{"doi":"10.1038/s44320-024-00064-3","title":"Proteome-wide copy-number estimation from transcriptomics","abstract":"Protein copy numbers constrain systems-level properties of regulatory networks, but proportional proteomic data remain scarce compared to RNA-seq. We related mRNA to protein statistically using best-available data from quantitative proteomics and transcriptomics for 4366 genes in 369 cell lines. The approach starts with a protein's median copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model linking mRNAs to protein. For dozens of cell lines and primary samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, empirical mRNA-to-protein ratios, and a proteogenomic DREAM challenge winner. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein complexes, suggesting mechanistic relationships. We use the method to identify a viral-receptor abundance threshold for coxsackievirus B3 susceptibility from 1489 systems-biology infection models parameterized by protein inference. When applied to 796 RNA-seq profiles of breast cancer, inferred copy-number estimates collectively re-classify 26-29% of luminal tumors. By adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility of contemporary proteomics.","journal":"Molecular Systems Biology","year":2024,"id":466461,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9118,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":797276,"name":"Cameron Griffiths","orcid":"0000-0002-1280-3615","position":1,"is_corresponding":false},{"id":576969,"name":"Sarah M. Groves","orcid":"0000-0002-3122-2605","position":2,"is_corresponding":false},{"id":373945,"name":"B. Bishal Paudel","orcid":"0000-0002-5426-7048","position":3,"is_corresponding":false},{"id":1298608,"name":"Lixin Wang","orcid":"0000-0002-0402-0434","position":4,"is_corresponding":false},{"id":608961,"name":"David F. Kashatus","orcid":"0000-0001-8007-0612","position":5,"is_corresponding":false},{"id":72659,"name":"Kevin A. Janes","orcid":"0000-0002-8028-6138","position":6,"is_corresponding":false},{"id":703724,"name":"Andrew J. Sweatt","orcid":"0000-0001-6588-7376","position":0,"is_corresponding":true}],"reference_count":113,"raw_metadata":null,"created_at":"2026-07-19T02:05:06.743102Z","pmid":"39333715","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}