTAC
408
+ ATCTAG
409
+ ATCTTA
410
+ ATCTTT
411
+ ATCTTC
412
+ ATCTTG
413
+ ATCTCA
414
+ ATCTCT
415
+ ATCTCC
416
+ ATCTCG
417
+ ATCTGA
418
+ ATCTGT
419
+ ATCTGC
420
+ ATCTGG
421
+ ATCCAA
422
+ ATCCAT
423
+ ATCCAC
424
+ ATCCAG
425
+ ATCCTA
426
+ ATCCTT
427
+ ATCCTC
428
+ ATCCTG
429
+ ATCCCA
430
+ ATCCCT
431
+ ATCCCC
432
+ ATCCCG
433
+ ATCCGA
434
+ ATCCGT
435
+ ATCCGC
436
+ ATCCGG
437
+ ATCGAA
438
+ ATCGAT
439
+ ATCGAC
440
+ ATCGAG
441
+ ATCGTA
442
+ ATCGTT
443
+ ATCGTC
444
+ ATCGTG
445
+ ATCGCA
446
+ ATCGCT
447
+ ATCGCC
448
+ ATCGCG
449
+ ATCGGA
450
+ ATCGGT
451
+ ATCGGC
452
+ ATCGGG
453
+ ATGAAA
454
+ ATGAAT
455
+ ATGAAC
456
+ ATGAAG
457
+ ATGATA
458
+ ATGATT
459
+ ATGATC
460
+ ATGATG
461
+ ATGACA
462
+ ATGACT
463
+ ATGACC
464
+ ATGACG
465
+ ATGAGA
466
+ ATGAGT
467
+ ATGAGC
468
+ ATGAGG
469
+ ATGTAA
470
+ ATGTAT
471
+ ATGTAC
472
+ ATGTAG
473
+ ATGTTA
474
+ ATGTTT
475
+ ATGTTC
476
+ ATGTTG
477
+ ATGTCA
478
+ ATGTCT
479
+ ATGTCC
480
+ ATGTCG
481
+ ATGTGA
482
+ ATGTGT
483
+ ATGTGC
484
+ ATGTGG
485
+ ATGCAA
486
+ ATGCAT
487
+ ATGCAC
488
+ ATGCAG
489
+ ATGCTA
490
+ ATGCTT
491
+ ATGCTC
492
+ ATGCTG
493
+ ATGCCA
494
+ ATGCCT
495
+ ATGCCC
496
+ ATGCCG
497
+ ATGCGA
498
+ ATGCGT
499
+ ATGCGC
500
+ ATGCGG
501
+ ATGGAA
502
+ ATGGAT
503
+ ATGGAC
504
+ ATGGAG
505
+ ATGGTA
506
+ ATGGTT
507
+ ATGGTC
508
+ ATGGTG
509
+ ATGGCA
510
+ ATGGCT
511
+ ATGGCC
512
+ ATGGCG
513
+ ATGGGA
514
+ ATGGGT
515
+ ATGGGC
516
+ ATGGGG
517
+ ACAAAA
518
+ ACAAAT
519
+ ACAAAC
520
+ ACAAAG
521
+ ACAATA
522
+ ACAATT
523
+ ACAATC
524
+ ACAATG
525
+ ACAACA
526
+ ACAACT
527
+ ACAACC
528
+ ACAACG
529
+ ACAAGA
530
+ ACAAGT
531
+ ACAAGC
532
+ ACAAGG
533
+ ACATAA
534
+ ACATAT
535
+ ACATAC
536
+ ACATAG
537
+ ACATTA
538
+ ACATTT
539
+ ACATTC
540
+ ACATTG
541
+ ACATCA
542
+ ACATCT
543
+ ACATCC
544
+ ACATCG
545
+ ACATGA
546
+ ACATGT
547
+ ACATGC
548
+ ACATGG
549
+ ACACAA
550
+ ACACAT
551
+ ACACAC
552
+ ACACAG
553
+ ACACTA
554
+ ACACTT
555
+ ACACTC
556
+ ACACTG
557
+ ACACCA
558
+ ACACCT
559
+ ACACCC
560
+ ACACCG
561
+ ACACGA
562
+ ACACGT
563
+ ACACGC
564
+ ACACGG
565
+ ACAGAA
566
+ ACAGAT
567
+ ACAGAC
568
+ ACAGAG
569
+ ACAGTA
570
+ ACAGTT
571
+ ACAGTC
572
+ ACAGTG
573
+ ACAGCA
574
+ ACAGCT
575
+ ACAGCC
576
+ ACAGCG
577
+ ACAGGA
578
+ ACAGGT
579
+ ACAGGC
580
+ ACAGGG
581
+ ACTAAA
582
+ ACTAAT
583
+ ACTAAC
584
+ ACTAAG
585
+ ACTATA
586
+ ACTATT
587
+ ACTATC
588
+ ACTATG
589
+ ACTACA
590
+ ACTACT
591
+ ACTACC
592
+ ACTACG
593
+ ACTAGA
594
+ ACTAGT
595
+ ACTAGC
596
+ ACTAGG
597
+ ACTTAA
598
+ ACTTAT
599
+ ACTTAC
600
+ ACTTAG
601
+ ACTTTA
602
+ ACTTTT
603
+ ACTTTC
604
+ ACTTTG
605
+ ACTTCA
606
+ ACTTCT
607
+ ACTTCC
608
+ ACTTCG
609
+ ACTTGA
610
+ ACTTGT
611
+ ACTTGC
612
+ ACTTGG
613
+ ACTCAA
614
+ ACTCAT
615
+ ACTCAC
616
+ ACTCAG
617
+ ACTCTA
618
+ ACTCTT
619
+ ACTCTC
620
+ ACTCTG
621
+ ACTCCA
622
+ ACTCCT
623
+ ACTCCC
624
+ ACTCCG
625
+ ACTCGA
626
+ ACTCGT
627
+ ACTCGC
628
+ ACTCGG
629
+ ACTGAA
630
+ ACTGAT
631
+ ACTGAC
632
+ ACTGAG
633
+ ACTGTA
634
+ ACTGTT
635
+ ACTGTC
636
+ ACTGTG
637
+ ACTGCA
638
+ ACTGCT
639
+ ACTGCC
640
+ ACTGCG
641
+ ACTGGA
642
+ ACTGGT
643
+ ACTGGC
644
+ ACTGGG
645
+ ACCAAA
646
+ ACCAAT
647
+ ACCAAC
648
+ ACCAAG
649
+ ACCATA
650
+ ACCATT
651
+ ACCATC
652
+ ACCATG
653
+ ACCACA
654
+ ACCACT
655
+ ACCACC
656
+ ACCACG
657
+ ACCAGA
658
+ ACCAGT
659
+ ACCAGC
660
+ ACCAGG
661
+ ACCTAA
662
+ ACCTAT
663
+ ACCTAC
664
+ ACCTAG
665
+ ACCTTA
666
+ ACCTTT
667
+ ACCTTC
668
+ ACCTTG
669
+ ACCTCA
670
+ ACCTCT
671
+ ACCTCC
672
+ ACCTCG
673
+ ACCTGA
674
+ ACCTGT
675
+ ACCTGC
676
+ ACCTGG
677
+ ACCCAA
678
+ ACCCAT
679
+ ACCCAC
680
+ ACCCAG
681
+ ACCCTA
682
+ ACCCTT
683
+ ACCCTC
684
+ ACCCTG
685
+ ACCCCA
686
+ ACCCCT
687
+ ACCCCC
688
+ ACCCCG
689
+ ACCCGA
690
+ ACCCGT
691
+ ACCCGC
692
+ ACCCGG
693
+ ACCGAA
694
+ ACCGAT
695
+ ACCGAC
696
+ ACCGAG
697
+ ACCGTA
698
+ ACCGTT
699
+ ACCGTC
700
+ ACCGTG
701
+ ACCGCA
702
+ ACCGCT
703
+ ACCGCC
704
+ ACCGCG
705
+ ACCGGA
706
+ ACCGGT
707
+ ACCGGC
708
+ ACCGGG
709
+ ACGAAA
710
+ ACGAAT
711
+ ACGAAC
712
+ ACGAAG
713
+ ACGATA
714
+ ACGATT
715
+ ACGATC
716
+ ACGATG
717
+ ACGACA
718
+ ACGACT
719
+ ACGACC
720
+ ACGACG
721
+ ACGAGA
722
+ ACGAGT
723
+ ACGAGC
724
+ ACGAGG
725
+ ACGTAA
726
+ ACGTAT
727
+ ACGTAC
728
+ ACGTAG
729
+ ACGTTA
730
+ ACGTTT
731
+ ACGTTC
732
+ ACGTTG
733
+ ACGTCA
734
+ ACGTCT
735
+ ACGTCC
736
+ ACGTCG
737
+ ACGTGA
738
+ ACGTGT
739
+ ACGTGC
740
+ ACGTGG
741
+ ACGCAA
742
+ ACGCAT
743
+ ACGCAC
744
+ ACGCAG
745
+ ACGCTA
746
+ ACGCTT
747
+ ACGCTC
748
+ ACGCTG
749
+ ACGCCA
750
+ ACGCCT
751
+ ACGCCC
752
+ ACGCCG
753
+ ACGCGA
754
+ ACGCGT
755
+ ACGCGC
756
+ ACGCGG
757
+ ACGGAA
758
+ ACGGAT
759
+ ACGGAC
760
+ ACGGAG
761
+ ACGGTA
762
+ ACGGTT
763
+ ACGGTC
764
+ ACGGTG
765
+ ACGGCA
766
+ ACGGCT
767
+ ACGGCC
768
+ ACGGCG
769
+ ACGGGA
770
+ ACGGGT
771
+ ACGGGC
772
+ ACGGGG
773
+ AGAAAA
774
+ AGAAAT
775
+ AGAAAC
776
+ AGAAAG
777
+ AGAATA
778
+ AGAATT
779
+ AGAATC
780
+ AGAATG
781
+ AGAACA
782
+ AGAACT
783
+ AGAACC
784
+ AGAACG
785
+ AGAAGA
786
+ AGAAGT
787
+ AGAAGC
788
+ AGAAGG
789
+ AGATAA
790
+ AGATAT
791
+ AGATAC
792
+ AGATAG
793
+ AGATTA
794
+ AGATTT
795
+ AGATTC
796
+ AGATTG
797
+ AGATCA
798
+ AGATCT
799
+ AGATCC
800
+ AGATCG
801
+ AGATGA
802
+ AGATGT
803
+ AGATGC
804
+ AGATGG
805
+ AGACAA
806
+ AGACAT
807
+ AGACAC
808
+ AGACAG
809
+ AGACTA
810
+ AGACTT
811
+ AGACTC
812
+ AGACTG
813
+ AGACCA
814
+ AGACCT
815
+ AGACCC
816
+ AGACCG
817
+ AGACGA
818
+ AGACGT
819
+ AGACGC
820
+ AGACGG
821
+ AGAGAA
822
+ AGAGAT
823
+ AGAGAC
824
+ AGAGAG
825
+ AGAGTA
826
+ AGAGTT
827
+ AGAGTC
828
+ AGAGTG
829
+ AGAGCA
830
+ AGAGCT
831
+ AGAGCC
832
+ AGAGCG
833
+ AGAGGA
834
+ AGAGGT
835
+ AGAGGC
836
+ AGAGGG
837
+ AGTAAA
838
+ AGTAAT
839
+ AGTAAC
840
+ AGTAAG
841
+ AGTATA
842
+ AGTATT
843
+ AGTATC
844
+ AGTATG
845
+ AGTACA
846
+ AGTACT
847
+ AGTACC
848
+ AGTACG
849
+ AGTAGA
850
+ AGTAGT
851
+ AGTAGC
852
+ AGTAGG
853
+ AGTTAA
854
+ AGTTAT
855
+ AGTTAC
856
+ AGTTAG
857
+ AGTTTA
858
+ AGTTTT
859
+ AGTTTC
860
+ AGTTTG
861
+ AGTTCA
862
+ AGTTCT
863
+ AGTTCC
864
+ AGTTCG
865
+ AGTTGA
866
+ AGTTGT
867
+ AGTTGC
868
+ AGTTGG
869
+ AGTCAA
870
+ AGTCAT
871
+ AGTCAC
872
+ AGTCAG
873
+ AGTCTA
874
+ AGTCTT
875
+ AGTCTC
876
+ AGTCTG
877
+ AGTCCA
878
+ AGTCCT
879
+ AGTCCC
880
+ AGTCCG
881
+ AGTCGA
882
+ AGTCGT
883
+ AGTCGC
884
+ AGTCGG
885
+ AGTGAA
886
+ AGTGAT
887
+ AGTGAC
888
+ AGTGAG
889
+ AGTGTA
890
+ AGTGTT
891
+ AGTGTC
892
+ AGTGTG
893
+ AGTGCA
894
+ AGTGCT
895
+ AGTGCC
896
+ AGTGCG
897
+ AGTGGA
898
+ AGTGGT
899
+ AGTGGC
900
+ AGTGGG
901
+ AGCAAA
902
+ AGCAAT
903
+ AGCAAC
904
+ AGCAAG
905
+ AGCATA
906
+ AGCATT
907
+ AGCATC
908
+ AGCATG
909
+ AGCACA
910
+ AGCACT
911
+ AGCACC
912
+ AGCACG
913
+ AGCAGA
914
+ AGCAGT
915
+ AGCAGC
916
+ AGCAGG
917
+ AGCTAA
918
+ AGCTAT
919
+ AGCTAC
920
+ AGCTAG
921
+ AGCTTA
922
+ AGCTTT
923
+ AGCTTC
924
+ AGCTTG
925
+ AGCTCA
926
+ AGCTCT
927
+ AGCTCC
928
+ AGCTCG
929
+ AGCTGA
930
+ AGCTGT
931
+ AGCTGC
932
+ AGCTGG
933
+ AGCCAA
934
+ AGCCAT
935
+ AGCCAC
936
+ AGCCAG
937
+ AGCCTA
938
+ AGCCTT
939
+ AGCCTC
940
+ AGCCTG
941
+ AGCCCA
942
+ AGCCCT
943
+ AGCCCC
944
+ AGCCCG
945
+ AGCCGA
946
+ AGCCGT
947
+ AGCCGC
948
+ AGCCGG
949
+ AGCGAA
950
+ AGCGAT
951
+ AGCGAC
952
+ AGCGAG
953
+ AGCGTA
954
+ AGCGTT
955
+ AGCGTC
956
+ AGCGTG
957
+ AGCGCA
958
+ AGCGCT
959
+ AGCGCC
960
+ AGCGCG
961
+ AGCGGA
962
+ AGCGGT
963
+ AGCGGC
964
+ AGCGGG
965
+ AGGAAA
966
+ AGGAAT
967
+ AGGAAC
968
+ AGGAAG
969
+ AGGATA
970
+ AGGATT
971
+ AGGATC
972
+ AGGATG
973
+ AGGACA
974
+ AGGACT
975
+ AGGACC
976
+ AGGACG
977
+ AGGAGA
978
+ AGGAGT
979
+ AGGAGC
980
+ AGGAGG
981
+ AGGTAA
982
+ AGGTAT
983
+ AGGTAC
984
+ AGGTAG
985
+ AGGTTA
986
+ AGGTTT
987
+ AGGTTC
988
+ AGGTTG
989
+ AGGTCA
990
+ AGGTCT
991
+ AGGTCC
992
+ AGGTCG
993
+ AGGTGA
994
+ AGGTGT
995
+ AGGTGC
996
+ AGGTGG
997
+ AGGCAA
998
+ AGGCAT
999
+ AGGCAC
1000
+ AGGCAG
1001
+ AGGCTA
1002
+ AGGCTT
1003
+ AGGCTC
1004
+ AGGCTG
1005
+ AGGCCA
1006
+ AGGCCT
1007
+ AGGCCC
1008
+ AGGCCG
1009
+ AGGCGA
1010
+ AGGCGT
1011
+ AGGCGC
1012
+ AGGCGG
1013
+ AGGGAA
1014
+ AGGGAT
1015
+ AGGGAC
1016
+ AGGGAG
1017
+ AGGGTA
1018
+ AGGGTT
1019
+ AGGGTC
1020
+ AGGGTG
1021
+ AGGGCA
1022
+ AGGGCT
1023
+ AGGGCC
1024
+ AGGGCG
1025
+ AGGGGA
1026
+ AGGGGT
1027
+ AGGGGC
1028
+ AGGGGG
1029
+ TAAAAA
1030
+ TAAAAT
1031
+ TAAAAC
1032
+ TAAAAG
1033
+ TAAATA
1034
+ TAAATT
1035
+ TAAATC
1036
+ TAAATG
1037
+ TAAACA
1038
+ TAAACT
1039
+ TAAACC
1040
+ TAAACG
1041
+ TAAAGA
1042
+ TAAAGT
1043
+ TAAAGC
1044
+ TAAAGG
1045
+ TAATAA
1046
+ TAATAT
1047
+ TAATAC
1048
+ TAATAG
1049
+ TAATTA
1050
+ TAATTT
1051
+ TAATTC
1052
+ TAATTG
1053
+ TAATCA
1054
+ TAATCT
1055
+ TAATCC
1056
+ TAATCG
1057
+ TAATGA
1058
+ TAATGT
1059
+ TAATGC
1060
+ TAATGG
1061
+ TAACAA
1062
+ TAACAT
1063
+ TAACAC
1064
+ TAACAG
1065
+ TAACTA
1066
+ TAACTT
1067
+ TAACTC
1068
+ TAACTG
1069
+ TAACCA
1070
+ TAACCT
1071
+ TAACCC
1072
+ TAACCG
1073
+ TAACGA
1074
+ TAACGT
1075
+ TAACGC
1076
+ TAACGG
1077
+ TAAGAA
1078
+ TAAGAT
1079
+ TAAGAC
1080
+ TAAGAG
1081
+ TAAGTA
1082
+ TAAGTT
1083
+ TAAGTC
1084
+ TAAGTG
1085
+ TAAGCA
1086
+ TAAGCT
1087
+ TAAGCC
1088
+ TAAGCG
1089
+ TAAGGA
1090
+ TAAGGT
1091
+ TAAGGC
1092
+ TAAGGG
1093
+ TATAAA
1094
+ TATAAT
1095
+ TATAAC
1096
+ TATAAG
1097
+ TATATA
1098
+ TATATT
1099
+ TATATC
1100
+ TATATG
1101
+ TATACA
1102
+ TATACT
1103
+ TATACC
1104
+ TATACG
1105
+ TATAGA
1106
+ TATAGT
1107
+ TATAGC
1108
+ TATAGG
1109
+ TATTAA
1110
+ TATTAT
1111
+ TATTAC
1112
+ TATTAG
1113
+ TATTTA
1114
+ TATTTT
1115
+ TATTTC
1116
+ TATTTG
1117
+ TATTCA
1118
+ TATTCT
1119
+ TATTCC
1120
+ TATTCG
1121
+ TATTGA
1122
+ TATTGT
1123
+ TATTGC
1124
+ TATTGG
1125
+ TATCAA
1126
+ TATCAT
1127
+ TATCAC
1128
+ TATCAG
1129
+ TATCTA
1130
+ TATCTT
1131
+ TATCTC
1132
+ TATCTG
1133
+ TATCCA
1134
+ TATCCT
1135
+ TATCCC
1136
+ TATCCG
1137
+ TATCGA
1138
+ TATCGT
1139
+ TATCGC
1140
+ TATCGG
1141
+ TATGAA
1142
+ TATGAT
1143
+ TATGAC
1144
+ TATGAG
1145
+ TATGTA
1146
+ TATGTT
1147
+ TATGTC
1148
+ TATGTG
1149
+ TATGCA
1150
+ TATGCT
1151
+ TATGCC
1152
+ TATGCG
1153
+ TATGGA
1154
+ TATGGT
1155
+ TATGGC
1156
+ TATGGG
1157
+ TACAAA
1158
+ TACAAT
1159
+ TACAAC
1160
+ TACAAG
1161
+ TACATA
1162
+ TACATT
1163
+ TACATC
1164
+ TACATG
1165
+ TACACA
1166
+ TACACT
1167
+ TACACC
1168
+ TACACG
1169
+ TACAGA
1170
+ TACAGT
1171
+ TACAGC
1172
+ TACAGG
1173
+ TACTAA
1174
+ TACTAT
1175
+ TACTAC
1176
+ TACTAG
1177
+ TACTTA
1178
+ TACTTT
1179
+ TACTTC
1180
+ TACTTG
1181
+ TACTCA
1182
+ TACTCT
1183
+ TACTCC
1184
+ TACTCG
1185
+ TACTGA
1186
+ TACTGT
1187
+ TACTGC
1188
+ TACTGG
1189
+ TACCAA
1190
+ TACCAT
1191
+ TACCAC
1192
+ TACCAG
1193
+ TACCTA
1194
+ TACCTT
1195
+ TACCTC
1196
+ TACCTG
1197
+ TACCCA
1198
+ TACCCT
1199
+ TACCCC
1200
+ TACCCG
1201
+ TACCGA
1202
+ TACCGT
1203
+ TACCGC
1204
+ TACCGG
1205
+ TACGAA
1206
+ TACGAT
1207
+ TACGAC
1208
+ TACGAG
1209
+ TACGTA
1210
+ TACGTT
1211
+ TACGTC
1212
+ TACGTG
1213
+ TACGCA
1214
+ TACGCT
1215
+ TACGCC
1216
+ TACGCG
1217
+ TACGGA
1218
+ TACGGT
1219
+ TACGGC
1220
+ TACGGG
1221
+ TAGAAA
1222
+ TAGAAT
1223
+ TAGAAC
1224
+ TAGAAG
1225
+ TAGATA
1226
+ TAGATT
1227
+ TAGATC
1228
+ TAGATG
1229
+ TAGACA
1230
+ TAGACT
1231
+ TAGACC
1232
+ TAGACG
1233
+ TAGAGA
1234
+ TAGAGT
1235
+ TAGAGC
1236
+ TAGAGG
1237
+ TAGTAA
1238
+ TAGTAT
1239
+ TAGTAC
1240
+ TAGTAG
1241
+ TAGTTA
1242
+ TAGTTT
1243
+ TAGTTC
1244
+ TAGTTG
1245
+ TAGTCA
1246
+ TAGTCT
1247
+ TAGTCC
1248
+ TAGTCG
1249
+ TAGTGA
1250
+ TAGTGT
1251
+ TAGTGC
1252
+ TAGTGG
1253
+ TAGCAA
1254
+ TAGCAT
1255
+ TAGCAC
1256
+ TAGCAG
1257
+ TAGCTA
1258
+ TAGCTT
1259
+ TAGCTC
1260
+ TAGCTG
1261
+ TAGCCA
1262
+ TAGCCT
1263
+ TAGCCC
1264
+ TAGCCG
1265
+ TAGCGA
1266
+ TAGCGT
1267
+ TAGCGC
1268
+ TAGCGG
1269
+ TAGGAA
1270
+ TAGGAT
1271
+ TAGGAC
1272
+ TAGGAG
1273
+ TAGGTA
1274
+ TAGGTT
1275
+ TAGGTC
1276
+ TAGGTG
1277
+ TAGGCA
1278
+ TAGGCT
1279
+ TAGGCC
1280
+ TAGGCG
1281
+ TAGGGA
1282
+ TAGGGT
1283
+ TAGGGC
1284
+ TAGGGG
1285
+ TTAAAA
1286
+ TTAAAT
1287
+ TTAAAC
1288
+ TTAAAG
1289
+ TTAATA
1290
+ TTAATT
1291
+ TTAATC
1292
+ TTAATG
1293
+ TTAACA
1294
+ TTAACT
1295
+ TTAACC
1296
+ TTAACG
1297
+ TTAAGA
1298
+ TTAAGT
1299
+ TTAAGC
1300
+ TTAAGG
1301
+ TTATAA
1302
+ TTATAT
1303
+ TTATAC
1304
+ TTATAG
1305
+ TTATTA
1306
+ TTATTT
1307
+ TTATTC
1308
+ TTATTG
1309
+ TTATCA
1310
+ TTATCT
1311
+ TTATCC
1312
+ TTATCG
1313
+ TTATGA
1314
+ TTATGT
1315
+ TTATGC
1316
+ TTATGG
1317
+ TTACAA
1318
+ TTACAT
1319
+ TTACAC
1320
+ TTACAG
1321
+ TTACTA
1322
+ TTACTT
1323
+ TTACTC
1324
+ TTACTG
1325
+ TTACCA
1326
+ TTACCT
1327
+ TTACCC
1328
+ TTACCG
1329
+ TTACGA
1330
+ TTACGT
1331
+ TTACGC
1332
+ TTACGG
1333
+ TTAGAA
1334
+ TTAGAT
1335
+ TTAGAC
1336
+ TTAGAG
1337
+ TTAGTA
1338
+ TTAGTT
1339
+ TTAGTC
1340
+ TTAGTG
1341
+ TTAGCA
1342
+ TTAGCT
1343
+ TTAGCC
1344
+ TTAGCG
1345
+ TTAGGA
1346
+ TTAGGT
1347
+ TTAGGC
1348
+ TTAGGG
1349
+ TTTAAA
1350
+ TTTAAT
1351
+ TTTAAC
1352
+ TTTAAG
1353
+ TTTATA
1354
+ TTTATT
1355
+ TTTATC
1356
+ TTTATG
1357
+ TTTACA
1358
+ TTTACT
1359
+ TTTACC
1360
+ TTTACG
1361
+ TTTAGA
1362
+ TTTAGT
1363
+ TTTAGC
1364
+ TTTAGG
1365
+ TTTTAA
1366
+ TTTTAT
1367
+ TTTTAC
1368
+ TTTTAG
1369
+ TTTTTA
1370
+ TTTTTT
1371
+ TTTTTC
1372
+ TTTTTG
1373
+ TTTTCA
1374
+ TTTTCT
1375
+ TTTTCC
1376
+ TTTTCG
1377
+ TTTTGA
1378
+ TTTTGT
1379
+ TTTTGC
1380
+ TTTTGG
1381
+ TTTCAA
1382
+ TTTCAT
1383
+ TTTCAC
1384
+ TTTCAG
1385
+ TTTCTA
1386
+ TTTCTT
1387
+ TTTCTC
1388
+ TTTCTG
1389
+ TTTCCA
1390
+ TTTCCT
1391
+ TTTCCC
1392
+ TTTCCG
1393
+ TTTCGA
1394
+ TTTCGT
1395
+ TTTCGC
1396
+ TTTCGG
1397
+ TTTGAA
1398
+ TTTGAT
1399
+ TTTGAC
1400
+ TTTGAG
1401
+ TTTGTA
1402
+ TTTGTT
1403
+ TTTGTC
1404
+ TTTGTG
1405
+ TTTGCA
1406
+ TTTGCT
1407
+ TTTGCC
1408
+ TTTGCG
1409
+ TTTGGA
1410
+ TTTGGT
1411
+ TTTGGC
1412
+ TTTGGG
1413
+ TTCAAA
1414
+ TTCAAT
1415
+ TTCAAC
1416
+ TTCAAG
1417
+ TTCATA
1418
+ TTCATT
1419
+ TTCATC
1420
+ TTCATG
1421
+ TTCACA
1422
+ TTCACT
1423
+ TTCACC
1424
+ TTCACG
1425
+ TTCAGA
1426
+ TTCAGT
1427
+ TTCAGC
1428
+ TTCAGG
1429
+ TTCTAA
1430
+ TTCTAT
1431
+ TTCTAC
1432
+ TTCTAG
1433
+ TTCTTA
1434
+ TTCTTT
1435
+ TTCTTC
1436
+ TTCTTG
1437
+ TTCTCA
1438
+ TTCTCT
1439
+ TTCTCC
1440
+ TTCTCG
1441
+ TTCTGA
1442
+ TTCTGT
1443
+ TTCTGC
1444
+ TTCTGG
1445
+ TTCCAA
1446
+ TTCCAT
1447
+ TTCCAC
1448
+ TTCCAG
1449
+ TTCCTA
1450
+ TTCCTT
1451
+ TTCCTC
1452
+ TTCCTG
1453
+ TTCCCA
1454
+ TTCCCT
1455
+ TTCCCC
1456
+ TTCCCG
1457
+ TTCCGA
1458
+ TTCCGT
1459
+ TTCCGC
1460
+ TTCCGG
1461
+ TTCGAA
1462
+ TTCGAT
1463
+ TTCGAC
1464
+ TTCGAG
1465
+ TTCGTA
1466
+ TTCGTT
1467
+ TTCGTC
1468
+ TTCGTG
1469
+ TTCGCA
1470
+ TTCGCT
1471
+ TTCGCC
1472
+ TTCGCG
1473
+ TTCGGA
1474
+ TTCGGT
1475
+ TTCGGC
1476
+ TTCGGG
1477
+ TTGAAA
1478
+ TTGAAT
1479
+ TTGAAC
1480
+ TTGAAG
1481
+ TTGATA
1482
+ TTGATT
1483
+ TTGATC
1484
+ TTGATG
1485
+ TTGACA
1486
+ TTGACT
1487
+ TTGACC
1488
+ TTGACG
1489
+ TTGAGA
1490
+ TTGAGT
1491
+ TTGAGC
1492
+ TTGAGG
1493
+ TTGTAA
1494
+ TTGTAT
1495
+ TTGTAC
1496
+ TTGTAG
1497
+ TTGTTA
1498
+ TTGTTT
1499
+ TTGTTC
1500
+ TTGTTG
1501
+ TTGTCA
1502
+ TTGTCT
1503
+ TTGTCC
1504
+ TTGTCG
1505
+ TTGTGA
1506
+ TTGTGT
1507
+ TTGTGC
1508
+ TTGTGG
1509
+ TTGCAA
1510
+ TTGCAT
1511
+ TTGCAC
1512
+ TTGCAG
1513
+ TTGCTA
1514
+ TTGCTT
1515
+ TTGCTC
1516
+ TTGCTG
1517
+ TTGCCA
1518
+ TTGCCT
1519
+ TTGCCC
1520
+ TTGCCG
1521
+ TTGCGA
1522
+ TTGCGT
1523
+ TTGCGC
1524
+ TTGCGG
1525
+ TTGGAA
1526
+ TTGGAT
1527
+ TTGGAC
1528
+ TTGGAG
1529
+ TTGGTA
1530
+ TTGGTT
1531
+ TTGGTC
1532
+ TTGGTG
1533
+ TTGGCA
1534
+ TTGGCT
1535
+ TTGGCC
1536
+ TTGGCG
1537
+ TTGGGA
1538
+ TTGGGT
1539
+ TTGGGC
1540
+ TTGGGG
1541
+ TCAAAA
1542
+ TCAAAT
1543
+ TCAAAC
1544
+ TCAAAG
1545
+ TCAATA
1546
+ TCAATT
1547
+ TCAATC
1548
+ TCAATG
1549
+ TCAACA
1550
+ TCAACT
1551
+ TCAACC
1552
+ TCAACG
1553
+ TCAAGA
1554
+ TCAAGT
1555
+ TCAAGC
1556
+ TCAAGG
1557
+ TCATAA
1558
+ TCATAT
1559
+ TCATAC
1560
+ TCATAG
1561
+ TCATTA
1562
+ TCATTT
1563
+ TCATTC
1564
+ TCATTG
1565
+ TCATCA
1566
+ TCATCT
1567
+ TCATCC
1568
+ TCATCG
1569
+ TCATGA
1570
+ TCATGT
1571
+ TCATGC
1572
+ TCATGG
1573
+ TCACAA
1574
+ TCACAT
1575
+ TCACAC
1576
+ TCACAG
1577
+ TCACTA
1578
+ TCACTT
1579
+ TCACTC
1580
+ TCACTG
1581
+ TCACCA
1582
+ TCACCT
1583
+ TCACCC
1584
+ TCACCG
1585
+ TCACGA
1586
+ TCACGT
1587
+ TCACGC
1588
+ TCACGG
1589
+ TCAGAA
1590
+ TCAGAT
1591
+ TCAGAC
1592
+ TCAGAG
1593
+ TCAGTA
1594
+ TCAGTT
1595
+ TCAGTC
1596
+ TCAGTG
1597
+ TCAGCA
1598
+ TCAGCT
1599
+ TCAGCC
1600
+ TCAGCG
1601
+ TCAGGA
1602
+ TCAGGT
1603
+ TCAGGC
1604
+ TCAGGG
1605
+ TCTAAA
1606
+ TCTAAT
1607
+ TCTAAC
1608
+ TCTAAG
1609
+ TCTATA
1610
+ TCTATT
1611
+ TCTATC
1612
+ TCTATG
1613
+ TCTACA
1614
+ TCTACT
1615
+ TCTACC
1616
+ TCTACG
1617
+ TCTAGA
1618
+ TCTAGT
1619
+ TCTAGC
1620
+ TCTAGG
1621
+ TCTTAA
1622
+ TCTTAT
1623
+ TCTTAC
1624
+ TCTTAG
1625
+ TCTTTA
1626
+ TCTTTT
1627
+ TCTTTC
1628
+ TCTTTG
1629
+ TCTTCA
1630
+ TCTTCT
1631
+ TCTTCC
1632
+ TCTTCG
1633
+ TCTTGA
1634
+ TCTTGT
1635
+ TCTTGC
1636
+ TCTTGG
1637
+ TCTCAA
1638
+ TCTCAT
1639
+ TCTCAC
1640
+ TCTCAG
1641
+ TCTCTA
1642
+ TCTCTT
1643
+ TCTCTC
1644
+ TCTCTG
1645
+ TCTCCA
1646
+ TCTCCT
1647
+ TCTCCC
1648
+ TCTCCG
1649
+ TCTCGA
1650
+ TCTCGT
1651
+ TCTCGC
1652
+ TCTCGG
1653
+ TCTGAA
1654
+ TCTGAT
1655
+ TCTGAC
1656
+ TCTGAG
1657
+ TCTGTA
1658
+ TCTGTT
1659
+ TCTGTC
1660
+ TCTGTG
1661
+ TCTGCA
1662
+ TCTGCT
1663
+ TCTGCC
1664
+ TCTGCG
1665
+ TCTGGA
1666
+ TCTGGT
1667
+ TCTGGC
1668
+ TCTGGG
1669
+ TCCAAA
1670
+ TCCAAT
1671
+ TCCAAC
1672
+ TCCAAG
1673
+ TCCATA
1674
+ TCCATT
1675
+ TCCATC
1676
+ TCCATG
1677
+ TCCACA
1678
+ TCCACT
1679
+ TCCACC
1680
+ TCCACG
1681
+ TCCAGA
1682
+ TCCAGT
1683
+ TCCAGC
1684
+ TCCAGG
1685
+ TCCTAA
1686
+ TCCTAT
1687
+ TCCTAC
1688
+ TCCTAG
1689
+ TCCTTA
1690
+ TCCTTT
1691
+ TCCTTC
1692
+ TCCTTG
1693
+ TCCTCA
1694
+ TCCTCT
1695
+ TCCTCC
1696
+ TCCTCG
1697
+ TCCTGA
1698
+ TCCTGT
1699
+ TCCTGC
1700
+ TCCTGG
1701
+ TCCCAA
1702
+ TCCCAT
1703
+ TCCCAC
1704
+ TCCCAG
1705
+ TCCCTA
1706
+ TCCCTT
1707
+ TCCCTC
1708
+ TCCCTG
1709
+ TCCCCA
1710
+ TCCCCT
1711
+ TCCCCC
1712
+ TCCCCG
1713
+ TCCCGA
1714
+ TCCCGT
1715
+ TCCCGC
1716
+ TCCCGG
1717
+ TCCGAA
1718
+ TCCGAT
1719
+ TCCGAC
1720
+ TCCGAG
1721
+ TCCGTA
1722
+ TCCGTT
1723
+ TCCGTC
1724
+ TCCGTG
1725
+ TCCGCA
1726
+ TCCGCT
1727
+ TCCGCC
1728
+ TCCGCG
1729
+ TCCGGA
1730
+ TCCGGT
1731
+ TCCGGC
1732
+ TCCGGG
1733
+ TCGAAA
1734
+ TCGAAT
1735
+ TCGAAC
1736
+ TCGAAG
1737
+ TCGATA
1738
+ TCGATT
1739
+ TCGATC
1740
+ TCGATG
1741
+ TCGACA
1742
+ TCGACT
1743
+ TCGACC
1744
+ TCGACG
1745
+ TCGAGA
1746
+ TCGAGT
1747
+ TCGAGC
1748
+ TCGAGG
1749
+ TCGTAA
1750
+ TCGTAT
1751
+ TCGTAC
1752
+ TCGTAG
1753
+ TCGTTA
1754
+ TCGTTT
1755
+ TCGTTC
1756
+ TCGTTG
1757
+ TCGTCA
1758
+ TCGTCT
1759
+ TCGTCC
1760
+ TCGTCG
1761
+ TCGTGA
1762
+ TCGTGT
1763
+ TCGTGC
1764
+ TCGTGG
1765
+ TCGCAA
1766
+ TCGCAT
1767
+ TCGCAC
1768
+ TCGCAG
1769
+ TCGCTA
1770
+ TCGCTT
1771
+ TCGCTC
1772
+ TCGCTG
1773
+ TCGCCA
1774
+ TCGCCT
1775
+ TCGCCC
1776
+ TCGCCG
1777
+ TCGCGA
1778
+ TCGCGT
1779
+ TCGCGC
1780
+ TCGCGG
1781
+ TCGGAA
1782
+ TCGGAT
1783
+ TCGGAC
1784
+ TCGGAG
1785
+ TCGGTA
1786
+ TCGGTT
1787
+ TCGGTC
1788
+ TCGGTG
1789
+ TCGGCA
1790
+ TCGGCT
1791
+ TCGGCC
1792
+ TCGGCG
1793
+ TCGGGA
1794
+ TCGGGT
1795
+ TCGGGC
1796
+ TCGGGG
1797
+ TGAAAA
1798
+ TGAAAT
1799
+ TGAAAC
1800
+ TGAAAG
1801
+ TGAATA
1802
+ TGAATT
1803
+ TGAATC
1804
+ TGAATG
1805
+ TGAACA
1806
+ TGAACT
1807
+ TGAACC
1808
+ TGAACG
1809
+ TGAAGA
1810
+ TGAAGT
1811
+ TGAAGC
1812
+ TGAAGG
1813
+ TGATAA
1814
+ TGATAT
1815
+ TGATAC
1816
+ TGATAG
1817
+ TGATTA
1818
+ TGATTT
1819
+ TGATTC
1820
+ TGATTG
1821
+ TGATCA
1822
+ TGATCT
1823
+ TGATCC
1824
+ TGATCG
1825
+ TGATGA
1826
+ TGATGT
1827
+ TGATGC
1828
+ TGATGG
1829
+ TGACAA
1830
+ TGACAT
1831
+ TGACAC
1832
+ TGACAG
1833
+ TGACTA
1834
+ TGACTT
1835
+ TGACTC
1836
+ TGACTG
1837
+ TGACCA
1838
+ TGACCT
1839
+ TGACCC
1840
+ TGACCG
1841
+ TGACGA
1842
+ TGACGT
1843
+ TGACGC
1844
+ TGACGG
1845
+ TGAGAA
1846
+ TGAGAT
1847
+ TGAGAC
1848
+ TGAGAG
1849
+ TGAGTA
1850
+ TGAGTT
1851
+ TGAGTC
1852
+ TGAGTG
1853
+ TGAGCA
1854
+ TGAGCT
1855
+ TGAGCC
1856
+ TGAGCG
1857
+ TGAGGA
1858
+ TGAGGT
1859
+ TGAGGC
1860
+ TGAGGG
1861
+ TGTAAA
1862
+ TGTAAT
1863
+ TGTAAC
1864
+ TGTAAG
1865
+ TGTATA
1866
+ TGTATT
1867
+ TGTATC
1868
+ TGTATG
1869
+ TGTACA
1870
+ TGTACT
1871
+ TGTACC
1872
+ TGTACG
1873
+ TGTAGA
1874
+ TGTAGT
1875
+ TGTAGC
1876
+ TGTAGG
1877
+ TGTTAA
1878
+ TGTTAT
1879
+ TGTTAC
1880
+ TGTTAG
1881
+ TGTTTA
1882
+ TGTTTT
1883
+ TGTTTC
1884
+ TGTTTG
1885
+ TGTTCA
1886
+ TGTTCT
1887
+ TGTTCC
1888
+ TGTTCG
1889
+ TGTTGA
1890
+ TGTTGT
1891
+ TGTTGC
1892
+ TGTTGG
1893
+ TGTCAA
1894
+ TGTCAT
1895
+ TGTCAC
1896
+ TGTCAG
1897
+ TGTCTA
1898
+ TGTCTT
1899
+ TGTCTC
1900
+ TGTCTG
1901
+ TGTCCA
1902
+ TGTCCT
1903
+ TGTCCC
1904
+ TGTCCG
1905
+ TGTCGA
1906
+ TGTCGT
1907
+ TGTCGC
1908
+ TGTCGG
1909
+ TGTGAA
1910
+ TGTGAT
1911
+ TGTGAC
1912
+ TGTGAG
1913
+ TGTGTA
1914
+ TGTGTT
1915
+ TGTGTC
1916
+ TGTGTG
1917
+ TGTGCA
1918
+ TGTGCT
1919
+ TGTGCC
1920
+ TGTGCG
1921
+ TGTGGA
1922
+ TGTGGT
1923
+ TGTGGC
1924
+ TGTGGG
1925
+ TGCAAA
1926
+ TGCAAT
1927
+ TGCAAC
1928
+ TGCAAG
1929
+ TGCATA
1930
+ TGCATT
1931
+ TGCATC
1932
+ TGCATG
1933
+ TGCACA
1934
+ TGCACT
1935
+ TGCACC
1936
+ TGCACG
1937
+ TGCAGA
1938
+ TGCAGT
1939
+ TGCAGC
1940
+ TGCAGG
1941
+ TGCTAA
1942
+ TGCTAT
1943
+ TGCTAC
1944
+ TGCTAG
1945
+ TGCTTA
1946
+ TGCTTT
1947
+ TGCTTC
1948
+ TGCTTG
1949
+ TGCTCA
1950
+ TGCTCT
1951
+ TGCTCC
1952
+ TGCTCG
1953
+ TGCTGA
1954
+ TGCTGT
1955
+ TGCTGC
1956
+ TGCTGG
1957
+ TGCCAA
1958
+ TGCCAT
1959
+ TGCCAC
1960
+ TGCCAG
1961
+ TGCCTA
1962
+ TGCCTT
1963
+ TGCCTC
1964
+ TGCCTG
1965
+ TGCCCA
1966
+ TGCCCT
1967
+ TGCCCC
1968
+ TGCCCG
1969
+ TGCCGA
1970
+ TGCCGT
1971
+ TGCCGC
1972
+ TGCCGG
1973
+ TGCGAA
1974
+ TGCGAT
1975
+ TGCGAC
1976
+ TGCGAG
1977
+ TGCGTA
1978
+ TGCGTT
1979
+ TGCGTC
1980
+ TGCGTG
1981
+ TGCGCA
1982
+ TGCGCT
1983
+ TGCGCC
1984
+ TGCGCG
1985
+ TGCGGA
1986
+ TGCGGT
1987
+ TGCGGC
1988
+ TGCGGG
1989
+ TGGAAA
1990
+ TGGAAT
1991
+ TGGAAC
1992
+ TGGAAG
1993
+ TGGATA
1994
+ TGGATT
1995
+ TGGATC
1996
+ TGGATG
1997
+ TGGACA
1998
+ TGGACT
1999
+ TGGACC
2000
+ TGGACG
2001
+ TGGAGA
2002
+ TGGAGT
2003
+ TGGAGC
2004
+ TGGAGG
2005
+ TGGTAA
2006
+ TGGTAT
2007
+ TGGTAC
2008
+ TGGTAG
2009
+ TGGTTA
2010
+ TGGTTT
2011
+ TGGTTC
2012
+ TGGTTG
2013
+ TGGTCA
2014
+ TGGTCT
2015
+ TGGTCC
2016
+ TGGTCG
2017
+ TGGTGA
2018
+ TGGTGT
2019
+ TGGTGC
2020
+ TGGTGG
2021
+ TGGCAA
2022
+ TGGCAT
2023
+ TGGCAC
2024
+ TGGCAG
2025
+ TGGCTA
2026
+ TGGCTT
2027
+ TGGCTC
2028
+ TGGCTG
2029
+ TGGCCA
2030
+ TGGCCT
2031
+ TGGCCC
2032
+ TGGCCG
2033
+ TGGCGA
2034
+ TGGCGT
2035
+ TGGCGC
2036
+ TGGCGG
2037
+ TGGGAA
2038
+ TGGGAT
2039
+ TGGGAC
2040
+ TGGGAG
2041
+ TGGGTA
2042
+ TGGGTT
2043
+ TGGGTC
2044
+ TGGGTG
2045
+ TGGGCA
2046
+ TGGGCT
2047
+ TGGGCC
2048
+ TGGGCG
2049
+ TGGGGA
2050
+ TGGGGT
2051
+ TGGGGC
2052
+ TGGGGG
2053
+ CAAAAA
2054
+ CAAAAT
2055
+ CAAAAC
2056
+ CAAAAG
2057
+ CAAATA
2058
+ CAAATT
2059
+ CAAATC
2060
+ CAAATG
2061
+ CAAACA
2062
+ CAAACT
2063
+ CAAACC
2064
+ CAAACG
2065
+ CAAAGA
2066
+ CAAAGT
2067
+ CAAAGC
2068
+ CAAAGG
2069
+ CAATAA
2070
+ CAATAT
2071
+ CAATAC
2072
+ CAATAG
2073
+ CAATTA
2074
+ CAATTT
2075
+ CAATTC
2076
+ CAATTG
2077
+ CAATCA
2078
+ CAATCT
2079
+ CAATCC
2080
+ CAATCG
2081
+ CAATGA
2082
+ CAATGT
2083
+ CAATGC
2084
+ CAATGG
2085
+ CAACAA
2086
+ CAACAT
2087
+ CAACAC
2088
+ CAACAG
2089
+ CAACTA
2090
+ CAACTT
2091
+ CAACTC
2092
+ CAACTG
2093
+ CAACCA
2094
+ CAACCT
2095
+ CAACCC
2096
+ CAACCG
2097
+ CAACGA
2098
+ CAACGT
2099
+ CAACGC
2100
+ CAACGG
2101
+ CAAGAA
2102
+ CAAGAT
2103
+ CAAGAC
2104
+ CAAGAG
2105
+ CAAGTA
2106
+ CAAGTT
2107
+ CAAGTC
2108
+ CAAGTG
2109
+ CAAGCA
2110
+ CAAGCT
2111
+ CAAGCC
2112
+ CAAGCG
2113
+ CAAGGA
2114
+ CAAGGT
2115
+ CAAGGC
2116
+ CAAGGG
2117
+ CATAAA
2118
+ CATAAT
2119
+ CATAAC
2120
+ CATAAG
2121
+ CATATA
2122
+ CATATT
2123
+ CATATC
2124
+ CATATG
2125
+ CATACA
2126
+ CATACT
2127
+ CATACC
2128
+ CATACG
2129
+ CATAGA
2130
+ CATAGT
2131
+ CATAGC
2132
+ CATAGG
2133
+ CATTAA
2134
+ CATTAT
2135
+ CATTAC
2136
+ CATTAG
2137
+ CATTTA
2138
+ CATTTT
2139
+ CATTTC
2140
+ CATTTG
2141
+ CATTCA
2142
+ CATTCT
2143
+ CATTCC
2144
+ CATTCG
2145
+ CATTGA
2146
+ CATTGT
2147
+ CATTGC
2148
+ CATTGG
2149
+ CATCAA
2150
+ CATCAT
2151
+ CATCAC
2152
+ CATCAG
2153
+ CATCTA
2154
+ CATCTT
2155
+ CATCTC
2156
+ CATCTG
2157
+ CATCCA
2158
+ CATCCT
2159
+ CATCCC
2160
+ CATCCG
2161
+ CATCGA
2162
+ CATCGT
2163
+ CATCGC
2164
+ CATCGG
2165
+ CATGAA
2166
+ CATGAT
2167
+ CATGAC
2168
+ CATGAG
2169
+ CATGTA
2170
+ CATGTT
2171
+ CATGTC
2172
+ CATGTG
2173
+ CATGCA
2174
+ CATGCT
2175
+ CATGCC
2176
+ CATGCG
2177
+ CATGGA
2178
+ CATGGT
2179
+ CATGGC
2180
+ CATGGG
2181
+ CACAAA
2182
+ CACAAT
2183
+ CACAAC
2184
+ CACAAG
2185
+ CACATA
2186
+ CACATT
2187
+ CACATC
2188
+ CACATG
2189
+ CACACA
2190
+ CACACT
2191
+ CACACC
2192
+ CACACG
2193
+ CACAGA
2194
+ CACAGT
2195
+ CACAGC
2196
+ CACAGG
2197
+ CACTAA
2198
+ CACTAT
2199
+ CACTAC
2200
+ CACTAG
2201
+ CACTTA
2202
+ CACTTT
2203
+ CACTTC
2204
+ CACTTG
2205
+ CACTCA
2206
+ CACTCT
2207
+ CACTCC
2208
+ CACTCG
2209
+ CACTGA
2210
+ CACTGT
2211
+ CACTGC
2212
+ CACTGG
2213
+ CACCAA
2214
+ CACCAT
2215
+ CACCAC
2216
+ CACCAG
2217
+ CACCTA
2218
+ CACCTT
2219
+ CACCTC
2220
+ CACCTG
2221
+ CACCCA
2222
+ CACCCT
2223
+ CACCCC
2224
+ CACCCG
2225
+ CACCGA
2226
+ CACCGT
2227
+ CACCGC
2228
+ CACCGG
2229
+ CACGAA
2230
+ CACGAT
2231
+ CACGAC
2232
+ CACGAG
2233
+ CACGTA
2234
+ CACGTT
2235
+ CACGTC
2236
+ CACGTG
2237
+ CACGCA
2238
+ CACGCT
2239
+ CACGCC
2240
+ CACGCG
2241
+ CACGGA
2242
+ CACGGT
2243
+ CACGGC
2244
+ CACGGG
2245
+ CAGAAA
2246
+ CAGAAT
2247
+ CAGAAC
2248
+ CAGAAG
2249
+ CAGATA
2250
+ CAGATT
2251
+ CAGATC
2252
+ CAGATG
2253
+ CAGACA
2254
+ CAGACT
2255
+ CAGACC
2256
+ CAGACG
2257
+ CAGAGA
2258
+ CAGAGT
2259
+ CAGAGC
2260
+ CAGAGG
2261
+ CAGTAA
2262
+ CAGTAT
2263
+ CAGTAC
2264
+ CAGTAG
2265
+ CAGTTA
2266
+ CAGTTT
2267
+ CAGTTC
2268
+ CAGTTG
2269
+ CAGTCA
2270
+ CAGTCT
2271
+ CAGTCC
2272
+ CAGTCG
2273
+ CAGTGA
2274
+ CAGTGT
2275
+ CAGTGC
2276
+ CAGTGG
2277
+ CAGCAA
2278
+ CAGCAT
2279
+ CAGCAC
2280
+ CAGCAG
2281
+ CAGCTA
2282
+ CAGCTT
2283
+ CAGCTC
2284
+ CAGCTG
2285
+ CAGCCA
2286
+ CAGCCT
2287
+ CAGCCC
2288
+ CAGCCG
2289
+ CAGCGA
2290
+ CAGCGT
2291
+ CAGCGC
2292
+ CAGCGG
2293
+ CAGGAA
2294
+ CAGGAT
2295
+ CAGGAC
2296
+ CAGGAG
2297
+ CAGGTA
2298
+ CAGGTT
2299
+ CAGGTC
2300
+ CAGGTG
2301
+ CAGGCA
2302
+ CAGGCT
2303
+ CAGGCC
2304
+ CAGGCG
2305
+ CAGGGA
2306
+ CAGGGT
2307
+ CAGGGC
2308
+ CAGGGG
2309
+ CTAAAA
2310
+ CTAAAT
2311
+ CTAAAC
2312
+ CTAAAG
2313
+ CTAATA
2314
+ CTAATT
2315
+ CTAATC
2316
+ CTAATG
2317
+ CTAACA
2318
+ CTAACT
2319
+ CTAACC
2320
+ CTAACG
2321
+ CTAAGA
2322
+ CTAAGT
2323
+ CTAAGC
2324
+ CTAAGG
2325
+ CTATAA
2326
+ CTATAT
2327
+ CTATAC
2328
+ CTATAG
2329
+ CTATTA
2330
+ CTATTT
2331
+ CTATTC
2332
+ CTATTG
2333
+ CTATCA
2334
+ CTATCT
2335
+ CTATCC
2336
+ CTATCG
2337
+ CTATGA
2338
+ CTATGT
2339
+ CTATGC
2340
+ CTATGG
2341
+ CTACAA
2342
+ CTACAT
2343
+ CTACAC
2344
+ CTACAG
2345
+ CTACTA
2346
+ CTACTT
2347
+ CTACTC
2348
+ CTACTG
2349
+ CTACCA
2350
+ CTACCT
2351
+ CTACCC
2352
+ CTACCG
2353
+ CTACGA
2354
+ CTACGT
2355
+ CTACGC
2356
+ CTACGG
2357
+ CTAGAA
2358
+ CTAGAT
2359
+ CTAGAC
2360
+ CTAGAG
2361
+ CTAGTA
2362
+ CTAGTT
2363
+ CTAGTC
2364
+ CTAGTG
2365
+ CTAGCA
2366
+ CTAGCT
2367
+ CTAGCC
2368
+ CTAGCG
2369
+ CTAGGA
2370
+ CTAGGT
2371
+ CTAGGC
2372
+ CTAGGG
2373
+ CTTAAA
2374
+ CTTAAT
2375
+ CTTAAC
2376
+ CTTAAG
2377
+ CTTATA
2378
+ CTTATT
2379
+ CTTATC
2380
+ CTTATG
2381
+ CTTACA
2382
+ CTTACT
2383
+ CTTACC
2384
+ CTTACG
2385
+ CTTAGA
2386
+ CTTAGT
2387
+ CTTAGC
2388
+ CTTAGG
2389
+ CTTTAA
2390
+ CTTTAT
2391
+ CTTTAC
2392
+ CTTTAG
2393
+ CTTTTA
2394
+ CTTTTT
2395
+ CTTTTC
2396
+ CTTTTG
2397
+ CTTTCA
2398
+ CTTTCT
2399
+ CTTTCC
2400
+ CTTTCG
2401
+ CTTTGA
2402
+ CTTTGT
2403
+ CTTTGC
2404
+ CTTTGG
2405
+ CTTCAA
2406
+ CTTCAT
2407
+ CTTCAC
2408
+ CTTCAG
2409
+ CTTCTA
2410
+ CTTCTT
2411
+ CTTCTC
2412
+ CTTCTG
2413
+ CTTCCA
2414
+ CTTCCT
2415
+ CTTCCC
2416
+ CTTCCG
2417
+ CTTCGA
2418
+ CTTCGT
2419
+ CTTCGC
2420
+ CTTCGG
2421
+ CTTGAA
2422
+ CTTGAT
2423
+ CTTGAC
2424
+ CTTGAG
2425
+ CTTGTA
2426
+ CTTGTT
2427
+ CTTGTC
2428
+ CTTGTG
2429
+ CTTGCA
2430
+ CTTGCT
2431
+ CTTGCC
2432
+ CTTGCG
2433
+ CTTGGA
2434
+ CTTGGT
2435
+ CTTGGC
2436
+ CTTGGG
2437
+ CTCAAA
2438
+ CTCAAT
2439
+ CTCAAC
2440
+ CTCAAG
2441
+ CTCATA
2442
+ CTCATT
2443
+ CTCATC
2444
+ CTCATG
2445
+ CTCACA
2446
+ CTCACT
2447
+ CTCACC
2448
+ CTCACG
2449
+ CTCAGA
2450
+ CTCAGT
2451
+ CTCAGC
2452
+ CTCAGG
2453
+ CTCTAA
2454
+ CTCTAT
2455
+ CTCTAC
2456
+ CTCTAG
2457
+ CTCTTA
2458
+ CTCTTT
2459
+ CTCTTC
2460
+ CTCTTG
2461
+ CTCTCA
2462
+ CTCTCT
2463
+ CTCTCC
2464
+ CTCTCG
2465
+ CTCTGA
2466
+ CTCTGT
2467
+ CTCTGC
2468
+ CTCTGG
2469
+ CTCCAA
2470
+ CTCCAT
2471
+ CTCCAC
2472
+ CTCCAG
2473
+ CTCCTA
2474
+ CTCCTT
2475
+ CTCCTC
2476
+ CTCCTG
2477
+ CTCCCA
2478
+ CTCCCT
2479
+ CTCCCC
2480
+ CTCCCG
2481
+ CTCCGA
2482
+ CTCCGT
2483
+ CTCCGC
2484
+ CTCCGG
2485
+ CTCGAA
2486
+ CTCGAT
2487
+ CTCGAC
2488
+ CTCGAG
2489
+ CTCGTA
2490
+ CTCGTT
2491
+ CTCGTC
2492
+ CTCGTG
2493
+ CTCGCA
2494
+ CTCGCT
2495
+ CTCGCC
2496
+ CTCGCG
2497
+ CTCGGA
2498
+ CTCGGT
2499
+ CTCGGC
2500
+ CTCGGG
2501
+ CTGAAA
2502
+ CTGAAT
2503
+ CTGAAC
2504
+ CTGAAG
2505
+ CTGATA
2506
+ CTGATT
2507
+ CTGATC
2508
+ CTGATG
2509
+ CTGACA
2510
+ CTGACT
2511
+ CTGACC
2512
+ CTGACG
2513
+ CTGAGA
2514
+ CTGAGT
2515
+ CTGAGC
2516
+ CTGAGG
2517
+ CTGTAA
2518
+ CTGTAT
2519
+ CTGTAC
2520
+ CTGTAG
2521
+ CTGTTA
2522
+ CTGTTT
2523
+ CTGTTC
2524
+ CTGTTG
2525
+ CTGTCA
2526
+ CTGTCT
2527
+ CTGTCC
2528
+ CTGTCG
2529
+ CTGTGA
2530
+ CTGTGT
2531
+ CTGTGC
2532
+ CTGTGG
2533
+ CTGCAA
2534
+ CTGCAT
2535
+ CTGCAC
2536
+ CTGCAG
2537
+ CTGCTA
2538
+ CTGCTT
2539
+ CTGCTC
2540
+ CTGCTG
2541
+ CTGCCA
2542
+ CTGCCT
2543
+ CTGCCC
2544
+ CTGCCG
2545
+ CTGCGA
2546
+ CTGCGT
2547
+ CTGCGC
2548
+ CTGCGG
2549
+ CTGGAA
2550
+ CTGGAT
2551
+ CTGGAC
2552
+ CTGGAG
2553
+ CTGGTA
2554
+ CTGGTT
2555
+ CTGGTC
2556
+ CTGGTG
2557
+ CTGGCA
2558
+ CTGGCT
2559
+ CTGGCC
2560
+ CTGGCG
2561
+ CTGGGA
2562
+ CTGGGT
2563
+ CTGGGC
2564
+ CTGGGG
2565
+ CCAAAA
2566
+ CCAAAT
2567
+ CCAAAC
2568
+ CCAAAG
2569
+ CCAATA
2570
+ CCAATT
2571
+ CCAATC
2572
+ CCAATG
2573
+ CCAACA
2574
+ CCAACT
2575
+ CCAACC
2576
+ CCAACG
2577
+ CCAAGA
2578
+ CCAAGT
2579
+ CCAAGC
2580
+ CCAAGG
2581
+ CCATAA
2582
+ CCATAT
2583
+ CCATAC
2584
+ CCATAG
2585
+ CCATTA
2586
+ CCATTT
2587
+ CCATTC
2588
+ CCATTG
2589
+ CCATCA
2590
+ CCATCT
2591
+ CCATCC
2592
+ CCATCG
2593
+ CCATGA
2594
+ CCATGT
2595
+ CCATGC
2596
+ CCATGG
2597
+ CCACAA
2598
+ CCACAT
2599
+ CCACAC
2600
+ CCACAG
2601
+ CCACTA
2602
+ CCACTT
2603
+ CCACTC
2604
+ CCACTG
2605
+ CCACCA
2606
+ CCACCT
2607
+ CCACCC
2608
+ CCACCG
2609
+ CCACGA
2610
+ CCACGT
2611
+ CCACGC
2612
+ CCACGG
2613
+ CCAGAA
2614
+ CCAGAT
2615
+ CCAGAC
2616
+ CCAGAG
2617
+ CCAGTA
2618
+ CCAGTT
2619
+ CCAGTC
2620
+ CCAGTG
2621
+ CCAGCA
2622
+ CCAGCT
2623
+ CCAGCC
2624
+ CCAGCG
2625
+ CCAGGA
2626
+ CCAGGT
2627
+ CCAGGC
2628
+ CCAGGG
2629
+ CCTAAA
2630
+ CCTAAT
2631
+ CCTAAC
2632
+ CCTAAG
2633
+ CCTATA
2634
+ CCTATT
2635
+ CCTATC
2636
+ CCTATG
2637
+ CCTACA
2638
+ CCTACT
2639
+ CCTACC
2640
+ CCTACG
2641
+ CCTAGA
2642
+ CCTAGT
2643
+ CCTAGC
2644
+ CCTAGG
2645
+ CCTTAA
2646
+ CCTTAT
2647
+ CCTTAC
2648
+ CCTTAG
2649
+ CCTTTA
2650
+ CCTTTT
2651
+ CCTTTC
2652
+ CCTTTG
2653
+ CCTTCA
2654
+ CCTTCT
2655
+ CCTTCC
2656
+ CCTTCG
2657
+ CCTTGA
2658
+ CCTTGT
2659
+ CCTTGC
2660
+ CCTTGG
2661
+ CCTCAA
2662
+ CCTCAT
2663
+ CCTCAC
2664
+ CCTCAG
2665
+ CCTCTA
2666
+ CCTCTT
2667
+ CCTCTC
2668
+ CCTCTG
2669
+ CCTCCA
2670
+ CCTCCT
2671
+ CCTCCC
2672
+ CCTCCG
2673
+ CCTCGA
2674
+ CCTCGT
2675
+ CCTCGC
2676
+ CCTCGG
2677
+ CCTGAA
2678
+ CCTGAT
2679
+ CCTGAC
2680
+ CCTGAG
2681
+ CCTGTA
2682
+ CCTGTT
2683
+ CCTGTC
2684
+ CCTGTG
2685
+ CCTGCA
2686
+ CCTGCT
2687
+ CCTGCC
2688
+ CCTGCG
2689
+ CCTGGA
2690
+ CCTGGT
2691
+ CCTGGC
2692
+ CCTGGG
2693
+ CCCAAA
2694
+ CCCAAT
2695
+ CCCAAC
2696
+ CCCAAG
2697
+ CCCATA
2698
+ CCCATT
2699
+ CCCATC
2700
+ CCCATG
2701
+ CCCACA
2702
+ CCCACT
2703
+ CCCACC
2704
+ CCCACG
2705
+ CCCAGA
2706
+ CCCAGT
2707
+ CCCAGC
2708
+ CCCAGG
2709
+ CCCTAA
2710
+ CCCTAT
2711
+ CCCTAC
2712
+ CCCTAG
2713
+ CCCTTA
2714
+ CCCTTT
2715
+ CCCTTC
2716
+ CCCTTG
2717
+ CCCTCA
2718
+ CCCTCT
2719
+ CCCTCC
2720
+ CCCTCG
2721
+ CCCTGA
2722
+ CCCTGT
2723
+ CCCTGC
2724
+ CCCTGG
2725
+ CCCCAA
2726
+ CCCCAT
2727
+ CCCCAC
2728
+ CCCCAG
2729
+ CCCCTA
2730
+ CCCCTT
2731
+ CCCCTC
2732
+ CCCCTG
2733
+ CCCCCA
2734
+ CCCCCT
2735
+ CCCCCC
2736
+ CCCCCG
2737
+ CCCCGA
2738
+ CCCCGT
2739
+ CCCCGC
2740
+ CCCCGG
2741
+ CCCGAA
2742
+ CCCGAT
2743
+ CCCGAC
2744
+ CCCGAG
2745
+ CCCGTA
2746
+ CCCGTT
2747
+ CCCGTC
2748
+ CCCGTG
2749
+ CCCGCA
2750
+ CCCGCT
2751
+ CCCGCC
2752
+ CCCGCG
2753
+ CCCGGA
2754
+ CCCGGT
2755
+ CCCGGC
2756
+ CCCGGG
2757
+ CCGAAA
2758
+ CCGAAT
2759
+ CCGAAC
2760
+ CCGAAG
2761
+ CCGATA
2762
+ CCGATT
2763
+ CCGATC
2764
+ CCGATG
2765
+ CCGACA
2766
+ CCGACT
2767
+ CCGACC
2768
+ CCGACG
2769
+ CCGAGA
2770
+ CCGAGT
2771
+ CCGAGC
2772
+ CCGAGG
2773
+ CCGTAA
2774
+ CCGTAT
2775
+ CCGTAC
2776
+ CCGTAG
2777
+ CCGTTA
2778
+ CCGTTT
2779
+ CCGTTC
2780
+ CCGTTG
2781
+ CCGTCA
2782
+ CCGTCT
2783
+ CCGTCC
2784
+ CCGTCG
2785
+ CCGTGA
2786
+ CCGTGT
2787
+ CCGTGC
2788
+ CCGTGG
2789
+ CCGCAA
2790
+ CCGCAT
2791
+ CCGCAC
2792
+ CCGCAG
2793
+ CCGCTA
2794
+ CCGCTT
2795
+ CCGCTC
2796
+ CCGCTG
2797
+ CCGCCA
2798
+ CCGCCT
2799
+ CCGCCC
2800
+ CCGCCG
2801
+ CCGCGA
2802
+ CCGCGT
2803
+ CCGCGC
2804
+ CCGCGG
2805
+ CCGGAA
2806
+ CCGGAT
2807
+ CCGGAC
2808
+ CCGGAG
2809
+ CCGGTA
2810
+ CCGGTT
2811
+ CCGGTC
2812
+ CCGGTG
2813
+ CCGGCA
2814
+ CCGGCT
2815
+ CCGGCC
2816
+ CCGGCG
2817
+ CCGGGA
2818
+ CCGGGT
2819
+ CCGGGC
2820
+ CCGGGG
2821
+ CGAAAA
2822
+ CGAAAT
2823
+ CGAAAC
2824
+ CGAAAG
2825
+ CGAATA
2826
+ CGAATT
2827
+ CGAATC
2828
+ CGAATG
2829
+ CGAACA
2830
+ CGAACT
2831
+ CGAACC
2832
+ CGAACG
2833
+ CGAAGA
2834
+ CGAAGT
2835
+ CGAAGC
2836
+ CGAAGG
2837
+ CGATAA
2838
+ CGATAT
2839
+ CGATAC
2840
+ CGATAG
2841
+ CGATTA
2842
+ CGATTT
2843
+ CGATTC
2844
+ CGATTG
2845
+ CGATCA
2846
+ CGATCT
2847
+ CGATCC
2848
+ CGATCG
2849
+ CGATGA
2850
+ CGATGT
2851
+ CGATGC
2852
+ CGATGG
2853
+ CGACAA
2854
+ CGACAT
2855
+ CGACAC
2856
+ CGACAG
2857
+ CGACTA
2858
+ CGACTT
2859
+ CGACTC
2860
+ CGACTG
2861
+ CGACCA
2862
+ CGACCT
2863
+ CGACCC
2864
+ CGACCG
2865
+ CGACGA
2866
+ CGACGT
2867
+ CGACGC
2868
+ CGACGG
2869
+ CGAGAA
2870
+ CGAGAT
2871
+ CGAGAC
2872
+ CGAGAG
2873
+ CGAGTA
2874
+ CGAGTT
2875
+ CGAGTC
2876
+ CGAGTG
2877
+ CGAGCA
2878
+ CGAGCT
2879
+ CGAGCC
2880
+ CGAGCG
2881
+ CGAGGA
2882
+ CGAGGT
2883
+ CGAGGC
2884
+ CGAGGG
2885
+ CGTAAA
2886
+ CGTAAT
2887
+ CGTAAC
2888
+ CGTAAG
2889
+ CGTATA
2890
+ CGTATT
2891
+ CGTATC
2892
+ CGTATG
2893
+ CGTACA
2894
+ CGTACT
2895
+ CGTACC
2896
+ CGTACG
2897
+ CGTAGA
2898
+ CGTAGT
2899
+ CGTAGC
2900
+ CGTAGG
2901
+ CGTTAA
2902
+ CGTTAT
2903
+ CGTTAC
2904
+ CGTTAG
2905
+ CGTTTA
2906
+ CGTTTT
2907
+ CGTTTC
2908
+ CGTTTG
2909
+ CGTTCA
2910
+ CGTTCT
2911
+ CGTTCC
2912
+ CGTTCG
2913
+ CGTTGA
2914
+ CGTTGT
2915
+ CGTTGC
2916
+ CGTTGG
2917
+ CGTCAA
2918
+ CGTCAT
2919
+ CGTCAC
2920
+ CGTCAG
2921
+ CGTCTA
2922
+ CGTCTT
2923
+ CGTCTC
2924
+ CGTCTG
2925
+ CGTCCA
2926
+ CGTCCT
2927
+ CGTCCC
2928
+ CGTCCG
2929
+ CGTCGA
2930
+ CGTCGT
2931
+ CGTCGC
2932
+ CGTCGG
2933
+ CGTGAA
2934
+ CGTGAT
2935
+ CGTGAC
2936
+ CGTGAG
2937
+ CGTGTA
2938
+ CGTGTT
2939
+ CGTGTC
2940
+ CGTGTG
2941
+ CGTGCA
2942
+ CGTGCT
2943
+ CGTGCC
2944
+ CGTGCG
2945
+ CGTGGA
2946
+ CGTGGT
2947
+ CGTGGC
2948
+ CGTGGG
2949
+ CGCAAA
2950
+ CGCAAT
2951
+ CGCAAC
2952
+ CGCAAG
2953
+ CGCATA
2954
+ CGCATT
2955
+ CGCATC
2956
+ CGCATG
2957
+ CGCACA
2958
+ CGCACT
2959
+ CGCACC
2960
+ CGCACG
2961
+ CGCAGA
2962
+ CGCAGT
2963
+ CGCAGC
2964
+ CGCAGG
2965
+ CGCTAA
2966
+ CGCTAT
2967
+ CGCTAC
2968
+ CGCTAG
2969
+ CGCTTA
2970
+ CGCTTT
2971
+ CGCTTC
2972
+ CGCTTG
2973
+ CGCTCA
2974
+ CGCTCT
2975
+ CGCTCC
2976
+ CGCTCG
2977
+ CGCTGA
2978
+ CGCTGT
2979
+ CGCTGC
2980
+ CGCTGG
2981
+ CGCCAA
2982
+ CGCCAT
2983
+ CGCCAC
2984
+ CGCCAG
2985
+ CGCCTA
2986
+ CGCCTT
2987
+ CGCCTC
2988
+ CGCCTG
2989
+ CGCCCA
2990
+ CGCCCT
2991
+ CGCCCC
2992
+ CGCCCG
2993
+ CGCCGA
2994
+ CGCCGT
2995
+ CGCCGC
2996
+ CGCCGG
2997
+ CGCGAA
2998
+ CGCGAT
2999
+ CGCGAC
3000
+ CGCGAG
3001
+ CGCGTA
3002
+ CGCGTT
3003
+ CGCGTC
3004
+ CGCGTG
3005
+ CGCGCA
3006
+ CGCGCT
3007
+ CGCGCC
3008
+ CGCGCG
3009
+ CGCGGA
3010
+ CGCGGT
3011
+ CGCGGC
3012
+ CGCGGG
3013
+ CGGAAA
3014
+ CGGAAT
3015
+ CGGAAC
3016
+ CGGAAG
3017
+ CGGATA
3018
+ CGGATT
3019
+ CGGATC
3020
+ CGGATG
3021
+ CGGACA
3022
+ CGGACT
3023
+ CGGACC
3024
+ CGGACG
3025
+ CGGAGA
3026
+ CGGAGT
3027
+ CGGAGC
3028
+ CGGAGG
3029
+ CGGTAA
3030
+ CGGTAT
3031
+ CGGTAC
3032
+ CGGTAG
3033
+ CGGTTA
3034
+ CGGTTT
3035
+ CGGTTC
3036
+ CGGTTG
3037
+ CGGTCA
3038
+ CGGTCT
3039
+ CGGTCC
3040
+ CGGTCG
3041
+ CGGTGA
3042
+ CGGTGT
3043
+ CGGTGC
3044
+ CGGTGG
3045
+ CGGCAA
3046
+ CGGCAT
3047
+ CGGCAC
3048
+ CGGCAG
3049
+ CGGCTA
3050
+ CGGCTT
3051
+ CGGCTC
3052
+ CGGCTG
3053
+ CGGCCA
3054
+ CGGCCT
3055
+ CGGCCC
3056
+ CGGCCG
3057
+ CGGCGA
3058
+ CGGCGT
3059
+ CGGCGC
3060
+ CGGCGG
3061
+ CGGGAA
3062
+ CGGGAT
3063
+ CGGGAC
3064
+ CGGGAG
3065
+ CGGGTA
3066
+ CGGGTT
3067
+ CGGGTC
3068
+ CGGGTG
3069
+ CGGGCA
3070
+ CGGGCT
3071
+ CGGGCC
3072
+ CGGGCG
3073
+ CGGGGA
3074
+ CGGGGT
3075
+ CGGGGC
3076
+ CGGGGG
3077
+ GAAAAA
3078
+ GAAAAT
3079
+ GAAAAC
3080
+ GAAAAG
3081
+ GAAATA
3082
+ GAAATT
3083
+ GAAATC
3084
+ GAAATG
3085
+ GAAACA
3086
+ GAAACT
3087
+ GAAACC
3088
+ GAAACG
3089
+ GAAAGA
3090
+ GAAAGT
3091
+ GAAAGC
3092
+ GAAAGG
3093
+ GAATAA
3094
+ GAATAT
3095
+ GAATAC
3096
+ GAATAG
3097
+ GAATTA
3098
+ GAATTT
3099
+ GAATTC
3100
+ GAATTG
3101
+ GAATCA
3102
+ GAATCT
3103
+ GAATCC
3104
+ GAATCG
3105
+ GAATGA
3106
+ GAATGT
3107
+ GAATGC
3108
+ GAATGG
3109
+ GAACAA
3110
+ GAACAT
3111
+ GAACAC
3112
+ GAACAG
3113
+ GAACTA
3114
+ GAACTT
3115
+ GAACTC
3116
+ GAACTG
3117
+ GAACCA
3118
+ GAACCT
3119
+ GAACCC
3120
+ GAACCG
3121
+ GAACGA
3122
+ GAACGT
3123
+ GAACGC
3124
+ GAACGG
3125
+ GAAGAA
3126
+ GAAGAT
3127
+ GAAGAC
3128
+ GAAGAG
3129
+ GAAGTA
3130
+ GAAGTT
3131
+ GAAGTC
3132
+ GAAGTG
3133
+ GAAGCA
3134
+ GAAGCT
3135
+ GAAGCC
3136
+ GAAGCG
3137
+ GAAGGA
3138
+ GAAGGT
3139
+ GAAGGC
3140
+ GAAGGG
3141
+ GATAAA
3142
+ GATAAT
3143
+ GATAAC
3144
+ GATAAG
3145
+ GATATA
3146
+ GATATT
3147
+ GATATC
3148
+ GATATG
3149
+ GATACA
3150
+ GATACT
3151
+ GATACC
3152
+ GATACG
3153
+ GATAGA
3154
+ GATAGT
3155
+ GATAGC
3156
+ GATAGG
3157
+ GATTAA
3158
+ GATTAT
3159
+ GATTAC
3160
+ GATTAG
3161
+ GATTTA
3162
+ GATTTT
3163
+ GATTTC
3164
+ GATTTG
3165
+ GATTCA
3166
+ GATTCT
3167
+ GATTCC
3168
+ GATTCG
3169
+ GATTGA
3170
+ GATTGT
3171
+ GATTGC
3172
+ GATTGG
3173
+ GATCAA
3174
+ GATCAT
3175
+ GATCAC
3176
+ GATCAG
3177
+ GATCTA
3178
+ GATCTT
3179
+ GATCTC
3180
+ GATCTG
3181
+ GATCCA
3182
+ GATCCT
3183
+ GATCCC
3184
+ GATCCG
3185
+ GATCGA
3186
+ GATCGT
3187
+ GATCGC
3188
+ GATCGG
3189
+ GATGAA
3190
+ GATGAT
3191
+ GATGAC
3192
+ GATGAG
3193
+ GATGTA
3194
+ GATGTT
3195
+ GATGTC
3196
+ GATGTG
3197
+ GATGCA
3198
+ GATGCT
3199
+ GATGCC
3200
+ GATGCG
3201
+ GATGGA
3202
+ GATGGT
3203
+ GATGGC
3204
+ GATGGG
3205
+ GACAAA
3206
+ GACAAT
3207
+ GACAAC
3208
+ GACAAG
3209
+ GACATA
3210
+ GACATT
3211
+ GACATC
3212
+ GACATG
3213
+ GACACA
3214
+ GACACT
3215
+ GACACC
3216
+ GACACG
3217
+ GACAGA
3218
+ GACAGT
3219
+ GACAGC
3220
+ GACAGG
3221
+ GACTAA
3222
+ GACTAT
3223
+ GACTAC
3224
+ GACTAG
3225
+ GACTTA
3226
+ GACTTT
3227
+ GACTTC
3228
+ GACTTG
3229
+ GACTCA
3230
+ GACTCT
3231
+ GACTCC
3232
+ GACTCG
3233
+ GACTGA
3234
+ GACTGT
3235
+ GACTGC
3236
+ GACTGG
3237
+ GACCAA
3238
+ GACCAT
3239
+ GACCAC
3240
+ GACCAG
3241
+ GACCTA
3242
+ GACCTT
3243
+ GACCTC
3244
+ GACCTG
3245
+ GACCCA
3246
+ GACCCT
3247
+ GACCCC
3248
+ GACCCG
3249
+ GACCGA
3250
+ GACCGT
3251
+ GACCGC
3252
+ GACCGG
3253
+ GACGAA
3254
+ GACGAT
3255
+ GACGAC
3256
+ GACGAG
3257
+ GACGTA
3258
+ GACGTT
3259
+ GACGTC
3260
+ GACGTG
3261
+ GACGCA
3262
+ GACGCT
3263
+ GACGCC
3264
+ GACGCG
3265
+ GACGGA
3266
+ GACGGT
3267
+ GACGGC
3268
+ GACGGG
3269
+ GAGAAA
3270
+ GAGAAT
3271
+ GAGAAC
3272
+ GAGAAG
3273
+ GAGATA
3274
+ GAGATT
3275
+ GAGATC
3276
+ GAGATG
3277
+ GAGACA
3278
+ GAGACT
3279
+ GAGACC
3280
+ GAGACG
3281
+ GAGAGA
3282
+ GAGAGT
3283
+ GAGAGC
3284
+ GAGAGG
3285
+ GAGTAA
3286
+ GAGTAT
3287
+ GAGTAC
3288
+ GAGTAG
3289
+ GAGTTA
3290
+ GAGTTT
3291
+ GAGTTC
3292
+ GAGTTG
3293
+ GAGTCA
3294
+ GAGTCT
3295
+ GAGTCC
3296
+ GAGTCG
3297
+ GAGTGA
3298
+ GAGTGT
3299
+ GAGTGC
3300
+ GAGTGG
3301
+ GAGCAA
3302
+ GAGCAT
3303
+ GAGCAC
3304
+ GAGCAG
3305
+ GAGCTA
3306
+ GAGCTT
3307
+ GAGCTC
3308
+ GAGCTG
3309
+ GAGCCA
3310
+ GAGCCT
3311
+ GAGCCC
3312
+ GAGCCG
3313
+ GAGCGA
3314
+ GAGCGT
3315
+ GAGCGC
3316
+ GAGCGG
3317
+ GAGGAA
3318
+ GAGGAT
3319
+ GAGGAC
3320
+ GAGGAG
3321
+ GAGGTA
3322
+ GAGGTT
3323
+ GAGGTC
3324
+ GAGGTG
3325
+ GAGGCA
3326
+ GAGGCT
3327
+ GAGGCC
3328
+ GAGGCG
3329
+ GAGGGA
3330
+ GAGGGT
3331
+ GAGGGC
3332
+ GAGGGG
3333
+ GTAAAA
3334
+ GTAAAT
3335
+ GTAAAC
3336
+ GTAAAG
3337
+ GTAATA
3338
+ GTAATT
3339
+ GTAATC
3340
+ GTAATG
3341
+ GTAACA
3342
+ GTAACT
3343
+ GTAACC
3344
+ GTAACG
3345
+ GTAAGA
3346
+ GTAAGT
3347
+ GTAAGC
3348
+ GTAAGG
3349
+ GTATAA
3350
+ GTATAT
3351
+ GTATAC
3352
+ GTATAG
3353
+ GTATTA
3354
+ GTATTT
3355
+ GTATTC
3356
+ GTATTG
3357
+ GTATCA
3358
+ GTATCT
3359
+ GTATCC
3360
+ GTATCG
3361
+ GTATGA
3362
+ GTATGT
3363
+ GTATGC
3364
+ GTATGG
3365
+ GTACAA
3366
+ GTACAT
3367
+ GTACAC
3368
+ GTACAG
3369
+ GTACTA
3370
+ GTACTT
3371
+ GTACTC
3372
+ GTACTG
3373
+ GTACCA
3374
+ GTACCT
3375
+ GTACCC
3376
+ GTACCG
3377
+ GTACGA
3378
+ GTACGT
3379
+ GTACGC
3380
+ GTACGG
3381
+ GTAGAA
3382
+ GTAGAT
3383
+ GTAGAC
3384
+ GTAGAG
3385
+ GTAGTA
3386
+ GTAGTT
3387
+ GTAGTC
3388
+ GTAGTG
3389
+ GTAGCA
3390
+ GTAGCT
3391
+ GTAGCC
3392
+ GTAGCG
3393
+ GTAGGA
3394
+ GTAGGT
3395
+ GTAGGC
3396
+ GTAGGG
3397
+ GTTAAA
3398
+ GTTAAT
3399
+ GTTAAC
3400
+ GTTAAG
3401
+ GTTATA
3402
+ GTTATT
3403
+ GTTATC
3404
+ GTTATG
3405
+ GTTACA
3406
+ GTTACT
3407
+ GTTACC
3408
+ GTTACG
3409
+ GTTAGA
3410
+ GTTAGT
3411
+ GTTAGC
3412
+ GTTAGG
3413
+ GTTTAA
3414
+ GTTTAT
3415
+ GTTTAC
3416
+ GTTTAG
3417
+ GTTTTA
3418
+ GTTTTT
3419
+ GTTTTC
3420
+ GTTTTG
3421
+ GTTTCA
3422
+ GTTTCT
3423
+ GTTTCC
3424
+ GTTTCG
3425
+ GTTTGA
3426
+ GTTTGT
3427
+ GTTTGC
3428
+ GTTTGG
3429
+ GTTCAA
3430
+ GTTCAT
3431
+ GTTCAC
3432
+ GTTCAG
3433
+ GTTCTA
3434
+ GTTCTT
3435
+ GTTCTC
3436
+ GTTCTG
3437
+ GTTCCA
3438
+ GTTCCT
3439
+ GTTCCC
3440
+ GTTCCG
3441
+ GTTCGA
3442
+ GTTCGT
3443
+ GTTCGC
3444
+ GTTCGG
3445
+ GTTGAA
3446
+ GTTGAT
3447
+ GTTGAC
3448
+ GTTGAG
3449
+ GTTGTA
3450
+ GTTGTT
3451
+ GTTGTC
3452
+ GTTGTG
3453
+ GTTGCA
3454
+ GTTGCT
3455
+ GTTGCC
3456
+ GTTGCG
3457
+ GTTGGA
3458
+ GTTGGT
3459
+ GTTGGC
3460
+ GTTGGG
3461
+ GTCAAA
3462
+ GTCAAT
3463
+ GTCAAC
3464
+ GTCAAG
3465
+ GTCATA
3466
+ GTCATT
3467
+ GTCATC
3468
+ GTCATG
3469
+ GTCACA
3470
+ GTCACT
3471
+ GTCACC
3472
+ GTCACG
3473
+ GTCAGA
3474
+ GTCAGT
3475
+ GTCAGC
3476
+ GTCAGG
3477
+ GTCTAA
3478
+ GTCTAT
3479
+ GTCTAC
3480
+ GTCTAG
3481
+ GTCTTA
3482
+ GTCTTT
3483
+ GTCTTC
3484
+ GTCTTG
3485
+ GTCTCA
3486
+ GTCTCT
3487
+ GTCTCC
3488
+ GTCTCG
3489
+ GTCTGA
3490
+ GTCTGT
3491
+ GTCTGC
3492
+ GTCTGG
3493
+ GTCCAA
3494
+ GTCCAT
3495
+ GTCCAC
3496
+ GTCCAG
3497
+ GTCCTA
3498
+ GTCCTT
3499
+ GTCCTC
3500
+ GTCCTG
3501
+ GTCCCA
3502
+ GTCCCT
3503
+ GTCCCC
3504
+ GTCCCG
3505
+ GTCCGA
3506
+ GTCCGT
3507
+ GTCCGC
3508
+ GTCCGG
3509
+ GTCGAA
3510
+ GTCGAT
3511
+ GTCGAC
3512
+ GTCGAG
3513
+ GTCGTA
3514
+ GTCGTT
3515
+ GTCGTC
3516
+ GTCGTG
3517
+ GTCGCA
3518
+ GTCGCT
3519
+ GTCGCC
3520
+ GTCGCG
3521
+ GTCGGA
3522
+ GTCGGT
3523
+ GTCGGC
3524
+ GTCGGG
3525
+ GTGAAA
3526
+ GTGAAT
3527
+ GTGAAC
3528
+ GTGAAG
3529
+ GTGATA
3530
+ GTGATT
3531
+ GTGATC
3532
+ GTGATG
3533
+ GTGACA
3534
+ GTGACT
3535
+ GTGACC
3536
+ GTGACG
3537
+ GTGAGA
3538
+ GTGAGT
3539
+ GTGAGC
3540
+ GTGAGG
3541
+ GTGTAA
3542
+ GTGTAT
3543
+ GTGTAC
3544
+ GTGTAG
3545
+ GTGTTA
3546
+ GTGTTT
3547
+ GTGTTC
3548
+ GTGTTG
3549
+ GTGTCA
3550
+ GTGTCT
3551
+ GTGTCC
3552
+ GTGTCG
3553
+ GTGTGA
3554
+ GTGTGT
3555
+ GTGTGC
3556
+ GTGTGG
3557
+ GTGCAA
3558
+ GTGCAT
3559
+ GTGCAC
3560
+ GTGCAG
3561
+ GTGCTA
3562
+ GTGCTT
3563
+ GTGCTC
3564
+ GTGCTG
3565
+ GTGCCA
3566
+ GTGCCT
3567
+ GTGCCC
3568
+ GTGCCG
3569
+ GTGCGA
3570
+ GTGCGT
3571
+ GTGCGC
3572
+ GTGCGG
3573
+ GTGGAA
3574
+ GTGGAT
3575
+ GTGGAC
3576
+ GTGGAG
3577
+ GTGGTA
3578
+ GTGGTT
3579
+ GTGGTC
3580
+ GTGGTG
3581
+ GTGGCA
3582
+ GTGGCT
3583
+ GTGGCC
3584
+ GTGGCG
3585
+ GTGGGA
3586
+ GTGGGT
3587
+ GTGGGC
3588
+ GTGGGG
3589
+ GCAAAA
3590
+ GCAAAT
3591
+ GCAAAC
3592
+ GCAAAG
3593
+ GCAATA
3594
+ GCAATT
3595
+ GCAATC
3596
+ GCAATG
3597
+ GCAACA
3598
+ GCAACT
3599
+ GCAACC
3600
+ GCAACG
3601
+ GCAAGA
3602
+ GCAAGT
3603
+ GCAAGC
3604
+ GCAAGG
3605
+ GCATAA
3606
+ GCATAT
3607
+ GCATAC
3608
+ GCATAG
3609
+ GCATTA
3610
+ GCATTT
3611
+ GCATTC
3612
+ GCATTG
3613
+ GCATCA
3614
+ GCATCT
3615
+ GCATCC
3616
+ GCATCG
3617
+ GCATGA
3618
+ GCATGT
3619
+ GCATGC
3620
+ GCATGG
3621
+ GCACAA
3622
+ GCACAT
3623
+ GCACAC
3624
+ GCACAG
3625
+ GCACTA
3626
+ GCACTT
3627
+ GCACTC
3628
+ GCACTG
3629
+ GCACCA
3630
+ GCACCT
3631
+ GCACCC
3632
+ GCACCG
3633
+ GCACGA
3634
+ GCACGT
3635
+ GCACGC
3636
+ GCACGG
3637
+ GCAGAA
3638
+ GCAGAT
3639
+ GCAGAC
3640
+ GCAGAG
3641
+ GCAGTA
3642
+ GCAGTT
3643
+ GCAGTC
3644
+ GCAGTG
3645
+ GCAGCA
3646
+ GCAGCT
3647
+ GCAGCC
3648
+ GCAGCG
3649
+ GCAGGA
3650
+ GCAGGT
3651
+ GCAGGC
3652
+ GCAGGG
3653
+ GCTAAA
3654
+ GCTAAT
3655
+ GCTAAC
3656
+ GCTAAG
3657
+ GCTATA
3658
+ GCTATT
3659
+ GCTATC
3660
+ GCTATG
3661
+ GCTACA
3662
+ GCTACT
3663
+ GCTACC
3664
+ GCTACG
3665
+ GCTAGA
3666
+ GCTAGT
3667
+ GCTAGC
3668
+ GCTAGG
3669
+ GCTTAA
3670
+ GCTTAT
3671
+ GCTTAC
3672
+ GCTTAG
3673
+ GCTTTA
3674
+ GCTTTT
3675
+ GCTTTC
3676
+ GCTTTG
3677
+ GCTTCA
3678
+ GCTTCT
3679
+ GCTTCC
3680
+ GCTTCG
3681
+ GCTTGA
3682
+ GCTTGT
3683
+ GCTTGC
3684
+ GCTTGG
3685
+ GCTCAA
3686
+ GCTCAT
3687
+ GCTCAC
3688
+ GCTCAG
3689
+ GCTCTA
3690
+ GCTCTT
3691
+ GCTCTC
3692
+ GCTCTG
3693
+ GCTCCA
3694
+ GCTCCT
3695
+ GCTCCC
3696
+ GCTCCG
3697
+ GCTCGA
3698
+ GCTCGT
3699
+ GCTCGC
3700
+ GCTCGG
3701
+ GCTGAA
3702
+ GCTGAT
3703
+ GCTGAC
3704
+ GCTGAG
3705
+ GCTGTA
3706
+ GCTGTT
3707
+ GCTGTC
3708
+ GCTGTG
3709
+ GCTGCA
3710
+ GCTGCT
3711
+ GCTGCC
3712
+ GCTGCG
3713
+ GCTGGA
3714
+ GCTGGT
3715
+ GCTGGC
3716
+ GCTGGG
3717
+ GCCAAA
3718
+ GCCAAT
3719
+ GCCAAC
3720
+ GCCAAG
3721
+ GCCATA
3722
+ GCCATT
3723
+ GCCATC
3724
+ GCCATG
3725
+ GCCACA
3726
+ GCCACT
3727
+ GCCACC
3728
+ GCCACG
3729
+ GCCAGA
3730
+ GCCAGT
3731
+ GCCAGC
3732
+ GCCAGG
3733
+ GCCTAA
3734
+ GCCTAT
3735
+ GCCTAC
3736
+ GCCTAG
3737
+ GCCTTA
3738
+ GCCTTT
3739
+ GCCTTC
3740
+ GCCTTG
3741
+ GCCTCA
3742
+ GCCTCT
3743
+ GCCTCC
3744
+ GCCTCG
3745
+ GCCTGA
3746
+ GCCTGT
3747
+ GCCTGC
3748
+ GCCTGG
3749
+ GCCCAA
3750
+ GCCCAT
3751
+ GCCCAC
3752
+ GCCCAG
3753
+ GCCCTA
3754
+ GCCCTT
3755
+ GCCCTC
3756
+ GCCCTG
3757
+ GCCCCA
3758
+ GCCCCT
3759
+ GCCCCC
3760
+ GCCCCG
3761
+ GCCCGA
3762
+ GCCCGT
3763
+ GCCCGC
3764
+ GCCCGG
3765
+ GCCGAA
3766
+ GCCGAT
3767
+ GCCGAC
3768
+ GCCGAG
3769
+ GCCGTA
3770
+ GCCGTT
3771
+ GCCGTC
3772
+ GCCGTG
3773
+ GCCGCA
3774
+ GCCGCT
3775
+ GCCGCC
3776
+ GCCGCG
3777
+ GCCGGA
3778
+ GCCGGT
3779
+ GCCGGC
3780
+ GCCGGG
3781
+ GCGAAA
3782
+ GCGAAT
3783
+ GCGAAC
3784
+ GCGAAG
3785
+ GCGATA
3786
+ GCGATT
3787
+ GCGATC
3788
+ GCGATG
3789
+ GCGACA
3790
+ GCGACT
3791
+ GCGACC
3792
+ GCGACG
3793
+ GCGAGA
3794
+ GCGAGT
3795
+ GCGAGC
3796
+ GCGAGG
3797
+ GCGTAA
3798
+ GCGTAT
3799
+ GCGTAC
3800
+ GCGTAG
3801
+ GCGTTA
3802
+ GCGTTT
3803
+ GCGTTC
3804
+ GCGTTG
3805
+ GCGTCA
3806
+ GCGTCT
3807
+ GCGTCC
3808
+ GCGTCG
3809
+ GCGTGA
3810
+ GCGTGT
3811
+ GCGTGC
3812
+ GCGTGG
3813
+ GCGCAA
3814
+ GCGCAT
3815
+ GCGCAC
3816
+ GCGCAG
3817
+ GCGCTA
3818
+ GCGCTT
3819
+ GCGCTC
3820
+ GCGCTG
3821
+ GCGCCA
3822
+ GCGCCT
3823
+ GCGCCC
3824
+ GCGCCG
3825
+ GCGCGA
3826
+ GCGCGT
3827
+ GCGCGC
3828
+ GCGCGG
3829
+ GCGGAA
3830
+ GCGGAT
3831
+ GCGGAC
3832
+ GCGGAG
3833
+ GCGGTA
3834
+ GCGGTT
3835
+ GCGGTC
3836
+ GCGGTG
3837
+ GCGGCA
3838
+ GCGGCT
3839
+ GCGGCC
3840
+ GCGGCG
3841
+ GCGGGA
3842
+ GCGGGT
3843
+ GCGGGC
3844
+ GCGGGG
3845
+ GGAAAA
3846
+ GGAAAT
3847
+ GGAAAC
3848
+ GGAAAG
3849
+ GGAATA
3850
+ GGAATT
3851
+ GGAATC
3852
+ GGAATG
3853
+ GGAACA
3854
+ GGAACT
3855
+ GGAACC
3856
+ GGAACG
3857
+ GGAAGA
3858
+ GGAAGT
3859
+ GGAAGC
3860
+ GGAAGG
3861
+ GGATAA
3862
+ GGATAT
3863
+ GGATAC
3864
+ GGATAG
3865
+ GGATTA
3866
+ GGATTT
3867
+ GGATTC
3868
+ GGATTG
3869
+ GGATCA
3870
+ GGATCT
3871
+ GGATCC
3872
+ GGATCG
3873
+ GGATGA
3874
+ GGATGT
3875
+ GGATGC
3876
+ GGATGG
3877
+ GGACAA
3878
+ GGACAT
3879
+ GGACAC
3880
+ GGACAG
3881
+ GGACTA
3882
+ GGACTT
3883
+ GGACTC
3884
+ GGACTG
3885
+ GGACCA
3886
+ GGACCT
3887
+ GGACCC
3888
+ GGACCG
3889
+ GGACGA
3890
+ GGACGT
3891
+ GGACGC
3892
+ GGACGG
3893
+ GGAGAA
3894
+ GGAGAT
3895
+ GGAGAC
3896
+ GGAGAG
3897
+ GGAGTA
3898
+ GGAGTT
3899
+ GGAGTC
3900
+ GGAGTG
3901
+ GGAGCA
3902
+ GGAGCT
3903
+ GGAGCC
3904
+ GGAGCG
3905
+ GGAGGA
3906
+ GGAGGT
3907
+ GGAGGC
3908
+ GGAGGG
3909
+ GGTAAA
3910
+ GGTAAT
3911
+ GGTAAC
3912
+ GGTAAG
3913
+ GGTATA
3914
+ GGTATT
3915
+ GGTATC
3916
+ GGTATG
3917
+ GGTACA
3918
+ GGTACT
3919
+ GGTACC
3920
+ GGTACG
3921
+ GGTAGA
3922
+ GGTAGT
3923
+ GGTAGC
3924
+ GGTAGG
3925
+ GGTTAA
3926
+ GGTTAT
3927
+ GGTTAC
3928
+ GGTTAG
3929
+ GGTTTA
3930
+ GGTTTT
3931
+ GGTTTC
3932
+ GGTTTG
3933
+ GGTTCA
3934
+ GGTTCT
3935
+ GGTTCC
3936
+ GGTTCG
3937
+ GGTTGA
3938
+ GGTTGT
3939
+ GGTTGC
3940
+ GGTTGG
3941
+ GGTCAA
3942
+ GGTCAT
3943
+ GGTCAC
3944
+ GGTCAG
3945
+ GGTCTA
3946
+ GGTCTT
3947
+ GGTCTC
3948
+ GGTCTG
3949
+ GGTCCA
3950
+ GGTCCT
3951
+ GGTCCC
3952
+ GGTCCG
3953
+ GGTCGA
3954
+ GGTCGT
3955
+ GGTCGC
3956
+ GGTCGG
3957
+ GGTGAA
3958
+ GGTGAT
3959
+ GGTGAC
3960
+ GGTGAG
3961
+ GGTGTA
3962
+ GGTGTT
3963
+ GGTGTC
3964
+ GGTGTG
3965
+ GGTGCA
3966
+ GGTGCT
3967
+ GGTGCC
3968
+ GGTGCG
3969
+ GGTGGA
3970
+ GGTGGT
3971
+ GGTGGC
3972
+ GGTGGG
3973
+ GGCAAA
3974
+ GGCAAT
3975
+ GGCAAC
3976
+ GGCAAG
3977
+ GGCATA
3978
+ GGCATT
3979
+ GGCATC
3980
+ GGCATG
3981
+ GGCACA
3982
+ GGCACT
3983
+ GGCACC
3984
+ GGCACG
3985
+ GGCAGA
3986
+ GGCAGT
3987
+ GGCAGC
3988
+ GGCAGG
3989
+ GGCTAA
3990
+ GGCTAT
3991
+ GGCTAC
3992
+ GGCTAG
3993
+ GGCTTA
3994
+ GGCTTT
3995
+ GGCTTC
3996
+ GGCTTG
3997
+ GGCTCA
3998
+ GGCTCT
3999
+ GGCTCC
4000
+ GGCTCG
4001
+ GGCTGA
4002
+ GGCTGT
4003
+ GGCTGC
4004
+ GGCTGG
4005
+ GGCCAA
4006
+ GGCCAT
4007
+ GGCCAC
4008
+ GGCCAG
4009
+ GGCCTA
4010
+ GGCCTT
4011
+ GGCCTC
4012
+ GGCCTG
4013
+ GGCCCA
4014
+ GGCCCT
4015
+ GGCCCC
4016
+ GGCCCG
4017
+ GGCCGA
4018
+ GGCCGT
4019
+ GGCCGC
4020
+ GGCCGG
4021
+ GGCGAA
4022
+ GGCGAT
4023
+ GGCGAC
4024
+ GGCGAG
4025
+ GGCGTA
4026
+ GGCGTT
4027
+ GGCGTC
4028
+ GGCGTG
4029
+ GGCGCA
4030
+ GGCGCT
4031
+ GGCGCC
4032
+ GGCGCG
4033
+ GGCGGA
4034
+ GGCGGT
4035
+ GGCGGC
4036
+ GGCGGG
4037
+ GGGAAA
4038
+ GGGAAT
4039
+ GGGAAC
4040
+ GGGAAG
4041
+ GGGATA
4042
+ GGGATT
4043
+ GGGATC
4044
+ GGGATG
4045
+ GGGACA
4046
+ GGGACT
4047
+ GGGACC
4048
+ GGGACG
4049
+ GGGAGA
4050
+ GGGAGT
4051
+ GGGAGC
4052
+ GGGAGG
4053
+ GGGTAA
4054
+ GGGTAT
4055
+ GGGTAC
4056
+ GGGTAG
4057
+ GGGTTA
4058
+ GGGTTT
4059
+ GGGTTC
4060
+ GGGTTG
4061
+ GGGTCA
4062
+ GGGTCT
4063
+ GGGTCC
4064
+ GGGTCG
4065
+ GGGTGA
4066
+ GGGTGT
4067
+ GGGTGC
4068
+ GGGTGG
4069
+ GGGCAA
4070
+ GGGCAT
4071
+ GGGCAC
4072
+ GGGCAG
4073
+ GGGCTA
4074
+ GGGCTT
4075
+ GGGCTC
4076
+ GGGCTG
4077
+ GGGCCA
4078
+ GGGCCT
4079
+ GGGCCC
4080
+ GGGCCG
4081
+ GGGCGA
4082
+ GGGCGT
4083
+ GGGCGC
4084
+ GGGCGG
4085
+ GGGGAA
4086
+ GGGGAT
4087
+ GGGGAC
4088
+ GGGGAG
4089
+ GGGGTA
4090
+ GGGGTT
4091
+ GGGGTC
4092
+ GGGGTG
4093
+ GGGGCA
4094
+ GGGGCT
4095
+ GGGGCC
4096
+ GGGGCG
4097
+ GGGGGA
4098
+ GGGGGT
4099
+ GGGGGC
4100
+ GGGGGG
4101
+ A
4102
+ T
4103
+ C
4104
+ G
4105
+ N
4106
+ <eos>
4107
+ <bos>