Genome-wide studies depend heavily on the quality and consistency of integrated annotation data from diverse computational and experimental sources. This thesis presents a generalized framework for detecting and managing discrepancies within and between annotation datasets by addressing biological identity, data relationships, source independence, and conflicts. The workflow identifies errors and either resolves or incorporates inconsistencies into downstream analyses. The framework’s utility is demonstrated through construction of a genome-wide mouse transcription factor binding map and classification of single nucleotide polymorphisms. We further examine the impact of annotation discrepancies on downstream analyses and discuss future extensions for biologically meaningful summarization of inconsistencies.
