Transcriptomic profiling of plant-bacterial interactions provides critical insights into the molecular mechanisms underlying parasitism, commensalism, and mutualism. RNA sequencing (RNA-seq) enables the simultaneous analysis of plant and bacterial transcriptomes during colonization; however, integrated computational workflows specifically tailored for co-transcriptome analysis remain limited. Here, we present a step-by-step bioinformatics pipeline for analyzing co-transcriptome landscapes in plant-bacterial interactions. This workflow includes: (1) quality control and processing of raw RNA-seq data from both plant host and in-planta bacterial populations; (2) statistical analyses for differential gene expression; (3) prediction of orthologous bacterial genes and functional annotation of bacterial transcripts using the KEGG database; (4) integration and comparative analysis across multiple bacterial strains; and (5) correlation-based analysis of transcriptional dynamics between plants and bacteria. Designed for researchers with basic familiarity with command-line tools and R programming, this pipeline enables comprehensive analysis of plant-bacterial transcriptional interplay and facilitates hypothesis generation in both pathogenic and symbiotic contexts.