“…Although detailed protocols, workflows, parameters, and versions for each tool need to be modified depending on the aims of experiments ( Hao et al, 2023 , Hong and Jeong, 2023 , Jang et al, 2022 , Ju et al, 2023 , Park et al, 2023 ), our guideline will be valuable for biologists who are not bioinformaticians to analyze their own and publicly available RNA-seq data. Users will need to refer to other papers for assessing microarray, scRNA sequencing data ( Ryu et al, 2023 , Hwang, 2023 , Kim, 2023 , Kim and Lee, 2023 ), and hardware requirements for transcriptome analysis, which we did not include in this guideline. In addition, users will need to investigate additional publications to understand how to combine RNA-seq data with other types of datasets at various biological levels, including proteins (proteomics), metabolites (metabolomics), transcription factor-binding regions (Chromatin ImmunoPrecipitation sequencing: ChIP-seq), DNA-protein interaction (Assay for Transposase-Accessible Chromatin using sequencing: ATAC-seq), and expression/splicing variations in quantitative trait loci (eQTL/sQTL).…”