A major focus of my lab is developing and applying innovative statistical and bioinformatics tools to better understand genomics and epigenomics changes (e.g., DNA methylations) in complex diseases such as cancers and neurodegenerative diseases. By making statistical and bioinformatics methodology available in open-source and user-friendly software, more researchers can then leverage our work to make new scientific discoveries. In the past few years, I have worked with my team to develop several R/Bioconductor packages for the analysis of genomics and epigenomics data. Specifically, we performed comprehensive evaluations of existing analysis tools for region-based DNA methylation analysis (Mallik et al. 2019, Briefings in Bioinformatics 20 (6) 2224-2235) and developed the new coMethDMR approach and software (Gomez et al. 2019, Nucleic Acids Research 47 (177): e98). In addition, we have also developed several additional open-source software packages, including MethReg (Silva et al. 2022, Nucleic Acids Research, gkac030), pathwayMultiomics (Odom et al. 2021, Frontiers in Genetics, 12: 783713), and PathwayPCA (Odom et al. 2020, Proteomics 20: e1900409) that perform integrative analysis of DNA methylations and other types of omics data, all of which have been shared with the public through Bioconductor repository or GitHub.