Detalle de: Quantifying annotation-driven bias in alternative splicing from EGAP metadata

Annotated coding sequences (CDSs) enable genome-wide estimates of alternative splicing. However, the quality and evidence support of these annotations can systematically bias estimates of splicing events across species. Here, we evaluate how annotation-related variables from the NCBI Eukaryotic Genome Annotation Pipeline affect inferred splicing levels. Analyzing 670 multicellular eukaryotes, we find that the percentage of CDSs supported by experimental evidence is the dominant predictor of variation in splicing estimates, whereas assembly quality and raw transcriptomic input play a minor role. To correct this annotation-driven bias, we introduce a normalization procedure based on polynomial regression, yielding an adjusted metric of alternative splicing. This novel metric preserves relative splicing complexity across species while mitigating annotation artifacts, with important implications for comparative genomics.