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.gitattributes

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+model.pkl filter=lfs diff=lfs merge=lfs -text

Predictor.py

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+# CUDA_VISIBLE_DEVICES=2 python -m torch.distributed.launch --nproc_per_node=1 --master_port 3303 Predictor.py --predict_file /home/ubuntu/Experimental_Data/v1_5UTR_seqs_with_v1Label.fasta --outdir /home/ubuntu/Experimental_Data/try --outfilename try_RVACv1
+
+
+import os
+from Bio import SeqIO
+import sys
+
+# import argparse
+# from argparse import Namespace
+# import pathlib
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+# import esm
+# from esm.data import *
+# from esm.model.esm2_secondarystructure import ESM2 as ESM2_SISS
+from esm.model.esm2 import ESM2 as ESM2_SISS
+# from esm.model.esm2_supervised import ESM2
+from esm import Alphabet, FastaBatchedDataset#, ProteinBertModel, pretrained, MSATransformer
+
+
+import numpy as np
+import pandas as pd
+import random
+# import math
+# import scipy.stats as stats
+# from scipy.stats import spearmanr, pearsonr
+# from sklearn import preprocessing
+# from copy import deepcopy
+from tqdm import tqdm#, trange
+# import matplotlib.pyplot as plt
+# import seaborn as sns
+# from sklearn.model_selection import KFold
+# from torch.optim.lr_scheduler import StepLR
+# import torch.distributed as dist
+# from torch.nn.parallel import DistributedDataParallel
+# from torch.utils.data.distributed import DistributedSampler
+from io import StringIO
+
+seed = 1337
+random.seed(seed)
+np.random.seed(seed)
+torch.manual_seed(seed)
+# torch.cuda.manual_seed(seed)
+# torch.cuda.manual_seed_all(seed)
+
+# parser = argparse.ArgumentParser()
+# parser.add_argument('--device_ids', type=str, default='0', help="Training Devices")
+# parser.add_argument('--local-rank', type=int, default=-1, help="DDP parameter, do not modify")
+
+# parser.add_argument('--outdir', type=str, default = '/home/ubuntu/Experimental_Data/try')
+# parser.add_argument('--outfilename', type=str, default = 'try_RVACv1')
+# parser.add_argument('--predict_file', type = str, default = '/home/ubuntu/Experimental_Data/v1_5UTR_seqs_with_v1Label.fasta')
+# args = parser.parse_args()
+# print(args)
+
+global modelfile, layers, heads, embed_dim, batch_toks, inp_len, device_ids, device
+modelfile = 'model.pkl'
+
+# model_info = modelfile.split('/')[-1].split('_')
+# for item in model_info:
+#     if 'layers' in item: 
+#         layers = int(item[0])
+#     elif 'heads' in item:
+#         heads = int(item[:-5])
+#     elif 'embedsize' in item:
+#         embed_dim = int(item[:-9])
+#     elif 'batchToks' in item:
+#         batch_toks = 4096
+        
+layers = 6
+heads = 16
+embed_dim = 128
+batch_toks = 4096
+
+inp_len = 50
+    
+# device_ids = list(map(int, args.device_ids.split(',')))
+# dist.init_process_group(backend='nccl')
+# device = torch.device('cuda:{}'.format(device_ids[args.local_rank]))
+device = "cpu"
+# torch.cuda.set_device(device)
+
+# local_rank = args.local_rank
+local_rank = -1
+# storage_id = int(device_ids[local_rank])
+storage_id = 0
+
+# repr_layers = [layers]
+include = ["mean"]
+    
+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()
+    y_pred_list, y_prob_list = [], []
+    ids_list, strs_list = [], []
+    with torch.no_grad():
+        # for (ids, strs, _, toks, _, _) in tqdm(dataloader):
+        for ids, strs, toks in tqdm(dataloader):
+            ids_list.extend(ids)
+            strs_list.extend(strs)
+            # toks = toks.to(device)
+            
+            # print(toks)
+            logits = model(toks) 
+
+            logits = logits.reshape(-1)
+            y_prob = torch.sigmoid(logits)
+            y_pred = (y_prob > threshold).long()
+            
+            
+            y_prob_list.extend(y_prob.cpu().detach().tolist())
+            y_pred_list.extend(y_pred.cpu().detach().tolist())
+            
+        data_pred = pd.DataFrame([ids_list, strs_list, y_prob_list, y_pred_list], index = ['ID', 'Sequence', "Probability as 5'UTR", "Prediction as 5'UTR"]).T
+    return data_pred
+
+
+
+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)
+    print(f"{len(dataset)} sequences")
+    return dataset, dataloader
+
+def read_fasta(file):
+    # 判断文件是否为空
+    if os.path.getsize(file) == 0:
+        print("Error: The file is empty!")
+        sys.exit()
+
+    ids = []
+    sequences = []
+
+    for record in SeqIO.parse(file, "fasta"):
+        # 检查序列的开头是否为">"
+#         if not record.id.startswith('>'):
+#             print(f"Error: The sequence '{record.id}' is not properly formatted, it does not start with '>'. Skipping...")
+#             continue
+
+        # 检查序列是否只包含A, G, C, T
+        sequence = str(record.seq).upper()[-inp_len:]
+        if not set(sequence).issubset(set("AGCT")):
+            print(f"Error: The sequence '{record.description}' contains invalid characters. Only A, G, C, T are allowed. Skipping...")
+            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 read_raw(raw_input):
+    ids = []
+    sequences = []
+
+    file = StringIO(raw_input)
+    for record in SeqIO.parse(file, "fasta"):
+        # 检查序列的开头是否为">"
+#         if not record.id.startswith('>'):
+#             print(f"Error: The sequence '{record.id}' is not properly formatted, it does not start with '>'. Skipping...")
+#             continue
+
+        # 检查序列是否只包含A, G, C, T
+        sequence = str(record.seq).upper()[-inp_len:]
+        if not set(sequence).issubset(set("AGCT")):
+            print(f"Error: The sequence '{record.description}' contains invalid characters. Only A, G, C, T are allowed. Skipping...")
+            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
+
+#######
+
+# alphabet = Alphabet(mask_prob = 0.0, standard_toks = 'AGCT')
+alphabet = Alphabet(prepend_toks=("<pad>", "<eos>", "<unk>"), standard_toks = 'AGCT', append_toks=("<cls>", "<mask>", "<sep>"))
+# print(alphabet.tok_to_idx)
+# 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}
+alphabet.tok_to_idx = {'<pad>': 0, '<eos>': 1, '<unk>': 2, 'A': 3, 'G': 4, 'C': 5, 'T': 6, '<cls>': 7, '<mask>': 8, '<sep>': 9}
+
+def predict_file(input_file):
+    print('====Load Data====')
+    ids, seqs = read_fasta(input_file)
+    _, dataloader = generate_dataset_dataloader(ids, seqs)
+        
+    model = CNN_linear().to(device)
+    # model.load_state_dict({k.replace('module.', ''):v for k,v in torch.load(modelfile, map_location=lambda storage, loc : storage.cuda(storage_id)).items()}, strict = False)
+    model.load_state_dict({k.replace('module.', ''):v for k,v in torch.load(modelfile, map_location=torch.device('cpu')).items()}, strict = False)
+    # model = DistributedDataParallel(model, device_ids=[device_ids[local_rank]], output_device=device_ids[local_rank], find_unused_parameters=True)
+
+    print('====Predict====')
+    pred = eval_step(dataloader, model)
+
+    print(pred)
+    # print('====Save Results====')         
+    # if not os.path.exists(args.outdir): os.makedirs(args.outdir)
+    # pred.to_csv(f'{args.outdir}/{args.outfilename}_prediction_results.csv', index = False)
+
+def predict_raw(raw_input):
+    print('====Parse Input====')
+    ids, seqs = read_raw(raw_input)
+    _, dataloader = generate_dataset_dataloader(ids, seqs)
+        
+    model = CNN_linear().to(device)
+    # model.load_state_dict({k.replace('module.', ''):v for k,v in torch.load(modelfile, map_location=lambda storage, loc : storage.cuda(storage_id)).items()}, strict = False)
+    model.load_state_dict({k.replace('module.', ''):v for k,v in torch.load(modelfile, map_location=torch.device('cpu')).items()}, strict = False)
+    # model = DistributedDataParallel(model, device_ids=[device_ids[local_rank]], output_device=device_ids[local_rank], find_unused_parameters=True)
+
+    print('====Predict====')
+    pred = eval_step(dataloader, model)
+
+    print(pred)

app.py

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+import streamlit as st
+from Bio import SeqIO
+from Predictor import predict_file, predict_raw
+
+st.title("5' UTR prediction")
+
+st.subheader("Input sequence")
+#x = st.slider('Select a value')
+# seq = ""
+seq = st.text_input("Input your sequence here", value="")
+st.subheader("Upload sequence file")
+uploaded = st.file_uploader("Sequence file in FASTA format")
+# if uploaded:
+    # predict_file(uploaded)
+    # seq = SeqIO.read(uploaded, "fasta").seq
+st.subheader("Prediction result:")
+if st.button("Predict"):
+    if uploaded:
+        predict_file(uploaded)
+    else:
+        predict_raw(seq)
+    # st.write("Sequence length = ", len(seq))

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:705ea278849702e12285d4059dc15d902cc445f458729415ed83b1bb6515f3d3
+size 4905915

requirements.txt

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+biopython
+pytorch
+numpy
+pandas
+tqdm