MassIVEapplication/xmlftp://massive-ftp.ucsd.edu/v06/MSV000092887/primaryOK200ProteomicsAmmar TahirX500R QTOFHomo Sapiens (ncbitaxon:9606)https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=31410a93240e4ac8aa73c8e116ca9c98ammar.tahir@univie.ac.atUniversity of Vienna/ Department of Pharmaceutical SciencesMassIVE4,325MS:1002864 - No post-translational-modifications are included in the identified peptides of this datasetA significant hurdle in untargeted lipid/metabolomics research lies in the absence of reliable, cross-validated spectral libraries, leading to a considerable portion of LC-MS features being labeled as unknowns. Despite continuous advancement in annotation tools and libraries, it is important to safeguard, publish and share acquired data through public repositories. Embracing this trend of data sharing not only promotes efficient resource utilization but also paves the way for future repurposing and in-depth analysis; ultimately advancing our comprehension of Covid-19 and other diseases. In this work, we generated an extensive MS-dataset of 39 Covid-19 infected patients versus age- and gender-matched 39 healthy controls. We implemented state of the art acquisition techniques including IDA and SWATH-DIA to ensure a thorough insight in the lipidome and metabolome, ensuring a repurposable dataset.A comprehensive IDA and SWATH-DIA Lipidomics and Metabolomics dataset: SARS-CoV-2 case control study.Tahir Ammar A, Draxler Agnes A, Stelzer Tamara T, Blaschke Amelie A, Laky Brenda B, Széll Marton M, Binar Jessica J, Bartak Viktoria V, Bragagna Laura L, Maqboul Lina L, Herzog Theresa T, Thell Rainer R, Wagner Karl-Heinz KHfalseExploring the Metabolic Consequences of SARSCoV2 Infection A comprehensive IDA and SWATH DIA Lipidomics and Metabolomics datasetIn this work, we generated an extensive dataset describing the metabolome and lipidome of 39 SARS-CoV-2 infected patients versus age- and gender-matched 39 healthy controls. We implemented state of the art acquisition techniques including IDA and SWATH-DIA to ensure a holistic insight in the lipidome and metabolome, ensuring a future-proof repurposable dataset.MSV00009288739266559