Advancement in high-throughput technologies has generated a large amount of “-omics” data that become an inevitable component of modern biomedical and public health research. Practical statistical and computational methods are needed to meta-analyze and compare “-omics” data from different studies or experiments. In this talk, I will introduce two problem-driven methods and one software for the meta-analysis and resemblance analysis of multiple transcriptomic studies. In the first part, we proposed a Bayesian hierarchical model for RNA-seq meta-analysis by modeling count data, integrating information across genes and across studies, and modeling differential signals across studies via latent variables. In the second part, as motivated by two PNAS papers presenting contradicting conclusions of mouse model resemblance to human studies, we proposed a novel method to quantify the continuous measure of resemblance across model organisms and characterize in what pathways they most agree or disagree. In addition, I will also briefly introduce a R-Shiny based modularized software suite called “MetaOmics” to meta-analyze multiple transcriptomic studies for seven biological purposes.