A framework to extend the application of triple quadrupole (QqQ) mass spectrometers to large-scale metabolite profiling. We aim to provide a foundation for designing QqQ multiple reaction monitoring (MRM) experiments for each of the 82 696 metabolites in the METLIN metabolite database. First, we identify common fragmentation products from the experimental fragmentation data in METLIN. Then, we model the likelihoods of each precursor structure in METLIN producing each common fragmentation product. With these likelihood estimates, we select ensembles of common fragmentation products that minimize our uncertainty about metabolite identities. We demonstrate encouraging performance and, based on our results, we suggest how our method can be integrated with future work to develop large-scale MRM experiments.
Provides a statistical analysis software. MarkerView is an application that permits users to distil complex datasets, find statistically significant differences and reveal key insights. It includes several statistical tools, such as principle components analysis (PCA), principle components variable grouping (PCVG) and T-tests. This software enables users to interpret results through interactive plots.
Identifies minimal precursors set demanded for metabolites production. Precursor is a qualitative method to determine bio-chemical producibility of metabolic reaction networks to determine minimal precursors sets required for target metabolites production.
Transforms signal intensity values into percent mole enrichment for each isotopologue measured. FluxFix is a web-based calculator that converts raw mass spec AUC values into the percent representation of each isotopologue measured. This program is agnostic to the mass spectrometry platform, generalizable to known or unknown metabolites, and it can take input data from either a theoretical natural isotopologue distribution or an experimentally measured one.
Aims to alleviate problems caused by missing not at random (MNAR) and facilitate targeted metabolomics studies. GSimp is a Gibbs sampler based missing value imputation approach that utilizes predictive information of variables and held a truncated normal distribution for each missing element simultaneously via embedding a prediction model into the Gibbs sampler framework. The software can handle left-censored missing values in targeted metabolomics studies.
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