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I wanted to see if high quality gene sequence data could be created through machine learning techniques. Unfortunately, the code provided is to illustrate my thought process and to accomplish a rudimentary implementation of the idea. Due to life events (primarily focused on buying a new home and selling the old one), I did not have as much time as I would have liked to focus on this project.
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Load the DNA-BERT tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("dsmilkov/dnabert-base")
model = AutoModelForSeq2SeqLM.from_pretrained("dsmilkov/dnabert-base")
# Initialize the RL agent
agent = DRL_Agent(model, tokenizer)
# Number of synthetic CRISPR gRNA encoding data sequences to generate
num_sequences = 100
# Generate the synthetic sequences
synthetic_sequences = []
for _ in range(num_sequences):
sequence = agent.generate_sequence()
synthetic_sequences.append(sequence)
# Evaluate the generated sequences using DNABert
predictions = model.generate(tokenizer(synthetic_sequences, return_tensors="pt").input_ids)
predictions = tokenizer.batch_decode(predictions, skip_special_tokens=True)
# Identify anomalous DNA edit sites
anomalous_sites = []
for i, prediction in enumerate(predictions):
if "N" in prediction:
anomalous_sites.append(i)
# Retrain the RL agent on the anomalous sequences
agent.retrain(anomalous_sites)
# Generate a new set of synthetic sequences using the retrained agent
new_synthetic_sequences = []
for _ in range(num_sequences):
sequence = agent.generate_sequence()
new_synthetic_sequences.append(sequence)
import numpy as np
import pandas as pd
import random
# Define the parameters for the synthetic data
num_samples = 1000 # Number of DNA samples to generate
seq_length = 1000 # Length of each DNA sequence
num_crispr_sites = 10 # Number of CRISPR-Cas9 binding sites per sequence
# Create a list of all possible CRISPR-Cas9 binding sites
possible_sites = list('NNNNNNNN')
# Generate a list of random DNA sequences
dna_sequences = []
for i in range(num_samples):
sequence = ''.join(random.choice('ATCG') for _ in range(seq_length))
dna_sequences.append(sequence)
# Generate a list of CRISPR-Cas9 binding sites for each sequence
crispr_sites = []
for sequence in dna_sequences:
sites = []
for i in range(num_crispr_sites):
# Select a random start position for the binding site
start = random.randint(0, seq_length - len(possible_sites))
# Select a random binding site from the list of possible sites
site = random.choice(possible_sites)
# Add the binding site to the list of sites for this sequence
sites.append(sequence[start:start + len(site)])
crispr_sites.append(sites)
# Create a pandas DataFrame with the DNA sequences and CRISPR-Cas9 binding sites
df = pd.DataFrame({'dna_sequence': dna_sequences, 'crispr_sites': crispr_sites})
import torch
import torch.nn as nn
import torch.nn.functional as F
class CNN(nn.Module):
def __init__(self):
super().__init__()
# 1D convolutional layers
self.conv1 = nn.Conv1d(in_channels=10, out_channels=30, kernel_size=3, padding=1)
self.conv2 = nn.Conv1d(in_channels=30, out_channels=100, kernel_size=5, padding=2)
self.conv3 = nn.Conv1d(in_channels=100, out_channels=200, kernel_size=7, padding=3)
# Max pooling layers
self.pool1 = nn.MaxPool1d(kernel_size=5)
self.pool2 = nn.MaxPool1d(kernel_size=5)
self.pool3 = nn.MaxPool1d(kernel_size=5)
# Dense layers
self.fc1 = nn.Linear(in_features=200 * 96, out_features=100)
self.fc2 = nn.Linear(in_features=100, out_features=10)
self.fc3 = nn.Linear(in_features=10, out_features=1)
def forward(self, x):
# Pass the input through the convolutional and max pooling layers
x = F.relu(self.conv1(x))
x = self.pool1(x)
x = F.relu(self.conv2(x))
x = self.pool2(x)
x = F.relu(self.conv3(x))
x = self.pool3(x)
# Flatten the output of the convolutional layers
x = x.view(x.size(0), -1)
# Pass the flattened output through the dense layers
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = torch.sigmoid(self.fc3(x))
return x
import torch
import numpy as np
import gym
# Define the environment
class CRISPREnv(gym.Env):
def __init__(self):
self.action_space = gym.spaces.Discrete(4) # 4 possible actions: A, C, G, T
self.observation_space = gym.spaces.Box(low=0, high=1, shape=(4,)) # 4 features: A, C, G, T
def reset(self):
# Initialize the environment with a random sequence of 20 nucleotides
self.sequence = np.random.choice(['A', 'C', 'G', 'T'], size=20)
return self.sequence
def step(self, action):
# Take an action and update the environment
self.sequence[self.position] = ['A', 'C', 'G', 'T'][action]
self.position += 1
# Calculate the reward
reward = 0
if self.sequence[self.position - 1] == 'G':
reward = 1
# Check if the episode is done
done = (self.position == 20)
# Return the observation, reward, done, and info
return self.sequence, reward, done, {}
# Define the agent
class CRISPRAgent(nn.Module):
def __init__(self):
super(CRISPRAgent, self).__init__()
self.fc1 = nn.Linear(4, 16)
self.fc2 = nn.Linear(16, 4)
def forward(self, x):
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
# Train the agent
env = CRISPREnv()
agent = CRISPRAgent()
optimizer = torch.optim.Adam(agent.parameters())
for episode in range(10000):
done = False
observation = env.reset()
while not done:
# Get the agent's action
action = agent(observation)
# Take the action and observe the environment's response
next_observation, reward, done, _ = env.step(action)
# Update the agent's parameters
loss = F.mse_loss(action, reward)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Update the observation
observation = next_observation
# Generate synthetic pcas-9 CRISPR gRNA encoding data
sequences = []
for _ in range(10000):
env.reset()
done = False
while not done:
# Get the agent's action
action = agent(observation)
# Take the action and observe the environment's response
next_observation, reward, done, _ = env.step(action)
sequences.append(''.join(observation))
# Save the synthetic data to a file
with open('synthetic_data.txt', 'w') as f:
for sequence in sequences:
f.write(sequence + '\n')
import torch
import torch.nn as nn
import torch.optim as optim
from transformers import DNABertModel
# Define the agent network
class Agent(nn.Module):
def __init__(self):
super(Agent, self).__init__()
self.dnabert = DNABertModel.from_pretrained("dsmiles/dnabert-base")
self.fc1 = nn.Linear(768, 256)
self.fc2 = nn.Linear(256, 1)
def forward(self, x):
x = self.dnabert(x)
x = self.fc1(x.last_hidden_state[:, 0])
x = self.fc2(x)
return x
# Define the environment
class Environment:
def __init__(self, data):
self.data = data
self.index = 0
def reset(self):
self.index = 0
def step(self, action):
reward = 0
if action == self.data[self.index][1]:
reward = 1
self.index += 1
done = self.index == len(self.data)
return reward, done
# Define the training loop
def train(agent, env, num_episodes):
optimizer = optim.Adam(agent.parameters(), lr=1e-3)
for episode in range(num_episodes):
env.reset()
done = False
while not done:
# Get the current state
state = env.data[env.index][0]
# Take an action
action = agent(state)
# Get the reward
reward, done = env.step(action)
# Update the agent's network
optimizer.zero_grad()
loss = -reward
loss.backward()
optimizer.step()
# Generate synthetic data
def generate_data(agent, num_samples):
data = []
for i in range(num_samples):
# Get a random DNA sequence
dna = "ACGT" * np.random.randint(1, 100)
# Get the agent's prediction
action = agent(dna)
# Add the data to the list
data.append((dna, action))
return data
# Main function
if __name__ == "__main__":
# Load the data
data = pd.read_csv("data.csv")
# Create the environment
env = Environment(data)
# Create the agent
agent = Agent()
# Train the agent
train(agent, env, 1000)
# Generate synthetic data
data = generate_data(agent, 10000)
# Save the data
pd.DataFrame(data).to_csv("synthetic_data.csv")
import torch
import numpy as np
class DNA_Generator(torch.nn.Module):
def __init__(self, num_actions):
super(DNA_Generator, self).__init__()
self.num_actions = num_actions
# Define the LSTM network
self.lstm = torch.nn.LSTM(input_size=1, hidden_size=64, num_layers=1, batch_first=True)
# Define the policy network (the head that outputs the action probabilities)
self.policy = torch.nn.Linear(64, num_actions)
def forward(self, x):
# Pass the input through the LSTM network
lstm_out, _ = self.lstm(x)
# Pass the LSTM output through the policy network
action_probs = self.policy(lstm_out)
# Return the action probabilities
return action_probs
# Initialize the DNA generator network
dna_generator = DNA_Generator(num_actions=4) # 4 actions: A, C, G, T
# Define the loss function and optimizer
loss_fn = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(dna_generator.parameters(), lr=0.01)
# Training loop
for epoch in range(1000):
# Generate the input data (random sequence of 1s and 0s)
input_data = torch.rand((1, 20, 1))
# Generate the target data (correct PCA-9 CRISPR gRNA sequence)
target_data = torch.tensor([1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1])
# Forward pass through the DNA generator network
action_probs = dna_generator(input_data)
# Compute the loss
loss = loss_fn(action_probs.squeeze(), target_data)
# Backpropagate and update the network parameters
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Generate a synthetic DNA sequence using the trained DNA generator network
input_data = torch.rand((1, 20, 1))
action_probs = dna_generator(input_data)
synthetic_dna = ''.join([['A', 'C', 'G', 'T'][i] for i in action_probs.argmax(dim=1).numpy().squeeze()])
print("Synthetic DNA sequence:", synthetic_dna)
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