bioRxivBiochemistry
Rapid QC-MS - Interactive Dashboard for Synchronous Mass Spectrometry Data Acquisition Quality Control
Abstract
Consistently collecting high-quality liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) data is a time-consuming hurdle for untargeted workflows. Analytical controls such as internal and biological standards are commonly included in high throughput workflows, helping researchers recognize low integrity specimens regardless of their biological source. However, evaluating these standards as data are collected has remained a considerable bottleneck - in both person-hours and accuracy. Here we present Rapid QC-MS, an automated, interactive dashboard for assessing LC-MS/MS data quality. Minutes after a new data file is written, a browser-viewable dashboard is updated with quality control results spanning multiple performance dimensions such as instrument sensitivity, in-run retention time shifts, and mass accuracy drift. Rapid QC-MS provides interactive visualizations that help users recognize acute deviations in these performance metrics, as well as gradual drifts over periods of hours, days, months, or years. Rapid QC-MS is open-source, simple to install, and highly configurable. By integrating open-source python libraries and widely used MS analysis software, it can adapt to any LC-MS/MS workflow. Rapid QC-MS runs locally and offers optional remote quality control by syncing with Google Drive. Furthermore, Rapid QC-MS can operate in a semi-autonomous fashion, alerting users to specimens with potentially poor analytical integrity via frequently used messaging applications. Rapid QC-MS offers a fast, straightforward approach to help users collect high-quality untargeted LC-MS/MS data by eliminating many of the most time-consuming steps in manual data curation. Download for free: https://github.com/czbiohub-sf/Rapid-QC-MS O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/578059v2_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@50d2b1org.highwire.dtl.DTLVardef@388373org.highwire.dtl.DTLVardef@3e31b5org.highwire.dtl.DTLVardef@1316c74_HPS_FORMAT_FIGEXP M_FIG C_FIG
- assay: metabolomics
- research focus: benchmarking
- research focus: database / resource
- research focus: software / tool
- approach: deep learning
- approach: statistical model
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