{"doi":"10.1038/nmeth.3901","title":"The Perseus computational platform for comprehensive analysis of (prote)omics data","abstract":"A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.","journal":"Nature Methods","year":2016,"id":9929,"datarank":12.546908658491471,"base_score":9.074062353683555,"endowment":9.074062353683555,"self_citation_contribution":1.3611093530525336,"citation_network_contribution":11.185799305438938,"self_endowment_contribution":1.3611093530525336,"citer_contribution":11.185799305438938,"corpus_percentile":91.2,"corpus_rank":1151,"citation_count":8725,"citer_count":154,"citers_with_citation_signal":154,"citers_with_endowment":154,"datacite_reuse_total":0,"is_dataset":false,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2016-06-27","authors":[{"id":85037,"name":"Tikira Temu","orcid":null,"position":1,"is_corresponding":false},{"id":85038,"name":"Pavel Sinitcyn","orcid":"0000-0002-2653-1702","position":2,"is_corresponding":false},{"id":85039,"name":"Arthur Carlson","orcid":null,"position":3,"is_corresponding":false},{"id":85040,"name":"Marco Y. Hein","orcid":"0000-0002-9490-2261","position":4,"is_corresponding":false},{"id":85041,"name":"Tamar Geiger","orcid":"0000-0002-9526-197X","position":5,"is_corresponding":false},{"id":2937,"name":"Matthias Mann","orcid":"0000-0003-1292-4799","position":6,"is_corresponding":false},{"id":79938,"name":"Jürgen Cox","orcid":null,"position":7,"is_corresponding":false},{"id":85036,"name":"Stefka Tyanova","orcid":"0000-0002-1974-5876","position":0,"is_corresponding":true}],"reference_count":76,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"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,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}