Ethylene is a gaseous hormone that is vital in plant growth, development, biotic or abiotic stresses, fruit ripening, and senescence. The ethylene signaling machinery includes several genes, many of which are multigene family members and exhibit spatial and temporal expression patterns. These genes also show divergence and expansion during evolution. Recent high-throughput technologies have promoted a more comprehensive profiling of genes regulated by ethylene signaling. The availability of a large amount of sequencing data from whole genome, transcriptome, small RNA, etc., in the public databases provides an informative resource for gene mining of ethylene. However, these omics data generally result in large gene lists that make it challenging to interpret the data and extract insights into biological significance. Different platforms have different architectures and designs, and even similar studies using the same platform may obtain data with significant variances because of the highly dynamic and flexible effects of plant hormones, making comparisons across different studies and platforms difficult. Hence, the method of analysis suggests the requirement of database for their respective analysis. Some of the databases reported to find the genomics, transcriptomics, and regulatory mechanism of ethylene are The Arabidopsis Information Resource (TAIR), Banana Genome Hub (BGH), Citrus genome database (CGD), OneKP database, miRDeep-2, miRDP, psRNATarget, Arabidopsis Hormone Database (AHD), and gene set–level analyses of hormone responses (GSHR). The present chapter deals with the online and offline methods and basic workflow for the detection of ethylene signaling machinery.