TopNEXt: automatic DDA exclusion framework for multi-sample mass spectrometry experiments.

ABSTRACT:

Motivation

Liquid Chromatography Tandem Mass Spectrometry experiments aim to produce high-quality fragmentation spectra, which can be used to annotate metabolites. However, current Data-Dependent Acquisition approaches may fail to collect spectra of sufficient quality and quantity for experimental outcomes, and extend poorly across multiple samples by failing to share information across samples or by requiring manual expert input.

Results

We present TopNEXt, a real-time scan prioritization framework that improves data acquisition in multi-sample Liquid Chromatography Tandem Mass Spectrometry metabolomics experiments. TopNEXt extends traditional Data-Dependent Acquisition exclusion methods across multiple samples by using a Region of Interest and intensity-based scoring syste

SUBMITTER: McBride R 

PROVIDER: S-EPMC10336026 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature