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README.md

@@ -1,3 +1,115 @@
----
-license: apache-2.0
----
+# GENERator-eukaryote-1.2b-base model
+
+## Abouts
+In this repository, we present GENERator, a generative genomic foundation model featuring a context length of 98k base pairs and 1.2B parameters, trained on an expansive dataset comprising 386 billion base pairs of eukaryotic DNA. The extensive and diverse pre-training data endow the GENERator with enhanced understanding and generation capabilities across various organisms. Our evaluations demonstrate that the GENERator consistently achieves state-of-the-art performance across a wide spectrum of benchmarks, including [Genomic Benchmarks](https://huggingface.co/datasets/katielink/genomic-benchmarks/tree/main), [NT tasks](https://huggingface.co/datasets/InstaDeepAI/nucleotide_transformer_downstream_tasks_revised), and our newly proposed [Gener tasks](https://huggingface.co/GenerTeam).
+
+Beyond benchmark performance, the GENERator adheres to the central dogma of molecular biology, accurately generating protein-coding DNA sequences that produce proteins structurally analogous to known families. Moreover, the GENERator showcases significant promise in sequence optimization, particularly in the design of promoter sequences that regulate gene activity during various biological stages, highlighting its potential for a series of biologically significant tasks. Our findings position the GENERator as a vital resource for genomic research and biotechnological advancement. By enhancing our capability to interpret and predict genomic sequences, the GENERator paves the way for profound improvements in our understanding of complex biological systems and the development of precise genomic interventions.
+
+For more technical details, please refer to our paper [GENERator: A Long-Context Generative Genomic Foundation Model](https://huggingface.co/GenerTeam).
+
+## How to use
+### Simple example1:  generation
+
+```python
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the tokenizer and model.
+tokenizer = AutoTokenizer.from_pretrained("GENERator-eukaryote-1.2b-base", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("GENERator-eukaryote-1.2b-base")
+config = model.config
+
+max_length = config.max_position_embeddings
+
+# Define input sequences.
+sequences = [
+    "ATGAGGTGGCAAGAAATGGGCTAC",
+    "GAATTCCATGAGGCTATAGAATAATCTAAGAGAAAT"
+]
+
+# Process the sequences
+sequences = [tokenizer.bos_token + sequence for sequence in sequences]
+
+# Tokenize the sequences
+tokenizer.padding_side = "left"
+inputs = tokenizer(
+    sequences,
+    add_special_tokens=False,
+    return_tensors="pt",
+    padding=True,
+    truncation=True,
+    max_length=max_length
+)
+
+# Generate the sequences
+with torch.inference_mode():
+    outputs = model.generate(**inputs, max_new_tokens=32)
+
+# Decode the generated sequences
+decoded_sequences = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+# Print the decoded sequences
+print(decoded_sequences)
+```
+
+### Simple example2: embedding
+
+```python
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the tokenizer and model.
+tokenizer = AutoTokenizer.from_pretrained("GENERator-eukaryote-1.2b-base", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("GENERator-eukaryote-1.2b-base")
+
+config = model.config
+max_length = config.max_position_embeddings
+
+# Define input sequences.
+sequences = [
+    "ATGAGGTGGCAAGAAATGGGCTAC",
+    "GAATTCCATGAGGCTATAGAATAATCTAAGAGAAAT"
+]
+
+# Tokenize the sequences with add_special_tokens=True to automatically add special tokens,
+# such as the BOS EOS token, at the appropriate positions.
+tokenizer.padding_side = "right"
+inputs = tokenizer(
+    sequences,
+    add_special_tokens=True,
+    return_tensors="pt",
+    padding=True,
+    truncation=True,
+    max_length=max_length
+)
+
+# Perform a forward pass through the model to obtain the outputs, including hidden states.
+with torch.inference_mode():
+    outputs = model(**inputs, output_hidden_states=True)
+
+# Retrieve the hidden states from the last layer.
+hidden_states = outputs.hidden_states[-1]  # Shape: (batch_size, sequence_length, hidden_size)
+
+# Use the attention_mask to determine the index of the last token in each sequence.
+# Since add_special_tokens=True is used, the last token is typically the EOS token.
+attention_mask = inputs["attention_mask"]
+last_token_indices = attention_mask.sum(dim=1) - 1  # Index of the last token for each sequence
+
+# Extract the embedding corresponding to the EOS token for each sequence.
+seq_embeddings = []
+for i, token_index in enumerate(last_token_indices):
+    # Fetch the embedding for the last token (EOS token).
+    seq_embedding = hidden_states[i, token_index, :]
+    seq_embeddings.append(seq_embedding)
+
+# Stack the embeddings into a tensor with shape (batch_size, hidden_size)
+seq_embeddings = torch.stack(seq_embeddings)
+
+print("Sequence Embeddings:", seq_embeddings)
+
+```
+
+## Citation
+TBD

config.json

@@ -0,0 +1,28 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 5632,
+  "max_position_embeddings": 16384,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 26,
+  "num_key_value_heads": 4,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.0",
+  "use_cache": true,
+  "vocab_size": 4128
+}

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.44.0"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:445f44396e3147477a1d1eddd1150420f3cd5ca584fc9040f28923ebf17bdb65
+size 2885419008

special_tokens_map.json

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+{
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "pad_token": "<pad>",
+  "unk_token": {
+    "content": "<oov>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.py

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+import itertools
+import re
+from typing import List, Optional, Tuple
+
+from transformers import PreTrainedTokenizer
+
+
+class DNAKmerTokenizer(PreTrainedTokenizer):
+    def __init__(self, k, **kwargs):
+        self.k = k
+        self.special_tokens = [
+            "<oov>",
+            "<s>",
+            "</s>",
+            "<pad>",
+            "<mask>",
+            "<bog>",
+            "<eog>",
+            "<bok>",
+            "<eok>",
+            "<+>",
+            "<->",
+            "<mam>",
+            "<vrt>",
+            "<inv>",
+            "<pln>",
+            "<fng>",
+            "<prt>",
+            "<cds>",
+            "<pseudo>",
+            "<tRNA>",
+            "<rRNA>",
+            "<ncRNA>",
+            "<misc_RNA>",
+            "<sp0>",
+            "<sp1>",
+            "<sp2>",
+            "<sp3>",
+            "<sp4>",
+            "<sp5>",
+            "<sp6>",
+            "<sp7>",
+            "<sp8>",
+        ]
+        self.kmers = [
+            "".join(kmer) for kmer in itertools.product("ATCG", repeat=self.k)
+        ]
+        self.vocab = {
+            token: i for i, token in enumerate(self.special_tokens + self.kmers)
+        }
+        self.ids_to_tokens = {v: k for k, v in self.vocab.items()}
+        self.special_token_pattern = re.compile(
+            "|".join(re.escape(token) for token in self.special_tokens)
+        )
+        self.dna_pattern = re.compile(f"[A-Z]{{{self.k}}}|[A-Z]+")
+        self.bos_token = "<s>"
+        self.eos_token = "</s>"
+        self.bos_token_id = self._convert_token_to_id(self.bos_token)
+        self.eos_token_id = self._convert_token_to_id(self.eos_token)
+        super().__init__(**kwargs)
+
+    @property
+    def vocab_size(self):
+        return len(self.vocab)
+
+    def get_vocab(self):
+        return dict(self.vocab)
+
+    def _tokenize(self, text, **kwargs) -> List[str]:
+        tokens = []
+        pos = 0
+        while pos < len(text):
+            special_match = self.special_token_pattern.match(text, pos)
+            if special_match:
+                tokens.append(special_match.group())
+                pos = special_match.end()
+            else:
+                dna_match = self.dna_pattern.match(text, pos)
+                if dna_match:
+                    dna_seq = dna_match.group()
+                    tokens.append(dna_seq)
+                    pos = dna_match.end()
+                else:
+                    tokens.append(text[pos])
+                    pos += 1
+        return tokens
+
+    def _convert_token_to_id(self, token: str) -> int:
+        return self.vocab.get(token, self.vocab["<oov>"])
+
+    def _convert_id_to_token(self, index: int) -> str:
+        return self.ids_to_tokens.get(index, "<oov>")
+
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        return "".join(tokens)
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        if token_ids_1 is None:
+            return [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
+        return (
+                [self.bos_token_id]
+                + token_ids_0
+                + [self.eos_token_id]
+                + token_ids_1
+                + [self.eos_token_id]
+        )
+
+    def get_special_tokens_mask(
+            self, token_ids_0, token_ids_1=None, already_has_special_tokens=False
+    ):
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0, token_ids_1, already_has_special_tokens=True
+            )
+        if token_ids_1 is None:
+            return [1] + ([0] * len(token_ids_0)) + [1]
+        return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
+
+    def prepare_for_model(self, *args, **kwargs):
+        encoding = super().prepare_for_model(*args, **kwargs)
+        if "token_type_ids" in encoding:
+            del encoding["token_type_ids"]
+        return encoding
+
+    def save_vocabulary(
+            self, save_directory: str, filename_prefix: Optional[str] = None
+    ) -> Tuple[str]:
+        import os
+
+        vocab_file = os.path.join(
+            save_directory,
+            (filename_prefix + "-" if filename_prefix else "") + "vocab.txt",
+        )
+        with open(vocab_file, "w", encoding="utf-8") as writer:
+            for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
+                writer.write(token + "\n")
+        return (vocab_file,)

tokenizer_config.json

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+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<oov>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "</s>",
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "DNAKmerTokenizer",
+  "use_fast": false,
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenizer.DNAKmerTokenizer",
+      null
+    ]
+  },
+  "unk_token": "<oov>",
+  "use_default_system_prompt": false,
+  "k": 6
+}

vocab.txt

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+<oov>
+<s>
+</s>
+<pad>
+<mask>
+<bog>
+<eog>
+<bok>
+<eok>
+<+>
+<->
+<mam>
+<vrt>
+<inv>
+<pln>
+<fng>
+<prt>
+<cds>
+<pseudo>
+<tRNA>
+<rRNA>
+<ncRNA>
+<misc_RNA>
+<sp0>
+<sp1>
+<sp2>
+<sp3>
+<sp4>
+<sp5>
+<sp6>
+<sp7>
+<sp8>
+AAAAAA
+AAAAAT
+AAAAAC
+AAAAAG
+AAAATA
+AAAATT
+AAAATC
+AAAATG
+AAAACA
+AAAACT
+AAAACC
+AAAACG
+AAAAGA
+AAAAGT
+AAAAGC
+AAAAGG
+AAATAA
+AAATAT
+AAATAC
+AAATAG
+AAATTA
+AAATTT
+AAATTC
+AAATTG
+AAATCA
+AAATCT
+AAATCC
+AAATCG
+AAATGA
+AAATGT
+AAATGC
+AAATGG
+AAACAA
+AAACAT
+AAACAC
+AAACAG
+AAACTA
+AAACTT
+AAACTC
+AAACTG
+AAACCA
+AAACCT
+AAACCC
+AAACCG
+AAACGA
+AAACGT
+AAACGC
+AAACGG
+AAAGAA
+AAAGAT
+AAAGAC
+AAAGAG
+AAAGTA
+AAAGTT
+AAAGTC
+AAAGTG
+AAAGCA
+AAAGCT
+AAAGCC
+AAAGCG
+AAAGGA
+AAAGGT
+AAAGGC
+AAAGGG
+AATAAA
+AATAAT
+AATAAC
+AATAAG
+AATATA
+AATATT
+AATATC
+AATATG
+AATACA
+AATACT
+AATACC
+AATACG
+AATAGA
+AATAGT
+AATAGC
+AATAGG
+AATTAA
+AATTAT
+AATTAC
+AATTAG
+AATTTA
+AATTTT
+AATTTC
+AATTTG
+AATTCA
+AATTCT
+AATTCC
+AATTCG
+AATTGA
+AATTGT
+AATTGC
+AATTGG
+AATCAA
+AATCAT
+AATCAC
+AATCAG
+AATCTA
+AATCTT
+AATCTC
+AATCTG
+AATCCA
+AATCCT
+AATCCC
+AATCCG
+AATCGA
+AATCGT
+AATCGC
+AATCGG
+AATGAA
+AATGAT
+AATGAC
+AATGAG
+AATGTA
+AATGTT
+AATGTC
+AATGTG
+AATGCA
+AATGCT
+AATGCC
+AATGCG
+AATGGA
+AATGGT
+AATGGC
+AATGGG
+AACAAA
+AACAAT
+AACAAC
+AACAAG
+AACATA
+AACATT
+AACATC
+AACATG
+AACACA
+AACACT
+AACACC
+AACACG
+AACAGA
+AACAGT
+AACAGC
+AACAGG
+AACTAA
+AACTAT
+AACTAC
+AACTAG
+AACTTA
+AACTTT
+AACTTC
+AACTTG
+AACTCA
+AACTCT
+AACTCC
+AACTCG
+AACTGA
+AACTGT
+AACTGC
+AACTGG
+AACCAA
+AACCAT
+AACCAC
+AACCAG
+AACCTA
+AACCTT
+AACCTC
+AACCTG
+AACCCA
+AACCCT
+AACCCC
+AACCCG
+AACCGA
+AACCGT
+AACCGC
+AACCGG
+AACGAA
+AACGAT
+AACGAC
+AACGAG
+AACGTA
+AACGTT
+AACGTC
+AACGTG
+AACGCA
+AACGCT
+AACGCC
+AACGCG
+AACGGA
+AACGGT
+AACGGC
+AACGGG
+AAGAAA
+AAGAAT
+AAGAAC
+AAGAAG
+AAGATA
+AAGATT
+AAGATC
+AAGATG
+AAGACA
+AAGACT
+AAGACC
+AAGACG
+AAGAGA
+AAGAGT
+AAGAGC
+AAGAGG
+AAGTAA
+AAGTAT
+AAGTAC
+AAGTAG
+AAGTTA
+AAGTTT
+AAGTTC
+AAGTTG
+AAGTCA
+AAGTCT
+AAGTCC
+AAGTCG
+AAGTGA
+AAGTGT
+AAGTGC
+AAGTGG
+AAGCAA
+AAGCAT
+AAGCAC
+AAGCAG
+AAGCTA
+AAGCTT
+AAGCTC
+AAGCTG
+AAGCCA
+AAGCCT
+AAGCCC
+AAGCCG
+AAGCGA
+AAGCGT
+AAGCGC
+AAGCGG
+AAGGAA
+AAGGAT
+AAGGAC
+AAGGAG
+AAGGTA
+AAGGTT
+AAGGTC
+AAGGTG
+AAGGCA
+AAGGCT
+AAGGCC
+AAGGCG
+AAGGGA
+AAGGGT
+AAGGGC
+AAGGGG
+ATAAAA
+ATAAAT
+ATAAAC
+ATAAAG
+ATAATA
+ATAATT
+ATAATC
+ATAATG
+ATAACA
+ATAACT
+ATAACC
+ATAACG
+ATAAGA
+ATAAGT
+ATAAGC
+ATAAGG
+ATATAA
+ATATAT
+ATATAC
+ATATAG
+ATATTA
+ATATTT
+ATATTC
+ATATTG
+ATATCA
+ATATCT
+ATATCC
+ATATCG
+ATATGA
+ATATGT
+ATATGC
+ATATGG
+ATACAA
+ATACAT
+ATACAC
+ATACAG
+ATACTA
+ATACTT
+ATACTC
+ATACTG
+ATACCA
+ATACCT
+ATACCC
+ATACCG
+ATACGA
+ATACGT
+ATACGC
+ATACGG
+ATAGAA
+ATAGAT
+ATAGAC
+ATAGAG
+ATAGTA
+ATAGTT
+ATAGTC
+ATAGTG
+ATAGCA
+ATAGCT
+ATAGCC
+ATAGCG
+ATAGGA
+ATAGGT
+ATAGGC
+ATAGGG
+ATTAAA
+ATTAAT
+ATTAAC
+ATTAAG
+ATTATA
+ATTATT
+ATTATC
+ATTATG
+ATTACA
+ATTACT
+ATTACC
+ATTACG
+ATTAGA
+ATTAGT
+ATTAGC
+ATTAGG
+ATTTAA
+ATTTAT
+ATTTAC
+ATTTAG
+ATTTTA
+ATTTTT
+ATTTTC
+ATTTTG
+ATTTCA
+ATTTCT
+ATTTCC
+ATTTCG
+ATTTGA
+ATTTGT
+ATTTGC
+ATTTGG
+ATTCAA
+ATTCAT
+ATTCAC
+ATTCAG
+ATTCTA
+ATTCTT
+ATTCTC
+ATTCTG
+ATTCCA
+ATTCCT
+ATTCCC
+ATTCCG
+ATTCGA
+ATTCGT
+ATTCGC
+ATTCGG
+ATTGAA
+ATTGAT
+ATTGAC
+ATTGAG
+ATTGTA
+ATTGTT
+ATTGTC
+ATTGTG
+ATTGCA
+ATTGCT
+ATTGCC
+ATTGCG
+ATTGGA
+ATTGGT
+ATTGGC
+ATTGGG
+ATCAAA
+ATCAAT
+ATCAAC
+ATCAAG
+ATCATA
+ATCATT
+ATCATC
+ATCATG
+ATCACA
+ATCACT
+ATCACC
+ATCACG
+ATCAGA
+ATCAGT
+ATCAGC
+ATCAGG
+ATCTAA
+ATCTAT
+ATCTAC
+ATCTAG
+ATCTTA
+ATCTTT
+ATCTTC
+ATCTTG
+ATCTCA
+ATCTCT
+ATCTCC
+ATCTCG
+ATCTGA
+ATCTGT
+ATCTGC
+ATCTGG
+ATCCAA
+ATCCAT
+ATCCAC
+ATCCAG
+ATCCTA
+ATCCTT
+ATCCTC
+ATCCTG
+ATCCCA
+ATCCCT
+ATCCCC
+ATCCCG
+ATCCGA
+ATCCGT
+ATCCGC
+ATCCGG
+ATCGAA
+ATCGAT
+ATCGAC
+ATCGAG
+ATCGTA
+ATCGTT
+ATCGTC
+ATCGTG
+ATCGCA
+ATCGCT
+ATCGCC
+ATCGCG
+ATCGGA
+ATCGGT
+ATCGGC
+ATCGGG
+ATGAAA
+ATGAAT
+ATGAAC
+ATGAAG
+ATGATA
+ATGATT
+ATGATC
+ATGATG
+ATGACA
+ATGACT
+ATGACC
+ATGACG
+ATGAGA
+ATGAGT
+ATGAGC
+ATGAGG
+ATGTAA
+ATGTAT
+ATGTAC
+ATGTAG
+ATGTTA
+ATGTTT
+ATGTTC
+ATGTTG
+ATGTCA
+ATGTCT
+ATGTCC
+ATGTCG
+ATGTGA
+ATGTGT
+ATGTGC
+ATGTGG
+ATGCAA
+ATGCAT
+ATGCAC
+ATGCAG
+ATGCTA
+ATGCTT
+ATGCTC
+ATGCTG
+ATGCCA
+ATGCCT
+ATGCCC
+ATGCCG
+ATGCGA
+ATGCGT
+ATGCGC
+ATGCGG
+ATGGAA
+ATGGAT
+ATGGAC
+ATGGAG
+ATGGTA
+ATGGTT
+ATGGTC
+ATGGTG
+ATGGCA
+ATGGCT
+ATGGCC
+ATGGCG
+ATGGGA
+ATGGGT
+ATGGGC
+ATGGGG
+ACAAAA
+ACAAAT
+ACAAAC
+ACAAAG
+ACAATA
+ACAATT
+ACAATC
+ACAATG
+ACAACA
+ACAACT
+ACAACC
+ACAACG
+ACAAGA
+ACAAGT
+ACAAGC
+ACAAGG
+ACATAA
+ACATAT
+ACATAC
+ACATAG
+ACATTA
+ACATTT
+ACATTC
+ACATTG
+ACATCA
+ACATCT
+ACATCC
+ACATCG
+ACATGA
+ACATGT
+ACATGC
+ACATGG
+ACACAA
+ACACAT
+ACACAC
+ACACAG
+ACACTA
+ACACTT
+ACACTC
+ACACTG
+ACACCA
+ACACCT
+ACACCC
+ACACCG
+ACACGA
+ACACGT
+ACACGC
+ACACGG
+ACAGAA
+ACAGAT
+ACAGAC
+ACAGAG
+ACAGTA
+ACAGTT
+ACAGTC
+ACAGTG
+ACAGCA
+ACAGCT
+ACAGCC
+ACAGCG
+ACAGGA
+ACAGGT
+ACAGGC
+ACAGGG
+ACTAAA
+ACTAAT
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+TGTGGC
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+TGCAAA
+TGCAAT
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+TGCAAG
+TGCATA
+TGCATT
+TGCATC
+TGCATG
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+TGGGGT
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+CCTGAG
+CCTGTA
+CCTGTT
+CCTGTC
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+CCTGCA
+CCTGCT
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+CCTGCG
+CCTGGA
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+CCCAAA
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+CCCCAA
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+CCCCAC
+CCCCAG
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+CCCCTG
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+CCCCGA
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+CCCGTC
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+CCCGCG
+CCCGGA
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+CCCGGG
+CCGAAA
+CCGAAT
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+CCGAAG
+CCGATA
+CCGATT
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+CCGAGC
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+CCGGGT
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+CGAAAA
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+CGTGAA
+CGTGAT
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+CGTGTA
+CGTGTT
+CGTGTC
+CGTGTG
+CGTGCA
+CGTGCT
+CGTGCC
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+CGTGGT
+CGTGGC
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+CGCATA
+CGCATT
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+CGCCTT
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+CGCCCC
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+CGCCGG
+CGCGAA
+CGCGAT
+CGCGAC
+CGCGAG
+CGCGTA
+CGCGTT
+CGCGTC
+CGCGTG
+CGCGCA
+CGCGCT
+CGCGCC
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+CGCGGA
+CGCGGT
+CGCGGC
+CGCGGG
+CGGAAA
+CGGAAT
+CGGAAC
+CGGAAG
+CGGATA
+CGGATT
+CGGATC
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+CGGACA
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+CGGAGT
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+CGGTTC
+CGGTTG
+CGGTCA
+CGGTCT
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+CGGTGG
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+CGGGCT
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+CGGGGT
+CGGGGC
+CGGGGG
+GAAAAA
+GAAAAT
+GAAAAC
+GAAAAG
+GAAATA
+GAAATT
+GAAATC
+GAAATG
+GAAACA
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+GAAAGA
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+GAAAGC
+GAAAGG
+GAATAA
+GAATAT
+GAATAC
+GAATAG
+GAATTA
+GAATTT
+GAATTC
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+GAATGA
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+GAATGC
+GAATGG
+GAACAA
+GAACAT
+GAACAC
+GAACAG
+GAACTA
+GAACTT
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+GAACCA
+GAACCT
+GAACCC
+GAACCG
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+GAACGT
+GAACGC
+GAACGG
+GAAGAA
+GAAGAT
+GAAGAC
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+GAAGTA
+GAAGTT
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+GAAGTG
+GAAGCA
+GAAGCT
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+GAAGCG
+GAAGGA
+GAAGGT
+GAAGGC
+GAAGGG
+GATAAA
+GATAAT
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+GATAAG
+GATATA
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+GATATC
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+GATGCT
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+GATGGT
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+GACAAA
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+GACAAC
+GACAAG
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+GACGAG
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+GACGCT
+GACGCC
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+GAGAAA
+GAGAAT
+GAGAAC
+GAGAAG
+GAGATA
+GAGATT
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+GAGATG
+GAGACA
+GAGACT
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+GAGAGT
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+GAGTAT
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+GAGTTT
+GAGTTC
+GAGTTG
+GAGTCA
+GAGTCT
+GAGTCC
+GAGTCG
+GAGTGA
+GAGTGT
+GAGTGC
+GAGTGG
+GAGCAA
+GAGCAT
+GAGCAC
+GAGCAG
+GAGCTA
+GAGCTT
+GAGCTC
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