SUMMARY: RNA fractionation followed by high-throughput sequencing (RNA-seq) is widely used to study RNA localization, translation, structure, stability and subcellular compartmentalization. Interpreting fractionated RNA-seq data poses a fundamental compositional challenge: library preparation and sequencing depth obscure the original proportions of RNA fractions, which can bias comparisons-particularly when biological changes shift RNA distribution across fractions. This bias compromises comparisons of fraction-specific RNA profiles and limits the utility of standard differential expression methods. Existing approaches using transcript frequency ratios or standard normalization fail to account for the compositional nature of fractionated samples and also cannot estimate the unrecoverable "lost" fraction. We developed FracFixR, a statistical framework that reconstructs original fraction proportions by modeling the compositional relationship between the whole and the fractionated RNA samples. Using non-negative linear regression on carefully selected transcripts, FracFixR estimates global fraction weights, corrects individual transcript frequencies, and quantifies the unrecoverable material. The framework includes methods for differential proportion testing between conditions using binomial GLM, logit, or beta-binomial models. We rigorously validated FracFixR using synthetic data with known ground truth based on naturally observed aligned read distributions and real polysome profiling data from multiple cell lines, demonstrating accurate reconstruction of fraction weights (Pearson correlation >0.85) and enabling detection of differentially translated transcripts between cancer subtypes. AVAILABILITY AND IMPLEMENTATION: FracFixR is implemented as an R package freely available on GitHub at https://github.com/Arnaroo/FracFixR as well as on the CRAN repository.