{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Shortt RL"],"funding":["National Cancer Institute","NCI NIH HHS","National Institute of General Medical Sciences","NIGMS NIH HHS"],"pagination":["3301-3307"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11622367"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["35(12)"],"pubmed_abstract":["Covalent labeling methods coupled to mass spectrometry have emerged in recent years for studying the higher order structure of proteins. Quantifying the extent of modification of proteins in multiple states (i.e., ligand free vs ligand-bound) can provide information on protein interaction sites and regions of conformational change. Though there are several software platforms that are used to quantify the extent of modification, the process can still be time-consuming, particularly for proteome-wide studies. Here, we present an open-source software for quantitation called Covalent labeling Automated Data Analysis Platform for high Throughput in R (coADAPTr). coADAPTr tackles the need for more efficient data analysis in covalent labeling mass spectrometry for techniques such as hydroxyl radi"],"journal":["Journal of the American Society for Mass Spectrometry"],"pubmed_title":["Covalent Labeling Automated Data Analysis Platform for High Throughput in R (coADAPTr): A Proteome-Wide Data Analysis Platform for Covalent Labeling Experiments."],"pmcid":["PMC11622367"],"funding_grant_id":["R01 GM094231","R35 GM144324","R01GM094231","U24 CA271037","R35GM144324","U24CA271037"],"pubmed_authors":["Ramirez CR","Polasky DA","Pino LK","Jones LM","Chea EE","Shortt RL","Nesvizhskii AI"],"additional_accession":[]},"is_claimable":false,"name":"Covalent Labeling Automated Data Analysis Platform for High Throughput in R (coADAPTr): A Proteome-Wide Data Analysis Platform for Covalent Labeling Experiments.","description":"Covalent labeling methods coupled to mass spectrometry have emerged in recent years for studying the higher order structure of proteins. Quantifying the extent of modification of proteins in multiple states (i.e., ligand free vs ligand-bound) can provide information on protein interaction sites and regions of conformational change. Though there are several software platforms that are used to quantify the extent of modification, the process can still be time-consuming, particularly for proteome-wide studies. Here, we present an open-source software for quantitation called Covalent labeling Automated Data Analysis Platform for high Throughput in R (coADAPTr). coADAPTr tackles the need for more efficient data analysis in covalent labeling mass spectrometry for techniques such as hydroxyl radi","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Dec","modification":"2025-04-03T23:52:06.416Z","creation":"2025-04-03T23:52:06.416Z"},"accession":"S-EPMC11622367","cross_references":{"pubmed":["39356573"],"doi":["10.1021/jasms.4c00196"]}}