Proteins, encoded by the genome, perform essential biological functions. A single gene can encode multiple versions of a protein, dubbed isoforms, with varying functionality. This level of cellular control is critical for multiple aspects of biology, such as neurobiology. Quantifying protein isoforms on a large scale is a major challenge, even with state-of-the-art technology, such as mass spectrometry. Standard “bottom up” mass spectrometry can assess only short portions of isoforms called peptides, and these peptides often map to more than one isoform. To address this challenge, we introduce PAQu, a novel Bayesian method that leverages multiomic information from the peptidome and transcriptome to provide accurate estimates of isoform abundance. PAQu offers several advantages over existing methods in a single and unified framework. It provides uncertainty quantification, integrates multiomic information for improved accuracy, highlights the potential presence of post-translational modifications, and provides a rigorous framework for hypothesis testing. Through extensive simulations, we show that PAQu consistently outperforms competiting methods in detecting differentially expressed protein isoforms and estimating their abundances. We apply PAQu to a real-world dataset to investigate differences in isoform abundance levels between the schizophrenic and control groups. These results demonstrate that PAQu can identify significant variations in isoform abundance levels, providing valuable insights into the underlying biological mechanisms of schizophrenia.