Data from a multi-year targeted proteomics study of a longitudinal birth cohort of type 1 diabetes
Abstract The deployment of liquid chromatography-mass spectrometry-based plasma proteomics experiments in a large cohort is sparse, leading to a lack of data available for benchmarking, method development or validation. Comprised of 6,426 plasma analyses, The Environmental Determinants of Diabetes i...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04249-1 |
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| author | Lisa M. Bramer Ernesto S. Nakayasu Javier E. Flores Jennifer E. Van Eyk Michael J. MacCoss Hemang M. Parikh Thomas O. Metz Bobbie-Jo M. Webb-Robertson |
| author_facet | Lisa M. Bramer Ernesto S. Nakayasu Javier E. Flores Jennifer E. Van Eyk Michael J. MacCoss Hemang M. Parikh Thomas O. Metz Bobbie-Jo M. Webb-Robertson |
| author_sort | Lisa M. Bramer |
| collection | DOAJ |
| description | Abstract The deployment of liquid chromatography-mass spectrometry-based plasma proteomics experiments in a large cohort is sparse, leading to a lack of data available for benchmarking, method development or validation. Comprised of 6,426 plasma analyses, The Environmental Determinants of Diabetes in the Young (TEDDY) proteomics validation study constitutes one of the largest targeted proteomics experiments in the literature to date. The proteomics data from this study were generated over the course of 2.5 years from over 900 study subjects, each providing up to 29 longitudinal samples. The data also includes 916 quality control samples. The targeted mass spectrometry assay was comprised of 694 peptides mapping to 167 proteins and the panel was measured in each subject and QC sample. The targeted proteomic dataset presented here can be used as a resource for new computational methods development, such as for batch correction, as well as for benchmarking and comparing the performance of different methods/tools. |
| format | Article |
| id | doaj-art-a1089d7936d945fba003a1bfb3f4901d |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Data |
| spelling | doaj-art-a1089d7936d945fba003a1bfb3f4901d2025-01-26T12:14:35ZengNature PortfolioScientific Data2052-44632025-01-011211810.1038/s41597-024-04249-1Data from a multi-year targeted proteomics study of a longitudinal birth cohort of type 1 diabetesLisa M. Bramer0Ernesto S. Nakayasu1Javier E. Flores2Jennifer E. Van Eyk3Michael J. MacCoss4Hemang M. Parikh5Thomas O. Metz6Bobbie-Jo M. Webb-Robertson7Biological Sciences Division, Pacific Northwest National LaboratoryBiological Sciences Division, Pacific Northwest National LaboratoryBiological Sciences Division, Pacific Northwest National LaboratoryDepartment of Cardiology, Advanced Clinical Biosystem Research Institue, Cedars-Sinai Medical CenterDepartment of Genome Sciences, University of WashingtonHealth Informatics Institute, University of South FloridaBiological Sciences Division, Pacific Northwest National LaboratoryBiological Sciences Division, Pacific Northwest National LaboratoryAbstract The deployment of liquid chromatography-mass spectrometry-based plasma proteomics experiments in a large cohort is sparse, leading to a lack of data available for benchmarking, method development or validation. Comprised of 6,426 plasma analyses, The Environmental Determinants of Diabetes in the Young (TEDDY) proteomics validation study constitutes one of the largest targeted proteomics experiments in the literature to date. The proteomics data from this study were generated over the course of 2.5 years from over 900 study subjects, each providing up to 29 longitudinal samples. The data also includes 916 quality control samples. The targeted mass spectrometry assay was comprised of 694 peptides mapping to 167 proteins and the panel was measured in each subject and QC sample. The targeted proteomic dataset presented here can be used as a resource for new computational methods development, such as for batch correction, as well as for benchmarking and comparing the performance of different methods/tools.https://doi.org/10.1038/s41597-024-04249-1 |
| spellingShingle | Lisa M. Bramer Ernesto S. Nakayasu Javier E. Flores Jennifer E. Van Eyk Michael J. MacCoss Hemang M. Parikh Thomas O. Metz Bobbie-Jo M. Webb-Robertson Data from a multi-year targeted proteomics study of a longitudinal birth cohort of type 1 diabetes Scientific Data |
| title | Data from a multi-year targeted proteomics study of a longitudinal birth cohort of type 1 diabetes |
| title_full | Data from a multi-year targeted proteomics study of a longitudinal birth cohort of type 1 diabetes |
| title_fullStr | Data from a multi-year targeted proteomics study of a longitudinal birth cohort of type 1 diabetes |
| title_full_unstemmed | Data from a multi-year targeted proteomics study of a longitudinal birth cohort of type 1 diabetes |
| title_short | Data from a multi-year targeted proteomics study of a longitudinal birth cohort of type 1 diabetes |
| title_sort | data from a multi year targeted proteomics study of a longitudinal birth cohort of type 1 diabetes |
| url | https://doi.org/10.1038/s41597-024-04249-1 |
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