This repository contains the supporting materials for the manuscript "The National Phenome Centre’s Open Platform for LC-MS-Based Metabolomics".
Contents:
- LC-MS Metabolite annotations: Metabolite annotations (retention time and m/z values) for 5 NPC LC-MS profiling assays.
- Reversed phase positive ion mode (RPOS)
- Reversed phase negative ion mode (RNEG)
- HILIC positive ion mode (HPOS)
- Lipidomic reversed phase positive ion mode (LPOS)
- Lipidomic reversed phase negative ion mode (LNEG)
- Protocols: Standard operating procedures (SOPs) and corresponding proformas (PRO) to document the outlined procedures in pdf format.
- NPC.SOP.CC004_v1.1: Sample sorting and LIMS logging
- NPC.SOP.CC005_v2.1: Formatting and replication of samples
- NPC.SOP.MS001_v2.1: Calibration and LockMass
- NPC.SOP.MS002_v2.1 and NPC.PRO.MS002_v2.1: UPLC, sample manager and Q-ToF system performance check
- NPC.SOP.MS003_v2.1 and NPC.PRO.MS003_v2.1: RPC UPLC-QTOF analysis of lipids in human plasma and serum
- NPC.SOP.MS004_v2.1 and NPC.PRO.MS004_v2.1: HILIC UPLC-QTOF analysis of small molecules in human plasma and serum
- NPC.SOP.MS005_v2.1 and NPC.PRO.MS005_v2.1: RPC UPLC-QTOF analysis of small molecules in human urine
- NPC.SOP.MS006_v2.1 and NPC.PRO.MS006_v2.1: HILIC UPLC-QTOF analysis of small molecules in human urine
- NPC_ExampleTraces.pfg: Representative gradient and system pressure traces for each chromatographic method (RPC and HILIC for small molecule profiling,
and RPC for lipid profiling).
NPC LC-MS metabolic profiling workflow
Additional Links:
- Targeted Extraction of Annotated Metabolites (PeakPantheR): Comprehensive training materials and documentation for the targeted extraction of annotated metabolites from LC-MS global profiling data, including vignettes and exemplar data.
- Data Pre-processing and Quality Control (nPYc-Toolbox): A Python implementation of the NPC toolchain for the import, pre-processing and quality control of metabolic profiling datasets.
- Data Pre-processing and Quality Control Tutorials (nPYc-Toolbox): Comprehensive training materials and documentation for data pre-processing and QC, including Jupyter notebooks, full documentation and exemplar data.