Deep Learning to Predict the Biosynthetic Gene Clusters in Bacterial Genomes

摘要

Biosynthetic gene clusters (BGCs) in bacterial genomes code for important small molecules and sec-ondary metabolites. Based on the validated BGCs and the corresponding sequences of protein family domains (Pfams), Pfam functions and clan information, we develop a deep learning method e-DeepBGC, that extends DeepBGC, for detecting the BGCs and their biosynthetic class in bacterial gen-omes. We show that e-DeepBGC leads to reduced false positive rates in BGC identification and an increased sensitivity in identifying BGCs compared to DeepBGC. We apply e-DeepBGC to 5,666 Ref Seq bacterial genomes and detect a total of 170, 685 BGCs with an average of 30.1 BGCs in each gen-ome. We summarize all the predicted BGCs, their functional classes and the distributions of the BGCs in different bacterial phyla.(c) 2022 Elsevier Ltd. All rights reserved.