MOTIVATION: The assessment of aberrant transcription events in rare disease patients holds great promise for enhancing the prioritization of causative genes-a strategy already widely adopted in clinical settings to improve diagnostic accuracy. Nevertheless, the accurate identification of causal genes remains a substantial challenge. RESULTS: We propose AXOLOTL, a novel ensemble method for identifying aberrant gene expression events in RNA expression matrices. AXOLOTL effectively accounts for gene correlation by incorporating coexpression constraints. We demonstrated the superior performance of AXOLOTL on representative RNA-seq datasets, including those from the GTEx healthy cohort, mitochondrial disease cohorts, and collagen VI-related dystrophy cohorts. Furthermore, we applied AXOLOTL to real-world cases of neurological disorders and demonstrated its ability to accurately identify aberrant gene expression and facilitate the prioritization of pathogenic variants. AVAILABILITY AND IMPLEMENTATION: AXOLOTL is freely available on GitHub (https://github.com/xuwenjian85/axolotl) and Zenodo (https://doi.org/10.5281/zenodo.17940844).