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jMorp User Guide Table of contents 1. Overview of jMorp 2. How to use jMorp website 3. Details of datasets included in jMorp 3.1. JG2.1.0 3.2. 38KJPN-SNV/INDEL 3.3. 38KJPN-HLA 3.4. 54KJPN-SNV/INDEL 3.5. 54KJPN-HLA (v20230626) 3.6. 54KJPN-HLA (v20230828) 3.7. 54KJPN-STR 3.8. JCNVv1 3.9. 8.3KJPN-SV 3.10. JSV1 3.11. ToMMo whole blood transcriptome (v20230828) 3.12. ToMMo ISO-Seq 3.13. IMM 3cell analysis 3.14. IMM cord blood 3.15. Proteome 3.16. Metabolome 2022 3.17. Metabolome 2023 3.18. MRI (v20240325) 3.19. PGx 3.20. Metagenome 16S-v4 3.21. Metagenome 16S-v3/v4 3.22. Metagenome shotgun 3.23. Data other than TMM analysis data References Contact jMorp User Guide 3. Details of datasets included in jMorp 3.17. Metabolome 2023 日本語版に切り替え 3.17. Metabolome 2023 Dataset CategoryMetabolome SummaryMetabolome analysis results derived from approximately 63,000 Japanese individuals References Koshiba et al. [9] Saigusa et al. [10] Saigusa et al. [11] Sato et al. [20] Samples analyzedPlasma # of samples and analysis platform Category Analysis platform Nunber of samples NMR NMR (Bruker: 600MHz), CryoProbe SampleJet 63,577 (Non pregnant: 49,188, Pregnant: 14,389, Repeat assessment: 4,597) LC-MS/G-Met v1 C18 column: UHPLC-Q-TOF/MS (Waters: Synapt G2-Si), HILIC column: HPLC-Q-FT/MS (Thermo Fisher Scientific: QExactive) 1,265 LC-MS/G-met v2 HPLC-Q-FT/MS (Thermo Fisher Scientific: QExactive) 2,971 LC-MS/T-Met UHPLC-MS/MS (Thermo Fisher Scientific: TSQ Quantiva) 2,363 LC-MS/kit180 UHPLC-MS/MS (Waters: Xevo TQ-S) 1,482 (Repeat assessment: 579) LC-MS/kit500 UHPLC-MS/MS (Waters: Xevo TQ-XS) 9,247 (Repeat assessment: 1,338) GC-MS/T-Met GC-MS/MS (Shimadzu: TQ8040) 4,608 (Repeat assessment: 645) Age / BMI distributions (non pregnant) Category Age distribution BMI distribution All NMR LC-MS/G-Met v1 LC-MS/G-met v2 LC-MS/T-Met LC-MS/kit180 LC-MS/kit500 GC-MS/T-Met Rule of metabolite ID convensionMetabolites in this dataset are assigned metabolite IDs in the form of TCx123456. The first two characters (TC) stands for ToMMo Compound ID (TC-ID). The third letter indicates how the metabolite is measured, and is divided as follows: Prefix Meaning TCN NMR TCZ LC-MS G-Met metabolome in HILIC mode ver.1 using HPLC TCI LC-MS G-Met metabolome in HILIC mode ver.2 using UHPLC TCO LC-MS G-Met metabolome in C18 mode using UHPLC TCL LC-MS T-Met metabolome TCB LC-MS T-Met metabolome in kit180 TCM LC-MS T-Met metabolome in kit500 TCS GC-MS T-Met metabolome The last six digits are numbers assigned to each metabolite within each data source. In MS metabolome, data sources can be further divided into positive and negative modes. A number less than 500,000 indicates that the metabolite was detected in negative mode; otherwise, it is a metabolite detected in positive mode. Automatic quantification of metabolitesThe concentrations of metabolites were automatically estimated from NMR spectra by using several regression models. Based on more than 1,000 concentration data carefully calculated by experts, both linear regression model and neural network model were built for each metabolite. We selected the model with best performance by using R-squared (R2) values as an evaluation index. We provide a reliability score of estimated concentration on a four-tiered scale: “Triple Stars (★★★)”, “Double Stars (★★☆)”, “Single Star (★☆☆)” and “Zero Star (☆☆☆)”. Each category corresponds to R2 value >=0.9, >=0.7, >=0.6, and <0.6, respectively. Outliers were defined by this protocol as those concentration is >10 SD after automatically estimation and were excluded in each compound. Note related to Hypoxanthine and InosineBecause values of metabolites, (TCN000044, TCI006689, TCS000091, TCO501589, TCZ000947) and Inosine (TCN000045, TCI010703), strongly depend on the stored time until specimen processing for banking, values of the samples that were not processed on the day were excluded for these two metabolites. About postprandial change plotPostprandial Changes in plasma metabolite level of metabolome 2022 dataset. Only participants between the ages of 60 and not 70 were included in the plot. Participants with a time interval exceeding 10 hours since their last meal are included in the “10<” group. Participants for whom the time interval from the last meal is unavailable are categorized into the “Unknown” group. The number of boxes was determined in 30-minute increments to ensure that the number of participants exceeded 50. In cases where this wasn’t achievable, the boxes were categorized into three groups: 0-5, 5-10, and 10<. About metabolic indexMetabolite concentrations of metabolome 2022 dataset were used to create an index stratified by age and sex. Data were analysed only on samples from the same day from collection to processing and storage, with the exception of pregnant women. Age was divided into 13 groups of 5 years each, with those aged 80 years and over grouped into one group to ensure that there were not too few people in the group compared to the other groups. Related pages on jMorp website Metabolite page Metabolite correlation network page GWAS page Download page Previous Next &＃169; Copyright 2024, jMorp developers. Built with Sphinx using a theme provided by Read the Docs.