Model organisms are instrumental substitute for human studies to expedite basic and clinical research. Despite their indispensable role in mechanistic investigation and drug development, resemblance of animal models to human has long been questioned and debated. Little effort has been made for an objective and quantitative congruence evaluation system for model organisms. We hereby propose a framework, namely Congruence Analysis for Model Organisms (CAMO), for transcriptomic response analysis by developing threshold-free differential expression analysis, quantitative resemblance score controlling data variabilities, pathway-centric downstream investigation and knowledge retrieval by text mining. Instead of a genome-wide dichotomous answer of “poorly/greatly” mimicking, CAMO assists researchers to quantify and visually identify biological functions that are best or least mimicked by model organisms, providing foundations for hypothesis generation and subsequent translational decisions.