Over time, different strategies have been designed to tackle the sometimes cumbersome task of finding bioactive secondary metabolites of bacterial origin, i.e., mainly bioactivity- or chem- istry-guided methods. An additional approach, complementary to the latter, arose in the late 1990s, when the first microbial genomes were sequenced, allowing genome mining. Thus, knowledge derived from bioinformatic analysis of microbial genomes paved the way to gain detailed insights into bacterial secondary metabolism. Since then, in silico methods for genome mining have enormously advanced and effective exper- imental approaches for connecting genomic and metabolic information have been developed [33-35].
Over time, different strategies have been designed to tackle the sometimes cumbersome task of finding bioactive secondary metabolites of bacterial origin, i.e., mainly bioactivity- or chem- istry-guided methods. An additional approach, complementary to the latter, arose in the late 1990s, when the first microbial genomes were sequenced, allowing genome mining. Thus, knowledge derived from bioinformatic analysis of microbial genomes paved the way to gain detailed insights into bacterial secondary metabolism. Since then, in silico methods for genome mining have enormously advanced and effective exper- imental approaches for connecting genomic and metabolic information have been developed [33-35].
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