A metagenomics-based technological framework has been proposed for evaluating the potential and utility of FEAST as an ARG profile-based source apportionment tool. To this end, a large panel of metagenomic data sets was analyzed, associating with eight source types of ARGs in environments. Totally, 1089 different ARGs were found in the 604 source metagenomes, and 396 ARG indicators were identified as the source-specific fingerprints to characterize each of the source types. With the source fingerprints, predictive performance of FEAST was checked using "leave-one-out" cross-validation strategy. Furthermore, artificial sink communities were simulated to evaluate the FEAST for source apportionment of ARGs. The prediction of FEAST showed high accuracy values (0.933±0.046) and specificity values (0.959±0.041), confirming its suitability to discriminate samples from different source types. The apportionment results reflected well the expected output of artificial communities which were generated with different ratios of source types to simulate various contamination levels. Finally, the validated FEAST was applied to track the sources of ARGs in river sediments. Results showed STP effluents were the main contributor of ARGs, with an average contribution of 76%, followed by sludge (10%) and aquaculture effluent (2.7%), which were basically consistent with the actual environment in the area.

已经提出了一个基于宏基因组学的技术框架，用于评估 FEAST 作为基于 ARG 配置文件的源分配工具的潜力和实用性。为此，分析了一大组宏基因组数据集，与环境中八种源类型的 ARG 相关联。总共在 604 个源宏基因组中发现了 1089 个不同的 ARG，并确定了 396 个 ARG 指标作为源特异性指纹来表征每种源类型。使用源指纹，使用“留一法”交叉验证策略检查 FEAST 的预测性能。此外，模拟人工水槽群落以评估 FEAST 的 ARGs 源分配。FEAST的预测显示出高精度值（0.933±0.046）和特异性值（0.959±0.041），确认其适用于区分不同来源类型的样品。分配结果很好地反映了人工群落的预期产出，人工群落以不同的源类型比例生成，以模拟各种污染水平。最后，应用经过验证的 FEAST 来追踪河流沉积物中 ARGs 的来源。结果表明，STP出水是ARGs的主要贡献者，平均贡献率为76%，其次是污泥（10%）和水产养殖出水（2.7%），与区域实际环境基本一致。验证的 FEAST 用于追踪河流沉积物中 ARGs 的来源。结果表明，STP出水是ARGs的主要贡献者，平均贡献率为76%，其次是污泥（10%）和水产养殖出水（2.7%），与区域实际环境基本一致。验证的 FEAST 用于追踪河流沉积物中 ARGs 的来源。结果表明，STP出水是ARGs的主要贡献者，平均贡献率为76%，其次是污泥（10%）和水产养殖出水（2.7%），与区域实际环境基本一致。