Add Plant NT model for H3K27ac prediction

README.md

@@ -1,3 +1,63 @@
----
-license: cc-by-nc-sa-4.0
----
+---
+license: cc-by-nc-sa-4.0
+widget:
+- text: AGTCGCCGCAACCCACACACGGACGGCTCGACGTGGCGATCTTAGCGGCTCATCCGCCCGGCCTCCCTCGCGCTCGATCGCTACGCAGCCTACGCTCGTTTCGCTCGGTTCGGTGGGTCGCCGATCTGGCGCCACGGCGGCTACCAACGACACCGCGATTGAGAAGGGTGCGTGGCCGTGGAGTCGTGGAGAAACGCCCGCGCGCGCGGGTGCGGCGAGGGACGACGACCGCGTCGTGCGGATCGATTGGCGGGGCAGCTCGGCGCCCCG
+tags:
+- DNA
+- biology
+- genomics
+---
+# Plant foundation DNA large language models
+
+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.  
+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.  
+
+
+**Developed by:** zhangtaolab
+
+### Model Sources
+
+- **Repository:** [Plant DNA LLMs](https://github.com/zhangtaolab/plant_DNA_LLMs)
+- **Manuscript:** [Versatile applications of foundation DNA language models in plant genomes]() 
+
+### Architecture
+
+The model is trained based on the InstaDeepAI/nucleotide-transformer-v2-100m-multi-species model with modified tokenizer that replaces k-mer to BPE.
+
+This model is fine-tuned for predicting H3K27ac histone modification.
+
+
+### How to use
+
+Install the runtime library first:
+```bash
+pip install transformers
+```
+
+Here is a simple code for inference:
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+
+model_name = 'plant-nucleotide-transformer-H3K27ac'
+# load model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained(f'zhangtaolab/{model_name}', trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(f'zhangtaolab/{model_name}', trust_remote_code=True)
+
+# inference
+sequences = ['GCTTTGGTTTATACCTTACACAACATAAATCACATAGTTAATCCCTAATCGTCTTTGATTCTCAATGTTTTGTTCATTTTTACCATGAACATCATCTGATTGATAAGTGCATAGAGAATTAACGGCTTACACTTTACACTTGCATAGATGATTCCTAAGTATGTCCT',
+             'TAGCCCCCTCCTCTCTTTATATAGTGCAATCTAATATATGAAAGGTTCGGTGATGGGGCCAATAAGTGTATTTAGGCTAGGCCTTCATGGGCCAAGCCCAAAAGTTTCTCAACACTCCCCCTTGAGCACTCACCGCGTAATGTCCATGCCTCGTCAAAACTCCATAAAAACCCAGTG']
+pipe = pipeline('text-classification', model=model, tokenizer=tokenizer,
+                trust_remote_code=True, top_k=None)
+results = pipe(sequences)
+print(results)
+
+```
+
+
+### Training data
+We use EsmForSequenceClassification to fine-tune the model.  
+Detailed training procedure can be found in our manuscript.
+
+
+#### Hardware
+Model was trained on a NVIDIA GTX1080Ti GPU (11 GB).

config.json

@@ -0,0 +1,45 @@
+{
+  "_name_or_path": "Plant_NT_H3K27ac",
+  "add_bias_fnn": false,
+  "architectures": [
+    "EsmForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auto_map": {
+    "AutoConfig": "esm_config.EsmConfig",
+    "AutoModelForMaskedLM": "modeling_esm.EsmForMaskedLM",
+    "AutoModelForSequenceClassification": "modeling_esm.EsmForSequenceClassification",
+    "AutoModelForTokenClassification": "modeling_esm.EsmForTokenClassification"
+  },
+  "emb_layer_norm_before": false,
+  "esmfold_config": null,
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 512,
+  "id2label": {
+    "0": "Not H3K27ac",
+    "1": "H3K27ac"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 2048,
+  "is_folding_model": false,
+  "label2id": {
+    "Not H3K27ac": 0,
+    "H3K27ac": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "mask_token_id": 2,
+  "max_position_embeddings": 2050,
+  "model_type": "esm",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 22,
+  "pad_token_id": 1,
+  "position_embedding_type": "rotary",
+  "problem_type": "single_label_classification",
+  "tie_word_embeddings": false,
+  "token_dropout": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.39.1",
+  "use_cache": false,
+  "vocab_list": null,
+  "vocab_size": 8003
+}

esm_config.py

@@ -0,0 +1,379 @@
+# coding=utf-8
+# Copyright 2022 Meta and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" ESM model configuration"""
+
+from dataclasses import asdict, dataclass
+from typing import Optional
+
+from transformers import PretrainedConfig, logging
+
+logger = logging.get_logger(__name__)
+
+# TODO Update this
+ESM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/resolve/main/config.json",
+    # See all ESM models at https://huggingface.co/models?filter=esm
+}
+
+
+class EsmConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`ESMModel`]. It is used to instantiate a ESM model
+    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the ESM
+    [facebook/esm-1b](https://huggingface.co/facebook/esm-1b) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*):
+            Vocabulary size of the ESM model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`ESMModel`].
+        mask_token_id (`int`, *optional*):
+            The index of the mask token in the vocabulary. This must be included in the config because of the
+            "mask-dropout" scaling trick, which will scale the inputs depending on the number of masked tokens.
+        pad_token_id (`int`, *optional*):
+            The index of the padding token in the vocabulary. This must be included in the config because certain parts
+            of the ESM code use this instead of the attention mask.
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout ratio for the attention probabilities.
+        max_position_embeddings (`int`, *optional*, defaults to 1026):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
+            Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query", "rotary"`.
+            For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
+            [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
+            For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
+            with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
+        is_decoder (`bool`, *optional*, defaults to `False`):
+            Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        emb_layer_norm_before (`bool`, *optional*):
+            Whether to apply layer normalization after embeddings but before the main stem of the network.
+        token_dropout (`bool`, defaults to `False`):
+            When this is enabled, masked tokens are treated as if they had been dropped out by input dropout.
+
+    Examples:
+
+    ```python
+    >>> from transformers import EsmModel, EsmConfig
+
+    >>> # Initializing a ESM facebook/esm-1b style configuration >>> configuration = EsmConfig()
+
+    >>> # Initializing a model from the configuration >>> model = ESMModel(configuration)
+
+    >>> # Accessing the model configuration >>> configuration = model.config
+    ```"""
+    model_type = "esm"
+
+    def __init__(
+        self,
+        vocab_size=None,
+        mask_token_id=None,
+        pad_token_id=None,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=1026,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        position_embedding_type="absolute",
+        use_cache=True,
+        emb_layer_norm_before=None,
+        token_dropout=False,
+        is_folding_model=False,
+        esmfold_config=None,
+        vocab_list=None,
+        add_bias_fnn=True,
+        **kwargs,
+    ):
+        super().__init__(
+            pad_token_id=pad_token_id, mask_token_id=mask_token_id, **kwargs
+        )
+
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.position_embedding_type = position_embedding_type
+        self.use_cache = use_cache
+        self.emb_layer_norm_before = emb_layer_norm_before
+        self.token_dropout = token_dropout
+        self.is_folding_model = is_folding_model
+        # Arguments needed for Dalmatian
+        self.add_bias_fnn = add_bias_fnn
+        if is_folding_model:
+            if esmfold_config is None:
+                logger.info(
+                    "No esmfold_config supplied for folding model, using default values."
+                )
+                esmfold_config = EsmFoldConfig()
+            elif isinstance(esmfold_config, dict):
+                esmfold_config = EsmFoldConfig(**esmfold_config)
+            self.esmfold_config = esmfold_config
+            if vocab_list is None:
+                logger.warning(
+                    "No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!"
+                )
+                self.vocab_list = get_default_vocab_list()
+            else:
+                self.vocab_list = vocab_list
+        else:
+            self.esmfold_config = None
+            self.vocab_list = None
+        if self.esmfold_config is not None and getattr(
+            self.esmfold_config, "use_esm_attn_map", False
+        ):
+            raise ValueError(
+                "The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!"
+            )
+
+    def to_dict(self):
+        """
+        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
+
+        Returns:
+            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
+        """
+        output = super().to_dict()
+        if isinstance(self.esmfold_config, EsmFoldConfig):
+            output["esmfold_config"] = self.esmfold_config.to_dict()
+        return output
+
+
+@dataclass
+class EsmFoldConfig:
+    esm_type: str = None
+    fp16_esm: bool = True
+    use_esm_attn_map: bool = False
+    esm_ablate_pairwise: bool = False
+    esm_ablate_sequence: bool = False
+    esm_input_dropout: float = 0
+
+    embed_aa: bool = True
+    bypass_lm: bool = False
+
+    lddt_head_hid_dim: int = 128
+    trunk: "TrunkConfig" = None
+
+    def __post_init__(self):
+        if self.trunk is None:
+            self.trunk = TrunkConfig()
+        elif isinstance(self.trunk, dict):
+            self.trunk = TrunkConfig(**self.trunk)
+
+    def to_dict(self):
+        """
+        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
+
+        Returns:
+            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
+        """
+        output = asdict(self)
+        output["trunk"] = self.trunk.to_dict()
+        return output
+
+
+@dataclass
+class TrunkConfig:
+    num_blocks: int = 48
+    sequence_state_dim: int = 1024
+    pairwise_state_dim: int = 128
+    sequence_head_width: int = 32
+    pairwise_head_width: int = 32
+    position_bins: int = 32
+    dropout: float = 0
+    layer_drop: float = 0
+    cpu_grad_checkpoint: bool = False
+    max_recycles: int = 4
+    chunk_size: Optional[int] = 128
+    structure_module: "StructureModuleConfig" = None
+
+    def __post_init__(self):
+        if self.structure_module is None:
+            self.structure_module = StructureModuleConfig()
+        elif isinstance(self.structure_module, dict):
+            self.structure_module = StructureModuleConfig(**self.structure_module)
+
+        if self.max_recycles <= 0:
+            raise ValueError(
+                f"`max_recycles` should be positive, got {self.max_recycles}."
+            )
+        if self.sequence_state_dim % self.sequence_state_dim != 0:
+            raise ValueError(
+                "`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
+                f" {self.sequence_state_dim} and {self.sequence_state_dim}."
+            )
+        if self.pairwise_state_dim % self.pairwise_state_dim != 0:
+            raise ValueError(
+                "`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
+                f" {self.pairwise_state_dim} and {self.pairwise_state_dim}."
+            )
+
+        sequence_num_heads = self.sequence_state_dim // self.sequence_head_width
+        pairwise_num_heads = self.pairwise_state_dim // self.pairwise_head_width
+
+        if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
+            raise ValueError(
+                "`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
+                f" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."
+            )
+        if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
+            raise ValueError(
+                "`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
+                f" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."
+            )
+        if self.pairwise_state_dim % 2 != 0:
+            raise ValueError(
+                f"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."
+            )
+
+        if self.dropout >= 0.4:
+            raise ValueError(
+                f"`dropout` should not be greater than 0.4, got {self.dropout}."
+            )
+
+    def to_dict(self):
+        """
+        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
+
+        Returns:
+            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
+        """
+        output = asdict(self)
+        output["structure_module"] = self.structure_module.to_dict()
+        return output
+
+
+@dataclass
+class StructureModuleConfig:
+    """
+    Args:
+        sequence_dim:
+            Single representation channel dimension
+        pairwise_dim:
+            Pair representation channel dimension
+        ipa_dim:
+            IPA hidden channel dimension
+        resnet_dim:
+            Angle resnet (Alg. 23 lines 11-14) hidden channel dimension
+        num_heads_ipa:
+            Number of IPA heads
+        num_qk_points:
+            Number of query/key points to generate during IPA
+        num_v_points:
+            Number of value points to generate during IPA
+        dropout_rate:
+            Dropout rate used throughout the layer
+        num_blocks:
+            Number of structure module blocks
+        num_transition_layers:
+            Number of layers in the single representation transition (Alg. 23 lines 8-9)
+        num_resnet_blocks:
+            Number of blocks in the angle resnet
+        num_angles:
+            Number of angles to generate in the angle resnet
+        trans_scale_factor:
+            Scale of single representation transition hidden dimension
+        epsilon:
+            Small number used in angle resnet normalization
+        inf:
+            Large number used for attention masking
+    """
+
+    sequence_dim: int = 384
+    pairwise_dim: int = 128
+    ipa_dim: int = 16
+    resnet_dim: int = 128
+    num_heads_ipa: int = 12
+    num_qk_points: int = 4
+    num_v_points: int = 8
+    dropout_rate: float = 0.1
+    num_blocks: int = 8
+    num_transition_layers: int = 1
+    num_resnet_blocks: int = 2
+    num_angles: int = 7
+    trans_scale_factor: int = 10
+    epsilon: float = 1e-8
+    inf: float = 1e5
+
+    def to_dict(self):
+        return asdict(self)
+
+
+def get_default_vocab_list():
+    return (
+        "<cls>",
+        "<pad>",
+        "<eos>",
+        "<unk>",
+        "L",
+        "A",
+        "G",
+        "V",
+        "S",
+        "E",
+        "R",
+        "T",
+        "I",
+        "D",
+        "P",
+        "K",
+        "Q",
+        "N",
+        "F",
+        "Y",
+        "M",
+        "H",
+        "W",
+        "C",
+        "X",
+        "B",
+        "U",
+        "Z",
+        "O",
+        ".",
+        "-",
+        "<null_1>",
+        "<mask>",
+    )

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:100163c725792213fb870569d51af69c7abe2868c810b44d463fb002739af7e5
+size 391148900

modeling_esm.py

@@ -0,0 +1,1446 @@
+# coding=utf-8
+# Copyright 2022 Meta and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch ESM model."""
+
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss, SiLU
+from transformers.file_utils import (
+    add_code_sample_docstrings,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+)
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    MaskedLMOutput,
+    SequenceClassifierOutput,
+    TokenClassifierOutput,
+)
+from transformers.modeling_utils import (
+    PreTrainedModel,
+    find_pruneable_heads_and_indices,
+    prune_linear_layer,
+)
+from transformers.utils import logging
+
+from .esm_config import EsmConfig
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "facebook/esm2_t6_8M_UR50D"
+_CONFIG_FOR_DOC = "EsmConfig"
+
+ESM_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "facebook/esm2_t6_8M_UR50D",
+    "facebook/esm2_t12_35M_UR50D",
+    # This is not a complete list of all ESM models!
+    # See all ESM models at https://huggingface.co/models?filter=esm
+]
+
+
+def rotate_half(x):
+    x1, x2 = x.chunk(2, dim=-1)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(x, cos, sin):
+    cos = cos[:, :, : x.shape[-2], :]
+    sin = sin[:, :, : x.shape[-2], :]
+
+    return (x * cos) + (rotate_half(x) * sin)
+
+
+def gelu(x):
+    """
+    This is the gelu implementation from the original ESM repo. Using F.gelu yields subtly wrong results.
+    """
+    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
+
+
+def symmetrize(x):
+    "Make layer symmetric in final two dimensions, used for contact prediction."
+    return x + x.transpose(-1, -2)
+
+
+def average_product_correct(x):
+    "Perform average product correct, used for contact prediction."
+    a1 = x.sum(-1, keepdims=True)
+    a2 = x.sum(-2, keepdims=True)
+    a12 = x.sum((-1, -2), keepdims=True)
+
+    avg = a1 * a2
+    avg.div_(a12)  # in-place to reduce memory
+    normalized = x - avg
+    return normalized
+
+
+class RotaryEmbedding(torch.nn.Module):
+    """
+    Rotary position embeddings based on those in
+    [RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer). Query and keys are transformed by rotation
+    matrices which depend on their relative positions.
+    """
+
+    def __init__(self, dim: int):
+        super().__init__()
+        # Generate and save the inverse frequency buffer (non trainable)
+        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
+        inv_freq = inv_freq
+        self.register_buffer("inv_freq", inv_freq)
+
+        self._seq_len_cached = None
+        self._cos_cached = None
+        self._sin_cached = None
+
+    def _update_cos_sin_tables(self, x, seq_dimension=2):
+        seq_len = x.shape[seq_dimension]
+
+        # Reset the tables if the sequence length has changed,
+        # or if we're on a new device (possibly due to tracing for instance)
+        if seq_len != self._seq_len_cached or self._cos_cached.device != x.device:
+            self._seq_len_cached = seq_len
+            t = torch.arange(x.shape[seq_dimension], device=x.device).type_as(
+                self.inv_freq
+            )
+            freqs = torch.outer(t, self.inv_freq)
+            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
+
+            self._cos_cached = emb.cos()[None, None, :, :]
+            self._sin_cached = emb.sin()[None, None, :, :]
+
+        return self._cos_cached, self._sin_cached
+
+    def forward(
+        self, q: torch.Tensor, k: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        self._cos_cached, self._sin_cached = self._update_cos_sin_tables(
+            k, seq_dimension=-2
+        )
+
+        return (
+            apply_rotary_pos_emb(q, self._cos_cached, self._sin_cached),
+            apply_rotary_pos_emb(k, self._cos_cached, self._sin_cached),
+        )
+
+
+class EsmContactPredictionHead(nn.Module):
+    """Performs symmetrization, apc, and computes a logistic regression on the output features"""
+
+    def __init__(
+        self,
+        in_features: int,
+        bias=True,
+        eos_idx: int = 2,
+    ):
+        super().__init__()
+        self.in_features = in_features
+        self.eos_idx = eos_idx
+        self.regression = nn.Linear(in_features, 1, bias)
+        self.activation = nn.Sigmoid()
+
+    def forward(self, tokens, attentions):
+        # remove eos token attentions
+        eos_mask = tokens.ne(self.eos_idx).to(attentions)
+        eos_mask = eos_mask.unsqueeze(1) * eos_mask.unsqueeze(2)
+        attentions = attentions * eos_mask[:, None, None, :, :]
+        attentions = attentions[..., :-1, :-1]
+        # remove cls token attentions
+        attentions = attentions[..., 1:, 1:]
+        batch_size, layers, heads, seqlen, _ = attentions.size()
+        attentions = attentions.view(batch_size, layers * heads, seqlen, seqlen)
+
+        # features: batch x channels x tokens x tokens (symmetric)
+        attentions = attentions.to(
+            self.regression.weight.device
+        )  # attentions always float32, may need to convert to float16
+        attentions = average_product_correct(symmetrize(attentions))
+        attentions = attentions.permute(0, 2, 3, 1)
+        return self.activation(self.regression(attentions).squeeze(3))
+
+
+class EsmEmbeddings(nn.Module):
+    """
+    Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.word_embeddings = nn.Embedding(
+            config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id
+        )
+
+        if config.emb_layer_norm_before:
+            self.layer_norm = nn.LayerNorm(
+                config.hidden_size, eps=config.layer_norm_eps
+            )
+        else:
+            self.layer_norm = None
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+        self.position_embedding_type = getattr(
+            config, "position_embedding_type", "absolute"
+        )
+        self.register_buffer(
+            "position_ids",
+            torch.arange(config.max_position_embeddings).expand((1, -1)),
+            persistent=False,
+        )
+
+        self.padding_idx = config.pad_token_id
+        self.position_embeddings = nn.Embedding(
+            config.max_position_embeddings,
+            config.hidden_size,
+            padding_idx=self.padding_idx,
+        )
+        self.token_dropout = config.token_dropout
+        self.mask_token_id = config.mask_token_id
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        position_ids=None,
+        inputs_embeds=None,
+        past_key_values_length=0,
+    ):
+        if position_ids is None:
+            if input_ids is not None:
+                # Create the position ids from the input token ids. Any padded tokens remain padded.
+                position_ids = create_position_ids_from_input_ids(
+                    input_ids, self.padding_idx, past_key_values_length
+                )
+            else:
+                position_ids = self.create_position_ids_from_inputs_embeds(
+                    inputs_embeds
+                )
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+
+        # Note that if we want to support ESM-1 (not 1b!) in future then we need to support an
+        # embedding_scale factor here.
+        embeddings = inputs_embeds
+
+        # Matt: ESM has the option to handle masking in MLM in a slightly unusual way. If the token_dropout
+        # flag is False then it is handled in the same was as BERT/RoBERTa. If it is set to True, however,
+        # masked tokens are treated as if they were selected for input dropout and zeroed out.
+        # This "mask-dropout" is compensated for when masked tokens are not present, by scaling embeddings by
+        # a factor of (fraction of unmasked tokens during training) / (fraction of unmasked tokens in sample).
+        # This is analogous to the way that dropout layers scale down outputs during evaluation when not
+        # actually dropping out values (or, equivalently, scale up their un-dropped outputs in training).
+        if self.token_dropout:
+            embeddings.masked_fill_(
+                (input_ids == self.mask_token_id).unsqueeze(-1), 0.0
+            )
+            mask_ratio_train = (
+                0.15 * 0.8
+            )  # Hardcoded as the ratio used in all ESM model training runs
+            src_lengths = attention_mask.sum(-1)
+            mask_ratio_observed = (input_ids == self.mask_token_id).sum(
+                -1
+            ).float() / src_lengths
+            embeddings = (
+                embeddings
+                * (1 - mask_ratio_train)
+                / (1 - mask_ratio_observed)[:, None, None]
+            ).to(embeddings.dtype)
+
+        if self.position_embedding_type == "absolute":
+            position_embeddings = self.position_embeddings(position_ids)
+            embeddings += position_embeddings
+
+        if self.layer_norm is not None:
+            embeddings = self.layer_norm(embeddings)
+        if attention_mask is not None:
+            embeddings = (embeddings * attention_mask.unsqueeze(-1)).to(
+                embeddings.dtype
+            )
+        # Matt: I think this line was copied incorrectly from BERT, disabling it for now.
+        # embeddings = self.dropout(embeddings)
+        return embeddings
+
+    def create_position_ids_from_inputs_embeds(self, inputs_embeds):
+        """
+        We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
+
+        Args:
+            inputs_embeds: torch.Tensor
+
+        Returns: torch.Tensor
+        """
+        input_shape = inputs_embeds.size()[:-1]
+        sequence_length = input_shape[1]
+
+        position_ids = torch.arange(
+            self.padding_idx + 1,
+            sequence_length + self.padding_idx + 1,
+            dtype=torch.long,
+            device=inputs_embeds.device,
+        )
+        return position_ids.unsqueeze(0).expand(input_shape)
+
+
+class EsmSelfAttention(nn.Module):
+    def __init__(self, config, position_embedding_type=None):
+        super().__init__()
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(
+            config, "embedding_size"
+        ):
+            raise ValueError(
+                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+                f"heads ({config.num_attention_heads})"
+            )
+
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        self.key = nn.Linear(config.hidden_size, self.all_head_size)
+        self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.position_embedding_type = position_embedding_type or getattr(
+            config, "position_embedding_type", "absolute"
+        )
+        self.rotary_embeddings = None
+        if (
+            self.position_embedding_type == "relative_key"
+            or self.position_embedding_type == "relative_key_query"
+        ):
+            self.max_position_embeddings = config.max_position_embeddings
+            self.distance_embedding = nn.Embedding(
+                2 * config.max_position_embeddings - 1, self.attention_head_size
+            )
+        elif self.position_embedding_type == "rotary":
+            self.rotary_embeddings = RotaryEmbedding(dim=self.attention_head_size)
+
+        self.is_decoder = config.is_decoder
+
+    def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
+        new_x_shape = x.size()[:-1] + (
+            self.num_attention_heads,
+            self.attention_head_size,
+        )
+        x = x.view(new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        output_attentions: Optional[bool] = False,
+    ) -> Tuple[torch.Tensor]:
+        mixed_query_layer = self.query(hidden_states)
+
+        # If this is instantiated as a cross-attention module, the keys
+        # and values come from an encoder; the attention mask needs to be
+        # such that the encoder's padding tokens are not attended to.
+        is_cross_attention = encoder_hidden_states is not None
+
+        if is_cross_attention and past_key_value is not None:
+            # reuse k,v, cross_attentions
+            key_layer = past_key_value[0]
+            value_layer = past_key_value[1]
+            attention_mask = encoder_attention_mask
+        elif is_cross_attention:
+            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
+            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
+            attention_mask = encoder_attention_mask
+        elif past_key_value is not None:
+            key_layer = self.transpose_for_scores(self.key(hidden_states))
+            value_layer = self.transpose_for_scores(self.value(hidden_states))
+            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
+            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
+        else:
+            key_layer = self.transpose_for_scores(self.key(hidden_states))
+            value_layer = self.transpose_for_scores(self.value(hidden_states))
+
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+
+        # Matt: Our BERT model (which this code was derived from) scales attention logits down by sqrt(head_dim).
+        # ESM scales the query down by the same factor instead. Modulo numerical stability these are equivalent,
+        # but not when rotary embeddings get involved. Therefore, we scale the query here to match the original
+        # ESM code and fix rotary embeddings.
+        query_layer = query_layer * self.attention_head_size**-0.5
+
+        if self.is_decoder:
+            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
+            # Further calls to cross_attention layer can then reuse all cross-attention
+            # key/value_states (first "if" case)
+            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
+            # all previous decoder key/value_states. Further calls to uni-directional self-attention
+            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
+            # if encoder bi-directional self-attention `past_key_value` is always `None`
+            past_key_value = (key_layer, value_layer)
+
+        if self.position_embedding_type == "rotary":
+            query_layer, key_layer = self.rotary_embeddings(query_layer, key_layer)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+        if (
+            self.position_embedding_type == "relative_key"
+            or self.position_embedding_type == "relative_key_query"
+        ):
+            seq_length = hidden_states.size()[1]
+            position_ids_l = torch.arange(
+                seq_length, dtype=torch.long, device=hidden_states.device
+            ).view(-1, 1)
+            position_ids_r = torch.arange(
+                seq_length, dtype=torch.long, device=hidden_states.device
+            ).view(1, -1)
+            distance = position_ids_l - position_ids_r
+            positional_embedding = self.distance_embedding(
+                distance + self.max_position_embeddings - 1
+            )
+            positional_embedding = positional_embedding.to(
+                dtype=query_layer.dtype
+            )  # fp16 compatibility
+
+            if self.position_embedding_type == "relative_key":
+                relative_position_scores = torch.einsum(
+                    "bhld,lrd->bhlr", query_layer, positional_embedding
+                )
+                attention_scores = attention_scores + relative_position_scores
+            elif self.position_embedding_type == "relative_key_query":
+                relative_position_scores_query = torch.einsum(
+                    "bhld,lrd->bhlr", query_layer, positional_embedding
+                )
+                relative_position_scores_key = torch.einsum(
+                    "bhrd,lrd->bhlr", key_layer, positional_embedding
+                )
+                attention_scores = (
+                    attention_scores
+                    + relative_position_scores_query
+                    + relative_position_scores_key
+                )
+
+        if attention_mask is not None:
+            # Apply the attention mask is (precomputed for all layers in EsmModel forward() function)
+            attention_scores = attention_scores + attention_mask
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs = self.dropout(attention_probs)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            attention_probs = attention_probs * head_mask
+
+        context_layer = torch.matmul(attention_probs, value_layer)
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(new_context_layer_shape)
+
+        outputs = (
+            (context_layer, attention_probs) if output_attentions else (context_layer,)
+        )
+
+        if self.is_decoder:
+            outputs = outputs + (past_key_value,)
+        return outputs
+
+
+class EsmSelfOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states += input_tensor
+        return hidden_states
+
+
+class EsmAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.self = EsmSelfAttention(config)
+        self.output = EsmSelfOutput(config)
+        self.pruned_heads = set()
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def prune_heads(self, heads):
+        if len(heads) == 0:
+            return
+        heads, index = find_pruneable_heads_and_indices(
+            heads,
+            self.self.num_attention_heads,
+            self.self.attention_head_size,
+            self.pruned_heads,
+        )
+
+        # Prune linear layers
+        self.self.query = prune_linear_layer(self.self.query, index)
+        self.self.key = prune_linear_layer(self.self.key, index)
+        self.self.value = prune_linear_layer(self.self.value, index)
+        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+        # Update hyper params and store pruned heads
+        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+        self.self.all_head_size = (
+            self.self.attention_head_size * self.self.num_attention_heads
+        )
+        self.pruned_heads = self.pruned_heads.union(heads)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+    ):
+        hidden_states_ln = self.LayerNorm(hidden_states)
+        self_outputs = self.self(
+            hidden_states_ln,
+            attention_mask,
+            head_mask,
+            encoder_hidden_states,
+            encoder_attention_mask,
+            past_key_value,
+            output_attentions,
+        )
+        attention_output = self.output(self_outputs[0], hidden_states)
+        outputs = (attention_output,) + self_outputs[
+            1:
+        ]  # add attentions if we output them
+        return outputs
+
+
+class EsmIntermediate(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+
+        self.dense = nn.Linear(
+            config.hidden_size,
+            int(config.intermediate_size * 2),
+            bias=config.add_bias_fnn,
+        )
+        self.activation_fn = SiLU()
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+
+        # GLU
+        x1, x2 = hidden_states.split(int(hidden_states.size(-1) / 2), -1)
+        hidden_states = self.activation_fn(x1) * x2
+
+        return hidden_states
+
+
+class EsmOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(
+            config.intermediate_size, config.hidden_size, bias=config.add_bias_fnn
+        )
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states += input_tensor
+        return hidden_states
+
+
+class EsmLayer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
+        self.attention = EsmAttention(config)
+        self.is_decoder = config.is_decoder
+        self.add_cross_attention = config.add_cross_attention
+        if self.add_cross_attention:
+            if not self.is_decoder:
+                raise RuntimeError(
+                    f"{self} should be used as a decoder model if cross attention is added"
+                )
+            self.crossattention = EsmAttention(config)
+        self.intermediate = EsmIntermediate(config)
+        self.output = EsmOutput(config)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+    ):
+        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
+        self_attn_past_key_value = (
+            past_key_value[:2] if past_key_value is not None else None
+        )
+        self_attention_outputs = self.attention(
+            hidden_states,
+            attention_mask,
+            head_mask,
+            output_attentions=output_attentions,
+            past_key_value=self_attn_past_key_value,
+        )
+        attention_output = self_attention_outputs[0]
+
+        # if decoder, the last output is tuple of self-attn cache
+        if self.is_decoder:
+            outputs = self_attention_outputs[1:-1]
+            present_key_value = self_attention_outputs[-1]
+        else:
+            outputs = self_attention_outputs[
+                1:
+            ]  # add self attentions if we output attention weights
+
+        cross_attn_present_key_value = None
+        if self.is_decoder and encoder_hidden_states is not None:
+            if not hasattr(self, "crossattention"):
+                raise AttributeError(
+                    f"If `encoder_hidden_states` are passed, {self} has to be instantiated"
+                    " with cross-attention layers by setting `config.add_cross_attention=True`"
+                )
+
+            # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
+            cross_attn_past_key_value = (
+                past_key_value[-2:] if past_key_value is not None else None
+            )
+            cross_attention_outputs = self.crossattention(
+                attention_output,
+                attention_mask,
+                head_mask,
+                encoder_hidden_states,
+                encoder_attention_mask,
+                cross_attn_past_key_value,
+                output_attentions,
+            )
+            attention_output = cross_attention_outputs[0]
+            outputs = (
+                outputs + cross_attention_outputs[1:-1]
+            )  # add cross attentions if we output attention weights
+
+            # add cross-attn cache to positions 3,4 of present_key_value tuple
+            cross_attn_present_key_value = cross_attention_outputs[-1]
+            present_key_value = present_key_value + cross_attn_present_key_value
+
+        layer_output = self.feed_forward_chunk(attention_output)
+
+        outputs = (layer_output,) + outputs
+
+        # if decoder, return the attn key/values as the last output
+        if self.is_decoder:
+            outputs = outputs + (present_key_value,)
+        return outputs
+
+    def feed_forward_chunk(self, attention_output):
+        attention_output_ln = self.LayerNorm(attention_output)
+        intermediate_output = self.intermediate(attention_output_ln)
+        layer_output = self.output(intermediate_output, attention_output)
+        return layer_output
+
+
+class EsmEncoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList(
+            [EsmLayer(config) for _ in range(config.num_hidden_layers)]
+        )
+        self.emb_layer_norm_after = nn.LayerNorm(
+            config.hidden_size, eps=config.layer_norm_eps
+        )
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=False,
+        output_hidden_states=False,
+        return_dict=True,
+    ):
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
+                    "`use_cache=False`..."
+                )
+                use_cache = False
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+        all_cross_attentions = (
+            () if output_attentions and self.config.add_cross_attention else None
+        )
+
+        next_decoder_cache = () if use_cache else None
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+            past_key_value = past_key_values[i] if past_key_values is not None else None
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs, past_key_value, output_attentions)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(layer_module),
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    past_key_value,
+                    output_attentions,
+                )
+
+            hidden_states = layer_outputs[0]
+            if use_cache:
+                next_decoder_cache += (layer_outputs[-1],)
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)
+                if self.config.add_cross_attention:
+                    all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
+
+        if self.emb_layer_norm_after:
+            hidden_states = self.emb_layer_norm_after(hidden_states)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [
+                    hidden_states,
+                    next_decoder_cache,
+                    all_hidden_states,
+                    all_self_attentions,
+                    all_cross_attentions,
+                ]
+                if v is not None
+            )
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            past_key_values=next_decoder_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+            cross_attentions=all_cross_attentions,
+        )
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPooler
+class EsmPooler(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+
+class EsmPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = EsmConfig
+    base_model_prefix = "esm"
+    _no_split_modules = ["EsmLayer", "EsmFoldTriangularSelfAttentionBlock"]
+
+    # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+
+ESM_START_DOCSTRING = r"""
+
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`EsmConfig`]): Model configuration class with all the parameters of the
+            model. Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+ESM_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `({0})`):
+            Indices of input sequence tokens in the vocabulary.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+        position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.max_position_embeddings - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+
+        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare ESM Model transformer outputting raw hidden-states without any specific head on top.",
+    ESM_START_DOCSTRING,
+)
+class EsmModel(EsmPreTrainedModel):
+    """
+
+    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
+    cross-attention is added between the self-attention layers, following the architecture described in [Attention is
+    all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
+    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
+
+    To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
+    to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
+    `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
+    """
+
+    supports_gradient_checkpointing = False
+
+    def __init__(self, config, add_pooling_layer=True):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = EsmEmbeddings(config)
+        self.encoder = EsmEncoder(config)
+
+        self.pooler = EsmPooler(config) if add_pooling_layer else None
+
+        self.contact_head = EsmContactPredictionHead(
+            in_features=config.num_hidden_layers * config.num_attention_heads, bias=True
+        )
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, EsmEncoder):
+            module.gradient_checkpointing = value
+
+    def get_input_embeddings(self):
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings.word_embeddings = value
+
+    def _prune_heads(self, heads_to_prune):
+        """
+        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+        class PreTrainedModel
+        """
+        for layer, heads in heads_to_prune.items():
+            self.encoder.layer[layer].attention.prune_heads(heads)
+
+    @add_start_docstrings_to_model_forward(
+        ESM_INPUTS_DOCSTRING.format("(batch_size, sequence_length)")
+    )
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=BaseModelOutputWithPoolingAndCrossAttentions,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
+        r"""
+        encoder_hidden_states  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+            the model is configured as a decoder.
+        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+        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)`):
+            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        """
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        if self.config.is_decoder:
+            use_cache = use_cache if use_cache is not None else self.config.use_cache
+        else:
+            use_cache = False
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You cannot specify both input_ids and inputs_embeds at the same time"
+            )
+        elif input_ids is not None:
+            input_shape = input_ids.size()
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        batch_size, seq_length = input_shape
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+        # past_key_values_length
+        past_key_values_length = (
+            past_key_values[0][0].shape[2] if past_key_values is not None else 0
+        )
+
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                ((batch_size, seq_length + past_key_values_length)), device=device
+            )
+
+        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
+            attention_mask, input_shape
+        )
+
+        # If a 2D or 3D attention mask is provided for the cross-attention
+        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+        if self.config.is_decoder and encoder_hidden_states is not None:
+            (
+                encoder_batch_size,
+                encoder_sequence_length,
+                _,
+            ) = encoder_hidden_states.size()
+            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+            if encoder_attention_mask is None:
+                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
+            encoder_extended_attention_mask = self.invert_attention_mask(
+                encoder_attention_mask
+            )
+        else:
+            encoder_extended_attention_mask = None
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        embedding_output = self.embeddings(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            past_key_values_length=past_key_values_length,
+        )
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            head_mask=head_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_extended_attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = encoder_outputs[0]
+        pooled_output = (
+            self.pooler(sequence_output) if self.pooler is not None else None
+        )
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPoolingAndCrossAttentions(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            past_key_values=encoder_outputs.past_key_values,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+            cross_attentions=encoder_outputs.cross_attentions,
+        )
+
+    def predict_contacts(self, tokens, attention_mask):
+        attns = self(
+            tokens,
+            attention_mask=attention_mask,
+            return_dict=True,
+            output_attentions=True,
+        ).attentions
+        attns = torch.stack(attns, dim=1)  # Matches the original model layout
+        # In the original model, attentions for padding tokens are completely zeroed out.
+        # This makes no difference most of the time because the other tokens won't attend to them,
+        # but it does for the contact prediction task, which takes attentions as input,
+        # so we have to mimic that here.
+        attns *= attention_mask.unsqueeze(1).unsqueeze(2).unsqueeze(3)
+        attns *= attention_mask.unsqueeze(1).unsqueeze(2).unsqueeze(4)
+        return self.contact_head(tokens, attns)
+
+
+@add_start_docstrings(
+    """ESM Model with a `language modeling` head on top.""", ESM_START_DOCSTRING
+)
+class EsmForMaskedLM(EsmPreTrainedModel):
+    _tied_weights_keys = ["lm_head.decoder.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        if config.is_decoder:
+            logger.warning(
+                "If you want to use `EsmForMaskedLM` make sure `config.is_decoder=False` for "
+                "bi-directional self-attention."
+            )
+
+        self.esm = EsmModel(config, add_pooling_layer=False)
+        self.lm_head = EsmLMHead(config)
+
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return self.lm_head.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head.decoder = new_embeddings
+
+    @add_start_docstrings_to_model_forward(
+        ESM_INPUTS_DOCSTRING.format("batch_size, sequence_length")
+    )
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=MaskedLMOutput,
+        config_class=_CONFIG_FOR_DOC,
+        mask="<mask>",
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, MaskedLMOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+        kwargs (`Dict[str, any]`, optional, defaults to *{}*):
+            Used to hide legacy arguments that have been deprecated.
+        """
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        outputs = self.esm(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = outputs[0]
+        prediction_scores = self.lm_head(sequence_output)
+
+        masked_lm_loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+
+            labels = labels.to(prediction_scores.device)
+            masked_lm_loss = loss_fct(
+                prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)
+            )
+
+        if not return_dict:
+            output = (prediction_scores,) + outputs[2:]
+            return (
+                ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+            )
+
+        return MaskedLMOutput(
+            loss=masked_lm_loss,
+            logits=prediction_scores,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def predict_contacts(self, tokens, attention_mask):
+        return self.esm.predict_contacts(tokens, attention_mask=attention_mask)
+
+
+class EsmLMHead(nn.Module):
+    """ESM Head for masked language modeling."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+    def forward(self, features, **kwargs):
+        x = self.dense(features)
+        x = gelu(x)
+        x = self.layer_norm(x)
+
+        # project back to size of vocabulary with bias
+        x = self.decoder(x) + self.bias
+        return x
+
+
+@add_start_docstrings(
+    """
+    ESM Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
+    output) e.g. for GLUE tasks.
+    """,
+    ESM_START_DOCSTRING,
+)
+class EsmForSequenceClassification(EsmPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.config = config
+
+        self.esm = EsmModel(config, add_pooling_layer=False)
+        self.classifier = EsmClassificationHead(config)
+
+        self.init_weights()
+
+    @add_start_docstrings_to_model_forward(
+        ESM_INPUTS_DOCSTRING.format("batch_size, sequence_length")
+    )
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=SequenceClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        outputs = self.esm(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = outputs[0]
+        logits = self.classifier(sequence_output)
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (
+                    labels.dtype == torch.long or labels.dtype == torch.int
+                ):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    ESM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
+    Named-Entity-Recognition (NER) tasks.
+    """,
+    ESM_START_DOCSTRING,
+)
+class EsmForTokenClassification(EsmPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.esm = EsmModel(config, add_pooling_layer=False)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        self.init_weights()
+
+    @add_start_docstrings_to_model_forward(
+        ESM_INPUTS_DOCSTRING.format("batch_size, sequence_length")
+    )
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=TokenClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, TokenClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+        """
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        outputs = self.esm(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        sequence_output = self.dropout(sequence_output)
+        logits = self.classifier(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+
+            labels = labels.to(logits.device)
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class EsmClassificationHead(nn.Module):
+    """Head for sentence-level classification tasks."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
+
+    def forward(self, features, **kwargs):
+        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
+        x = self.dropout(x)
+        x = self.dense(x)
+        x = torch.tanh(x)
+        x = self.dropout(x)
+        x = self.out_proj(x)
+        return x
+
+
+def create_position_ids_from_input_ids(
+    input_ids, padding_idx, past_key_values_length=0
+):
+    """
+    Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
+    are ignored. This is modified from fairseq's `utils.make_positions`.
+
+    Args:
+        x: torch.Tensor x:
+
+    Returns: torch.Tensor
+    """
+    # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
+    mask = input_ids.ne(padding_idx).int()
+    incremental_indices = (
+        torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length
+    ) * mask
+    return incremental_indices.long() + padding_idx

special_tokens_map.json

@@ -0,0 +1,6 @@
+{
+  "cls_token": "<cls>",
+  "mask_token": "<mask>",
+  "pad_token": "<pad>",
+  "unk_token": "<unk>"
+}

tokenizer_config.json

@@ -0,0 +1,44 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<mask>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<cls>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<cls>",
+  "eos_token": null,
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "tokenizer_class": "EsmTokenizer",
+  "unk_token": "<unk>"
+}

vocab.txt

@@ -0,0 +1,8003 @@
+<unk>
+<pad>
+<mask>
+<cls>
+<eos>
+<bos>
+TT
+AA
+CC
+GG
+AT
+AG
+CT
+AC
+GT
+ATT
+GC
+AAT
+TTT
+AGG
+GAA
+ACC
+NN
+CAA
+CTT
+GTT
+ACT
+AGT
+GCC
+GAT
+CAT
+GGT
+AAAA
+GCT
+GAG
+NNNN
+ATTT
+CCT
+GAC
+CGG
+CAG
+CAC
+ATAT
+AATT
+AAAT
+CGT
+TTTT
+NNNNNNNN
+GAGG
+GTTT
+GATT
+CATT
+CTTT
+ACAA
+AGAA
+GAAT
+ACTT
+ACAT
+ACCT
+CGC
+CCTT
+CAAT
+AGTT
+CCAA
+GCTT
+AGAT
+ATTTT
+CTCT
+GGTT
+AGGT
+ATGT
+GCAA
+CACC
+GACC
+ATAA
+GGAA
+GAGT
+AGCT
+GTGT
+GCCT
+CCAT
+GCAT
+CGAA
+GACT
+CACT
+GGAT
+CTAA
+CTCC
+CAGG
+GGCC
+CGCC
+AGCC
+GGGT
+GGCT
+CAGT
+AAGT
+GTTTT
+CGAT
+CTTTT
+AGAG
+CTAT
+GGGG
+ACAC
+AAAAT
+CGGT
+CGAG
+GTAT
+GTCC
+CAAAA
+GTAA
+CGTT
+CCCC
+GATTT
+CGAC
+GAAAA
+GTGG
+CATTT
+CCCT
+ATCT
+GAATT
+CGCT
+ATGG
+CAATT
+AAATT
+GTCT
+AAGG
+GCGG
+AACT
+AATTT
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+ATTAT
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+CTGT
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+AAAATT
+ACATT
+ATCC
+CGAGG
+CTGG
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+AGCTT
+GCCTT
+ACGG
+GCGC
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+CTCTT
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+GCATT
+CTCAA
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+AGGTT
+CCTTT
+GAGGT
+GCACC
+GGAGG
+CTTGT
+AGCAT
+GGTTT
+CCTCC
+GCAAT
+CACCT
+GTTGT
+ATTGT
+AACAA
+CACAT
+AGAC
+GATAT
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+AAAC
+CTTCT
+GATGT
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+GAAGG
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+CCGG
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+GGTGT
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+GAAAAT
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+CTCCT
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+AATGAA
+GGGGT
+AGTAGT
+CCACC
+ACGCC
+AAAAAAAA
+ATTGTT
+CGTCT
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+GCGCT
+GCGAG
+CGGGT
+CGCAT
+CCTCCT
+ACACAT
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+GGGCT
+GCGGT
+GCACAA
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+CTTCAA
+GTCAT
+GGCCT
+GAGGTT
+GTTGAT
+AATCAT
+CAATAT
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+AATAAAA
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+ACACTT
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+GTTGAA
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+CATTAT
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+ACGAT
+ACCTTT
+CGCAA
+GAATAT
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+CACTTT
+GTCAA
+GAAGAT
+AGCCTT
+CGGCGG
+CTTCAT
+GCAATT
+AATGAT
+GTTTAT
+CGTAT
+AATGTT
+GCATAA
+GCACTT
+GCACAT
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+CCAAT
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+CATGTT
+CTGAA
+AAATGT
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+CCTTTT
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+ACATGT
+GTTCAT
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+GAAGGT
+CACCAA
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+CTTGTT
+GCGTT
+AGCGT
+GTTTCT
+CTTGGT
+ACGAC
+AGACAA
+ACACCT
+CACCAT
+CTTGAA
+CATGAT
+AAAAAAT
+CTTCGG
+AAATCT
+CATATT
+CAGTTT
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+GCCACC
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+ACGCT
+CTGCT
+CTGAT
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+GAGATT
+CTGTT
+CTAAT
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+GTCCGGT
+CAGCAG
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+GCTTAA
+GGAAGG
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+CAGTAT
+ACAAATT
+CACTGT
+GGATGG
+AGCTGT
+AAAATAA
+GCTGAT
+AAATGG
+GCTAAT
+AAATCAA
+CTCCCT
+GCTGCC
+CAATGG
+ACTTGG
+CGAGAA
+GGCTGT
+ACAGGT
+AGATGG
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+AGCTCC
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+GCTTTTT
+AGATCC
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+ACCAAAA
+ATAACT
+AGCTTTT
+CTAATTT
+ACATGG
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+GCACAC
+CAATAAT
+GTTAGT
+AGGGAT
+CTTTCTT
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+ACTAGT
+GCTTGG
+CTCTCTCT
+AGCGCC
+CGATAT
+GACGCC
+ACAAGG
+GGATCC
+GACTTTT
+GCTCGT
+AGGTGG
+GAGAC
+CGAAGG
+GACACT
+AATTCAT
+GCAGAG
+ATGTCT
+CCTTGG
+AGACAC
+GTATT
+AATTCAA
+CGAGCT
+CATTCAT
+GCGGGG
+GACAGG
+CACGAA
+CAGTTTT
+GAGTAA
+AAATTAT
+AAGTAA
+CCTATT
+CGGGCC
+AGGTAA
+GTAAT
+GGAACT
+CTAAAAT
+ACCAC
+GCCTAA
+ACCATTT
+ATTAGT
+ACTGCT
+GGGTAA
+ATTGCT
+AAATCAT
+AGGTTTT
+AAAACTT
+CACCCC
+CCTAAT
+CGGTGT
+GCGACC
+GAGCAG
+GGTTCC
+GTTGCC
+ATTATTT
+GTTTGAT
+GAGCGG
+CAAGGT
+CGTCAT
+ATTAAAA
+GGAAAAT
+CTATCT
+GAAGCC
+GACAAAA
+ACTTCC
+GCGGCT
+CCAATTT
+CGATGT
+CCATGG
+ACAGCT
+CACTCT
+AATTTAT
+CGCCGG
+GAGGAGG
+AAATAAAT
+CCTGAA
+CTCCACC
+GAGCAC
+ATTAATT
+ATTTTTAT
+GCGTGG
+AGCAATT
+GCAAGG
+GATACC
+ATATGG
+CGCCAA
+GTGTCT
+GCTGGT
+CAGAGT
+GACCCC
+AGCCCC
+AGGCCT
+CAAAATT
+CAAATTT
+CATTGG
+GTGTAA
+AGCGAA
+ATGTCC
+GCAAAAT
+CAGGGT
+CGTCTT
+AGAACC
+GCGGAA
+ATTCCT
+GACTCT
+CAGTGT
+GACAGT
+GAATGG
+GCATGG
+AAGTCT
+GGGTCC
+GCTAGT
+ACAAGAA
+GAGTCC
+ACGGAA
+CAAACC
+CACCGT
+ATTAAAT
+GTTTTGT
+TTTCC
+CTAACT
+GTTTGTT
+GGCCGT
+CGTCCT
+CTTTTGT
+CACAGG
+GAGAAAT
+CGTCAA
+GATTTAT
+CGATCT
+CACGCC
+CATGGT
+GGAACC
+GAACCT
+TTTTTTT
+CAGAAAA
+GAAATTT
+ACCTGT
+CAGACT
+GAAAATT
+GAAACC
+GCTGTT
+AGTTCC
+ACAACAA
+ATTACT
+AAGTCC
+AATATTT
+GGGTCT
+CATGCC
+AGTCCT
+AGAGAGAG
+AATAATT
+GTTGAG
+TTTGG
+CAGACC
+GTTGTGT
+CAATTAT
+GATTGG
+GACGAT
+AGTAAAA
+CATTTAT
+CACAAAT
+CTTGAG
+CGCCGCC
+CCTAGG
+ACTAAAA
+GACTGT
+CCTTCTT
+GAAGAG
+AATAAAT
+AATATAT
+ACTCGT
+GCGGAT
+AGTTGG
+GCTAC
+CAGAC
+AATCCT
+AATTGG
+CGGTCC
+GCTTCGG
+ATTGGT
+GATCAAT
+GTTTCTT
+CGATCC
+ATGTGG
+CACTCC
+GCTACT
+CAGGCC
+CGACGT
+CAGTAA
+GAAAC
+GCGAAT
+GATAGT
+CTTTTCT
+AATTAATT
+AAATAAAA
+GGACAGTCCGGT
+GACCCT
+AGTTTAT
+GCGGGT
+CTCCTTT
+GATGCC
+CTTTGTT
+CGGCTT
+AGCCGT
+GATGAAT
+AGAGTTT
+CAGGCT
+CACAGT
+CAATCAA
+CATCGG
+CACAAGT
+GGAATTT
+CGCCCC
+CCAAATT
+GTATCT
+ACGAAT
+GGTAGT
+GCGCGT
+GCACGT
+GCTACC
+AGCGAT
+AATAGT
+ATATGTTT
+GGCCCT
+CGTGTT
+AGGACT
+CAGCGG
+GCCTGT
+CTTGCC
+AAATGAT
+ACGTGT
+AATGGT
+GCGTCC
+ATAACC
+GCGAGT
+GAGACC
+ATATGAT
+CGATTTT
+GTCCGGTGT
+ATATGTT
+CCTGCT
+ACTTGTT
+CGTGCC
+CTAACC
+CGGTGG
+CAAAATTT
+CGCTCC
+CGAACC
+ATATCAT
+GAGGGG
+GGAAGAA
+GCACTTT
+AGAAGAT
+CGCAGG
+GGTCCT
+CGGGTT
+AATTCTT
+ACCAGT
+CAAAAAA
+GTGTGG
+CGGCCT
+GCGGTT
+CGACGG
+AATACT
+AAAATTAT
+AATTGAA
+ACACGT
+CTCCGT
+CGGACC
+ATTTGCT
+GAATCAA
+AAATGAA
+GTAATTT
+GCGCAT
+GTGTCC
+GCAACAA
+GACATGT
+CTTTGAT
+GATTGAT
+CTTTCAA
+GTTTTCT
+CGGATT
+CTTTCAT
+ACACTTT
+GGCCGG
+CGATGG
+CGGCGT
+ACATCAT
+GACGGT
+AATTGAT
+ACAACTT
+ACTGGT
+ATATGAA
+CAGTCC
+AGCGTT
+ATTTAAAA
+CTTTTTTT
+ACATCAA
+AGTGCC
+CATTCTT
+GGGTGG
+ACAACAT
+ATTTTATT
+GCGATT
+GCTTCTT
+GCTAGG
+ACTTCAA
+CCAAGAA
+CAGTCT
+CTCCGG
+ACATGTT
+AGCAAAT
+GCCAGG
+GGTCGT
+CATTTTTT
+ACCGAA
+AACTT
+AGGTCT
+CTTTGAA
+CACATAT
+CCTGAT
+GAAAATTT
+AGCCCT
+CCTGTT
+CGAAAAT
+CATCAAT
+CACATTT
+AGGGAG
+GCTCAG
+GTTTGAA
+GGCCCC
+CCCCCT
+CTAAATT
+GCATCAA
+CACATGT
+AGGTGTT
+ACTAGG
+AAATTAA
+AATTGTT
+CTAAGG
+GTTTTAA
+AGATGAA
+GTTTTTTT
+AGAATAT
+CCCTGT
+ACTACTACTACT
+CGCCCT
+GGCTGG
+CATCAG
+GATGAC
+ACGGAT
+GCCAAAA
+ACTTTAT
+CACCGG
+ATTTGGT
+GGAAATT
+CTTCAG
+GCCTTTT
+ACTACC
+CGTGAA
+CAAAACC
+ACGTGG
+CATTATT
+GGACAGT
+CGCATT
+GTTTCAT
+GTCCCC
+AAATGTT
+CCTTCAT
+AGGTCC
+ATTTTGG
+CTTTATT
+ACATATT
+GATTTTTT
+CGGTAT
+GTTTCAA
+CGTGAT
+CGGGGT
+AATGCT
+GACCGT
+CGAACT
+AATTTTAT
+CCAAACC
+GAGTGG
+ATATCAA
+AGAGAGG
+GTCCCT
+AAAATGT
+ACATGAA
+GATAAAA
+CTTTTAA
+AATTAAAA
+CCTACT
+GGTACT
+ATAAGG
+CGGAC
+GAGAGAA
+GATTCAA
+GCCCGT
+GGTACC
+AGGACC
+CTTCCTT
+CTTGCTT
+GAATCAT
+GTAACT
+CGTTTTT
+GGTGGTT
+CTTCTTT
+CGCTGT
+CTTAAAA
+GGGTCGG
+CAAGCT
+CAGTGG
+ATTTAATT
+CAATATT
+GCGACT
+CTTAGG
+CAAAACT
+CAATTAA
+GATGAG
+AATACC
+GTATCC
+CGCAC
+CATAAAA
+CCTGGT
+GAATATT
+GTTAAAA
+CACACAA
+CGGAGT
+ACCCTAA
+CACCTTT
+GTTGTTT
+GCGTTT
+AGATCAA
+GGTGGAG
+CGTTAT
+GCACCTT
+ATTTAGT
+GATACT
+CCAACAA
+GCACACC
+ATTTTCC
+CACACAT
+AGCTGG
+AGGGCC
+AGAACAA
+ACGCAA
+CAATCAT
+GCTTGAT
+CGGGCT
+CTTACT
+AGCCTTT
+CCTCTCC
+AGACTTT
+GGATGAA
+ACCAAAT
+GCCAGT
+CAGCAC
+CATTTAA
+ACGAGT
+CCTCTCT
+CATTCAA
+GAATGAA
+GTCTGT
+CTATCC
+AGCGGT
+GATTATT
+CTTAGT
+AGCATAT
+GCCGCT
+GGACACT
+CGTGGT
+CATACT
+CATAGT
+ACGTTT
+ACGCTT
+GATTTGT
+GCAAATT
+AAAAACT
+GATAAAT
+AGCACTT
+ACAGCC
+TTTTGT
+ATGGTTT
+GTTACT
+AGCGCT
+CGATAA
+GTTACC
+AGATTAT
+CTATGG
+CGTGCT
+AGAATAA
+ATCTGT
+AATTTTTTT
+GCGCAC
+AGAAAAAT
+CGGAAT
+AGGGCT
+AACTGT
+GCAAGAA
+CATACC
+TTTTAA
+AGACAAT
+ACAGAG
+AAAAATAT
+ATTATAT
+GACATTT
+CCTAGT
+CGTTGG
+CAATCTT
+ACGATT
+AGCGAC
+ACGGTT
+AGTTCAA
+AGAGGAA
+AAATTGT
+GAATAAT
+GATTTCT
+GGAACAA
+GACGAG
+CGGTCT
+CTCTGG
+AAAAATAA
+CATTTGT
+CTTCAC
+AGTAGG
+CCTGCC
+GACAAAT
+CCTTGTT
+CCATCAA
+AGTACC
+CGGCGC
+GGTGAG
+ACTTGAA
+GCATCAT
+CATTAAT
+GTTTATT
+CAGGGG
+AGTGAG
+CCTAAGG
+TTTTTTTTT
+GAATTTTT
+AAAAAAAAT
+CTTTAAT
+ATTTTTGT
+ACAATAT
+CAGAAAT
+AAAAAGG
+GAAAACT
+CCTCCTT
+GTTCGT
+AGATGAT
+GATTCTT
+AGAGCAA
+ATATCTT
+ACAAGAT
+GAATGAT
+CCAAAGT
+CACGAT
+GGTCGAT
+ACTTCTT
+ACATAAT
+TTTTCT
+AGTTGAA
+CATGGCT
+CATTTCT
+CACAACT
+CCCCCC
+ACTCAG
+CGCGCT
+CGCTCT
+ATTTTCAT
+GTAACC
+GGAGAGG
+GAATTAA
+ATTTTAAT
+ACAAAATT
+ACAAAGT
+ACGACC
+GTTTGGT
+ATTTTTAA
+AGTTCTT
+AGACATT
+ACCTCAA
+ACGGCC
+AGCTCAA
+GCTTCAA
+GTTTTCC
+ACTTGAT
+CTTTTCC
+GAGCATT
+AAATTTTT
+CCTTGAT
+ACAAAGG
+AGTAGTAGTAGT
+GCTTTAT
+GAAATGGT
+GAGGCAA
+CATATTT
+GGATGAT
+AAAAGAT
+AATTTAA
+GGTTGAT
+GCGCAG
+GGTGAC
+CACGTT
+ACGCCT
+AGAAAATT
+AAAAGTT
+AGATATT
+GATTAAT
+CACACTT
+CCATCTT
+AAAAAATT
+ACCGCC
+ACCAGG
+CGGCAT
+AGTTCAT
+CCTTGCT
+GATTGAA
+CGGTAA
+AGTTGAT
+AGCGAG
+GGGGGG
+CAAATAT
+AAAAAGT
+CGGGAA
+AATTAGT
+GCAATAT
+GTTTAAT
+AGATAAT
+AGAAACT
+CCAAGTT
+CTTTGCC
+ACACATT
+CTTTGGT
+ATTTTGTT
+GCTTCAT
+ACACCAT
+CAATTTTT
+AGAGGTT
+ATAACAA
+ATGTGAT
+CTTACC
+ACTGCC
+AGAAAGT
+CCTTCAA
+ACTTCAT
+CAATGAA
+GAGGTTT
+GAGCCAA
+ACAAGTT
+GGTTCAA
+GTGGGGT
+ACATGAT
+GCATGAA
+CACAATT
+ATAACAT
+TTTTTTTT
+CTTTGCT
+ATTTACT
+AGTTTGT
+CAATGAT
+GCGTCT
+CACTGG
+GAATCTT
+CATTGAT
+AAATCTT
+AGCACCT
+ATTAGG
+AATGCC
+GCATGTT
+GATTCAT
+GAGAATT
+CATTGTT
+GAGAAGAA
+ATTTCCT
+CAATTCT
+ACGACT
+GAAAACC
+AGAAAGG
+CGACCTT
+ACATTAT
+ACCTTAT
+ACCTGG
+GGTAGG
+GACAAGG
+CAAATCC
+AGTTAAT
+ATTGTTT
+GAGTTTTT
+GAATTGT
+CAGATTT
+CACGCT
+GAGCGC
+CACATAA
+AGGTGAT
+GCCATTT
+CCATCAT
+ATGTCAT
+ACGTAT
+AGAACAT
+GCCTCGG
+GAATGTGAT
+GACTTGT
+GAGTTAT
+CGCTAT
+GACATGG
+GCATGAT
+CCTCAG
+GACACAA
+CCTTCGG
+ATTACC
+CTTCTCC
+GGTAAAA
+ACCTTGT
+ATTTATAT
+GCGTAT
+ACTAATT
+GGACTGTCCGGT
+AGTTGTT
+CAAGCC
+ACAGTTT
+ACAATAA
+GATTTAA
+ACGGGT
+CGCAAT
+AGACGG
+CCTCTTT
+CAGCCAA
+CGTTCC
+GTTCGG
+AGCACAT
+ATTTTTCT
+GGAGGAT
+ACATGCC
+AATTTGT
+GACGAGG
+CCTCCCC
+AGAAGTT
+CATCGT
+ACCAAGT
+GGACAT
+ACTTATT
+CTAACAA
+GGAAGAT
+ATAAGAA
+AGTAATT
+GCCTGG
+GAGCTTT
+AGAGAAT
+ACTTTGT
+AGTTATT
+CCTTCGT
+ACTAAAT
+GCAGTTT
+AGGAAAT
+GAGAAGG
+GAAAATGGT
+AGCAGTT
+AGGCGT
+GAGATTT
+GAGAAAG
+GACCGTT
+CAGCGT
+GCTTGTT
+GTTTTGG
+CCTTGAA
+CTCTCTT
+CATATAT
+CGAGGTT
+GAGAAGT
+AAAATCT
+ACCCGT
+ACAAACT
+GAGCGT
+GAATTCT
+ACACCTT
+CTTTCTTT
+AGTGTTT
+AATTAAAT
+AGTCGT
+CGCGAA
+CGACTTT
+GTAAGG
+AAAAATTT
+ACTTAAT
+CCCCGT
+GCTCAC
+GATATAT
+GACAAGT
+GTATGG
+GGACGT
+AGCTTGT
+ATGTAAT
+CGAATTT
+GTTAGG
+CCAAACT
+ATTCTTT
+ACCACAT
+CGTTCT
+ATGTCAA
+AGGAGAA
+CCCCTTT
+GGAACAT
+GCTAAAA
+CAACCT
+GGTTGAA
+CCGAGCCCT
+GCCTCGCC
+ATTGCC
+CGGACT
+GGTTGTT
+GCATTAT
+GTCCGT
+GACATAT
+ACTTGGT
+AAATACC
+GAGGAAT
+AGTATTT
+AGACGT
+ATTGAAT
+GGATCAA
+GACTCAA
+GGAGGAA
+GACGCT
+CCTCCAA
+GCAAGAT
+AGTAAAT
+CCTTAAT
+GATTTGG
+CAGAATT
+ACTTTAA
+CGGTTTT
+GATTGTT
+GAGATAT
+ATTTTGGT
+ATTTGTTT
+GCGGCGG
+ATTTTCTT
+GAGCTCC
+ATTTTGAT
+GAAAAGG
+GGTTAGT
+GCAACAT
+GACGTT
+AGATGTT
+ACCGGT
+ACAAGCC
+AGGAGTT
+GGCCTTT
+GAGATGG
+GCCGGT
+ACACGG
+ACAAAAAT
+GAGATGT
+GGGTCTT
+GCATGGT
+GCAAAATT
+ACGGCT
+CCTCGC
+CATCTTT
+GAGAGAG
+GTGTGTGT
+GCTTGAA
+CTCTCAA
+CCATGTT
+ATTTTTTTT
+GCAAACT
+ACCATAT
+GACCGG
+GCCTCCT
+AATTTCT
+GCAGCAA
+AGGATTT
+ACTTCCT
+AATAAAAT
+GAGATAA
+CGAAGAA
+CGGCAG
+GATGATT
+ACGGGG
+CACGGT
+GAGAGAT
+CATTGAA
+GGCCACACAA
+CAATGTT
+CCAAGCT
+GGAAACATAT
+AATTTTGT
+AGTTTAA
+CCATGAA
+ATTTTCAA
+GGCCGAA
+CACAAGG
+GCACCAT
+AATATAA
+CACCCCAC
+ATATATTT
+GGAAAGG
+ACCCGG
+AGGTTGT
+AGAAAGAA
+AGATCAT
+GTTTGTTT
+AAAGCAA
+ATATAAAA
+GCTTCGT
+AGCCCAA
+ATTTCTTT
+ATTCGG
+GCTTTGT
+CGCACT
+AGGTGAA
+AAAGAAAA
+CTATGGCAA
+CGCGGT
+GAAAAATT
+CAGCCTT
+CACTTAT
+AAGGTTT
+CATAAAT
+GCCAAAT
+ACACAAT
+ATGTGAA
+CAGATCT
+CATGTTT
+GAGCTCT
+CACCACC
+ATAAACT
+CATCATT
+GGCTCAA
+CGCAGT
+CGCTAA
+AGAAGCT
+GAAAAGAA
+GATAGG
+ACCAATT
+GAGTTGT
+CCAAAGG
+GAGCACT
+GACTGG
+CACTTGT
+CATTTTAT
+ATTATTTT
+AGCTTAT
+GCAGGTT
+CACCAAAA
+AGAATTTT
+CCAACTT
+ATTCATT
+CACAAAAT
+CAGCCAT
+AGCCGG
+ACGCAT
+CTCTTCT
+GTGGTTT
+GCAATAA
+GGGGTTT
+CTATGTT
+GCCTCAT
+ACAATCT
+CCTACC
+GAGACAA
+AGTGGAA
+AGCACAA
+GAGCCCT
+GACCTTT
+CACAACC
+GTTGCTT
+GTCCTTT
+AAATTCT
+AATGTTT
+AGTTTCT
+GACAATT
+AGTGATT
+GGACGG
+ACGTAA
+AGACCAA
+GAGTTCT
+CATGCAT
+AGATTTTT
+CAGAGAA
+AATCAAT
+GCACCAA
+CGCGCGG
+AAACAAAA
+AAATATTT
+AGTGAAT
+ATTGATT
+GGTTCTT
+AATTATTT
+AGTTGGT
+GCCTTCT
+CACATGG
+AGAGGAT
+ATAACTT
+GACAGAA
+AGTCGG
+GGTTTGT
+AAAGAAT
+GGGGAGG
+ATTCAAT
+GCAAAGG
+CTTTCCT
+CCATGAT
+GCACGG
+GACTTAT
+GAATAGG
+AGCTAAT
+AGACCTT
+GACTTCT
+GCCCGG
+ATGTGTT
+GGTGGAA
+GGCTCGG
+CGAGCCT
+AGATTAA
+AGCTCAT
+GAATGTT
+ATTGAG
+GACACAT
+AAAATTAA
+ACATTAA
+GCCACAA
+GGTTCAT
+CCTTGCCAT
+AGAGAAAA
+ATTCGT
+CTTATTT
+ACAATTTT
+GGTAAGT
+GCTTAAT
+CGTTAA
+GTATGAG
+GAGGGAA
+GCCAAGG
+ATAAATAT
+CATTTCC
+GCTTTCT
+GCCGAT
+CAGAAGT
+GTGTCTT
+GGGGGTTGTGT
+ACTATTT
+CTCCCAA
+AGTTTTTT
+ACGC
+CAATTGT
+AACCTTT
+GAGCAAT
+ACCGAT
+ACATTTTT
+AATCTTT
+CACATTCTTGG
+ACCAAAAT
+AGCCGAA
+ACAAGCT
+GCTTCCT
+CTTGCAA
+AGAAATCCAA
+GACTCCT
+CGTCGCC
+CTTGTTT
+CCAACAT
+ACTTAGG
+GCCAAGT
+AGGTTAT
+GACATCT
+AAAAAAATT
+ACGTCC
+AATAGG
+CCTTATT
+GGTGTTT
+CACACAC
+GGTTCGG
+GGGGGGGG
+CATGTAT
+ATTTAGG
+GAGTGAA
+ACTTGCT
+ACTCTTT
+CTTCAAT
+CGAGGAA
+GCCCGC
+CGTAAT
+GCGACTT
+CAGGTTT
+GCCTTGT
+CTCAAGT
+ACATGCAT
+AGGCGG
+CCTTCCT
+CTTCTCT
+GGTGAGG
+ATATATATAT
+CACCTCC
+ACATAAAA
+GTTGAC
+CTTTAGAA
+CCAAGAT
+AGAGCAT
+CGCTGG
+ACCGCT
+AGTGAC
+AGCTTCT
+GGCCATTGAT
+ACAGAAT
+GGTTATT
+ATTTATGT
+ACTTTCT
+ATTTAAAT
+GCACCACC
+ATATATGT
+GCGAAAA
+CACATCT
+TTTTCC
+AGTTCCT
+ACAGATT
+GAAAAGT
+CTTTCAAT
+ACAATGT
+ACACCAA
+GAGAGAGG
+AATTAGG
+AGCCATT
+GTAAATT
+AATTGGT
+CACCCTT
+CATCCTT
+GACTTAA
+GCTCCGCC
+TTTTGG
+ATTTATTTT
+AGCCGTT
+CACTCAA
+ACCTTAA
+CAAAAGG
+AGAACTT
+ATTTCTAA
+GGGTGTT
+CACAAGAA
+CGTAGT
+GCTCTCT
+CAGACAT
+ATTTGATT
+CGAAATT
+GGTGTGT
+ATAAGTT
+GAAATAT
+GCTCCAA
+ACATTGT
+ACAGCAA
+GGTTTAT
+CTTCATT
+CACCATT
+CTATCAA
+AGAAAAAA
+CTTGAAT
+AATTACT
+GAGGCGG
+AGTATAT
+GCATGCAT
+GGCTGAA
+AACCGT
+ACGTCT
+AGCTGAA
+GGTCCAA
+CATCACT
+ACCACAA
+GCCGTT
+CAAAAGAA
+AATTTTAA
+AGCATAA
+AGAATGT
+GCGCACC
+GCGTAA
+GAGCAGG
+CAAATCAA
+CGTACT
+ACAGAGG
+CGCCGAA
+CCCCCCCC
+CCTCAAT
+GTTTGCT
+CATTTTTGT
+AAAAACC
+AGTCCCT
+GGCCCAA
+CGGGAT
+GCTCGC
+CTATATT
+GGAGCAA
+CGTATT
+GCGCCAT
+CACTTAA
+CTTCTAA
+AAAATTTTT
+AAATAATT
+CAGCTTT
+GAGGAGT
+GCATTTTT
+GCATATT
+AGCCTCT
+AGAAACC
+GTGTGTT
+CACTCAT
+ACATCTT
+CAGATAT
+CGAAGAT
+ATCCTTT
+CGATGAA
+AGAGCTT
+GACATAA
+AATTCCT
+ACTTACC
+AGGAGGT
+AGAGATT
+CACAAAG
+AGATCTT
+GAGCTAT
+CATTGCT
+AGAATCT
+GCATTAA
+GCAGGAT
+GAGAGTT
+CGAAGGT
+CCCTGG
+GCCACCT
+CCTTAGG
+CCTCCAT
+CTCTTGT
+AAAATAAT
+CGAGTTT
+ACAAACC
+ATTTTGAA
+CGCATGG
+ACTATAT
+ATAAATTT
+AAGTCAA
+GCCATGT
+CGAAGCT
+CTCTTTTT
+CGTCGC
+GATGCAT
+ACATATAT
+ACAAATAT
+CGAGGAT
+AGAGTAT
+GGATCAT
+GAGTGTT
+CTATCTT
+GCAAGCC
+CAGAGCT
+AGCATGT
+CTCCCTT
+CTAAAGG
+CACCCAA
+AATGCTT
+GAGAGGT
+GGATTT
+CCTAGCT
+CCTAAAA
+GGTGCTT
+GCAACTT
+GACAACC
+AGACCAT
+ACTTTTTT
+AGCAGCT
+AGTCAAT
+CATCAC
+GTTGATT
+ATAAGAT
+AGCCTAT
+ATCCGT
+CAGCGC
+ATGTCTT
+CACCGGACT
+CTCCAAT
+GAGAAAAT
+GAGAACAA
+CTATGAT
+GGTTGCT
+GGTGAAT
+GCCGAGG
+GAGGTGT
+GCAAGTT
+ATGTATT
+GGTATTT
+GCTTTCC
+AGTCTTT
+AGATGCT
+CATTACT
+AGAAATAT
+AGCAGCC
+CGGTCGG
+GACACTT
+AAGTGG
+GGAAACT
+GACTTGG
+GCCCAC
+GCTCGGACTCGG
+AGTTGCT
+CGGCGCC
+GTTAATT
+CCCCGG
+CATGCTT
+AAATACT
+GCCCAG
+ACCTTTTT
+GAGGATT
+GGATATT
+CTTTTCTT
+GGTGGGT
+CTCTCCT
+GGATGTT
+CGGGCGG
+GTTTTTAT
+ACATACAT
+GCATTGT
+GCGATTT
+CAATTCC
+GAGCTAA
+GTTTTCAA
+AAATAGT
+ACAAATTT
+CTTTACC
+GAGAACT
+GTCTGG
+AATCAG
+AATATCC
+CGATGAT
+GAATGCT
+GTTGTGG
+AGATTGT
+CCATATT
+AAATGGT
+CGCGAT
+AGCAACT
+CACTAAT
+CTCCACT
+GGAAGCT
+ACACACAC
+CTTTTGG
+ATAAAGT
+GCAATGT
+CAAAAATT
+CACAAATT
+CGAAGCC
+GGCCGCC
+GAGTCTT
+CTACC
+GAGAGCT
+GAGTATT
+CTAAACT
+CATAATT
+CAGAACT
+CTAAGAA
+CAGCAGG
+AATGAAT
+GATGTTT
+GATTCCT
+CATGAAT
+GGATCTT
+ACAAGGT
+AGCAGAA
+GGAGCTT
+GTCCGGTGCGCC
+CAAAAACT
+GATTTTAT
+ATATGGT
+AGGAATT
+CTTTACT
+GATATTT
+AGTGGTT
+CAGAGAT
+CATCGACC
+CCAAGGT
+AGAGTAA
+ATATACT
+GATTAATT
+CTATAAT
+ACCGTT
+CACCAAT
+CCATAAT
+ACATTCT
+AGTAGAA
+CTATCAT
+CGAATAT
+ATCTCAA
+GAAAGAA
+GATTTCC
+GAATAAAA
+GCATAAT
+ACATGGT
+GATTGGT
+AGGGGGAG
+ACGACAT
+AGCTGCT
+GCGAGTT
+ACTTAGT
+GTTCTTT
+CGCCACC
+GAGGTCC
+CAAAACAA
+AGGAAGT
+GCAATTTT
+AAATGCT
+GTGTGAT
+GACGTGG
+GCTTGGT
+ACCAAAG
+ATTTTATTTT
+GCATGCT
+CAGAGCC
+TTTTTTAT
+GTTTAGT
+AAAAAGAA
+ATATGCT
+CCTTTAT
+CGAGGCC
+CCTAACC
+GCAAAGT
+AGCCTCC
+GCTTGCT
+ACATGCT
+ACTAAGT
+GTTGAGG
+ATTTGAAT
+ACGAAAA
+GCATCTT
+CACTTTTT
+GATGGTT
+GAGTCAT
+CCTTGCC
+AGCAGAT
+CCTACAA
+GAAATGG
+CAGCACC
+ATATAAAT
+ACCATGT
+GCAAAAAT
+CACACCT
+GCCTCTT
+ACCCTCT
+AGCAGGT
+GTGTGAA
+AGAGAGT
+GGAGAT
+CCTCAGG
+GTTTGGTT
+AGCCCGAGT
+GACAAGAA
+GAAATCC
+GAGAGCC
+GAGATCC
+CTCTAAT
+CCTAAAT
+ACTTTCC
+GCACTAA
+GCACATT
+CAGCAGT
+GTCTCTT
+GATTGCT
+CTTTTTAT
+GATAATT
+CCTCGCC
+CGACCCC
+GAAATAA
+GGGTATT
+GCGACCT
+GAGCTCAA
+GCGAGGT
+GAAATGT
+CGACCATTTT
+GGTTGGT
+GCCTTGG
+AGCAAGT
+GTTATTT
+GAATTCC
+CAGCCCC
+ACCTTCGG
+ACTACTT
+AGGCAAT
+GAGTCAA
+CCTCGGT
+AGCATTTT
+GGTGGCC
+AAAATAAAA
+CAGCTAT
+GAAGCAA
+GAGTTAA
+GTTAAAT
+GCAGAGG
+GACAACT
+ATTATGT
+CAGAGTT
+CAAATAA
+GAAGGCTAA
+CATGCAA
+AGTCCTT
+CGAGCTT
+GAGAAATT
+CTGGTTT
+CTTCTTTT
+ACTGTTT
+GACGAGT
+CACATCC
+AGTACTT
+ACCCAAT
+CATTCCT
+AGAAACAA
+CAGATGT
+CTCCATT
+CAGTTAT
+GAGACAT
+GGAAGTT
+ATATATAA
+AAAATATT
+GAAGTTT
+CAGCAAT
+GAGCCTT
+GGCTGCT
+AGCATCT
+GGCCCGG
+ATTTTAAAA
+AACTAAT
+ACCACTT
+ACAGAC
+GGTGCAA
+ACCTCCT
+CATAGG
+CGAACTCC
+AACAAGT
+CAGATAA
+AAATAGG
+CGACCCCAGG
+AGCTAGCT
+CACCTCT
+ACAGGTT
+CAAGAAT
+ACCTTCT
+ATTACAT
+GATCGG
+GATGTAT
+GAGACTT
+GATCGT
+GGATCGG
+AATATAAT
+GCCATGG
+AGTAGTT
+CACATTTT
+CCAAACAT
+CATTAAAA
+CACTATT
+GACGGCC
+CCTAATT
+ACACTAT
+GTAAAGG
+CAACGG
+CTTAATT
+GAGTAAT
+ACAACGT
+GATGCTT
+ACGGAG
+AGACTAT
+GCATGTGT
+GAAAACAA
+GACCCAA
+ACCCTTT
+AGGAAGG
+CACCTAT
+CACAATTT
+AATGATT
+AATATATT
+GTTTTGGT
+ACGCGG
+ACCTGTT
+CCTTTGT
+GCTTATT
+GAGATCT
+CACCTGT
+CTCTTGG
+CACCACT
+GCACAGG
+CCAAGCC
+ACCGTCC
+GCAATCT
+GGATGGT
+CTAACTT
+AATATGT
+CCAACGG
+CCATGGT
+ACCTCGG
+GCCTCAA
+ACACACAT
+CAGTCAA
+GCCACAT
+AGCTTCGG
+AGTGCTT
+GTTGCAT
+CAGAAGG
+AAAAGCT
+ACGAATT
+CACAACAA
+GAAGAC
+CGAGATT
+GGAAACC
+CACGAC
+CATGAC
+GAAGAAT
+ATTTATAA
+GAAACAA
+CTCTTAT
+GGTGCACC
+GGAAAGT
+GCAAGCT
+GCGAATT
+GTATATT
+GTGGCAT
+CCATCCT
+ACAGGAT
+AATCCTT
+CCAAATTT
+GCTTTAA
+ATTTTCGAC
+AAAGATT
+AGGGACT
+GAGGCAT
+GGGGCTT
+CGTACC
+CGCGTGG
+CTTTTTCT
+GTTTCCT
+AACCCAA
+GGTCCTT
+GTTACAA
+CTTGATT
+ACTCAAT
+CAGACTT
+GAATAGT
+CAGGAGG
+CTAAGAT
+AGAAATTT
+GCGCGCC
+GCGGCAA
+GCGACAA
+CTCTATT
+GGCCATT
+AGAGGGG
+AATATTTT
+CACACGG
+GAGGAAGAA
+GCCTACT
+ACCTACT
+AATCATT
+GACAGCC
+ATATAATT
+CGACCAA
+CTATGAA
+GCCTTAT
+ACCAACT
+CTCTTCC
+AGCGCCT
+CTCCTTTT
+AGGGAC
+CAGATGG
+ACTACAA
+AGCGGAGTT
+GTTGAAT
+AGCTCCT
+CACAGCC
+CGATTAT
+AGTGACT
+GTTCATT
+GAATTTAT
+AGGGTTTT
+GAGGTAT
+GCCACTT
+CCTTTCC
+ATCTGG
+CAATGGT
+GTTGCAA
+GAGCCCC
+CAGCATT
+GTGTCAT
+CGGTGAA
+GCATTCT
+AAGGATT
+GATCATT
+CTTAAAT
+AGTGGAT
+AAAATTGT
+GTATGAA
+GCAGGAA
+GATTTTTTT
+GGAAGAGG
+ATTATCC
+CACAGAA
+CTAACAT
+AGCCCAT
+ACAGCTT
+GAATGGT
+CGTTCTT
+CGTTGCC
+CTTTGTTT
+GCTATAT
+GAGGCTT
+GATCCAA
+CGCGTT
+CTCTCAT
+CACTTCT
+GGCTGCC
+ACCTATT
+GAGGTGTATGAG
+ATTATATT
+GAGAAAC
+GCAGAAT
+GGGTGAA
+GGAGCGG
+ATTTTATTT
+GTTGGAT
+CAAAGAA
+GGTCATT
+CAAACTT
+GAGCAGT
+AGGAGAT
+GTTTTCTT
+CAGCTCC
+AGCAAAAT
+CAGACAA
+GGAAGAC
+GCCATCT
+AAAACCT
+CAGAGGT
+GAGCACC
+AGTCATT
+GAAGAGG
+GAGCTGG
+AAATCCT
+ACTCTAA
+GCACAAT
+ACCTCTT
+GCTTTGG
+GCCCATT
+CCTTTGG
+AGGGATT
+AAGTGTT
+GGGTGAT
+GCCGAGT
+GAATCCT
+GGCCACC
+AGCTCTT
+AAAATGG
+AGGTTAA
+AATTGCT
+AGAGGGT
+AGGATAT
+CAAAACAT
+ACAAACAA
+GTGTAAT
+AAAGCTT
+CTCTGAT
+CAAACAT
+CCTTTCT
+GGAAGCC
+CACTCCT
+GCTGAC
+AGAGGCT
+AGATAAAA
+ATTACTT
+CACCGCC
+ACCTCAT
+CTCTGTT
+GTATGTT
+ATTTTTTAT
+CAAAACTT
+ATTATAA
+GAAATTTT
+GGAGCCT
+GGTTTCT
+AGAGGCTCAT
+ACGAGTT
+AAACTTTT
+GTTTTTGT
+CACTTGG
+GAAGCTT
+AGATTCT
+GAAGATT
+GCCTTCGG
+CGCCTCC
+GTGTACT
+ACGTGTT
+GAGCCAT
+CAATAAAA
+AGTCGAT
+GAAACAT
+GCAGCTT
+GACCATT
+GAGAACC
+AATCGT
+AATGAC
+GTTTATTT
+CTAAGTT
+AGGTGGT
+AAAATCC
+GAGTGAT
+GAGGGTT
+AGTACAA
+GCCTTCC
+GTATGAT
+AGATCCT
+CCTTGGT
+ACCATAA
+AGGTTTTT
+AGCGAGG
+CCATGCT
+ACACTCT
+AGAGAGAA
+AGTGTAA
+GGAACTT
+AGGCAC
+CGACGAC
+CATTAATT
+ACATATTT
+ACAGAAAA
+AGTGCAA
+GGCTATT
+CAATACT
+AGTCCAA
+ACTTAAAA
+AGCTTCC
+GATCAGT
+GCAGCGG
+GCTAAGT
+AACTCAA
+ATCTCTT
+ACAGAGT
+CGATCTT
+AGCTGAT
+AGAAGGT
+ACCCAC
+GTTTCTTT
+ACTAGTT
+GAGGTAA
+AGCTGTT
+CAAGTGAA
+GTCCATT
+GTGTATT
+CTCCCAT
+ACATTGG
+ATTTTTTTTT
+CAATTTAT
+GCCATAT
+AATAATTT
+AGATGGT
+ACGATTT
+CACAGTT
+CACTGTT
+GTGTCAA
+GTATCAA
+AAATAAAAT
+ACACACT
+AGGGAGG
+CGGTGAT
+ATTATTATTATT
+ACCAACC
+CATTTTTTT
+CATTTGG
+GCACCCC
+CTTCCGG
+ACCATCC
+ACCACCT
+CTTTCCTT
+AATTTTTTTT
+ACGGAC
+AATAATAATAAT
+AGAGACT
+CATTGGT
+AATAAATT
+AGCTATT
+CCCTCAT
+CTTTAGT
+CGACCCAGG
+GCAACGG
+GGAGGCC
+GACATCC
+GAAGGAT
+GACCTAT
+CTCAACC
+AGAAACAT
+GACAAATT
+CTTTTCAA
+CTTTTGTT
+ACACTAA
+CAGAACC
+GGAGAAT
+CACCCAT
+CCTTTAA
+GCCTCGT
+GGTATGT
+GAAAACAT
+GACTCGG
+ACGAAAT
+GTAACAA
+CTTTCTCT
+GCTAGGT
+GCAATCC
+CAATCCT
+GGTTAAT
+AGGAAGAA
+CAGCTCT
+CGCGAC
+AGTTTGG
+CACCCCT
+ATTTCATT
+ACTTTTAT
+ACCTAAT
+ATTGCTT
+GCTACAA
+CTTTAAAA
+ACGCAC
+GACTAAT
+GATATGT
+GCTTTTTT
+CATCAGG
+GGCCGAC
+GTATAAT
+AGGATAA
+AGTAGAT
+GGCCACT
+ACACAAAA
+GAAGGTT
+GCCGAC
+AAGTAAT
+ACCTTCC
+GCAGTGG
+AGTACAT
+ATTTAGTT
+GAGGAAGG
+CGAGCAA
+ACCATGG
+CAGCGCC
+CGAACAA
+GGATGCT
+CCAAACTT
+CACCAGG
+CATCCAT
+CCTCAC
+GGTCTGG
+AATTATAT
+GGTGGTGCACC
+AATTTGG
+CGAATGT
+CGCTTTT
+AAAAACAA
+GTTTAGG
+GGATAAT
+AGGTCTT
+CACTCTT
+CCCTCTT
+GTTTTGTT
+AGAAGAG
+ACACACC
+ACTCTCT
+GACCTTTT
+CAGTTGT
+GCGCCAA
+ACAAAAAA
+GGATCCT
+GCTCAGG
+AGCCATGAA
+CTCCCCT
+GATCAC
+GCAAGAGG
+CGACGAA
+CATGATT
+GCACTGT
+CGACTCC
+CAGGTAT
+CATCCAA
+CGGAGTT
+CATATAA
+ACGCGT
+ACTCATT
+AAAAGGAA
+CGTAGG
+CGATCAA
+ATTTTTGACCAA
+GACAGTT
+AGCTAGT
+ATTGGTT
+CAGTCTT
+GAGGGGT
+GCGGTGG
+ACAAAGTT
+CTCAACT
+GGTTTGG
+ACCAAATT
+GCCAATT
+GACTGAA
+GTGGAGG
+GTTATAT
+ATTTGTAA
+ATTTGTTTT
+ACTAAATT
+AGTGTGT
+AGTAAGT
+ACACTTTT
+AAAGTTTT
+ACTATAA
+ACGCAG
+AGTCACT
+AGCTCGG
+ACCTGAA
+GATTTATT
+GCCTTTTT
+AGGTATT
+CCATACC
+GGTTAGG
+GATGAGG
+GAATTGG
+AGTGCAT
+GGTGATT
+CGTCTCC
+GCTGGGG
+AGTCTCT
+GCTCTTT
+AGTATAA
+ATGTGTGT
+CAAATAAT
+AGAGTTTT
+GAATAAAT
+CTAAATTT
+CCTCCGT
+GAGTGGT
+GAGGAAAA
+AGCCTTCGGT
+CATCACC
+CCTCATT
+CTTTTTGT
+GCTGCTT
+CATTTTGT
+AGGTAAT
+GATTTTGT
+GGAGATT
+GTATCTT
+CGCAGCC
+GAAGGCAA
+AATTTTCT
+GGTGCAT
+ACTCCAA
+CGAGGCT
+AACCAAT
+CCTCCCT
+CGTCGGT
+CACTGAT
+AGCAACC
+CCTATTT
+AATTTATT
+ACATTTAT
+GTTTGAAT
+GGGGAAT
+GGTAATT
+CTCTGAA
+AGGTTCT
+CGGGCGAGG
+CGCGCGC
+GATGGAT
+AGTAACT
+AGTTAATT
+GATCTTT
+GTTTAATT
+GCAACCT
+AAAGCAT
+AGTGAGT
+GAGGTGG
+ATTATCT
+ATATGAAT
+CACCTACGG
+CTCTAGG
+AGTTTTAT
+AGCTACT
+AGTACCT
+AGTCCAT
+AGCCAAT
+AAAACAAAT
+AAAAACAT
+GGTGGAT
+CTTCTTCTT
+CAGTTCT
+AAAATCAA
+CAGAAGAA
+GCAGGCT
+CGCTCGG
+GACCCAT
+CGTTCAT
+CTTTATTT
+CGATTCC
+CAGTTTTT
+ACCCGAA
+ACAAACTT
+CAATTTGT
+CGAGAGG
+CACCTTTT
+CCCAACC
+GAATTTGT
+GTTTTATT
+AGCTTAA
+AAGTGAT
+AAGTATT
+GACTATT
+ATGTATGT
+CAGGGTT
+CTCTGCT
+CTTTGATT
+ATTTTGCT
+GTTTTTAA
+GTTTGATT
+GGGGGCT
+CAATTCAA
+CTTTTATT
+GAGAATTT
+ACCATCT
+GAAAAAGG
+GCACTCC
+AAAGATAT
+GGTACCT
+AGTCAC
+GCCTAAT
+CAATGCT
+ACATAAAT
+GATTTGAT
+CCTGAC
+GTCTCAA
+GCGTTTT
+ACGAAGT
+ACTATTTT
+GGTAAAT
+GAAAGAT
+CTTTTGCT
+AAAATTCT
+CAGGCAA
+GGTTGATT
+AGTTTCC
+GGTTTAA
+GCAGTAT
+CAAGGCAA
+AGAGTGT
+AATAACAA
+CAGAAAAT
+CAGACCT
+CGCAGGT
+GGTCGAC
+GGCCAGG
+CGGCTCT
+CCTCACT
+CGAACTT
+CGTTTCT
+CATTTTCT
+GGTCCCT
+CTAAACC
+GCACTCT
+GACTTTTT
+GTGGCAA
+GATTAGG
+CACAGCT
+GGCTCACC
+GCATATAT
+ACTCCAT
+CGCCTTT
+CTTTTGAA
+CTATAAAT
+CTCCGCC
+GGGGGAA
+AGCAAGG
+CCCCCAA
+CGTTTAT
+GAGTTGG
+CACCAAAAT
+GCTTAATT
+CGCCCTT
+GAATGAAT
+CAAATCTT
+ACTAAAAT
+ACAGCAG
+AAGTCAT
+GTGTGGT
+ATTTCAAT
+CTCATCT
+AATGCAA
+ATTTTAATT
+CTCTTAA
+AGAGACC
+ATTTAATTT
+ACAAATGT
+ATATCCT
+ACTTGTTT
+ACACCCC
+GTTTTTCT
+GACAGCT
+CCGTAGGTGTT
+CGGCAC
+GACAGGT
+CACCAAAT
+ACCCATT
+CGACCAT
+GCCCAAT
+CAGCACT
+GAGACAC
+GACGAAT
+CACTTCC
+GGCTGAT
+GGTCAC
+AGTGGAG
+CTCCCCCT
+CAGATCC
+AGTAAAAT
+AGGCCTT
+CAAATGAA
+AGAAGCC
+CATTAAAT
+ACAAACAT
+CGGCGGT
+GAACAGT
+GCGAAGG
+GAATACT
+CGTTCATGGCT
+ACAACCT
+CACAAAC
+GCGACGT
+GATGACC
+AAAAGGT
+CAAATCT
+ACTTGCC
+GTCCGG
+GCATAATT
+ACAACAAT
+GAAATAAT
+GTTTACT
+AGTGTAT
+AATTTCC
+AGCGGAT
+CGTCGAT
+GTTTGCC
+CATACAT
+CACAGGT
+ATGGATT
+GCTCACT
+ACGGAGG
+CATTTGAT
+AGATACT
+CTATAAAA
+ACGAGGT
+GGGGGAT
+GTTGTAA
+GCTCAAT
+GGTCCCC
+ACGACTT
+GCATGCC
+GCTCATT
+GACGACC
+GCAATGG
+GTGGGAA
+GAGGGAT
+GGACAAT
+ATTTTTGG
+GAGATTTT
+CGAAGTT
+GAAATCAA
+CAATTGG
+CAGTGTT
+CACAGAT
+ACAGGAA
+GACCTCT
+CAAATATT
+CGAGGGT
+CTTTTCAT
+GCAGTCT
+GACACAC
+CAGTGGT
+CAGAAATT
+GAAAAAGAA
+GCATCCT
+AGCCACC
+GCCGCCGT
+ACAGCAT
+GAATAATT
+GACAAAG
+CTTTAATT
+GACTCAT
+CAGTTGG
+GCAAACAT
+CTAAAGT
+AGAGAAAT
+GCTAAAT
+GCACTAT
+CAGGATT
+ATTATAAT
+AGGTCAA
+GACCAAT
+CAGCCCT
+AAAAATGG
+CGACTAT
+GGAAGGT
+GTGGAGT
+CGAAACC
+CGACACC
+ACAGACT
+GTTCAAT
+GACAGAT
+GCCGAG
+ATTAAATT
+GAAACTT
+ACTAGAT
+GTTGGTT
+GGGTCAA
+AGAACCT
+GGCCCAT
+GCAAACC
+CAATTAAT
+GCCAACT
+GCGAGAT
+CAGTTAA
+GCAGCAG
+GCCCTTT
+GTTTTTGG
+GTTTATAT
+AGAAATAA
+GCTATTT
+GCGCAGG
+GAAGCAT
+CGATTAA
+ATTCACC
+ATTCCAA
+GGTCGC
+AGAGGCC
+AGGTGCC
+ACATACT
+GATCCTT
+ATTTTTCC
+CTCATCC
+GCTCCCC
+GATCAG
+ATTAAAAT
+AGTTAAAA
+AGTATGT
+GTAACAT
+GATGTGG
+CGAGTAT
+GGGGCGG
+GCAGTGT
+GGATAGG
+CACCTAA
+GATTTGTT
+GAATACC
+GAGGGCT
+CCAAGAG
+CACTCGT
+ATTAATTT
+AGGAACT
+CAGTAGT
+GCATTCC
+ACTAACT
+GCTGCAA
+GTTTCTAA
+CAAATGG
+CTTCACC
+AATATGAA
+GCGGTCC
+ATATGCC
+GCAGGGG
+GGCTAGG
+CGCAAAA
+GACATTTT
+ATCTCAT
+CGACGCC
+GGTTAAAA
+GCGAGAA
+AGCCTAA
+AAACAAAT
+AATTGATT
+CTTCCAA
+GCAGGCC
+CACTACT
+AGAGATTT
+GAAGGAA
+ATTAGTT
+ACCAGCC
+GGTATAT
+CGAGGGG
+GGAGGCT
+GCTAGCT
+CATTTCAT
+GACAATTT
+ATTACAA
+AGGATGT
+GCGACAT
+AGTGACC
+GCGCGAA
+GCTCCAT
+GGAGAAAA
+CCAACCTT
+ACCAAGG
+ATGGAAT
+CGGCGGCGG
+GACTTCC
+GTAAGCCT
+AGGTCAT
+AATTAAATT
+CTCAATTT
+GTTGGAA
+CCTCGAT
+CTCATAA
+GACCTCC
+GATTGAGT
+ACAATCC
+GGTCGAA
+CAGGATAA
+GAGCTGT
+ACATGGTT
+AGCGCTT
+GTTTTGAT
+GTTTGGGT
+CGGCCAT
+GAATTTAA
+AGTCAG
+GACACCT
+ACATACC
+GACAAAAT
+GCGATGG
+GTAACTT
+CTTTCTTTT
+ATGGTTTT
+CCAAATAT
+AGTAGTGG
+AGCTCACT
+CGTCAC
+AGGTGCT
+CGCAAGG
+GTTTTGAA
+CGCCTCT
+AGACTCT
+ATTTAAATT
+CAGTAAT
+ACATGTTT
+GTAACACC
+CAAATCAT
+CGGCGAT
+CAGGTAA
+CTCCGAA
+CAGCTAA
+AGCGGTT
+ACGACAA
+ATTTACC
+AACAACT
+GCAGCAT
+CGTTGAA
+CAGTGAA
+ACTTCGT
+GCCATAA
+GCGCCTT
+GGCTCCT
+ATTTTAGT
+GCAGATT
+GCTTACC
+CAACAAT
+CCTTCAG
+CGCGGCC
+GGTGACC
+GAGTTCC
+GCCAGAA
+AATACTT
+GTTGGGT
+ACTTTGG
+GCCACGT
+GTCCCTT
+GATTAAAT
+CGTGAG
+CGAGTCC
+ACAGTAT
+GGGGCAA
+AAAAATGT
+GGTGAGT
+AACTCAT
+CGAGAAT
+GGAGGGT
+ACATTTGT
+ACTCAGG
+GCGCCCC
+CCTGCTT
+GAGCTCTT
+CCTTGCTT
+GGCCGACC
+CATTCTTT
+GATTAGT
+CAGAAAG
+GACTGTT
+AGCTTCTT
+GTGTGCT
+CAATAATACC
+ACGGTTT
+CAAAGTT
+GTATCAT
+GTCCACT
+GAGATGAA
+AGCCACT
+GACTCTT
+CAGCTGG
+ATTTGGTT
+AGCTAGG
+CTCTACT
+GGCTAAT
+CGTTGAT
+CAGTGAT
+AAATGCTT
+CAAGGAA
+ATTTGGAT
+CGGCCAA
+GATTTAAT
+CGTTTCC
+GCATGTTT
+ACGCGC
+AGATATAT
+CGGGGAT
+AGCCGCC
+AATTCATT
+CCTTACT
+CTTTTTAA
+CGAGCGG
+ACAAAGAT
+GTGTGTTT
+ACACATAT
+GCCAACC
+CGCGGGG
+CCCCATT
+GCCTGCT
+GCGAAGT
+ACCGAGG
+GCTTAGT
+AATATCAT
+ACATATGT
+CATTTTAA
+CGTATGT
+ACCTTGG
+GCCCTGG
+GAATCGG
+GCGGAGG
+CACTCGG
+CAGGTGT
+ACATAATT
+CGGTCAA
+CGAATCT
+GCCTAGG
+AAAAATAAAA
+GTCCCAA
+CTAAATAT
+CGTTTGT
+CAATTCTT
+GCATACT
+AGTATCT
+AAATGTTT
+CAGAATTT
+ACGAAGG
+GATGCAA
+ATTTTCCT
+AGGCATT
+ACATGTAT
+GCTGAG
+GATGGAA
+GAATGACC
+ACGTTTT
+GGTGTGG
+CCAACCT
+CGGCCGCC
+GATGACT
+GTAAGAA
+CAATAAAT
+ACCGGTT
+CTTTTCTTT
+CATATCAT
+AGCATGG
+CTTGGCC
+CCTTGTTT
+AGCCCGG
+CAAACAA
+AATAACAT
+GAGCCGAGG
+GGTCTTT
+GGTGGCT
+CCTTCGAA
+CAGTCAT
+AGACTTTT
+CGGCACC
+GATTATGT
+CTTGGTT
+CCTCACC
+CGATTCT
+GTTGGGG
+CAATTTCT
+ACCCCTT
+CGGCCCC
+GAATATTT
+GCGCTCC
+CTCTCTCC
+CATTATTT
+ACAATGG
+AGGCTTT
+AATTATAA
+CAAAAACAT
+GATAACT
+CTCTCTTT
+AAGTCTT
+AGGACTT
+AATCTAA
+CAATTGGTAT
+ACCCGC
+GATGGGT
+CTCTCGT
+GGTCAGG
+CCATCCTT
+CATTTCAA
+AGTTACT
+CATGACC
+CATTATAT
+GGTTGCC
+GCGCCGT
+GGACATT
+AGGAACC
+CTGTCAA
+CACGAAT
+GAAATATT
+GCGAACC
+AGTCTAA
+CAATACAA
+CGGAGAT
+CAGAAAC
+CTTGGAT
+GCTTGCC
+AATTAATTT
+AGACGAA
+ATGGCAT
+ACACGAA
+CTTTCGT
+ATTTTAAAT
+GCTGGAA
+CACGGCC
+CACGAG
+GCTTTTAT
+ATTTGAGT
+ATTTGTGT
+CGTCGTT
+GCGACGG
+GAAATGAA
+ACAAAGAA
+GGGGATT
+ACATATATAT
+GAATATAT
+GGTGTGGT
+AAAAGAAAA
+CGACCGT
+GTGGAAT
+GTTCTCT
+GTAAACT
+GATGTGT
+CGTAGCT
+GTTATGT
+GCGAGCC
+GGCTCAT
+GCTCCCT
+GATAAAAT
+CATTTCTT
+CTTTTTCC
+GAAGAAGAA
+AGAATGG
+GGACGAA
+GATATAA
+AGCTAGCC
+ACTTACT
+ACCATTTT
+ACGGCGG
+ACATTCC
+ACAAATAA
+AGAATTTTT
+GGTTCCT
+GCCAGAT
+CAATAGT
+CGCGGCGG
+AGGAAAAT
+CTTCACT
+GGGTGGT
+AAAAAAGAA
+CACGACC
+GTATGTTT
+AGAAGAC
+CGCATCC
+CATAAAG
+CTTCCCC
+GCAAAAAA
+GCTATGT
+CACGAGG
+AGCGAGT
+ACCACAC
+CAAGTTT
+GTGGGCC
+GACGGGG
+GCTCCTT
+GGTCAGT
+GAGGTCT
+CGCCGCCGCC
+GATGAGT
+CGACAAT
+AATTTTATT
+AGTTAGT
+GATTACT
+AAATGAAT
+GGCTACT
+CAATTCAT
+ATTTTACT
+CTTTCGG
+GCGCGCGC
+GTCTCAT
+CTTGAGG
+AGTTGCTT
+CTTATAT
+GCTATAATT
+CATTCATT
+CCATGCC
+CGGGACC
+AGCAATTT
+GCACACAT
+ACCAAAC
+GAATTTCT
+GGTAGCT
+AGCCTTTT
+GCCTATT
+AATTTAAT
+GACCCCT
+AGAAGAGG
+GATTTTCT
+CTTGGAA
+GCTTTCAA
+AGGCCAA
+ATTTGATTT
+AAAAATAAT
+CCTTTTAT
+GAGCGAG
+GGTTTCC
+CATTTAAT
+CCTTCCTT
+ATATAGT
+ACAGTTTT
+GACTACT
+GAAAAACT
+AGATTTAT
+GAATTGAA
+AGGAGCT
+CAATCAAT
+CATATGT
+AAGTGAA
+GGAGGGG
+GTTTGAG
+AATTGTGT
+CCCTCAA
+CGCCCAA
+GGCCAAT
+ATTTCTAT
+CTCAAGG
+CAAATGTT
+GGTACTT
+CACACTAT
+ACAGACC
+AATAAGAA
+GCATTGG
+CTTTTGAT
+CACTGCT
+ACAGTAA
+GTTTAAAA
+AAATAAATT
+GCCTTAA
+AAGAATTT
+AACTGG
+CTTGTGT
+ACAGTGG
+CGATTTTT
+GTTATAA
+CATACAA
+CTTCTCCT
+AGACTAA
+CACTGAA
+GCTTCTTT
+GTGGATT
+AAGAAGT
+CGTCGAC
+GCCATCC
+ACTTAATT
+ATATGATT
+AACTAAAA
+GCAAGAG
+CTCTCCCC
+AGGCTAA
+GTTTCGG
+AGCTTGG
+CCTTCCCC
+AGACACT
+CACTAAAA
+GACCACC
+GTTTTCAT
+ATTTTTATT
+AGATGCAT
+GACAACAA
+AACTATT
+AGCATCC
+CCTTCTCC
+AATCCAA
+ACAATTAT
+ATATGTAT
+CAGTATT
+CTTTTCTTTT
+CGAGACC
+AGAATTAT
+CTTTGAAT
+AAAAATCAT
+GCATTTAT
+ATTATTATT
+CGGTGGT
+GACACGT
+CCAAAGTT
+AGGGGAA
+CGCGAGG
+AACTCTT
+CTTTGGAA
+AAATCAAT
+GGCTGTT
+AGAGTCT
+GGCTCTT
+GAGTAGT
+GATATGG
+AGGGCAT
+ATTTAAAAT
+ACCTGCT
+GACCACT
+CATCGAT
+AGGTTGG
+CGGAGGT
+CATTGATT
+GCTTCTCC
+ACATTCAA
+AAGTACT
+ATTATCAA
+GCGCTGG
+ACCGAAT
+ATTTTAGG
+ATTTGTAT
+ATCTATT
+GCACACT
+GACGGAA
+GGGGGAG
+AGAATCC
+GAAAATAGCCAT
+AGCTCGT
+CATTAGT
+CCTGTTT
+AATGCAT
+CACTGCC
+ACCTGAT
+GGCCGGCC
+CAGGGAA
+CACGCTT
+GATTTTAA
+CTTCCAT
+AGCGACT
+GATGTCAT
+ACTTGATT
+AACCGG
+AGCGGCT
+GCGTGGT
+AAATCCAA
+AAAAAAAATT
+GTTCCTT
+ACATAGT
+AATCCAT
+CCTGAGG
+GCGAGCT
+CGTCACC
+CGTCGAA
+GTTCCAA
+CATTTATT
+GATAAGG
+AATTTCAT
+CTTTTTGGT
+CGGTTAT
+AAAACAAT
+GAATTCAA
+CCCTCGG
+AGATAGT
+GGCCGTGT
+GACATTGT
+CACGGGT
+CTTCCGT
+CTAACCT
+GCTCTCC
+AGCCTCGG
+CAGGGAT
+AGGATCT
+CATTGCC
+AATCGG
+GGTCAG
+ATTTCAC
+AACAATTT
+CGAATCC
+AGGCTGG
+CTATACT
+CCAAGCAA
+GACGCCT
+GCATGGTT
+AATACAA
+CAATTTAA
+GGACGGTCC
+ATTTGCTT
+CGCGAG
+GCTCGCC
+GTTTCAG
+CACTAGT
+GCTGAGT
+GTTGAGGT
+ACTCGAT
+AGCGCAT
+CAGGCGG
+ATTATCAT
+AATAGAA
+AGACCCC
+ATTTGGGT
+GCATAAAA
+AATATCT
+GAATTATT
+ACCAGAA
+GCTAATT
+GATCGAT
+AAACACT
+ATGTGGT
+GGGGGTT
+ATTTTCGG
+AGACTCAT
+ATTTCGT
+GCAGTAA
+AGGCAG
+ATCCCAA
+ACCCCAA
+GCAAGGT
+CGAGCAT
+AGAGTCC
+AGCTTTTT
+GCATACAA
+GCCTGAT
+AATTCAAT
+CAGTTCC
+AATAAGT
+GATTTGAA
+GGATACT
+AGGTTCC
+CGCCCGAGG
+GCTCACC
+CCTGCAAAA
+ACAACTTT
+AAAGAAAT
+CGGAGCT
+ATTATGAA
+GCTCTAT
+CAAATGAT
+GCGCCCT
+GTTCAG
+ACGAGAT
+GGAAAGAA
+GCACCGG
+AAAAAAAAAA
+CGGGTGT
+CGCTCGT
+GGTTCGT
+ACTAGAA
+AAAAATCT
+GCAACCCT
+AGTTGATT
+CCCAAGG
+GACCATGT
+CTCTCTCAA
+ATTGTGT
+GAATTTGG
+GAGTAGG
+ACTCCTT
+ATAAACC
+AATTGAG
+GTTTGTAT
+GCTAGAA
+ACTTTATT
+ATTTGAG
+CGGAGCC
+TTTTTTAA
+AATCTCC
+ATTTCCTT
+ACACAAAT
+AGATCGG
+GCACGAA
+CTTGGGT
+ACATGAAT
+GCAAATAT
+AGATTTGT
+ACATCCT
+AATTGAAT
+GATTTCTT
+GAGGCCT
+ACCAATTT
+CATGAG
+AAAAGAAT
+AAAACTTT
+GCGGGAA
+ACCTAGG
+ATTTTCTTT
+GAGTACT
+GTTGTAT
+CCATGAGG
+GATTATAT
+AGTGCCT
+AAATTTTTT
+GTTACTT
+AGCGAAT
+GGTCTAT
+GCTACTT
+CGTGTTT
+CGAGACT
+ACAAAATTT
+AAAAAGAT
+AGTCGTT
+GAGGAGAA
+CGAAAGG
+AACCCGG
+AAATACTT
+GGTCAAT
+GGGTGCC
+GGTGGGG
+AGTAGCT
+GATGTCT
+ATTCTAT
+GTTAGTT
+CAAAGAT
+GGTAGAA
+CAACCTT
+AATTATTTT
+CGTCAG
+GTGGCTT
+GACCTAA
+GATAGAA
+AGGAGCC
+AATGGAA
+GAAAGTT
+AATAAATTT
+CCACACC
+AATCTAT
+CTCTGCC
+GCGCTTT
+ATGTACT
+GGAGCAT
+CGGTTCT
+CAGGCTT
+AAAAGATT
+ATTCAC
+AGGATGG
+GGATGATT
+CGCTGCC
+GCTAGAT
+AAAAAAGG
+ATATGCAT
+AGGTACT
+GGCCGAGG
+CGCTTGT
+CGGCCTT
+CAAGCTT
+GCAACAAT
+ATTGCAT
+AATTAAAAT
+CGGCCGG
+AATATGTT
+GGGTCGGGCGG
+CGGTTGT
+GCAGGGT
+CCTCCTCC
+CACGCCT
+GGTATAA
+AAGAAAAT
+AATTATGT
+CATAACT
+CCATGCAT
+GGTCTCC
+CAGATTTT
+CGTGATT
+GCAGTCC
+AAAACATT
+ACAAGTTT
+CGACTTTT
+ATTTGCC
+CACGCGG
+CGGTTCC
+CAACATT
+GCGATGT
+CCTTGGTT
+CATGGAA
+AGCCTGT
+AAATATTTT
+GACGGAGG
+CTTTTGTTT
+AAATTATT
+CGGCTTT
+AATTTTGG
+GAAAAATAT
+GTGGACC
+CTTCTGG
+CGGTGTT
+CGCTGCT
+CCTCGAA
+CGATCAT
+GCCGCCGCC
+GAGGTTTT
+GGTATGG
+CAGCCGT
+AATTAGAA
+GATTCATT
+CACCAAGG
+GATTGAAT
+CGCATGT
+CGTGGCC
+AGTGGCT
+CGTCCCC
+CTTGTAA
+GAACTTT
+AGCCTGG
+AGAAAAGT
+GCAGCCT
+CGCCCAT
+ATTATGTT
+AATCAC
+GGAACCT
+ATTATGAT
+AGTCTAT
+AATAACT
+GAAATCAT
+GGAGCTCT
+ACTACAT
+CAAAAGT
+CGACGAT
+GCGATCT
+GAACCAT
+ACAACAC
+AAAAATTTT
+AAAAACTT
+AGCGATT
+AAAAATATT
+CGCGCGT
+ATTTCTTTT
+AGGGGAT
+AGCCCCT
+CGCTTCC
+GAGACCT
+AGATGCTT
+ATTTACTT
+CTCTCCCT
+ATATATTTT
+CGTGAC
+GTTCCAT
+AGCCCTT
+ACAGGGG
+GCAGAAAA
+AATCACT
+GGTGCCT
+ATCCCGT
+ACTATGT
+AGTAAATT
+CTTTTTTTTT
+ACAAGATT
+CATATATT
+CAATGAAT
+ACTCACT
+ACAACATT
+AAAAGTTT
+CGAAGGCT
+AGAGCCT
+CCTCCGG
+AGCGGCC
+GCTGAGG
+GTTCAC
+AATAGTT
+ATATGCTT
+GTGGTTTT
+GGGTGAAT
+AATATCAA
+CCCCTTTT
+CTTGTGAA
+CACTTCAA
+ACAAAACT
+CTTTAGG
+ATTTCAG
+GACCTGT
+GAAATCTT
+CGTGTGT
+CCTCTGGTCGAT
+AGGCTCT
+CAGTGCT
+CGAGTCT
+AATGGTT
+CATGTAA
+ATTGGGT
+GCTCCACT
+CATGTGT
+GTTGCCT
+AAAAATCC
+CCAAGATT
+AAAAAGTT
+CTCCAAAT
+ATTTTTCTT
+CCATCTTT
+CCAACGT
+ACTTCCTT
+ACTATCT
+GCCAAAG
+ACTCTAT
+CGAATAA
+CGCGCCT
+CAGCTGT
+CAATATAT
+GATTGATT
+GGATACC
+AATTACC
+CTAAGCT
+GGAGGAGG
+ACCTCGT
+CGAGTGT
+CGAACCT
+CTCTTCTT
+CCCAACT
+CTCTGGT
+GTGGCGG
+CATTAGG
+GCGAAAT
+AAAAAATTT
+GATAAGT
+AGATAGG
+CCATGTTT
+AACCATT
+GACGTCC
+ATAAACTT
+CACACAAT
+CATATCTT
+GGTTGCTT
+CAAAAGTT
+CACGTGT
+AGTAAGG
+ACTAATTT
+CAGCGAA
+GCATGAAT
+GAAAAGTT
+GGGTCAT
+GACCAAAA
+CCTCTGT
+GATGATTT
+AAAATAAAAT
+GAGCGAA
+AGTGGCC
+CCAAAGAA
+CAGCCGG
+ATTGTTTT
+AAATAATTT
+GAGTACAA
+ACACACAA
+CAATATTT
+GCACCCT
+CTCTAAGG
+ATATGAGT
+ATATACAT
+CAAAAGAT
+GAAAATCT
+GACTAGG
+CAGCGCT
+ACCCACC
+GCACTTTT
+AAAATTCC
+ATATAGG
+ACACATTT
+GGAGAAAT
+CATTTGAA
+AGTTGTTT
+GATTATTT
+ACACCCT
+CTTGTAT
+GATACAT
+GCACCGT
+ATTTATAGCC
+CACCGAA
+CAAGAG
+CGCAACC
+CACGTCC
+GTTCTCC
+AAGAACT
+AGGGAAT
+ACAAGAG
+CAAAAACC
+CTATCCT
+GGTCACT
+GTTTTTTTTT
+ACAGTGT
+GAGGGAGG
+GCGCCGCC
+AGAAGCAA
+GCTTCGCC
+AAATTCC
+GTTGAGT
+GTTGGCT
+GCAGAGT
+GTCTGAA
+GTTTCGT
+CGATGCC
+GATCCAT
+CAACAC
+CTTGTACT
+CCAACGGT
+AATACAT
+AATTCGT
+GTAAATTT
+AGCTGCC
+ACTGGTT
+CCAAACCT
+AGGGAATT
+AAAAAGAAAA
+GCAAAAGT
+ATTTTGTTT
+AGTTTTAA
+GATACTT
+AGATAGAT
+CGATCGG
+ACTGATT
+ATTCTAA
+GTTGTTTT
+AATTCTTT
+CATGGTT
+CGAGATGT
+AGGCGC
+GCTGATT
+ACTCAC
+GCTGCAT
+ATTGTAT
+GACTAAAT
+CAGGAAT
+ATTAAGT
+CTTTGGTT
+GCTTTTCT
+GGTAATCGATT
+AGTTTTGT
+AAAATTTAA
+CCAAACAA
+AGGGTAT
+ACCCTCC
+GCGAACT
+GGAGTTTT
+AGTCTCC
+GTAAGTT
+AGAAATGT
+AATTGTTT
+CATCTCT
+CTGGCAA
+GCTGGAT
+AGCGACC
+CGGCGCT
+AGCACGT
+ACGTCAA
+GGTGGAGG
+AGCCGAT
+GGGGCCT
+GAAGAGT
+CGTCGCT
+CAACCACC
+ATTTATAGG
+GGTAACT
+GGGGTTTT
+CCTAGGT
+AGTAAAG
+GCGCTCT
+ATAAACAT
+AGAAGGAA
+CGACTGT
+CCCTAAT
+CATACTT
+CACCAAGT
+GGTACAA
+ACGACGT
+GTGGGTT
+GAGGCAG
+GATCTCT
+CATATTTT
+GCTGGTT
+CAGTACT
+AATATAAAA
+ACACAAGT
+CGTTTGG
+AGATGAAT
+CCTGCAA
+CCTTCGC
+GACGACT
+GTTGTCT
+AGAATCAA
+CGAGTGG
+GCATGATT
+CGACTCT
+ATATATATATAT
+CCATCCCC
+ACGTGCC
+GCATATGT
+CCACTTTT
+CTTTCATT
+CGCCGCT
+GCCTGAA
+ATGTGCC
+GATGTCGT
+GATATCT
+GGTGTCT
+GGAGACC
+GATCGAA
+GCGGTTT
+AGGACAT
+CCTGAAT
+ATTCCTT
+GGTGTCC
+AAATACAT
+TTTTTTCT
+GAAAACTT
+GGAGAGT
+ACTAAGG
+CTTTCAACC
+ATTTTTAAT
+ATTTTTTAA
+ACTAACC
+GATTTCAA
+AGGAGCAA
+GACAAAC
+GTTATCC
+ACCCCCT
+ACTGCAA
+CATATCT
+GAACAC
+GCATGTAT
+GCACAAAA
+AGCCAAAA
+TTTTTTGT
+GCAGTTTT
+GCGTGCT
+CCATCGG
+GACGGCGG
+ATGGCTT
+AATAATAAT
+AGGTACC
+GGTGACT
+AAATTGG
+GTGTAGT
+ATGTGTTT
+CGTGTCT
+CGTTCAA
+CGACGAGG
+GCACCGGACT
+CTGGATT
+GACGCGG
+GTGTGCTT
+CAGCAAGG
+AGATTGG
+GCTATAA
+CGGGTAA
+CGACATT
+GCTTTTGT
+CTTGCAT
+GCATTTCT
+AATATGAT
+CGTCCTT
+CGCACTT
+GGTCACC
+AAGAAGG
+GGCCGCT
+GAAAAGAAAA
+GCCTGCC
+CAAGTCAA
+CGAAAAAT
+ACACTGT
+AGTGGGT
+GGAACGG
+ACCCTAT
+ACCTTCAT
+GAGCGCC
+ACGAACT
+CGCTTCT
+CGCACGT
+CGCACGG
+CGCCGTCTTCC
+GAAATCT
+ACAGTCT
+AAAAAAGT
+CATCTAA
+ACACTTGT
+ACTGCAT
+CAGCCAC
+AGCCAAAT
+GCCTATAAAT
+CGGACTT
+AGACTGT
+CGGGTTT
+ACTTTCTT
+CAATAATT
+ATTATTTTTT
+GACGGGT
+CCTTAC
+GAGTGCC
+GTTATCT
+CTTTGCTT
+GGTGAAGAA
+GATAGTT
+CTTCTAT
+GGTATGAAT
+GGACACC
+GGCCGAACC
+ATTTACAT
+CTTTTAAT
+ATTAAATAT
+GACGAAGG
+CACTAGG
+CCAAGAGG
+GATGGCC
+AGAGATAT
+ATTTATCAT
+GATTGTTT
+CCAAGTCC
+ACTCCCTCC
+CTTTAAAT
+CCATCTCC
+GCTAAGG
+ACTGAAT
+CGATAAT
+ATTTAGAA
+GGATAGT
+GGGGAGAA
+CGATGTT
+CACGTGG
+ACTTTTGT
+GAGTACC
+CCTATAT
+AACCCTT
+CGGTTGG
+CACGGCT
+GATGTAAT
+GTCTGGT
+ATTGGCT
+ATAAAGG
+CATTGTTT
+GCCCGCC
+ATTTTTCAA
+CCATCCAT
+CATTTTCC
+CAATTATT
+GAGTTCAT
+AGACAAAA
+CGAATTTT
+ACACAGG
+AGATAAAT
+AGGTCCT
+CGGGATT
+ACAGAAAT
+GTCCTTTT
+CTTTGAG
+CGCTTGG
+ACATTATT
+ATCCAAT
+CGCCTGG
+GAATATGT
+AGTTTTTTT
+CGGAAGACGG
+CAATGCC
+AGCGGGT
+CCGGCGG
+ATTATCTT
+AATTTTTAT
+GACGGTT
+GTTTTAAT
+GACCAAAT
+CGCCTAA
+AGACCCT
+AGACAGT
+GTGTGAC
+CGCACCT
+ATGGGAT
+GCATATTT
+CCATAGG
+CGCCTTTT
+AGAAAATTT
+CATGTGG
+AGTTTCAA
+AACTACT
+CTCCCTCT
+GCTTCAC
+ACACTCC
+ACCCACT
+GACCCTT
+ACGAGAA
+ACCGAGGTT
+GTGTAGG
+AAAATGAA
+GCTCTCAA
+GTCCGGTGCACC
+ACTTGGTT
+ACAATAAT
+AGTTGAGT
+ATGGCAA
+AAAAATCAA
+CTGGCTT
+ACGGTGG
+ACTCACC
+GGAAATTT
+AGAGAGAT
+CACACGT
+GCCCACT
+ACAAAAGT
+ATTGTAAT
+ACTTTCAA
+GTTACAT
+AGATTTAA
+AGCATTAT
+AGATTCC
+CACCTTCT
+CGGCTCC
+GCAAGATT
+CCTATGT
+ACTTATTT
+CGCCTAT
+GAGTACAT
+AAACACAT
+CGCCTGT
+AATTACAT
+CGTTGTT
+CCTTCTTT
+CGTCGACC
+GATATATT
+GCTCGAT
+AGAAAGAT
+CGCAGCT
+AGAAGAAT
+CATCTCC
+GTTTAAAT
+CGGGAGG
+GGATGCC
+GCTCGAA
+CCAAGAGCAT
+ATTTACAA
+AGGACAA
+AGCGTTT
+GCACTTGT
+GCGTGCC
+CTCTAGT
+CAGGAAAA
+ACATATAA
+GGTCGCC
+ACCAGCT
+ATTTTATAT
+CTTGTCAA
+GTCCGGTGG
+ACCAGAT
+GCAGTAGGGG
+CCATCGT
+GATTTGTTTT
+ACTGCTT
+CCTACGT
+ATTTTGATT
+ATTAACAT
+ACTAGCT
+ACATTTTTT
+GACACGG
+CAATGTTT
+GACTGAT
+GGCCATGT
+AGTGAGG
+ACCTTCTT
+ACTTCATT
+GCTGGCT
+AGGTAGG
+AGTTAGG
+AGTAATTT
+CGATTGT
+GACCGAA
+CCTGAG
+GCGCGCGG
+GCTACAT
+GGAAGCTT
+CCCCCTCT
+GGAGACT
+CCTCTAT
+GCTGCCT
+AAATTCAA
+ATTATATTT
+GTGGAGATT
+ACGACGG
+AATATCTT
+CTTATACT
+GACTTGTT
+GTCTATT
+GCGCCGG
+ACAACAG
+AAAATAAAT
+ACCTGGT
+CATATCC
+CCATCCAA
+CAATCCAA
+AGCGTGT
+GCAAAGTT
+CCTAGAT
+ACTCGAA
+CTGGGCC
+GCATTTGT
+ATATATATT
+CGACTAA
+ACATTGTT
+GATTGGTT
+AAATTAAT
+CGTGTGG
+GAAGTGT
+CGCATCT
+GGTCTACT
+GAGGTATCC
+CATTTGTT
+AATAAATAT
+CAATATAA
+ATTTATTTTT
+CGCAGAA
+ACGCCAT
+GATTTTGG
+GCCCGAGG
+ATTTATCT
+ACAATTTTT
+ATTTCCAA
+CACCTGG
+ACGAGCT
+AGATAATT
+ATTTCTCT
+GAAATGAT
+GACCGTTT
+GCATGCAA
+CGTTTTTT
+CGTGGAA
+GCTTAGG
+GGTCGGT
+GCTTTTAA
+ACCCTGT
+GGTGTAT
+AGTATTTT
+GTCTAAT
+AAAATTAAT
+ACAGCCT
+GTCTGTT
+ATTTGAATT
+GTTTGTGT
+GGCTATGT
+GCTATCT
+CAAATAAAA
+AACTAGTTT
+GAAAGCT
+CTTATTTT
+GCTTGATT
+GACCTGG
+CACATCAA
+GAACATT
+GCGTGTT
+GCCCCAA
+AATTAAGT
+GCATCAC
+GGGGACTT
+GATATCTTT
+AGGTAAAT
+GACTGCT
+GGTAGAT
+ACCTAAAA
+AGACCGT
+CGTCTTT
+GAAAATCC
+CCGGGCT
+ATTAACT
+GTTTGGAA
+GATTTCAT
+CCTATGG
+CAGCGGT
+GCCATTTT
+AAAGCAAT
+AATGTAA
+GCCCTCT
+ACCGAGT
+GCCTCTCC
+GATCCCT
+CAATCGG
+AAATACAA
+CGCAACT
+ACAGGCT
+CCATAGT
+GATTCTTT
+GGGTCGT
+AAGGCAA
+ACCAGTT
+GCAAATGT
+AGCAATTAA
+GGTATCT
+GCGTCGCC
+CATATGAA
+ACTTCGG
+CCTTCTCT
+CGCACAT
+GTATCCT
+GCAAATAA
+GCTTCATT
+GCAGCAGG
+CTCCAAAA
+CGCGTCC
+CTCATCAA
+AGTTTCAT
+ACGCCTT
+GAATTCAT
+GCGTTGG
+GAAATGTT
+GTTTATGT
+AGACTCC
+GCGTTGT
+AAATCATT
+GATTAAAA
+ACGGCAA
+ACAGGGT
+GAATTAATT
+ACCATCAA
+ACTTATGT
+ACATTAAT
+GCTAAGACC
+GCAACAC
+GCTGAAT
+GTTTGGAT
+ATTATGG
+CAAAAAGG
+GCAAAGAA
+GAATTAAT
+GTGGACT
+ACAATCAA
+GGGTAAT
+ATTTGGAA
+AATAAGTT
+GGTAGTT
+GGCTGCTT
+ACATCATT
+CCTTGATT
+AAACATAT
+CATTCATGGCT
+CGGCGAC
+GCCCTCC
+CAATCAC
+GTTTGAC
+AACCTTTT
+GCTAGTT
+CGACCGTT
+ACTAGCC
+AGTTTGTT
+GCCTAGCT
+GCATAGT
+GCTAACT
+GATAGAT
+ACCGAC
+ACTTAACT
+ACTGTCC
+CCTGGCC
+AGTCGCC
+ACCGAG
+GCTGTGT
+CCTTGGCC
+ACAATATT
+GTTGTCTT
+AAATGATT
+CGGATGT
+CGTGTCC
+GAAAATGG
+AGGGGTT
+GCGTAGT
+AACAACC
+AGAAACTT
+CTTTTCCT
+CCTTAATT
+CGAGTAA
+ATATGTGT
+CTATATTT
+CATTATAA
+GGAAACAA
+AGACTGG
+CGTATCC
+ACACAGT
+ACAAATCT
+CTAAACAA
+GACCGCC
+GATCACC
+CTGTAAT
+CATGGAT
+GAGGTTGT
+GATCGC
+CAGAGAG
+CACTTTAT
+ACTATGG
+GGAAACAT
+ATTTTTGTT
+ACTTCTTT
+AGTTTCTT
+GCTTCCTT
+GTTTTTCC
+AGACAAAT
+AGAATTAA
+GGCCCTT
+CGGTTTTT
+CGTCATT
+GATCTAT
+GGTCGTT
+GCAAAATTT
+ATTCTCT
+AATAACTT
+CCAACAAT
+ACATCTTT
+CGCGGAA
+CTAAACTT
+CTCAAAAT
+CAGGTCC
+CTGTCTT
+AACAAAAT
+ATGTCCT
+CGGAGAA
+GGTATCC
+GCAAGAAT
+AAATCTTT
+GAGTGAG
+GGCGGAT
+GACTTGAT
+CTCATGG
+ATTTTTTTAT
+CATATAAT
+GCCGCCT
+CAGGTTTT
+GGATAAAA
+CGCTGAA
+AAGAAGAA
+CGAACAT
+ACCCAG
+AGGTAAAA
+GGCCGCGG
+GCGTCGT
+CCATGAAT
+ACGATGT
+CGTTTAA
+AGTGATTT
+GCATGAC
+AAATCAG
+CTTTCTCC
+ATTCAG
+ATTTAAGT
+GGGCGCGG
+GCCTAAGT
+ATTGGAA
+GCATTCAA
+AGTAGACC
+AGAGAGAGG
+ATGTATAA
+CCTAAGT
+GAATCAC
+GCCAACGGT
+GCTGGGT
+AAATAAGT
+ACTCTTTT
+GCACCGGACAGT
+GATATGAT
+GTTAAGG
+CATCTGG
+ATTTATGG
+CTTCCTTTT
+GTTTGCTT
+ATTGCAA
+GACGAACTCC
+ACGATGG
+CCCCCTT
+GGTGGCAA
+ACCTCTCT
+GGATGAC
+GGATGAAT
+CCCTCCT
+ACATTTCT
+GTTGTGTT
+AGTACATT
+AAATGTGT
+CAGTCGG
+AAAATCAT
+ACGGTCC
+AGGTTCAA
+GTCTCCT
+GATCGTT
+GCCTTGTTCTT
+ATAAATTTT
+GAGATTGT
+GCAGACC
+GTTTGCAA
+ATGTATAT
+CGTGAGG
+GAGCCCAA
+GCATAGG
+CGCATTT
+CCATAAAA
+AAAATGAT
+GTAACCCT
+AGTTCTTT
+AATGACT
+CGTGAAT
+GCATCGG
+GACCAAGT
+ACTTTTCT
+ACCTGCC
+AAGGAAAA
+ACATGATT
+AGCTGCAA
+AGGGGCT
+GAGACACC
+CGTGCTT
+ATTTCGG
+GGATGAGG
+GCATCCAA
+CACATGAA
+CTCAACAA
+GTCCCAT
+GGAGCAG
+CATTTCTAA
+GTTTTTTTT
+AGTCGAA
+AATAGAT
+CCCTCGCC
+ATCTGAA
+GAGCAAAA
+CATTACC
+AATTAGTT
+ATTATAAAA
+CGACCCT
+GCGTCAT
+CAGTACC
+GCGGCGC
+GTTTAGTT
+ATTGTAA
+ACATGTGT
+CAGGGCC
+AGTTTGAA
+CCAAACCTT
+AGAAGGCAA
+CACGCAA
+CAATAGG
+CACCTTAT
+GCATTAAAT
+GGCCGAAT
+GAGGGACT
+ACTTTTAA
+ACATGCAA
+ACAAGAAAA
+CGTAGAT
+GAAGGCT
+CGATGACAT
+GATGGCT
+CTGTCAT
+ATAAGGT
+GATCCAAT
+CCATACT
+AGGCTCGG
+CAAATGGT
+ATCCATT
+ACCTCTTT
+GTTAAGT
+ATCCGG
+CATAACTT
+GTCCCCAA
+GTTCTCAAAT
+CATCCTTTT
+CTAAGCC
+AGTTGCC
+GGAAGAG
+AGTGTGG
+AGAGGTTT
+AATCGATT
+CAGCTCAA
+CAATCTTT
+GGGTGCT
+CCAAGCTT
+ACTCAGT
+ATTGTGAT
+GACAGAG
+AGATGCC
+GGTCGCT
+CACGGAT
+GCAATCAA
+GCTTGAGG
+ATTTCACT
+GAAGAAGAAGAA
+GCTTTCTT
+ATATAATTT
+CCGGGAT
+AGGTACCCAA
+GCCATCAA
+GTTGTCC
+GAGCTCAT
+AATATAAAT
+GACGTGT
+CGAAAAAA
+AGAGTTTTT
+GACCATTT
+AGTTTATT
+ACTCGCC
+CACCTTCC
+AGTTGCAT
+CGTCGTCC
+CTGGTTTT
+CCTGCGG
+CCATCAAT
+GGAAGGAA
+GATTTTCC
+ACATTCAT
+GAGTGCT
+GCACCTTT
+GAATTGTT
+CCATGATT
+CCTAGCC
+GATGTGCT
+AGAAAAGG
+AATGTGTT
+CTTATCAA
+GGTCTGT
+TTTAAGTT
+GAGAGCAA
+ATGGAGG
+CGATATT
+CTCTACC
+CTATCTTT
+ATATCAAT
+GACTATTT
+CTGGAGG
+GCTTACT
+AGTGTCT
+CGATTGG
+CTTGGATT
+ATCTAAT
+CCATGGAT
+GAGTTTAT
+CGAGGAGG
+GCTCTCGG
+GATTCGT
+AGGTCGG
+GCATAAACCT
+ATTTTAATTT
+ATAAGCT
+GGTACAT
+AACCACT
+GCCCGAA
+ACTTAAAT
+AATTTTCC
+GGTTGTGT
+ACGAACC
+GAAAAGAT
+GTCCAAT
+GTTGGCC
+GTTGTGCT
+CGTCAGG
+ACCAACAA
+AATTTTTAA
+AGCAACAA
+GAGCCGG
+CTATAATT
+CTTGGCT
+GGTTACT
+CTCCGAT
+ACGGGCT
+AAATAGAA
+CGGTTAA
+GCAAAGAT
+GATTTTTTTT
+GTTTCATT
+GGTTACAA
+AGCGCAA
+GGATATTAT
+AGTGTTTT
+GTTAGAA
+CGGTGCC
+ACGATAT
+ACACAATT
+GGGGGAGG
+AGGGTAA
+AAAATTCAA
+GTTTCAAT
+GCAAGTGT
+ATTTGCAT
+GCGATCC
+CGTCAAT
+GGCTGAGG
+AGGTAGT
+GAGTTCAA
+ATATCATT
+GTTTGAGT
+CGGAAAA
+GACGGAT
+ACTTGCTT
+AGCTGGT
+ACATGCTT
+CACTCCTT
+GAGCCGT
+CCATCAC
+GTTTGGTTT
+CAAAAACAA
+GCGATAT
+CCTCTAA
+GGACAGG
+ATTTTCAAT
+CAAGTAT
+GCACTGG
+GTTTGGGGT
+GAGGGTCC
+ATATACC
+CGTAAAA
+CATAAATTT
+ACACCTTT
+GCCTTCAA
+ACGACCT
+CAGCTTTT
+CCTAGTT
+CGCGCTT
+CTCCGGT
+AAGGAGAA
+CCTAGAA
+CGTGGCT
+AATAAAGT
+ACACGCC
+AAAGAGG
+GCGTGAA
+AATGTAT
+AGGTTTGG
+ACGATCT
+ACCATAATT
+CGATGGT
+AGATTCAA
+AGCAATTTT
+AAAATGTT
+CGTGTAA
+AAATGGAT
+ACCCTCAA
+GATTGCC
+AGTATGG
+CTTGTGAT
+ATATATCT
+CAGAGGTT
+AGCAATAT
+GCCTGTT
+ATTGAC
+CATCAGT
+ATTAACTT
+ACTTTTTTT
+GAATTATAGCGG
+GGTCTCT
+CGGCACT
+GAGCTTTT
+CGATTCAT
+AAAACAC
+GGCTCAAT
+GCCCCTTT
+GGTCATTT
+ACCTAGT
+CCATATTT
+GCAGACT
+ACCCTTTT
+CGCGCGCC
+AGAGTGG
+GACGTTT
+CCTATAA
+AAGGTTTT
+ACCGGGT
+GAAAAAGAT
+GGCTGTGT
+ACTTACAA
+AGAAGCTT
+ACATAGG
+CGTGGTT
+ACCCCCAA
+CACCCGG
+CCAAGTTT
+GGTGGTGG
+GTTTTTTTTTT
+GCATAAAT
+GCGAAAAT
+CCTCAGT
+ACCCGAT
+AGAGAGTT
+GCTCTAA
+GCTGGAG
+CCCTGAA
+AGTAGGT
+AATGAG
+CAGGCAT
+GCACGCC
+CAGCCCAA
+ACGGGAT
+CAGAACAA
+CTTGAGT
+GAGTCTAGTTT
+CGAAACT
+ACGTGAA
+AGCTTTAT
+CTTGTGTT
+CCATAAAT
+CGAAGCAA
+CAACCAA
+ATTTAACT
+CATTCAAT
+AGAGCAC
+AATTTGTT
+AGAAAACT
+GGGGGGT
+CTCCCTCC
+CTTCTGT
+ACCAAGAA
+GGACGGT
+CCCTCGT
+AGATGTGT
+CATAAGT
+CTATCTCT
+AATAATAT
+AAAGAGT
+CCTACAT
+ATATCTTT
+GATATTTT
+CCATGCAA
+GGCTGAC
+CGACGGCGG
+CACATAGG
+ACAAGCAA
+ATATAAGT
+GAACCTT
+AAAAAACT
+GAGGACT
+ATTATTAT
+CATATCAA
+ATTAAACT
+CCTCTGG
+CAGAAGCT
+GATGTTTT
+CATGTCAT
+GAATCTTT
+GATTCAAT
+CCATCATT
+AAAGAGAA
+ACAAAACC
+GATAATTT
+GCGTCAA
+CTCCAGG
+GTTATTTT
+AATGTGT
+CGAGTTTT
+GCTCTGT
+GTTCAGT
+GAACGG
+AGATATTT
+AGCGGAA
+GATCTAA
+CAGGTGG
+GCCGACC
+CGCTCATGGCT
+GGGTAGG
+GGGTGACGT
+CCAACACC
+CGCTTCACTT
+GGTTGAGT
+CCCTAGG
+GAGGAGGT
+CATTACTT
+CGCTGAT
+GAAGAATTT
+GAGCAGCT
+ACAGTCC
+GCTTTGTAT
+CGTCCCAA
+CACACATT
+CGGGTCC
+CGGATCT
+GCCGGCC
+GAGATCAA
+ATTTGAC
+CGGTCTT
+GAAGAAAA
+CGCCCGCC
+GAATGTTT
+GGTTGGCC
+AATAAACT
+AATTTCAA
+AGCTCATT
+AAGGGTT
+CGCACAA
+ACCATGCTCTT
+GCTTGCTT
+GAGAAAGG
+AGTGGTTT
+GATTAAGT
+GGTTGGTT
+CGTAAAT
+ATAAACAA
+GGGGAAAA
+CGTCTCT
+GAAAAAAT
+AATTACAA
+GAATAGAA
+AGACCACC
+GATGTAA
+ATATACTT
+CATACCT
+CGTGCAA
+CACGAGT
+ATTAAGTT
+ACAATTCT
+ACTTTAAT
+GAGAGAGAA
+CGTCCAT
+ATATAAATT
+GCCCGGT
+GAAGCAG
+GGTGTAA
+GCATATAA
+CTTTGCAT
+GCTGGCC
+AATCTATT
+AATTTTTGT
+CGGGGGT
+GAATATAA
+AGCCAGG
+GCCAACAT
+GATGGCGT
+GATATCAA
+AAGGAAT
+GATACAA
+CACAACAT
+GATATCTT
+AATTTTTTTTT
+AATTCCTT
+GACCAGG
+ATATAAAAT
+AATTATCT
+CATTTTCTT
+GCCAAGCC
+ACATGGAA
+GCCTCCAA
+CTTTCTCAA
+ACAATTAA
+CTGTGTT
+GCGGATT
+AGTCAGT
+CGTTCCT
+GTTAAGTT
+ACATCAAT
+GGAGAGAT
+CCACCTTT
+CTTTTTCTT
+GAGACAAT
+CACGATT
+AATTCTAT
+GAGTTTTGG
+AGAGATAA
+GTTTCCTT
+ATTGTATT
+GGAAGTGG
+GAACAAT
+CGTGCCT
+GGACAAAA
+CTTTTTTTTTT
+CTTACCT
+GAAATAAAA
+GATAAATT
+GCTAAAAT
+ATTTTATAA
+GAGAGTTT
+CGCAAGT
+CCAAGGCT
+ATATAAGG
+AATTAACT
+GATCTCC
+GGGCGTGT
+GAGCATTT
+AGGGGTCC
+GCCTTCTT
+CCCTCGGCT
+GTCCCCT
+CGGTGCT
+ACATGTAA
+CAAAATCC
+AGCAAAGG
+AGAACAC
+GTCTCGT
+CATCCCT
+GCTAACC
+CATAAAAT
+AGAATAAT
+GAACCAA
+AGGAAGTT
+GGATGGAT
+CTTGGAAT
+CATTCCAA
+AAGTCGTT
+CGCTCCT
+GCACACAA
+GGTATCAA
+ATTTTAAAAT
+ATGGGTT
+CGCAAAT
+AGTTATAT
+AGTTTTCT
+CACAAGTT
+AGAAAACC
+CCATCTCT
+AGTTAAAT
+CCTCCACC
+CAATCCTT
+GCAATTAT
+GTTTTCTTT
+GAATGCTT
+CGGCGTTT
+ATTGGAAT
+CCTGACC
+GACAATGT
+CAGTGCC
+AGTCACC
+ATTTGCAA
+GACTCGT
+ACGAAAAT
+GAGGAAGT
+AGCTTGAA
+GTAACCCC
+CAATCCACT
+CTTGTGG
+AAAAGCC
+CAGTGCAA
+CAGCGAC
+GGACGCC
+ACTCCCC
+ACCAAGTT
+ACTTTGAT
+CAATACC
+AGATCATT
+GGTAAGG
+GTTTATAA
+CAAAAATAT
+GCTGTCC
+AGTCCCC
+GCAGAAAT
+CTTTTATAGCC
+AGCTCGC
+GGAAGAAT
+CCTAAAAT
+AGGTTTAT
+CCTGATT
+AGAGAATT
+GAGCGAT
+AGGGACC
+AAGTAAAT
+CTCCCCCTT
+GTTTCAC
+GGGGTCGGGCGG
+CTTCAGT
+GTTCGCC
+ACCCTAGG
+GAGGAAAT
+GCCTTCTGT
+ATCTGAT
+ATCCTTTT
+AGGGAGAA
+ATTTCATTT
+GATTCGG
+CATTGGAT
+AGACGAT
+AGCGTAT
+AGAAGACT
+CAATTGAA
+GGGCGAG
+AAAAAATAT
+GATGTGAT
+AGGTGCTCT
+GTTTTTGTT
+GAAAAAGAAAA
+CGCTCAA
+ACTTTGAA
+CGCGGCT
+CAGACAC
+GGGGCAT
+CACGGAA
+CTTATGT
+CTTTGGAT
+ATTTTCATT
+ATTTTCCTT
+GACAAGCT
+AGCCATGT
+CGCAACAA
+AGCCACAA
+CGCATAA
+GAGGACCTT
+GGGGACGG
+GCCGAAGCT
+CCTACCT
+AGCCACGT
+ACTGGAA
+GGCGGAA
+GACTTTGT
+AGATCAC
+AGATTTCT
+ACTCCCT
+AGACTTGT
+AGTTAGTCC
+AGAAGATT
+AAAGAATT
+GCTTCTCAA
+CATTGCAA
+CGCGGGT
+GTGTCCT
+ACTTTAGGT
+GAAAAAGT
+GCCAAATT
+ACAACCTT
+GCTTTGTT
+ATTTATGTT
+AAGGGAA
+ACTACCT
+GCGCGCT
+ATTGGAT
+GCATCGT
+GCCTCCTT
+ACTCCTCT
+GCGTCGG
+CCAACAGT
+ACCAGGT
+CTTCTCCTT
+ACTCCTTT
+GCCAGCT
+ATTTAGAT
+GACGTCT
+GAGACGG
+GATAGCT
+AAATGTAA
+CATAACAT
+GAGTAAAA
+GCGGTGT
+CCAACCAA
+GGGGCTTGCTT
+GATATAAT
+CAAGAGG
+CAGCGAT
+CTAAACAT
+GGTGCCAT
+ACTGTAA
+AGGAAATT
+CATTCGT
+GCCCCTT
+GCCAAGAA
+CTGGAAT
+GTTGACT
+GTTTGAGG
+ATTCTATT
+GATTTTTCT
+GGGGAGT
+GGGGACC
+CCTGCAT
+ATTTTTTGT
+CACGACT
+GAGCTTCC
+ATATGGTT
+GCAAACAA
+GAGAGGAA
+GAATTAAAA
+ATCCCTT
+GGTAACAA
+CGAGCCCT
+AGGATCGG
+AAAATTATT
+CATATGCT
+CTATGCT
+AGTTCAAT
+CGAGCGT
+GTTGGCTT
+CGGTCAT
+ATTTTTTTTTT
+CCATCACC
+CGGTCGT
+GGGTAAAA
+AGTCGAC
+GATGCCT
+GACCATAT
+CACATATT
+CTTTGTAT
+ACTGAC
+AAAAGGGACC
+CAGCAAAA
+GTTCACT
+GCCAACACAA
+GATATCAT
+CCTCCGAA
+ACAAATTTT
+GAGTTTAA
+AATATAATT
+CGATGCT
+AGCCCAAT
+ATAAAGAT
+ACGGTGT
+CTCTCCTT
+AGACACC
+CACAAAAAT
+GATGGCTT
+GACTAAATT
+CATTGAAT
+CGCCATT
+AAACAGT
+GGGTGAG
+AAATAGAT
+AGTTTGAT
+CTCCACCT
+GCACTTAA
+GATAAGAT
+ACGCATT
+CAGGTCT
+ACTTAGTT
+GCAACTCT
+GCAAAGGT
+GGCGGCGG
+ATGGGAA
+ACATGTTTT
+ATGTGCT
+CCTCTTTT
+GGAAATAT
+GATGGAAT
+AAGTGCT
+CTTTCAG
+GTTATGAA
+CATAGAA
+AATTCTAA
+CTTCTCTT
+AGGACCT
+ACATTTAA
+GGTGCGCC
+GGACTTTT
+CCTGCCT
+GCGGGGAA
+ATTCCAAT
+ACTCGAC
+GACATCAA
+GGCTGGT
+CCTCGTT
+AATCTAAT
+GCCAGTT
+GCACAAAT
+ACTGTAT
+GAGCGGT
+CCGTGAA
+CCTCGCT
+ACGCCGT
+ATTGTGTT
+GACTTAAT
+GAGTTGAA
+GACAAAGG
+GGCCGCCT
+AATTACTT
+CTATAGT
+CCAAGAAT
+GCGTCGC
+GCGGCGT
+GAAGAAGAT
+ACGTGCT
+AGTTGAAT
+CATTGAGG
+CACCGAC
+CTCTTCAA
+ACGTACC
+AATAAGAT
+CAATTGAT
+AGCCTTCT
+CTTGTGGT
+CCTTACC
+CCTCTTGG
+GCATTTAA
+CCAACTCT
+ACACCCAA
+AATTTGAT
+GCCCAGG
+CCTTAAAA
+CATTTTTAT
+ATAACAAT
+CACTTGTT
+CTTAAACC
+AGTGATGAT
+AGCCAGCC
+AGGGCTT
+GCAAGGAT
+AGCTTCAT
+ACAAGTGT
+CGGGGTT
+ACATGGAT
+GGGGGAGAT
+GGCTAGT
+ACACTTAT
+GCAAATTT
+GAGAAGAGAA
+GCGCTGT
+CACACCTT
+CGTTTTCC
+CTCTCAAT
+GACATGAA
+CTTGAC
+AGAAAAAAAA
+ACCGCCT
+AGAGCAAACT
+AATTGGACT
+CAGGGGT
+GTGGCCAA
+AATCGAA
+ACGAATTT
+ACGCTTT
+GTTCTTTT
+CATTGTAA
+AATGTCAT
+AGCTACC
+AATTGGTT
+CTGTGAA
+CAAAAAGT
+CTTTATAT
+ACAAGGAA
+GCCAGAG
+CGAATGG
+CAGATGGGT
+CTAACTAA
+GTTTACC
+CCATGGCC
+AGGGTGT
+AGAATATT
+GCTCGTT
+AACCAAAA
+GAATGATT
+CGCGCAC
+CCTACTT
+ATTTTGTAT
+AGATTTGG
+GGATCGT
+AGAAAGTT
+CCTGGAA
+CGGGGCC
+ACGCCAA
+AGGGGGTT
+GAATTCTT
+GCCGTCC
+ACAATCAT
+GAATCCTT
+ATTTCCAT
+AGAACACC
+AAAGACT
+GCAAAACT
+GCGTGAT
+ACATACAA
+GCTTCAG
+ATATGTAA
+CCTCCACT
+ATTTGACT
+AAAACTTTT
+ACATGCCAT
+GAGACGT
+ACAAATCC
+AAATCGT
+AGGGTCT
+ACAACAAAAT
+GAACTAA
+CCCAATTT
+CGAACGT
+GTTATATT
+AGCTAAAA
+AGATTTTTT
+CTCGGGGGCT
+CTCTCCAA
+AGTCTCAA
+CATTGTAT
+CACTGGT
+CACATTTTT
+GGGGACT
+GCCAAAAT
+CAGAGAGG
+ACGGATACCT
+CTGTGAT
+AATTAGAT
+GTTGACC
+AAAATTGG
+AGCCGTTGG
+CTTCTTCTTCTT
+CAGTCGT
+AGGCAGG
+AAAAAAATAT
+GCAATCCAA
+CGACGGT
+GCTAAAG
+AGTTATAA
+AGTTCGT
+CTTCCACT
+GAGGACC
+GCAACAG
+CACCATGT
+GTTCGAT
+AGAGTTAT
+CGCAATT
+GCAGAAGT
+GAATGTGT
+ATTTCTCC
+AGAAGGTT
+GGGGTCGG
+GATTATAA
+GGCCCAAGG
+AAAAAAAC
+CTTATCT
+GTCCACC
+CAGAAACT
+GTTATGG
+GGAAAGAT
+CAGCGTT
+GTTGGATT
+GTAAAGT
+AGAATACC
+CCTTAGT
+GAGGGCC
+GCATCAAT
+GGAGTAGGTTT
+CGATCGAT
+CCCTGTT
+CAAATGCT
+CTTAGAT
+CCAACAC
+CCTGACT
+GCATGTCT
+ACGTTGT
+GGTTACC
+GAATTGAT
+CCATAATT
+AGAAAGAG
+AGAAAAGAA
+GAGTTTGT
+ACGATAA
+GCTATGG
+GCATGAGTT
+GCGGTAA
+CGCCGAT
+GGTGGTTGAATT
+ATTAAATTT
+ACAGCCAA
+AGTATTATT
+GAGCCCGAGT
+GAACGT
+GCTGTGG
+ACAAGTAT
+ACTGTGT
+CTTTGGCAT
+CGGAAGG
+GATGTCAA
+GACGCAT
+GACAATAT
+AGTTATTT
+CATTATGT
+ATTTTCTCT
+ACAGCGAA
+AGTACAC
+CATTAGCT
+GAGAGAAT
+AGAAGTTT
+ACAAGAAT
+CTAAGGAT
+AATTTCTT
+CGCCAAAA
+GAAAAAGTT
+AAATCGG
+CATTTGGT
+AGATATAA
+GTTCGGT
+AGTCTTTT
+CGAACAC
+ATTCTTTT
+CCCCCATTTT
+CCACAAAA
+ATTTTATGT
+CTCTTTTTT
+GGCCAAAA
+GCTCCGG
+GGTGCAC
+GTTTCTTTT
+CAGGACC
+GACGATT
+CCTGTGT
+GCAATTAA
+AGAGCGT
+CAAGCAA
+ACACATGT
+CCTCCGCC
+GAGGAGGAGG
+CATATGAT
+AAATGTAT
+AATATATTT
+CTTCTCAT
+GGAGAATT
+AGAGAAGT
+CACAAGCTT
+AACTGAA
+AAAAATTAT
+CGCCTTAGAG
+ACTTGAAT
+AGTTTAAT
+CACCGAT
+CAATTTGG
+CGTCAGT
+GGTTTTAT
+ACCAAACC
+ATTAGAA
+CACCACAA
+AGATACC
+GGCTAAAA
+CACGTTT
+CAACTTT
+GGATGAG
+ACGATCC
+CATTGGTT
+CACTCAAT
+GGTCCAT
+ACTTGTAT
+ACTTATAT
+GGGTGAGT
+ACGCACC
+ATTTCAGT
+GCGCGCCT
+GCGTTCT
+AATGTAAT
+CGTTGCT
+CTTGTAAT
+AATTGTAT
+GGCCTTTT
+AGTTTAGT
+CTTCCCT
+CAAAAGCT
+AGAGACAT
+GCTGCTGCT
+CTAAAGTT
+CGCTAGG
+AAACATTT
+CTTGACT
+AGAGCAAT
+GTCTGAT
+CTAAGTTT
+GAGAGCTT
+AGCTCAC
+CCCCACAA
+GAGGAATT
+GCTTCTCT
+ACACGCT
+AGCAGTTT
+GTCTACT
+GGTAACCCC
+AGGCGAC
+AGCACCTT
+ATTAGAT
+GACCAAAAT
+CGGCGAA
+CAAAAACTCGT
+CGGGTAT
+ACCTACTCCCC
+CGCCGAC
+ACTTTCAT
+GTTCTAT
+ATTCATTT
+CATGTGAT
+ATGTGGAT
+CATTTTGG
+GACTAGT
+GCATCATT
+AGGAAGAT
+CCTATCT
+GAGAAGAT
+GAATCGT
+AGTTACC
+GCGGGGT
+GGGGGCC
+GAACCTTTT
+AAATTCAT
+GCGTCAC
+CTTTGTGT
+CAAGATT
+AGGAACAA
+ACGAGCC
+CACTACC
+CCCTGCT
+CGTGGGT
+CTTTTTTTT
+ACTCCGT
+CCTTCAAT
+ACATCAC
+CCAAGGAA
+CGGCTAA
+ATTTTTTCT
+ACAATAAAA
+AAGTAAAA
+CCTTGCAA
+AGCACTTT
+GATATGTT
+GTTTGTTTT
+ACAATGAA
+ATTTTGTAA
+AATGTGAT
+ACTGGGT
+GTTGGGCAATT
+AGATGGAT
+ACCTCATT
+CACATCAT
+AAGGCTT
+AATAACC
+ACTAGGT
+AGCTCTTT
+GTGGGAT
+ATAAAATAT
+AGATGGTT
+CTTGTCTT
+AGAAGAAAA
+ATTATTTTT
+ACTCGC
+CATTTTCAA
+ATTGTGAA
+ATTCTGAA
+ACCCCAT
+AGAAATGG
+AGAGGCTAA
+GAGCATGT
+CGCTTAT
+GGCCAAAT
+CACTCCCTT
+GAATGCC
+CAGAGCAA
+GGGTAGAA
+CCAAGGCAGCT
+ACAATTGT
+GTTGCAAT
+AGTATCAA
+CTTAACAA
+CTATGGT
+GCACAGT
+AGGGAGT
+CCACGTTGTGG
+ACTTGGTTT
+CTCAAATT
+GCCTTCCT
+CTTTGGGT
+AGAGGCAA
+GTCTGCT
+GTTTATGG
+GCCTCATGG
+ACAAGAGG
+ACATCCAT
+ACCTCAC
+CTCCCGT
+CCTGGTT
+CGCTCGCC
+CCTCTATT
+ACACGAT
+AAGGAAAT
+ACTTCTCT
+ACAAGCAT
+CAATTAATT
+GAGAGAC
+CATCAAAA
+ACACCGT
+ACTCAGCT
+ATGTAGG
+ACAACCAA
+CAAGTGT
+ATAAAGAA
+CCTAACT
+CGTTGGT
+GCGGCGAT
+CACTTGAT
+CTTGTATT
+AATTGGAA
+CACCAACC
+AGAGAGGT
+ACTTCAAT
+CAACCAAT
+CCCCAAT
+CATGGCAT
+CTCCAGT
+CTCTCTCTT
+AATGGAAT
+ACCCAAAA
+AGCCACAT
+AGTTAGTT
+CTCTAAGT
+CGGCTCGG
+GTTAAATT
+AAAATTTAT
+CGCTCAT
+GAGTTTCT
+GAGTCCT
+GAATCAAT
+GCACATAT
+CATGGTGT
+CGCGACC
+CCTTCAC
+AAATCAC
+ATTTTTAGT
+CCCCCATT
+GCTAAATT
+GAACGAA
+CTTATAA
+CTTTCAC
+CCAAGTGAA
+ACTCCAC
+CGGGAC
+AGACATGT
+CTTCCTTT
+GAAGTGG
+CTTTTGGT
+CGCAGTT
+AATAATATT
+GGGGGGAA
+AGCTCCAA
+AAGAACC
+AGCGAGCC
+CGACAGCC
+ACATGAC
+ACACTCAA
+CCTCTGCAG
+GTCTCAC
+AGCTTCAA
+CGGCCACC
+GAGAAGCAA
+AATAAAATTTT
+GCATTATT
+GAGACTTT
+ATTCTCAT
+CACAAAGAA
+AGAGGAGG
+GACCGAGG
+AGTCAAAA
+GAAAAGCT
+ACAAAACTT
+CCCCGCC
+GGAGAGCC
+CTTTTTGG
+CAGGCCT
+CTCTATCT
+ACTGTTTT
+GAAATAAAT
+CATCATCAT
+GCCTCAG
+GAGTAATT
+CTAACTTT
+ATTTAAGG
+CTAAGTAT
+GCATTCAT
+CGAAGAAGCT
+GTAAGGT
+GCGGAAT
+GACTGGT
+ACATCGCC
+ACGCAGG
+GAGCTAAAT
+CTAAGGT
+GAGTCGG
+GAGCACAT
+GGCTCGT
+AAACAATT
+GTTTAATTT
+CACCAGT
+CACCCGT
+CGCCGAGG
+GACATCTT
+GCCGCCGCCGCC
+AGAGTTGT
+GTGTGGAT
+GTGGCCT
+CACCCCCC
+GATTCCTT
+CGAAAGAA
+AGATCAAT
+AGTTTTGG
+GAATAAAAT
+CTGTATT
+AGCCCGACC
+GCATCCGGGG
+AGGCACT
+CACCATAA
+GCCTAGT
+AATTGCTT
+GATGATCAA
+GATTTGGT
+CTCTATTT
+GATTGGAT
+AGCACCAA
+ACAACTTTT
+AGTTTGGT
+AAATGCC
+CGGACCT
+GCTGTTT
+AGATGTCT
+CCTCTCAA
+GCTTCACTT
+GCACGAT
+AGCGTCC
+CAGCAAGT
+GCAAGGCT
+ACAAAGATT
+GAGATTAT
+GCGGTGGT
+CTTCTGAA
+AATGTCTT
+GCAGCGT
+CAGTTTAT
+GTTATGTT
+CTAAATTTT
+GCACCTCC
+AATCCCAT
+AAATGGTT
+CACATAAT
+AGGAAAG
+AGCAAATTT
+GTCCAGT
+AAACACC
+GGCCGAT
+AGACAATT
+ATAAAGTT
+ATATGGAT
+ACTAACAT
+AACCCAT
+AGATGTTT
+GCCCGCT
+GTTGTGAA
+CCCTATT
+ACCTTGTT
+GAGAAAGAA
+AATTCCAA
+CATGTCAA
+ACCACACC
+CTTTATTTT
+CCATGCTT
+CTATGTTT
+CACAATAA
+GTTTTATTT
+GCGCTAT
+ACCTTCAA
+CGACTTCT
+GAACGGT
+GTCTGCC
+CGGCCGT
+GAGTTGTT
+ACAAGAC
+CGGATCC
+CGCGTGT
+GAGCCTCT
+GTTTTAAAT
+CGGCTCCTAGCT
+GAAAAACTT
+ACGTTGG
+ATCTGTT
+CAATGAC
+AATAATCC
+GAGAGAAAA
+ACCATGAA
+AGCATGAA
+ACGAAATT
+CTCTAAAA
+GGAAACTT
+AGATAGAA
+AGATATGT
+AATGTATT
+ACCCCTAATTAT
+CTAAAGAA
+GCCTCCCT
+GACCGTTGG
+GCTGTCAA
+GGTCTCGG
+AGGCCAT
+GGTGAACT
+AGACCGG
+GAAAAACC
+CACTAGAA
+CCCTTAGG
+CATCGCC
+GCAAGCAA
+CGTCGAG
+CATGTAAT
+AGTCGGT
+ATTCAGG
+GCCTTTGT
+ACCCTGAA
+AAAAATGAA
+CTTAAGT
+GCTACCT
+CACAGCAA
+ACTTAAGT
+CGGGAG
+GCAGAGCT
+CCTCTAGG
+GTTGTATT
+CTTTGTAA
+CGTCCCT
+GACTAAAA
+GACTTCAA
+GCTTGTGT
+AGTAACC
+GGACCGTCC
+GAGGACAA
+GAGTATAT
+CTTGGGCC
+AGGATCAA
+CTTTGTGG
+GTTATAAT
+ACTCTGT
+CATTTTTTTT
+GCACATGT
+GAGTTGAAGT
+CGGCCAC
+ATTCTAAT
+CAAGTACC
+AGTTGTGT
+ATTTTCTTTT
+CACAATAT
+ACTTGAG
+ATAAGTTT
+AAAAATAATT
+CGAAGGAT
+CGCGAGCT
+ATTTTTCCT
+ACAATTTAT
+GGCCAGCC
+ATATCTAT
+ATTCCTTTT
+AAATAACT
+CGAAAGT
+ATTTCAAAA
+CACTTTCT
+ACGCGCC
+GAGCAAGG
+GGGCGTCC
+GGAGAGAA
+CAGTTTCT
+ACACACCT
+GCCCGTT
+GCGCGCAA
+GGAGCACC
+ATTGTCAA
+ACAGGCC
+GCACATTTCC
+GATCACT
+AATTCTTTT
+AGGTACAT
+GCCAAAGG
+AAAAGGTT
+CGTCTCAA
+CACCTATT
+ACTGTCT
+ATAATTTAT
+CGATGAAT
+AAGAACAA
+AGGATGAA
+AGTTCCAA
+GGGCATAT
+ACCGATT
+GTTCTCCT
+GCTTCCAT
+GCAATAAT
+GACCAACT
+AAAACGG
+ATTTTATTTTT
+GGTTGGGGCTAT
+AGGGTCC
+GAGCACTT
+CTCTTCGG
+AGCATTTTT
+CCAACTTT
+CGCCCGAA
+CAGGTTAT
+ACTACTTT
+GTTGTGCC
+AGCCCCCGAGG
+ATTGGATT
+GCAACATT
+GAATTTCC
+CGCAGAT
+CCATAGAT
+GTGGCGT
+AGAGACAA
+ACGTTTTT
+CTTCCCAA
+AGAAATCT
+CAGTAGCC
+ATTGGGCATGTT
+GCATACAT
+CCCAAAAT
+CCATCGCC
+GCCCCCT
+GGTAAAAT
+CTCAAGAA
+CTTACTT
+GGTTGTTT
+CAGAGTTT
+GCGTTCC
+CATATCCT
+CAAAATCAA
+GGTGGACT
+CACCAACT
+ATTTTGAAT
+AACAAATT
+CAGCGAG
+CATTGCTT
+AGAAAGAAAA
+CTTCTCCAA
+GATTTGCTT
+CCTTCTGT
+GAGACTAA
+CATGTGTT
+CTTGTAGTTGT
+AGTATAAT
+GAAGAACT
+GTTTTGCT
+AAGGAGG
+GGGTAGT
+ATTTTAAATT
+ACGCAAT
+CCACAGG
+CGTTCGCC
+GATATGAA
+CATCTAT
+CAATGATT
+ATTTGAAAA
+AGATACAT
+CCCCACC
+GTATGCT
+CTTAGTT
+CAGGAACT
+GCATTTTTT
+GAGATATT
+CGCCGCGG
+ACTGGAT
+CAGGCAC
+CCAAGAC
+GCACTCAA
+GCCCAAGT
+GGCCACCT
+GGTTTCGC
+GGAACAC
+AAAACTAA
+CCTCAAAA
+CATCGAC
+CGAACCCCT
+AGAATTGT
+ATGTGATAAT
+CGCCGGT
+GATAAGTT
+CTTAATTT
+ACTTTGGCT
+CATCTTTT
+GCAATTTTT
+GCCTACC
+GCCCGAT
+CAGGGAC
+ACCTCTTTT
+GAATAAATT
+GCCAGGT
+CAGCCGAA
+AAGTCCAT
+CGAACATGCC
+AGAATGGT
+GGCCGTCC
+GCCGCGG
+CTTTAATTT
+GCATCGC
+ATTTATATT
+ATGTCGT
+GAGCTCGGT
+CCAAAACTCAT
+ACTTGGAA
+CAATTTCC
+GATCTGG
+CCATGAC
+GGCACC
+AATTTTAAT
+GATCAACTT
+CAACTCCT
+AGGTGATCCTGT
+ATTTTCGT
+GAATCATT
+GTCCAAAA
+CACATTCAT
+AGTTTTTTTT
+AGAAGTAT
+AAATAGTT
+GAATGGTT
+CAGAGAC
+ACTCGTT
+GAAGGCC
+AGAAGCAT
+CTTAAGG
+GTTGGCAA
+GTTGTGAT
+ACGCGGT
+CAGGGAGG
+ACTCAATT
+GTAAATAT
+ACGCAGT
+AATTCGG
+AGGCAAGT
+GGAAGGAT
+GCTTGTTT
+ATGTGATT
+ATTTTAGTT
+ACGCTAA
+CATTGCCT
+CAGTTTGT
+CGATCCT
+CGTGACC
+CGAGAGT
+CACAGAG
+CCTCCCAA
+GCGCTAA
+CAATATCT
+CAGCCTCGG
+CACACCAT
+CGCGGACT
+GCAGAGGT
+AAAAAATAA
+AGATGATT
+CACATATAT
+CTTCTATT
+CATCGAA
+CATCAATT
+GTTTCAAAT
+GGGTCTTT
+AAAGCCAT
+CTTCGCT
+GGGGGTGT
+AATCCCT
+GCCTCCGT
+CCACACAA
+CGAGAAAA
+ATTAAAGT
+ACTTTGTT
+CACCATTT
+GGAAGGAG
+GTCCATAT
+ACCAAACTT
+GGTTGGGAA
+CACCTTGT
+GTTATCTT
+GGATCCTCT
+AGGGTATT
+GCGCATT
+GAGGGAG
+ACTTACTT
+AGCAATGT
+AGTGGAAT
+AGTAGGAT
+GAGAAGCT
+GGTCTTGTT
+GCACATTT
+CTTTCCAA
+CGTAGAA
+CCTTGGGG
+ATGGGCT
+CTTTCTAA
+ACATATTTT
+CACTTCTTCTAA
+GAGATGGAT
+CATGTGAA
+CTTCGAA
+CGCGAAT
+CAGGAGGT
+CACTTAAT
+CTCTCGG
+GCTTGTGAA
+GCATACC
+GCTTTGAA
+CCTGGAT
+GTTTTGTTT
+CAGAGATT
+CGCTTAA
+ACACACTT
+CCCAAACC
+AGAGTTCT
+GAAAAACAA
+GCGCCACC
+CATGAGT
+GATTTAATT
+CACGCGT
+CTCCCGG
+GAATTGTAT
+CGGTGGCGG
+CTTTTTTAT
+GTGTGCC
+AACCGAA
+AAAAAAAAAT
+GAACCTCGGT
+CATTAAGG
+ACCTTGAA
+AGCATCAA
+AGTGAAGAA
+ATATATATGT
+AAAACCAA
+ATATAGAT
+ATGTGCTT
+GACGGAC
+CGTCCAA
+GACCCAAT
+ACTATCAA
+GTTAATTT
+AAAATTGAA
+AGGGCAA
+CCTTCATT
+GTTCTAA
+GTTGGCGG
+ACACCTCT
+ATATCCAA
+ATCTCCT
+GCAGGTTT
+GAGGGATT
+GACTACC
+CCCCACT
+GCAGCAC
+ACAGAATT
+GAGAATAA
+CTTTGTTTT
+ACCTCCTTT
+AGAGGCTCGT
+CCGTCAA
+CCATGGAA
+AGCAGCAA
+GACGGCT
+GTGGAGATTCCT
+ATGGACT
+GACACAAT
+ACACTGG
+AGAGCGG
+AAAACAAAA
+AATTAAGAA
+CAGTTGAA
+GATCTGT
+GGTTCAC
+GATGTGTT
+AATCCAAT
+GTTTGACT
+GCATAAGT
+CACTCTCT
+CTGGGTT
+CTTCGC
+GCCGATT
+CTATCTAT
+AGCTCCTT
+AAAGCCT
+ACCTCTCC
+CAGGGGTT
+AGAGCTCT
+CTTCTTAA
+CACCAAATT
+ATTATAATT
+ACAACTCT
+GCTTTGAT
+CGAGGTCT
+AATGGTTTT
+CATGTATT
+AAATGCAA
+GCAATGAA
+AGATGGAA
+CAAATGCAA
+AGTGGGG
+CACACATAT
+CGTAATT
+ACATAATTT
+CTTTATGT
+AACTGAT
+GCTCAGT
+AGAATTCT
+AGCAATAA
+GAGCTTGT
+AGCTTTGT
+TTTTTTCC
+AGACAGG
+GTTCAATT
+CGATCAC
+AGTTGGTT
+CACCATGG
+CTTTGCCGAGT
+CCTCCATT
+AGTAGCC
+ATATCAC
+CGACTTGT
+GCGAGGAT
+CCTCTCTT
+AGAACAAT
+GCTCGGT
+GATGGAG
+GCCCTAA
+GAATAAGT
+AGCTGACC
+GACAGAGG
+GCCACGG
+ATTTCCAAT
+AGGGTCAA
+GCCCCGG
+GGTGAATTTT
+GCTTGGTT
+AAATGGGT
+GCCCTCGG
+ACCTAATT
+GAGTCATT
+GGTGTTTT
+AAAAAAAG
+CTCTACTT
+GTTTCACT
+ACTTTTGG
+ACGCGAC
+GGTGGAC
+GCCGGTT
+AAACGAA
+GGAAGCAA
+ACTCTCC
+GAGGAGAT
+ACGTTAT
+AAAAGAG
+AGACACAT
+AGACATCT
+GATGATGG
+ACAAAGGT
+CTTTGACC
+AGTTGTAT
+GATTAAATT
+CAGCCTTT
+ACTTATAA
+GGTCCATT
+AGCCAAAGTCC
+CGAAGCAG
+CAGAATGT
+AATTTGAA
+AGTCAGG
+CAAATAAAT
+CTAAGGTT
+ACAGAGAA
+AGATTATT
+GTTCGTT
+AGGGTTTTT
+CAGCTTCC
+GCGGTCT
+ACCATCTT
+ACTTGTTTT
+GAATGTAT
+AAGGGAT
+GCTGCGT
+CGTGGAT
+CAAGTCAT
+GCATGTCC
+CACCACAT
+AGCACCAT
+GAGGAGTT
+CCATGACC
+CGGCAAT
+GCACAACT
+CTTATATT
+CGACTGG
+GCCCCAGCC
+AGCTAAAT
+CAAAACAAT
+ATGTACAT
+CGAGCCCC
+CCTATCC
+GGTGCGG
+GACACCAA
+GAGACATT
+GGACTGG
+AGCAAAAGT
+AAAACCCT
+AATCTCT
+AGAGATGT
+CAAAAATTT
+CACTAATT
+AGAGGGGGGT
+AGGATTTT
+ACACATAA
+GGTTCCGGTGG
+GAGAGAGT
+GATTGCCT
+AGTAAGAT
+CTTGTCC
+CCTTGGAA
+CAATTGTT
+CACGGTT
+CTAAGGAA
+GCGCAAGAGCT
+CGCCCTATT
+ATGTGTAT
+GTTTTAGT
+CAGCTTCT
+ATATATGG
+ACGTACT
+GGTGGTTT
+GCCACAC
+AAATAAAG
+GCGCGGGG
+GTTCCATT
+GACCACTAT
+ATATACAA
+ACGAAGAAAT
+AGGCTAT
+GCAAGAC
+GATTACC
+GTTTCTCT
+ACTTAGCTT
+GGAAGACGG
+AAATCTAT
+CACACTTT
+CGCCACT
+GATGATGAT
+ATGTAATT
+CCCCAGG
+GCAGCAGT
+AGATCGT
+GCACACTT
+GGATGGAA
+AGATGAAAA
+GTTGTCAT
+CTTTATCT
+CAATGTAT
+CCACAGT
+GCATTCTT
+GCCAGCC
+ACATAAGT
+GAGTCGT
+ATGTAGT
+ATTTTTGAA
+ATTCCATT
+CCGGCAA
+AGAAAATAA
+CACAACTT
+ACATACCTT
+GCATGGAA
+GACTTGAA
+CAATGGAA
+CTTAACTT
+GAGTTTGG
+AGAAATCC
+AAACACAA
+GGCTGCACC
+CGACGCT
+GGAGCGT
+NNNNNNNNNNNN
+CGTTCGTT
+CAAAACCCT
+GTTTACTT
+GCTCATTT
+GAAGGAGAA
+GGCGGGG
+GTTTCTAT
+ATATAACT
+AATATCCT
+CAGATTCT
+CTAAAGAT
+CCTTCTTCT
+GTTTGCCTCAT
+GCCCTGT
+GAGGAGCT
+AAGAAATT
+CAGAGGAA
+CTAAGCAA
+CTTTATAA
+GGGTACC
+GGTAGCC
+GCTTCTTTT
+CTAAGATT
+CGCGGTT
+CATGGCAA
+AGGGGGG
+GAGACCCT
+CCAACTCC
+GTTTGTAA
+CGAAGAC
+CGACTTGG
+AGTATATT
+GCTCCAAT
+ACTATATT
+ATGTATTT
+AAATTGAA
+CATTGACC
+CATTATCT
+GAGTGGTT
+AATTCCAT
+GTTGATTT
+ACATAGTT
+CCACGAA
+GCAGAGGTT
+GTTTATCT
+CTTTGAC
+AAAAGCAA
+ACGAAGAA
+CCCCCGGAT
+CAGGATCT
+AGGGAAAA
+GCATGCTT
+ACATTTTAT
+GCAGCCAT
+GAGTGATT
+AAAAAACAA
+ACGGTAT
+GTTAGAT
+GCCCTAT
+GCTCCTTT
+CTCCGTCC
+GTAACACT
+ACCAACTT
+GCATTCCCTT
+AGTCCTCT
+ACTTATCT
+CCCCCGG
+AGGGGCAA
+AGATTCAT
+GATACCAATT
+ACTTAGAAAT
+CAAATTCAA
+CACTTTGT
+AATCCATT
+AGCAGGAA
+CCAAAGAT
+AAAAATAAAT
+GACACTTT
+GGACAGTCT
+AATGTCAA
+AATTTTTTAT
+CACCATCT
+ATAAGAAT
+CGTACCT
+ACATGTCT
+GTTGCAC
+AGGATCC
+ACCTCCCT
+ATGTAAAT
+GCATCACT
+CGGCGGCGGCGG
+ACTATCC
+GAAAATGAA
+GATTATGG
+CGGATTT
+CAAATATTT
+AGTTGAG
+GACATCAT
+ACAAGCTT
+GGAGCAGG
+CGGGCGAGGCGG
+CGAGCTAGG
+CAGATCAA
+GAGTGTGT
+CGGTAAT
+GCCCTTTT
+GATGTCGG
+CATCCACT
+CTTATCTT
+CGGCCCT
+GGGCCGG
+GCCCATTT
+GATGAATT
+GCAGAATT
+GCCAATTT
+CATGTCT
+GCTGCGG
+GTTAACT
+ACAACCAT
+GAAGTATT
+CGCCCTCC
+GGATCTTT
+GACTTTCT
+ACGCCGCC
+ACTTGTAA
+CACCACCT
+GTTCGAG
+ACGATTTT
+CGAGGTCC
+CGGAAGTT
+AAATCCTT
+GCTATTTT
+GAATCGAA
+CGCTGAC
+CGGAATT
+GATGGATT
+GTTGGTTTT
+ATATAGTT
+CGTTGAGG
+AGAGGTGT
+ATTTCAATT
+AAAAAAACT
+CAGCATCT
+CAGGCGT
+GAATAAAG
+GCACATAA
+CAGTAAAA
+CTTATAAT
+ATGTAAAA
+AGCCAGCT
+GAGCGAGG
+ACTTGGAT
+GCAGCAAT
+ACCGACC
+GAAGCCT
+GTTATCAA
+CTTTCCAT
+GCTTGGAT
+AATTCACT
+CGTTAAT
+CAAGTGAT
+ATATGAG
+AGATGCAA
+CATTAGTT
+CACAAAGT
+GCAGAGAA
+CGGAACC
+AGAGGAGT
+GCCAACAA
+AGATGAC
+ATTATGGT
+CGCGACT
+GATAAACT
+GTTTATTTT
+CAAGATTT
+CATCTGGCT
+ACGTCAT
+GAATATCC
+GGTGGCGG
+GAGATGAT
+CTTCCGAA
+CGCCGTT
+GCAAAAGG
+GCGATTTT
+ATTGGTTTT
+AGCATTAA
+GCGGAGTT
+GACCATCT
+CAATTCCT
+GCATGTAA
+GGATGAGGCAA
+AGGTCGT
+CCTCCCCT
+CACAAGAT
+GGGCATAA
+GGCCATTT
+GTATCCCGGAGG
+CCAAGTAA
+AAAATATTT
+ACAACTGT
+AAATTCTT
+ACCACCAA
+CACTAAAT
+GGTTGCCT
+CACATTGT
+CACCATCC
+GGAAGGGT
+GTTGTCAA
+AGAAATTTT
+GCCGGGG
+ACGCTCC
+GGCCCCT
+GAGGGGCT
+AGCAAGAA
+CACCTCAA
+GATCATTT
+AAACACTT
+GCAGCGC
+AGGCTCC
+ATAACTTT
+CCAAATTTT
+GCTAGCC
+CGACCTCC
+CTTTTGATT
+AAAATATAT
+CTTCCATT
+CAGGCGC
+GAGAGGGT
+AAGTAGT
+GCTTCAAT
+ATTATTGG
+CACCCCGT
+CTCTTGCT
+ACCTTGGT
+AATTTTCAA
+AAAATTAAAA
+GGAGAGGT
+CTTTTATTT
+CACGTAT
+GTTCTGT
+ATAACCT
+ACCTATTT
+CATCCCC
+AGCAAATT
+ACTAAACT
+CTTCGCC
+CGTATTT
+GCACGGT
+GAGCTTCT
+CCTTTTCT
+CAATAATAT
+GCTTTGATT
+CTGGGAT
+CCAAGGCC
+CAACAACAA
+GGAAGCCT
+CATAGTT
+GCTGATTT
+CTTCGTT
+GCCACGAA
+CCTCATTTT
+CCAAAGCT
+GATGATGAA
+CATTTGGTT
+GTTTTAATT
+AATGGGT
+AAATCTAA
+AGTAGAAT
+GAGGATAA
+GGCTCGGT
+AAAATAATT
+GATTATTTT
+AGGGGAGG
+CACACCAA
+GTATGGT
+GAAAAGGT
+GAGGGGAA
+GATTAACT
+GCGTGAG
+AATATGG
+GTTAGGT
+CTTTCCTTT
+CCACACT
+ACCATATT
+ACATGAG
+GGTGGTCCTT
+GCCGTCGT
+GAGTTGAT
+GCAAAACC
+CAATGCAT
+CGCACCGGACT
+GCTGACC
+GCGTTAT
+ACATTGAA
+CAAATACT
+CATTCTCT
+CATGGCC
+GATCATAT
+CACCTATTCACC
+AGATTTTCT
+GTTACCT
+AGTTGGAA
+AAAATTTTAT
+CTTCTTTTTT
+GATTTATTT
+ACACAGAT
+AGTGCAAAT
+CCTGTAA
+AATGGCAA
+CGGAAAT
+GCCGCGC
+AGAATGAA
+AGCACGG
+CGAAGAG
+CGGCGAGG
+AGGGCCT
+CGCCGTCC
+CAGTTCAA
+GCCCACC
+GCATTTTGT
+CATGACT
+ACCCACAA
+AGGAATTT
+GAGTCAATT
+CTTTAACT
+CTTTCCCAA
+ACAAACCT
+CTTGTGCT
+CAATCCAT
+GCCCCCC
+GATAAGAA
+CACTTGAA
+AGCGCGT
+CCAACCACC
+GCAACGT
+ATTTCCCC
+GAATCTAA
+AATGTGAA
+AGCATAAT
+ATATCTAA
+ATTTGTGG
+AGACCTTT
+CAAAAGGT
+CAGACTTT
+CGAGCTCC
+AAATGGAA
+ACCGTTT
+GCATATCT
+CATTGTGT
+AATGGATT
+ACGGTTTT
+ACAACGTGGAGT
+ATATCTCT
+AACAAGG
+AATTTGTAA
+GAGAAAAAT
+CACATGAT
+CGACGGGT
+GATTCTCT
+CAATAACT
+CTTTACTT
+GGTTGTGG
+CGACATAA
+AAAAAAAAAAAA
+ACCGGACAT
+GCCTAAAA
+CAGCATTT
+CACCCTCC
+ACAGTTGT
+CCCTCGAT
+ACACGTT
+CTTGGTTTT
+CCTGAGT
+GTTTTGTTTT
+GGACAGGT
+CGTTTTAT
+AGGGTGTT
+CGTACAA
+GACTTTAT
+GACATATT
+GGTTCTACT
+GCTAGTTT
+ACACCTAT
+CGCTCTT
+CCTCCCTT
+AAAAGTAT
+ACGTCGG
+CGCCGCCT
+ACGGGAA
+GTTATCAT
+GGTGATTT
+CTTTCTTTTT
+GGCCCAC
+GATGGTTTT
+GACAGGGG
+AATGGAT
+GACGAGGT
+CAATTTCAA
+AGCTAGGT
+GATGAAGT
+ATTTTTCAT
+GGTTAATT
+GTCCGAA
+CTTTACAA
+GAGAGGTT
+GGCCCCCAA
+GAAAACAGT
+CTTTCCCC
+GCTGTAA
+CGAGCAGG
+GGTTCATT
+CGACAGT
+AGAGCTAT
+CCTTCGTCC
+ATTTGAGG
+AAATAACC
+CGCATAT
+AGTGGCAA
+CATTTCCT
+CAGTCATT
+CTTTGGCT
+CGAGCAC
+AGTGGACT
+GCGAAGAA
+ACATCGT
+CTTTAACC
+CAAATCAAT
+ACCATAAT
+CATCCAC
+GCGCACT
+GCGTGAC
+GTTTGGCT
+GCTCAGCCCC
+ACGGTCT
+ATTGGTTT
+CACCGTT
+GAGTATGT
+CGGGAAT
+ACGCTAT
+AGTAAGTT
+GTTGATCTTGAT
+ACATACCT
+GATTCAGG
+GACTCAG
+AATTATTTGT
+CTAAAGGGAGAA
+GCACCGGACAT
+GAGGCACC
+ATTTATCC
+CTTTCTCTT
+ATAACATT
+GCTTTGGT
+CCAAGTGGGT
+GAGTTCTT
+ATATCTCAA
+CGGGCTT
+GCCACCTT
+AGTCCTTTT
+AACAACAA
+CACAAACC
+GAAGTGACC
+GAGATTTTT
+AGAAGTAA
+AGCCAAGT
+CAGCATAT
+AGTATCC
+GAGGTGTACGAG
+AGATCCAA
+CAGTCCT
+GAAAAGGAT
+AGATTGAA
+CACATTAT
+CCTTATAT
+GACACACC
+AATTGTAA
+GATTTTGCT
+CTTGTAAGT
+GAAAAATTT
+GGGGGTTACC
+CTTGTCT
+GGGGGCAA
+GAAATAGG
+GTGTAAGG
+GAGAAACT
+GGTTGAGG
+CTCCACAA
+ATATGTGG
+CATAAGTT
+AAAAAAATTT
+ACATCTAT
+ACCGGAA
+AAAAAGGT
+GGCTGAAT
+CATTTTATT
+CAGGTTGT
+GGTCTAA
+CCTTAACC
+ATATGTTTT
+AGCGTAA
+GCCAAAC
+CGTGGGG
+CATTTATTT
+AGGGGGT
+AAAAATGAT
+AGAAAATTTT
+AGTCGATT
+GAGCGAC
+CAGGAATT
+CTGGGAA
+GCCACCGT
+AGCACTAA
+CTTAGAA
+CTCTGGTT
+ACCTACC
+ACATCGG
+AGATAAGT
+ACATTGAT
+CGAACGG
+AATCGAT
+ATTGTACT
+ATTTTTAAAA
+CTTGGGAA
+GTTTGTCT
+GGGTACT
+AATCACC
+GCAAGGCAA
+AGATCTAT
+GCGCCTCC
+GCACAACTT
+CATTTGCT
+GAGATATTT
+ATTGTCAT
+GCAATGCT
+GTTTAAGT
+GCAATGAAT
+GTTTTCCT
+AGAGCTTT
+CAATAATACT
+CTTTTCTTTTT
+ACCAGTTTT
+AATTTAATT
+CATATGTT
+ACACACGT
+AGAGACTT
+GATGTATT
+GCAACGGT
+GACCCGG
+GTTTGATTT
+AGTAGTAT
+GAATAACC
+GGTGGTGGT
+GACGCTT
+CAGCAGTT
+CTTTGGCTTT
+CGATTTAT
+GTTTTGATT
+GCCTCACT
+GTTTCCAA
+CAATGCTT
+GCATGAG
+ACCAACAT
+CGGTGGAA
+GTTATGAT
+AGCCGCT
+CTTTTGCTT
+GTGTCGT
+GTTTTTTAT
+CATTACAA
+GTCCGAT
+GACGAAGCT
+AGGGGGAGAA
+AATATACT
+GACCCTTT
+CGAGAAGG
+GGTTGAAT
+GACATTTTT
+CAATACTT
+CTCCATGT
+GTTGGGTT
+CACTCTGT
+GACTAATT
+GGATGCAT
+GAAGGATT
+ACCATTTCTT
+CTCTCTAA
+GAATTAAGAA
+GCGGGCGG
+CGAAGCAT
+GTAAGAT
+CCTTTGCAG
+AGTTCAC
+AGAAGGGG
+GTTTGCAT
+GACCACCT
+AATCTGAA
+CTTTGTCT
+GGTAACC
+CGTAC
+GGGTATTT
+ATTCACT
+GGGAGATAT
+CATCATCT
+CATGTCTT
+ACACCATGAA
+CGCGGAT
+GCCCCAT
+GGAAACACT
+AGCGTCT
+GACCCCCC
+AGAACATT
+CCCTGAT
+CGCCATGG
+CCTTGAC
+CAAAAGGTAT
+ACTTCAG
+GAGCCAACGGT
+AAATAATTTT
+GATGGCCT
+CGGTCCT
+CGTCACT
+ACAAGTGG
+ATTAAGAT
+CGGCAGG
+ATTATTCC
+AGATCTTT
+ATATGCAA
+AGCACTCT
+CAATATCC
+GAAAACAG
+AGCCGACC
+CAGTAGG
+CCTTATTT
+AGTCCTTT
+GAGGATGT
+AGATTAAT
+GTTTAAGG
+CACTTCGG
+GACATGTT
+CAGACGT
+ACTCCATT
+CAAAACATT
+ACTTCACT
+AGCTGCTT
+GAGGATTT
+CGTTGCGAAAA
+ACATAGAT
+CACTTCTT
+CGGCCGAA
+GGAAAGGT
+ACTATCAT
+GGAGACAA
+AGTTGACT
+GCATCGCC
+GTGTTGAA
+ATTTTATCT
+GACACCTT
+GCCGACT
+CATGGAAT
+CGAAAACC
+ATATATAAT
+GACAACAT
+GTTCATTT
+GGTAATTT
+CAATCTCT
+GCGCGGCC
+CGTAGGAGT
+CCTTAAAT
+GATTGCTT
+CGTTATT
+ACAGAGAT
+AAAATCTT
+ACAAGGAT
+GCATATGG
+ATTTTCTAT
+CATCCATT
+CTTCTACT
+GTCTAGT
+ATTTTGGTT
+AGATTCTT
+AGCTTCAG
+CGTGTAT
+CAATACAT
+GTCCCGG
+GGGTCCT
+GCTCGCT
+GAATAGAT
+GAAATCCT
+AAGGAGAGG
+GATTTGATT
+ATTTTTATTT
+GCGATAA
+CACTCTTT
+AAAAATATTT
+GCTTATTT
+GCGTCTCC
+GAGTAAAT
+GACAAAATT
+GAAGACT
+GCAACCTT
+GCCGGACC
+GCATTGAT
+CCCTAATT
+CCTAGGGT
+GAAGTCTCT
+GGTTGCACT
+ACGTGAT
+GACCCTAA
+GAAGGAAT
+GAGTTGGT
+AATAAACTGT
+CCAAGTGG
+ATTCTGAT
+GACATCAAGT
+GAGAATAT
+GGTATGAA
+CGACACGG
+GATTCAC
+AGCCACTT
+GACTCAAAT
+GAAAAGGAA
+GGCCTGG
+CAGCACAA
+GGGAGAGG
+CTTCGGT
+CAGGAAAT
+GACAAGTT
+GGGGATAA
+GACACATT
+GCCTCCCC
+CAGAATAT
+GAGGCGGT
+GCTTAAGT
+ATGTACC
+ACGTATT
+ATATAGAA
+GCTTGGAA
+GTTGTAAT
+CAGGGTAT
+CGCCAGG
+CACTATTT
+AGTCTGT
+CCTCTCCT
+GAATAAGG
+AGAATCAT
+ACACTTAA
+GACATTAT
+GTGTTTTTT
+GACAAAAAT
+GCATACTT
+GAGACGCC
+GTTTACCT
+GCAAGGTT
+ACCTTTCC
+GGCTGAGT
+GCACGTT
+ACGCGAA
+AGTGGAC
+AGCATGAT
+CAGGCAGG
+GATCCATT
+GATCTCGTT
+ACAAAAATT
+CCGGCAT
+GAGCAAAT
+GCCTTGATT
+ACAACTAT
+GTCCAAAT
+GCAAATGG
+CAGGATCC
+GAGATCAT
+GAGGATGG
+CGCCCGT
+AGATCAATT
+GGTGAAGT
+GAGCTTCGG
+CACTATAT
+ACAAGTAA
+AATTCATTT
+AATAATAA
+GGGTGACC
+ACCCATTT
+GAATCTAT
+CAATCATT
+GACTGGGG
+ATATATATAA
+GCTCAACAT
+CTCTATCC
+GCCCAGT
+GGCCCGGCC
+CAGCAGAA
+AAAACTAT
+CCTCCTTTT
+AGGTGTAAGCCT
+ACTTCTAA
+CTTTGAGT
+CCATGTTCC
+CGTCGAGG
+CCCTAGT
+ATTTTGTTTT
+GCATAATTT
+ACAAAATAT
+GTGGAGAA
+ATTGTCTT
+GAGAATTTT
+GACAATGG
+CAAAAGGGG
+CGCAGCAA
+GATCTAAT
+CGAAGCTT
+GGTATCAG
+ACCATCAT
+CCGTCGG
+CATCAAATT
+AGAAAACAA
+CGAGCTCT
+GCATGGAT
+CGCCAAT
+GTGTGTCT
+GATTGGCC
+ATTTTACC
+CCTTCCAT
+CCTTAACAT
+CAAAACCT
+GACAGCAA
+AGCCACTTGG
+AAGGGGGT
+CAATGCAA
+AGTGTGAT
+GAGACGAA
+GCACAATT
+AGTTCGG
+AGAGGTAT
+CCAACACT
+AGTATGAA
+AGCCACCT
+GACCAAATT
+AGAAAGGT
+GCCTCACC
+AAAAATGTT
+GGCTACC
+CTTTTCCTTT
+GCAGATCC
+GAATGGAA
+AAAGAGAT
+ACAATAAAT
+GAGGGTCGG
+ATTATAAAT
+ACAGGTTT
+CAGCCTCT
+GA
+CAATCGT
+GAAGGAAAA
+CTTTTAATT
+CGACATCT
+CTTGGCAA
+CCTTAGGAA
+ACAATGAT
+ATCCCAT
+GCGGCCT
+CTTAACT
+GGTTGATTT
+CTGGCAT
+CGAAACAA
+ACATGCTTT
+CTTAAAGG
+AGAGCCAA
+GCATTGAA
+CGTCCGTTGG
+AGCAAAATT
+GGACGAC
+GGTATTTTT
+GAGTGACT
+GCTGACT
+AATTAACAT
+GGATATTT
+AGTTGTGG
+ACAGCTTT
+CAGCAGCT
+CAGCAATT
+GCCAAGAT
+AATCCTTTT
+CCCCAAAA
+CAAATAGT
+GAAGGGAA
+AGACTCAA
+CACTTCGCC
+GGCCGGT
+AGCTTCGT
+GCAGATGT
+ATTTTGCC
+AGCATACT
+CAATGAG
+AGGTATAT
+GACTATGT
+ATCCACT
+CATATACT
+CTTTCTGT
+GCAAGAACC
+CGCCCCT
+GAGCCCCT
+GCTTTTGG
+ACAAAAGG
+GCGGTTTAAGTT
+CGCACAC
+GGAACCCAA
+GCAATTGT
+CATTTTTAA
+CGACCGG
+GCAATATT
+GACATGAT
+GCACAAGT
+ATGTAACT
+AGCAAGAT
+GGAGCCTCT
+GCGCGGT
+ATTGGGCC
+AACCACAA
+CTTTTTCAA
+AATTAATTTT
+GCATCCAT
+AAAAATTCT
+CAGATTGT
+GAAGAAATT
+CCAAGGTT
+GGAAGAGGT
+GTCTCGG
+ACAGTGTT
+CTTGGGTT
+GCTTGGCAT
+GAGGCAC
+AGACTTAA
+CTCCATTT
+AGTGGAGG
+GCCGCCGG
+ATGTATCT
+CTTTGCGG
+AAAAAACAT
+CTAAGTGTT
+GGAGCAC
+GATTGCAA
+ACCTCGCC
+CCATGGTT
+GTTAACAT
+GTATAGG
+ACCTTGAT
+CGAAGGCC
+CTTAACAT
+GAATGCAT
+ACGGATT
+GGTAGGT
+CGGCTAGG
+ATTATACT
+CTTTTCTCT
+GAGAGAGAGAG
+AGTAACAT
+GATTTTTAT
+CTTCAGG
+AATAAAGG
+ACGTCGCC
+ATGTCATT
+CTCCGCCTCGCC
+AATGGCTCT
+AAAAGTAA
+GAGAGATT
+GAGTAAGT
+CTTTAGTT
+CGAAACGCC
+GTGTGGTT
+GTATCGT
+GACACTAT
+GTTGAACC
+GATAACAT
+GCCTTGAAT
+GAATTGGT
+ACAAAGCT
+CAAGGTTTT
+CCAAGCAT
+AGAGAAGG
+GGGTATGG
+CGGATAT
+CCTTGACC
+GCTTAGAA
+CAGTAAAT
+AAGTGCAA
+GCTCTGG
+AATAGGT
+AAAATATAA
+CAGTCAGG
+CTCTAAGAA
+CGGGTGG
+CATAAACT
+GAGAAAATT
+GACGTAA
+ACCCGCT
+GACCAAGG
+GCTGCAC
+GTATAAAA
+AGGCCACAA
+CCTTCGTT
+ATGTACTT
+GAACTAT
+CTTTTGTGT
+GCATGAAAA
+CGAGAC
+CACCGGAA
+ACTAACTT
+GAGCAATT
+CGCAAAG
+CGCCTCCT
+ATTTAAATTT
+ACCAGAG
+GAGGTTAT
+GAGTATGG
+CGATTTGT
+ATTTTGTGT
+CGCGCAA
+CTCATCAT
+GCTCTACTT
+CCTCCGAGG
+AGACTTAT
+GCACGAGTT
+CTAAGTAA
+AAATTAAAA
+GGTTGGAT
+ACACCTAA
+ACCTAACC
+CAAGTCTT
+AGCTCAAT
+CGACACT
+ACGTTCAT
+CTCCGTTCC
+CGCTTTTT
+CAGAACAT
+CGAAGGTT
+CACACGCC
+ATTGTGG
+GCCAAACC
+GGAAACATT
+GCTCAAAA
+GCACTTAT
+CCAACCCAA
+CTTATCAT
+ACCTAAAT
+AATGTCT
+AATAAGG
+CTTGGGCCATTT
+ACATTTTGT
+ACGGCGT
+CCCCCTCC
+GGTGAGTT
+AATACATT
+CGGCTAT
+GGTCGAG
+CATTATGG
+AGAACTAA
+GACTCTAT
+CTTCCCTT
+CCTATGCT
+ATTTGATCT
+ACTCATTTT
+GGCCGAGT
+CCCCACAT
+AGTGGAGT
+AATCAATT
+CGGATGG
+ACTACTAT
+CCTCCTTT
+T
+A
+C
+G
+N