Estimating protein isoform abundances with [Formula: see text]

A single gene can encode multiple versions of a protein, dubbed isoforms, with varying functionality. Cellular control of isoform abundances is critical for multiple aspects of biology and is only partially regulated by transcript levels. While long-read sequencing facilitates transcript quantification, quantifying the resulting protein isoforms on a large scale is a major challenge, complicating biological interpretation of transcript alterations. Standard "bottom up" mass spectrometry can assess only short portions of isoforms called peptides, and these peptides often map onto more than one isoform. We introduce [Formula: see text] (Protein isoform Abundance Quantification), a Bayesian method that leverages multiomic information from the peptidome and transcriptome to provide accurate estimates of isoform abundance even when peptide mapping is ambiguous. [Formula: see text] offers several advantages over existing methods in a unified framework. It provides uncertainty quantification, integrates multiomic information for improved accuracy, and provides a rigorous framework for hypothesis testing. Extensive simulations show that [Formula: see text] consistently outperforms competing methods in detecting differentially abundant protein isoforms and estimating their abundances. We use [Formula: see text] to investigate differences in isoform abundance levels between people with schizophrenia and control subjects, confirming a long-held hypothesis that levels of the C4A isoform of Complement Component 4 are increased in schizophrenia while C4B is not. These results demonstrate that [Formula: see text] can identify significant variations in isoform abundance levels not previously possible.