{"id":393,"date":"2016-09-23T13:42:02","date_gmt":"2016-09-23T16:42:02","guid":{"rendered":"http:\/\/pages.cnpem.br\/proteomicsworkshop\/?page_id=393"},"modified":"2016-09-23T13:42:02","modified_gmt":"2016-09-23T16:42:02","slug":"patternlab-for-proteomics-4-0-a-one-stop-shop-for-analyzing-shotgun-proteomic-data","status":"publish","type":"page","link":"http:\/\/pages.cnpem.br\/proteomicsworkshop\/patternlab-for-proteomics-4-0-a-one-stop-shop-for-analyzing-shotgun-proteomic-data\/","title":{"rendered":"PatternLab for proteomics 4.0: A one-stop shop for analyzing shotgun proteomic data"},"content":{"rendered":"<p><strong>Paulo Carvalho<br \/>\nFiocruz, Curitiba<\/strong><\/p>\n<p>PatternLab is a freely available, widely adopted integrated computational environment for analyzing shotgun proteomic data. It was launched in 2008 and, thanks to the continual influx of invaluable feedback from researchers,\u00a0 it has undergone continual improvement as well as expansion [1].\u00a0 PatternLab is very user-friendly thanks to its graphical user interface and offers a complete arsenal for analyzing all sorts of proteomic experiments, be them labeled (e.g., isobaric tags), or label-free (e.g., spec count or XIC).\u00a0 In this talk, I will provide a brief overview of three of the modules that set it apart.\u00a0 The first, PepExplorer [2], unlocks the quantitative proteomics of unsequenced organisms; its foundations are rooted in solid machine learning techniques that enable combining de novo sequencing results with a sequence alignment algorithm tailored for proteomics while providing dynamic and simplified reports at the protein level.\u00a0 The second module, Isobaric Analyzer, enables the quantitative analysis of isobarically labeled peptides. Its hallmark is the use of a peptide-centric statistical approach; this enables the immediate spotting, in some specific cases, of only those portions of the protein that appear to have undergone changes in abundance.\u00a0 This usually happens due to a mutation or PTM not considered by the search, commonly in scenarios originating from clinical proteomics.\u00a0 In widely adopted approaches, such information is shadowed by a false result pointing towards the differential expression of the entire protein.\u00a0 In this regard, our approach brings to the forefront the discussion of reconsidering how we do algorithms for differential proteomics.\u00a0 Finally, the third module, SIM-XL, is a complete solution for analyzing data from cross-linking mass spectrometry experiments (XL-MS) [3].\u00a0 This approach is invaluable for experiments targeting protein-protein interaction or aiming at further exploring protein structures.\u00a0 SIM-XL is the first to provide results as an interactive graphical representation of the region (or regions) in question; for example, clicking on portions of the graph opens annotated mass spectra supporting that region.<\/p>\n<p>PatternLab has been consolidated by enabling the analyses of billions of mass spectra over the years through the hands of various groups around the world.\u00a0 It is freely available at <a href=\"http:\/\/patternlabforproteomics.org\/\">http:\/\/patternlabforproteomics.org<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Paulo Carvalho Fiocruz, Curitiba PatternLab is a freely available, widely adopted integrated computational environment for analyzing shotgun proteomic data. It was launched in 2008 and, thanks to the continual influx of invaluable feedback from researchers,\u00a0 it has undergone continual improvement as well as expansion [1].\u00a0 PatternLab is very user-friendly thanks to its graphical user interface&hellip;<\/p>\n","protected":false},"author":8,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-393","page","type-page","status-publish","hentry","description-off"],"_links":{"self":[{"href":"http:\/\/pages.cnpem.br\/proteomicsworkshop\/wp-json\/wp\/v2\/pages\/393","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/pages.cnpem.br\/proteomicsworkshop\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/pages.cnpem.br\/proteomicsworkshop\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/pages.cnpem.br\/proteomicsworkshop\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"http:\/\/pages.cnpem.br\/proteomicsworkshop\/wp-json\/wp\/v2\/comments?post=393"}],"version-history":[{"count":0,"href":"http:\/\/pages.cnpem.br\/proteomicsworkshop\/wp-json\/wp\/v2\/pages\/393\/revisions"}],"wp:attachment":[{"href":"http:\/\/pages.cnpem.br\/proteomicsworkshop\/wp-json\/wp\/v2\/media?parent=393"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}