Machine-learning guided discovery of emergent antimicrobial activity from dynamic covalent assemblies（Escherichia coli， RNA seq)

This dataset contains prokaryotic RNA-seq data from Escherichia coli samples collected under 3 treatment conditions to assess transcriptional responses to antimicrobial intervention. A total of 9 samples were analyzed, including 3 biological replicates in the A5B5 treatment group (AB5-1, AB5-2, AB5-3), 3 biological replicates in the PBS-treated control group (PBS-1, PBS-2, PBS-3), and 3 biological replicates in the CHX-treated positive control group (CHX1-1, CHX1-2, CHX1-3). Total RNA was extracted from bacterial samples using a bacterial RNA extraction workflow, and ribosomal RNA was depleted using the TIANSeq rRNA Depletion Kit (G-Bacteria). The remaining RNA was fragmented and used for first-strand cDNA synthesis with random hexamer primers. Strand-specific libraries were generated through dUTP-based second-strand synthesis followed by UNG digestion, and then processed through adapter ligation, purification, end repair, A-tailing, size selection, and PCR amplification. Library quality was evaluated using an Agilent 2100 Bioanalyzer, and qualified libraries were sequenced on an Illumina platform (paired-end RNA-seq). This dataset was generated to compare global gene expression patterns in E. coli across A5B5, PBS, and CHX conditions and to support downstream analyses of antimicrobial-response-associated pathways.