Biomarkers associated with environmental and disease resilience traits can be leveraged in breeding and management strategies. However, their discovery has been limited by the complexity of molecular systems and the cost of omics tools used to understand them. Advances in computational approaches including machine learning algorithms, together with the wealth of genomic data that has amassed, enable powerful meta-analyses for improved biomarker discovery in aquaculture species. Omics datasets from a variety of published studies on Pacific oyster with varying thermotolerance were systematically reanalyzed using open-access, reproducible bioinformatics pipelines, and updated references, and annotations. Data integration approaches revealed new and previously identified biomarkers associated with thermotolerance. Meta-analyses of omics datasets from Pacific oyster with different resilience traits and datasets from other shellfish species will reveal additional biomarkers and potential cross-species and/or cross-condition biomarkers. An example of the data analysis workflow and use of a comprehensive database containing these identified resilience biomarkers will be demonstrated during the presentation to promote the use of these products by the aquaculture community. These products will enable molecular tool development for more efficient phenotype selection and health monitoring, selection methods that use a systems biology approach for simultaneous improvement of multiple traits, and ultimately increased animal fitness.