✨生物大语言模型Evo2——解码基因密码的AI革命🚀

🌟 2025：生物AI的"DeepSeek时刻"

当整个中文互联网为国产大语言模型DeepSeek欢呼时，生命科学界正悄然掀起一场静默革命——由Arc Institute领衔，斯坦福、UC Berkeley、哥大、UCSF携手英伟达等顶尖AI企业，共同推出百亿参数级生物语义理解引擎Evo2！这个能直接"读懂"核苷酸语言的神奇模型，正在重新定义我们对基因密码的认知方式🧬

 

🌟模型亮点速览：

• 🧬 直接解析核苷酸序列的"生物语言"

• 🚀 支持8192长度（base模型）1 百万（完整模型）超长基因片段处理

• 🌍 跨物种基因理解能力升级

• 🔓 完全开源！支持NVIDIA NIM云端部署和本地运行

（👉小贴士：想了解Evo1到Evo2的架构革命？快在评论区催更技术解析专题！）

 

🔍本期实战目标：

　　🛠️ 从零开始的保姆级教程

　　🚀用Embedding+DNN实现BRCA1突变效应预测

　　📊性能飞跃：相较于仅用score函数差值预测的0.7 AUROC（Fair级），Embedding+DNN方案直冲0.9 AUROC（Good级）📈

 

 ✨ 小贴士：

我从零开始，租用新的Auto-dl服务器，搭建环境，重跑code，以保证每个新手小白都能有成功感地一次性运行成功，不产生任何报错。已准备好开箱即用的Auto-dl镜像，评论区@zylAK（我的博客园昵称）即刻获取🚀 如果觉得帖子不错欢迎转发zylAK的帖子给小伙伴们，你们的支持是我更新的动力。如果还想了解Evo2更多的应用，例如如何设计新的核酸序列、如何获得可解释的大语言模型理解等，都可以在评论区催更。

 

废话不多说，以Auto-dl云服务器为例，直接上代码：

一、前期准备：
1.1 云服务器配置：

 

1.2 开启Auto-dl的学术加速并从github上下载Evo2项目文件，激活：
命令行是：

source /etc/network_turbo #开启学术加速
 2 git clone https://github.com/ArcInstitute/evo2.git #下载Evo2项目文件
 3 cd evo2 #进入项目路径

4 git clone https://github.com/Zymrael/vortex.git #手动安装vortex依赖

python setup.py install #项目激活

 

1.3 从hugging face镜像站hf-mirror上下载对应Evo2模型，并存储在本地，以供后续调用。目前4090机器的配置可以运行1b和7b的模型（完整和base版均可），40b模型可能需要内存更高的机器且需要多卡GPU部署，这一期暂不讨论。三种参数量的模型效果相差不那么明显。
命令行是：

cd /root/autodl-tmp/evo2/evo2_models #在项目文件夹中单独创建一个evo2_model文件夹用于保存下载的模型，这样就不用每次调用时重新下载了 2  3 #设置huggingface-cli下载的镜像网址 4 export HF_ENDPOINT=https://hf-mirror.com 5  6 #下载evo2_1b_base模型为例 7 huggingface-cli download --resume-download arcinstitute/evo2_1b_base --local-dir /root/autodl-tmp/evo2/evo2_models

正确的下载运行时会有如下的输出（进度条逐渐增加）

 

二、模型加载与Embeddings提取

2.1 导入项目需要用到的所有packages

from Bio import SeqIO  2 import gzip  3 import matplotlib.pyplot as plt  4 import numpy as np  5 import pandas as pd  6 import os  7 import seaborn as sns  8 from sklearn.metrics import roc_auc_score  9 import numpy as np 10 import torch 11 import torch.nn as nn 12 from torch.utils.data import DataLoader, TensorDataset 13 from sklearn.model_selection import train_test_split 14 from sklearn.metrics import roc_auc_score 15 from torch.optim.lr_scheduler import ReduceLROnPlateau 16 from pathlib import Path 17 from tqdm.notebook import tqdm 18 import transformer_engine.pytorch as te 19 from transformer_engine.common import recipe

 可能会出现报错：

 但完全不影响后续代码运行。如果后期flash-attn包升级造成冲突，可以指定安装2.7.4版本。

 

2.2 加载模型

os.chdir('/root/autodl-tmp/evo2/evo2') model_path = "/root/autodl-tmp/evo2/evo2_models/evo2_1b_base/evo2_1b_base.pt" from evo2.models import Evo2 model = Evo2(model_name='evo2_1b_base',local_path=model_path)

 

2.3 加载并解析输入数据——BRCA1数据，包括序列数据，突变位点，突变效应分类。详细说明请见Evo2项目案例介绍：https://github.com/ArcInstitute/evo2/tree/main/notebooks/brca1

 1 os.chdir('/root/autodl-tmp/evo2')
   brca1_df = pd.read_excel(

    os.path.join('notebooks', 'brca1', '41586_2018_461_MOESM3_ESM.xlsx'),  3     header=2,  4 )  5 brca1_df = brca1_df[[  6     'chromosome', 'position (hg19)', 'reference', 'alt', 'function.score.mean', 'func.class',  7 ]]  8   9 # 对列名重命名 10 brca1_df.rename(columns={ 11     'chromosome': 'chrom', 12     'position (hg19)': 'pos', 13     'reference': 'ref', 14     'alt': 'alt', 15     'function.score.mean': 'score', 16     'func.class': 'class', 17 }, inplace=True) 18  19 # 将突变效应命名为二分类标签 20 brca1_df['class'] = brca1_df['class'].replace(['FUNC', 'INT'], 'FUNC/INT') 21  22 WINDOW_SIZE = 8192 23  24 # 读取17号染色体参考基因组序列 25 with gzip.open(os.path.join('notebooks', 'brca1', 'GRCh37.p13_chr17.fna.gz'), "rt") as handle: 26     for record in SeqIO.parse(handle, "fasta"): 27         seq_chr17 = str(record.seq) 28         break 29  30 def parse_sequences(pos, ref, alt): 31     """ 32     解析参考序列（未突变序列）和突变序列 33     """ 34     p = pos - 1 # Convert to 0-indexed position 35     full_seq = seq_chr17 36  37     ref_seq_start = max(0, p - WINDOW_SIZE//2) 38     ref_seq_end = min(len(full_seq), p + WINDOW_SIZE//2) 39     ref_seq = seq_chr17[ref_seq_start:ref_seq_end] 40     snv_pos_in_ref = min(WINDOW_SIZE//2, p) 41     var_seq = ref_seq[:snv_pos_in_ref] + alt + ref_seq[snv_pos_in_ref+1:] 42  43     # 数据合理性检查 44     assert len(var_seq) == len(ref_seq) 45     assert ref_seq[snv_pos_in_ref] == ref 46     assert var_seq[snv_pos_in_ref] == alt 47  48     return ref_seq, var_seq 49  50 # 给参考序列一个索引值 51 ref_seqs = [] 52 ref_seq_to_index = {} 53  54 # 解析序列并存储索引值 55 ref_seq_indexes = [] 56 var_seqs = [] 57  58 for _, row in brca1_df.iterrows(): 59     ref_seq, var_seq = parse_sequences(row['pos'], row['ref'], row['alt']) 60  61     # 给当前循环到的参考序列获取/创建索引 62     if ref_seq not in ref_seq_to_index: 63         ref_seq_to_index[ref_seq] = len(ref_seqs) 64         ref_seqs.append(ref_seq) 65      66     ref_seq_indexes.append(ref_seq_to_index[ref_seq]) 67     var_seqs.append(var_seq) 68  69 ref_seq_indexes = np.array(ref_seq_indexes)

 

2.4 以BCRA1序列为输入，提取全部全部层的Embedding并保存下来

# ========== 配置参数 ==========  2 device = next(model.model.parameters()).device  3 candidate_layers = [f"blocks.{i}.pre_norm" for i in range(25)]  4 batch_size = 8  # 根据GPU显存调整  5 save_dir = "extract_embeddings"  6 os.makedirs(save_dir, exist_ok=True)  7   8 # ========== 批量嵌入提取  ==========  9 def process_sequences(seq_list, layer_name, desc, prefix="ref"): 10     """批量处理序列嵌入并确保文件保存""" 11     # 生成标准化文件名 12     sanitized_layer = layer_name.replace('.', '_') 13     memmap_path = os.path.join(save_dir, f"{prefix}_{sanitized_layer}.npy") 14      15     # 创建内存映射文件并立即保存头信息 16     emb_mmap = np.lib.format.open_memmap( 17         memmap_path,  18         dtype=np.float32, 19         mode='w+', 20         shape=(len(seq_list), 1920) 21     ) 22      23     try: 24         # 分批处理 25         for i in tqdm(range(0, len(seq_list), batch_size), desc=desc, leave=False): 26             batch_seqs = seq_list[i:i+batch_size] 27              28             # Tokenize并填充 29             batch_tokens = [] 30             for seq in batch_seqs: 31                 tokens = model.tokenizer.tokenize(seq) 32                 batch_tokens.append(torch.tensor(tokens, dtype=torch.long)) 33              34             max_len = max(len(t) for t in batch_tokens) 35             padded_tokens = torch.stack([ 36                 torch.nn.functional.pad(t, (0, max_len - len(t))) for t in batch_tokens 37             ]).to(device) 38              39             # 前向传播 40             with torch.no_grad(): 41                 _, emb_dict = model.forward( 42                     padded_tokens, 43                     return_embeddings=True, 44                     layer_names=[layer_name] 45                 ) 46              47             # 写入内存映射文件 48             batch_emb = emb_dict[layer_name].float().mean(dim=1).cpu().numpy() 49             emb_mmap[i:i+len(batch_emb)] = batch_emb 50              51             # 立即刷新写入磁盘 52             emb_mmap.flush() 53              54     finally: 55         # 确保文件关闭 56         del emb_mmap 57      58     return memmap_path 59  60 # ========== 主流程 ========== 61 # 预先生成全局索引文件 (只需保存一次) 62 np.save(os.path.join(save_dir, "ref_idx.npy"), ref_seq_indexes) 63  64 for layer_name in tqdm(candidate_layers, desc="🔍 Processing Layers"): 65     # 处理参考序列 (生成 ref_blocks_0_pre_norm.npy) 66     _ = process_sequences( 67         ref_seqs,  68         layer_name, 69         f"🧬 Ref {layer_name}", 70         prefix="ref" 71     ) 72      73     # 处理变异序列 (生成 var_blocks_0_pre_norm.npy) 74     _ = process_sequences( 75         var_seqs, 76         layer_name, 77         f"🧬 Var {layer_name}", 78         prefix="var" 79     )

正确运行后有如下输出显示：

 

三、基于保存的Embeddings开发下游的突变效应预测器：

3.1 Embedding数据加载函数的定义

# ========== 新增配置参数 ==========  2 embed_dir = Path("extract_embeddings")  3 layers_to_train = [f"blocks.{i}.pre_norm" for i in range(25)]  # 需要训练的层列表，全部的25层  4 results_dir = Path("training_results")  5 results_dir.mkdir(exist_ok=True)  6   7 # ========== 数据加载函数 ==========  8 def load_layer_data(layer_name):  9     """加载指定层的嵌入数据和标签""" 10     sanitized = layer_name.replace('.', '_') 11      12     # 加载嵌入数据（内存映射模式） 13     ref_emb = np.load(embed_dir/f"ref_{sanitized}.npy", mmap_mode='r') 14     var_emb = np.load(embed_dir/f"var_{sanitized}.npy", mmap_mode='r') 15     ref_idx = np.load(embed_dir/"ref_idx.npy") 16      17     # 拼接特征 18     X = np.concatenate([ref_emb[ref_idx], var_emb], axis=1) 19      20     # 获取标签（从原始数据框） 21     y = brca1_df['class'].map({'FUNC/INT':0, 'LOF':1}).values 22      23     return X, y

 

3.2 Embeddings数据正确性检验。主要是验证数据是否存在，以及是否符合Evo2_1b_base模型中间层的维度（1920维）

# 示例，检查第24层文件是否存在  2 assert os.path.exists("extract_embeddings/ref_blocks_24_pre_norm.npy")  3 assert os.path.exists("extract_embeddings/var_blocks_24_pre_norm.npy")  4   5 # 验证嵌入维度  6 ref_emb = np.load("extract_embeddings/ref_blocks_24_pre_norm.npy", mmap_mode='r')  7 var_emb = np.load("extract_embeddings/var_blocks_24_pre_norm.npy", mmap_mode='r')  8 print(f"参考序列嵌入维度: {ref_emb.shape}")  # 应为 (N_ref, 1920)  9 print(f"变异序列嵌入维度: {var_emb.shape}")  # 应为 (N_ref, 1920) 10  11 for layer_name in layers_to_train: 12     print(layer_name) 13     X, y = load_layer_data(layer_name) 14     print(X.shape)

 

3.3 （重要）定义分离器DNN的结构（仿照原文中的DNN结构）

# ========== 模型架构 ==========  2 class BRCA1Classifier(nn.Module):  3     def __init__(self, input_dim):  4         super().__init__()  5         self.net = nn.Sequential(  6             nn.Linear(input_dim, 512),  7             nn.ReLU(),  8             nn.BatchNorm1d(512),  9             nn.Dropout(0.3), 10              11             nn.Linear(512, 128), 12             nn.ReLU(), 13             nn.BatchNorm1d(128), 14             nn.Dropout(0.3), 15              16             nn.Linear(128, 32), 17             nn.ReLU(), 18             nn.BatchNorm1d(32), 19              20             nn.Linear(32, 1), 21             nn.Sigmoid() 22         ) 23          24     def forward(self, x): 25         return self.net(x)

 

3.4 （重要）训练流程定义

# ========== 训练流程 ==========   2 def train_for_layer(layer_name):   3     """单层训练流程"""   4     print(f"n=== 开始训练层 {layer_name} ===")   5        6     # 加载数据   7     X, y = load_layer_data(layer_name)   8        9     # 数据划分  10     X_temp, X_test, y_temp, y_test = train_test_split(  11         X, y, test_size=0.2, random_state=42, stratify=y  12     )  13     X_train, X_val, y_train, y_val = train_test_split(  14         X_temp, y_temp, test_size=0.25, random_state=42, stratify=y_temp  15     )  16       17     # 转换为PyTorch Dataset  18     train_dataset = TensorDataset(torch.FloatTensor(X_train), torch.FloatTensor(y_train).unsqueeze(1))  19     val_dataset = TensorDataset(torch.FloatTensor(X_val), torch.FloatTensor(y_val).unsqueeze(1))  20     test_dataset = TensorDataset(torch.FloatTensor(X_test), torch.FloatTensor(y_test).unsqueeze(1))  21       22     # ========== 训练配置 ==========  23     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  24     model = BRCA1Classifier(X.shape[1]).to(device)  25   26     # 优化器和损失函数  27     optimizer = torch.optim.Adam(model.parameters(), lr=3e-4)  28     criterion = nn.BCELoss()  29   30     # 学习率调度器  31     scheduler = ReduceLROnPlateau(  32         optimizer,   33         mode='max',   34         factor=0.5,   35         patience=20,   36         min_lr=1e-6  37     )  38       39     # 数据加载器  40     train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)  41     val_loader = DataLoader(val_dataset, batch_size=128)  42     test_loader = DataLoader(test_dataset, batch_size=128)  43   44     # ========== 训练循环 ==========  45     best_auc = 0  46     patience_counter = 0  47     max_patience = 100  48   49     for epoch in range(500):  50         # 训练阶段  51         model.train()  52         train_loss = 0  53         for inputs, labels in train_loader:  54             inputs, labels = inputs.to(device), labels.to(device)  55               56             optimizer.zero_grad()  57             outputs = model(inputs)  58             loss = criterion(outputs, labels)  59           60             # 梯度裁剪  61             torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)  62           63             loss.backward()  64             optimizer.step()  65             train_loss += loss.item() * inputs.size(0)  66       67         # 验证阶段  68         model.eval()  69         val_loss = 0  70         y_true, y_pred = [], []  71         with torch.no_grad():  72             for inputs, labels in val_loader:  73                 inputs, labels = inputs.to(device), labels.to(device)  74                 outputs = model(inputs)  75                 val_loss += criterion(outputs, labels).item() * inputs.size(0)  76                 y_true.extend(labels.cpu().numpy())  77                 y_pred.extend(outputs.cpu().numpy())  78       79         # 计算指标  80         train_loss /= len(train_loader.dataset)  81         val_loss /= len(val_loader.dataset)  82         val_auc = roc_auc_score(y_true, y_pred)  83       84         # 学习率调整  85         scheduler.step(val_auc)  86       87         # 早停机制  88         if val_auc > best_auc:  89             best_auc = val_auc  90             patience_counter = 0  91             torch.save(model.state_dict(), 'best_model.pth')  92         else:  93             patience_counter += 1  94             if patience_counter >= max_patience:  95                 print(f"早停触发于第{epoch}轮")  96                 break  97       98         # 打印进度  99         print(f"Epoch {epoch+1}: " 100               f"Train Loss: {train_loss:.4f} | " 101               f"Val Loss: {val_loss:.4f} | " 102               f"Val AUROC: {val_auc:.4f}") 103          104     # ========== 最终评估 ========== 105     model.load_state_dict(torch.load('best_model.pth')) 106     model.eval() 107     y_test_true, y_test_pred = [], [] 108     with torch.no_grad(): 109         for inputs, labels in test_loader: 110             inputs, labels = inputs.to(device), labels.to(device) 111             outputs = model(inputs) 112             y_test_true.extend(labels.cpu().numpy()) 113             y_test_pred.extend(outputs.cpu().numpy()) 114  115     test_auc = roc_auc_score(y_test_true, y_test_pred) 116     print(f"n最终测试集AUROC: {test_auc:.4f}") 117      118     # 保存结果 119     sanitized = layer_name.replace('.', '_') 120     torch.save(model.state_dict(), results_dir/f"best_model_{sanitized}.pth") 121     np.save(results_dir/f"test_pred_{sanitized}.npy", y_test_pred) 122      123     return test_auc

 

 

3.5 执行训练流程

# ========== 主执行流程 ==========  2 if __name__ == "__main__":  3     results = {}  4     for layer in tqdm(layers_to_train, desc="Training Layers"):  5         try:  6             auc = train_for_layer(layer)  7             results[layer] = auc  8         except Exception as e:  9             print(f"训练层 {layer} 时出错: {str(e)}") 10             results[layer] = None 11      12     # 保存最终结果 13     with open(results_dir/"summary.txt", "w") as f: 14         for layer, auc in results.items(): 15             f.write(f"{layer}: {auc:.4f}n")

运行成功后会有类似如下输出：

 最后，我们可以通过检查summary.txt获取训练最优的训练结果是利用哪一层Embedding。我训练的结果显示第12层embedding训练得到的DNN预测器效果最好，小伙伴伴们也可以自己尝试不同的模型下，不同的DNN结构，哪一层能获得最好的预测效果。