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Functional-enrichment computational approaches may predict the membrane-trafficking regulations but do not resolve the directionality of the transport between cellular compartments. We present Traffickome, a deterministic method organizing 754 trafficking proteins into 11 directed transitions across seven compartments and 1,837 signaling proteins across 22 pathways. Implemented as an interactive browser and open-source Python package, Traffickome infers compartment-to-compartment transport, cargo fate and signaling-pathway activity and enables in silico perturbation from a single proteomic comparison. Across 29 proteomic comparisons spanning 14 studies encompassing over 66,000 gene-level observations across over 10,000 genes, Traffickome ranked the expected trafficking transition first in 23 cases (79%), outperforming the best conventional enrichment methods (17 of 29, 59%). Mean accuracy fell to 18% after randomizing the protein-to-transition assignments. To illustrate the power of Traffickome, we performed time-resolved analysis of the phosphoproteome in EGF-stimulated cells with the goal to distinguish signaling by internalized versus plasma-membrane EGFR. In the resulting datasets, comprising over 18,000 phosphosite-level measurements, Traffickome revealed localization-dependent differences in EGF-induced phosphorylation and prioritized trafficking regulators for testing their effects on signaling processes.<\/jats:p>","DOI":"10.64898\/2026.09.20.753016","type":"posted-content","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T18:25:13Z","timestamp":1790360713000},"source":"Crossref","is-referenced-by-count":0,"title":["Traffickome infers directed, compartment-resolved membrane trafficking from proteomic data"],"prefix":"10.64898","author":[{"given":"Rushikesh","family":"Deshpande","sequence":"first","affiliation":[{"name":"Department of Cell Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Itziar","family":"Pinilla-Macua","sequence":"additional","affiliation":[{"name":"Department of Cell Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4291-413X","authenticated-orcid":false,"given":"Joao","family":"Paulo","sequence":"additional","affiliation":[{"name":"Department of Cell Biology, Harvard Medical School, Boston, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steven P.","family":"Gygi","sequence":"additional","affiliation":[{"name":"Department of Cell Biology, Harvard Medical School, Boston, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Sorkin","sequence":"additional","affiliation":[{"name":"Department of Cell Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026092707464838000_2026.09.20.753016v1.1","doi-asserted-by":"publisher","DOI":"10.1038\/nrm2748"},{"key":"2026092707464838000_2026.09.20.753016v1.2","doi-asserted-by":"publisher","DOI":"10.1038\/nrc2521"},{"key":"2026092707464838000_2026.09.20.753016v1.3","doi-asserted-by":"publisher","DOI":"10.1038\/nrn3896"},{"key":"2026092707464838000_2026.09.20.753016v1.4","doi-asserted-by":"crossref","unstructured":"Badia-i-Mompel, P. , et al. decoupleR: ensemble of computational methods to infer biological activities from omics data. 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