图1 BGC在序列和功能上的特征示意图（以青霉素的生物合成为例）

Fig. 1 Schematic diagram for sequences and functions of BGC (with penicillin biosynthesis as an example)

图2 BGC挖掘的整体过程（该过程包括：宏基因组数据的整合，基因和潜在BGC的预测，内源表达或异源表达、天然产物的鉴定等。本图中选用的案例是诺糖环肽A2，是从地衣Nostoc属ATCC53789中提取分离的天然产物，可作为20S蛋白酶体的抑制剂，具有抗癌活性［45］）

Fig. 2 Overall process for BGC mining(This process includes the integration of metagenomic data, prediction of genes and potential BGC, endogenous or heterologous expression, identification of natural products, etc. The case chosen in this figure is Nostocyclopeptide A2, which is extracted from Nostoc sp. ATCC53789 isolated from lichen. It can be used as an inhibitor of 20S proteasome and exhibits anticancer activity[45].)

图3 BGC挖掘的一般分析流程及相关方法［从宏基因组数据中挖掘BGC，主要包括：BGC的挖掘方法（序列比对、特征比对等）和BGC的优化方法（数据库搜索、进化分析等）。其中BGC的挖掘方法主要有序列比对和特征比对两大类：序列比对主要是BLAST等方法，特征比对既包括隐马尔科夫模型（HMM）比对等传统方法，也包括基于数据模型的深度学习等方法。其中BGC的优化方法主要有数据库搜索、进化分析等：数据库搜索包括BGC序列数据库的搜索，以及BGC相关小分子质谱数据库的搜索，而进化分析的主要目标是分析BGC的演化和变异模式［54］］

Fig. 3 Overall flow for BGC analysis and mining[It mainly includes: BGC mining methods (sequence alignment, feature characterization, etc.) and BGC optimization methods (database searching, evolutionary analysis, etc.). Among them, the mining methods of BGC mainly include sequence alignment and feature characterization. Sequence alignment mainly uses BLAST and other methods, while feature characterization employs both traditional methods such as hidden Markov model (HMM) alignment and deep learning based on data model. The optimization methods of BGC mainly include database searching, evolutionary analysis, etc. Database searching includes the searching of BGC sequence database and BGC related small molecule mass spectrometry database, and the main purpose of evolutionary analysis is to analyze the evolution and variation patterns of BGC[54].]

图4 建立BGC和次生代谢产物关联性的分析方法［58］（a）逆生物合成：从已知化合物开始，预测生产该化合物所需的活性酶（主干酶和裁剪酶），并从这些预测中找到与基因组中需求匹配的假定簇。本图中选用的案例为青霉素G［59］。（b）同源搜索：从物种1产生的已知化合物和物种2产生的相同或相似的化合物开始，使用来自物种2的已知基因集群在物种1的基因组中搜索相似的基因集群，从而确定感兴趣的基因集群。（c）比较基因组学：从一组生物开始，其中一些生物产生目标化合物，而另一些生物则不产生，有可能在生产中识别同源基因簇，并在非生产中没有同源基因的基础上进行筛选，从而识别候选基因簇

Fig. 4 Analytical methods for establishing correlation between BGC and the production of secondary metabolites[58](a) Retro-biosynthesis: starting with a known compound but no related gene clusters identified, it is possible for predicting enzyme(s) to catalyze the synthesis of such a compound (backbone and tailoring enzymes), and with these predictions putative gene clusters matching the requirements can be found in the genome. The selected case in this figure is penicillin G[59]. (b) Homology searching: starting with a known compound produced by organism 1 and the same or similar compound produced by organism 2 with gene cluster identified, it is possible to use the known gene cluster from organism 2 to search for a similar gene cluster in the genome of organism 1, and thereby identify the gene cluster of interest. (c) Comparative genomics: starting with a group of organisms, some of which produce compounds of interest and some of which do not, it is possible to identify homologous gene clusters in the species that produce them and to screen on the basis of the absence of homologous genes in the species that does not produce them, thereby identifying candidate gene clusters.

图5 序列数据的类型，以及相应的人工智能分析方法DNN—深度神经网络；CNN—卷积神经网络；NN—神经网络；TL—迁移学习；GCN—图卷及网络；HMM—隐马尔科夫模型

Fig. 5 Types of sequence data and corresponding AI analysis methodsDNN— deep neural network; CNN— convolutional neural network; NN— neural network; TL— transfer learning; GCN— graph convolutional network; HMM— hidden markov model

图6 利用人工智能进行BGC挖掘的现状和趋势（从数据出发，通过人工智能方法进行数据挖掘和模型构建，进而服务于合成生物学的转化研究，产生更多的多模态数据，形成良性循环）

Fig. 6 Status quo and trend of BGC mining using artificial intelligence(Starting from the data, data mining and model construction are carried out with artificial intelligence methods, thus serving the transformation research of synthetic biology, generating more multimodal data and forming a virtuous cycle.)

图7 人工智能数据挖掘和培养组学的各自优缺点和互补性（相关方法优缺点的罗列，是基于互相比较和与传统分子生物学方法比较的结果）

Fig. 7 Advantages, disadvantages and complementarities of artificial intelligence data mining and culturomics(The list of advantages and disadvantages of the relevant methods is based on the results of comparison with each other and with traditional molecular biological methods as well.)

图8 BGC在系统生物学与合成生物学中的核心地位（生物合成基因簇的智能发掘与验证转化的研究，不但在数据上打通了数据库和实体库，而且在技术上打通了人工智能挖掘和培养实验验证。生物合成基因簇的智能发掘与验证转化的研究，能够紧密连接系统生物学与合成生物学，实现从数据到模型、从验证到应用的无缝转化）

Fig. 8 BGC's central role in systems biology and synthetic biology(Research on intelligent mining and verification transformation of biosynthetic gene clusters not only connects BGC database with entity database, but also connects artificial intelligence mining and culture experiment verification. Research on intelligent discovery and transformation verification for biosynthetic gene clusters can closely link systems biology and synthetic biology, and realize seamless transformation from data to model and from verification to application.)