CliqueMS: a computational tool for annotating in-source metabolite ions from LC-MS untargeted metabolomics data based on a coelution similarity network

Abstract

Motivation The analysis of biological samples in untargeted metabolomic studies using LC-MS yields tens of thousands of ion signals. Annotating these features is of the utmost importance for answering questions as fundamental as, e.g. how many metabolites are there in a given sample. Results Here, we introduce CliqueMS, a new algorithm for annotating in-source LC-MS1 data. CliqueMS is based on the similarity between coelution profiles and therefore, as opposed to most methods, allows for the annotation of a single spectrum. Furthermore, CliqueMS improves upon the state of the art in several dimensions: (i) it uses a more discriminatory feature similarity metric; (ii) it treats the similarities between features in a transparent way by means of a simple generative model; (iii) it uses a well-grounded maximum likelihood inference approach to group features; (iv) it uses empirical adduct frequencies to identify the parental mass and (v) it deals more flexibly with the identification of the parental mass by proposing and ranking alternative annotations. We validate our approach with simple mixtures of standards and with real complex biological samples. CliqueMS reduces the thousands of features typically obtained in complex samples to hundreds of metabolites, and it is able to correctly annotate more metabolites and adducts from a single spectrum than available tools. Availability and implementation https://CRAN.R-project.org/package=cliqueMS and https://github.com/osenan/cliqueMS. Supplementary information Supplementary data are available at Bioinformatics online.

In-Source CID Ramping and Covariant Ion Analysis of Hydrophilic Interaction Chromatography Metabolomics

“…The hypotheses of metabolite neutral masses are scored based on the matching results and optionally the empirical frequency of the adducts. 9,19 From these scores the most plausible neutral mass is returned as the result. Our data suggest that the performance of such algorithms can be improved by having a more complete adduct list generated from COVINA ( Figure S3).…”

“…6 The annotation step recognizes and annotates the adduct ions and natural isotopic ions. [7][8][9] These data processing steps dramatically reduce the complexity of the metabolomics data. Mahieu et al reported the analysis of an E. coli.…”

“…6,8,11,12 The other approach is to read the extracted ion chromatograms (XICs) in the raw data and calculate the correlation of chromatographic peaks. 7,9 Even though each of the tools has demonstrated its utility, the adduct ions in a metabolomics dataset are often insufficiently annotated. Glutamate, for example, was reported to produce more than 100 spectral peaks, but many of the complex adducts did not have chemical annotation.…”

Enhanced Electrospray In-source Fragmentation for Higher Sensitivity Data Independent Acquisition and Autonomous METLIN Molecular Identification

“…[3][4][5] However, the annotation and identification of the thousands of features typically detected in LC-MS experiments remains a critical challenge. [6][7][8] It is well known that in addition to molecular ions, LC-MS 1 data also contains adducts, isotopes, multimers, in-source fragments, and contaminants, etc., which can significantly impact annotation. 6,7,9 In-source fragmentation (ISF) is a naturally occurring phenomenon in atmospheric ion sources.…”

“…[6][7][8] It is well known that in addition to molecular ions, LC-MS 1 data also contains adducts, isotopes, multimers, in-source fragments, and contaminants, etc., which can significantly impact annotation. 6,7,9 In-source fragmentation (ISF) is a naturally occurring phenomenon in atmospheric ion sources. [10][11][12][13] ESI is considered among the softest ionization technologies with the least ISF, however, even in the ESI source, unintentional ISF widely exists.…”