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import streamlit as st
from Bio import SeqIO
import torch
import torch.nn as nn
import pandas as pd
import esm
from esm.data import *
from esm.model.esm2_secondarystructure import ESM2 as ESM2_SISS
from esm import Alphabet, FastaBatchedDataset
from io import StringIO
seed = 1337
torch.manual_seed(seed)
global modelfile, layers, heads, embed_dim, batch_toks, inp_len, device
modelfile = 'model.pkl'
layers = 6
heads = 16
embed_dim = 128
batch_toks = 1024
inp_len = 50
device = "cpu"
alphabet = Alphabet(standard_toks = 'AGCT')
assert alphabet.tok_to_idx == {'<pad>': 0, '<eos>': 1, '<unk>': 2, 'A': 3, 'G': 4, 'C': 5, 'T': 6, '<cls>': 7, '<mask>': 8, '<sep>': 9}
class CNN_linear(nn.Module):
def __init__(self,
border_mode='same', filter_len=8, nbr_filters=120,
dropout1=0, dropout2=0):
super(CNN_linear, self).__init__()
self.embedding_size = embed_dim
self.border_mode = border_mode
self.inp_len = inp_len
self.nodes = 40
self.cnn_layers = 0
self.filter_len = filter_len
self.nbr_filters = nbr_filters
self.dropout1 = dropout1
self.dropout2 = dropout2
self.dropout3 = 0.5
self.esm2 = ESM2_SISS(num_layers = layers,
embed_dim = embed_dim,
attention_heads = heads,
alphabet = alphabet)
self.conv1 = nn.Conv1d(in_channels = self.embedding_size,
out_channels = self.nbr_filters, kernel_size = self.filter_len, padding = self.border_mode)
self.conv2 = nn.Conv1d(in_channels = self.nbr_filters,
out_channels = self.nbr_filters, kernel_size = self.filter_len, padding = self.border_mode)
self.dropout1 = nn.Dropout(self.dropout1)
self.dropout2 = nn.Dropout(self.dropout2)
self.dropout3 = nn.Dropout(self.dropout3)
self.relu = nn.ReLU()
self.flatten = nn.Flatten()
self.fc = nn.Linear(in_features = embed_dim, out_features = self.nodes)
self.linear = nn.Linear(in_features = self.nbr_filters, out_features = self.nodes)
self.output = nn.Linear(in_features = self.nodes, out_features = 1)
self.direct_output = nn.Linear(in_features = embed_dim, out_features = 1)
self.magic_output = nn.Linear(in_features = 1, out_features = 1)
def forward(self, tokens, need_head_weights=True, return_contacts=False, return_representation=True):
x = self.esm2(tokens, [layers], need_head_weights, return_contacts, return_representation)
# x = self.esm2(tokens, [layers])
x = x["representations"][layers][:, 0]
x_o = x.unsqueeze(2)
x = self.flatten(x_o)
o_linear = self.fc(x)
o_relu = self.relu(o_linear)
o_dropout = self.dropout3(o_relu)
o = self.output(o_dropout)
return o
def eval_step(dataloader, model, threshold=0.5):
model.eval()
logits_list= []
# y_pred_list, y_prob_list = [], []
ids_list, strs_list = [], []
my_bar = st.progress(0, text="Running UTR_LM")
with torch.no_grad():
for i, (ids, strs, _, toks, _, _) in enumerate(dataloader):
ids_list.extend(ids)
strs_list.extend(strs)
# toks = toks.to(device)
my_bar.progress((i+1)/len(dataloader), text="Running UTR_LM")
# print(toks)
logits = model(toks)
logits = logits.reshape(-1)
# y_prob = torch.sigmoid(logits)
# y_pred = (y_prob > threshold).long()
logits_list.extend(logits.tolist())
# y_prob_list.extend(y_prob.tolist())
# y_pred_list.extend(y_pred.tolist())
st.success('Done', icon="✅")
# data_pred = pd.DataFrame({'ID':ids_list, 'Sequence':strs_list, "Translation Efficiency":logits_list, "prob":y_prob_list, "pred":y_pred_list})
data_pred = pd.DataFrame({'ID':ids_list, 'Sequence':strs_list, "Translation Efficiency":logits_list})
return data_pred
def read_raw(raw_input):
ids = []
sequences = []
file = StringIO(raw_input)
for record in SeqIO.parse(file, "fasta"):
# 检查序列是否只包含A, G, C, T
sequence = str(record.seq.back_transcribe()).upper()[-inp_len:]
if not set(sequence).issubset(set("AGCT")):
st.write(f"Record '{record.description}' was skipped for containing invalid characters. Only A, G, C, T(U) are allowed.")
continue
# 将符合条件的序列添加到列表中
ids.append(record.id)
sequences.append(sequence)
return ids, sequences
def generate_dataset_dataloader(ids, seqs):
dataset = FastaBatchedDataset(ids, seqs, mask_prob = 0.0)
# dataset = FastaBatchedDataset(ids, seqs)
batches = dataset.get_batch_indices(toks_per_batch=batch_toks, extra_toks_per_seq=2)
dataloader = torch.utils.data.DataLoader(dataset,
collate_fn=alphabet.get_batch_converter(),
batch_sampler=batches,
shuffle = False)
# dataloader = torch.utils.data.DataLoader(dataset, batch_sampler=batches, shuffle = False)
st.write(f"{len(dataset)} sequences")
return dataset, dataloader
def predict_raw(raw_input):
# st.write('====Parse Input====')
ids, seqs = read_raw(raw_input)
_, dataloader = generate_dataset_dataloader(ids, seqs)
model = CNN_linear()
model.load_state_dict(torch.load(modelfile, map_location=torch.device('cpu')), strict = False)
# st.write('====Predict====')
pred = eval_step(dataloader, model)
# print(pred)
return pred
st.title("5' UTR prediction")
st.subheader("Input sequence")
seq = st.text_area("FASTA format only", value="")
st.subheader("Upload sequence file")
uploaded = st.file_uploader("Sequence file in FASTA format")
if st.button("Predict"):
if uploaded:
result = predict_raw(uploaded.getvalue().decode())
# result_file = result.to_csv(index=False)
# st.download_button("Download", result_file, file_name="UTR_LM_prediction.csv")
# st.dataframe(result)
else:
result = predict_raw(seq)
result_file = result.to_csv(index=False)
st.download_button("Download", result_file, file_name="UTR_LM_prediction.csv")
st.dataframe(result)
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