Dataset Information

Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements.

ABSTRACT: Liquid chromatography coupled to mass spectrometry (LC-MS) has become a prominent tool for the analysis of complex proteomics and metabolomics samples. In many applications multiple LC-MS measurements need to be compared, e. g. to improve reliability or to combine results from different samples in a statistical comparative analysis. As in all physical experiments, LC-MS data are affected by uncertainties, and variability of retention time is encountered in all data sets. It is therefore necessary to estimate and correct the underlying distortions of the retention time axis to search for corresponding compounds in different samples. To this end, a variety of so-called LC-MS map alignment algorithms have been developed during the last four years. Most of these approaches are well documented, but they are usually evaluated on very specific samples only. So far, no publication has been assessing different alignment algorithms using a standard LC-MS sample along with commonly used quality criteria.We propose two LC-MS proteomics as well as two LC-MS metabolomics data sets that represent typical alignment scenarios. Furthermore, we introduce a new quality measure for the evaluation of LC-MS alignment algorithms. Using the four data sets to compare six freely available alignment algorithms proposed for the alignment of metabolomics and proteomics LC-MS measurements, we found significant differences with respect to alignment quality, running time, and usability in general.The multitude of available alignment methods necessitates the generation of standard data sets and quality measures that allow users as well as developers to benchmark and compare their map alignment tools on a fair basis. Our study represents a first step in this direction. Currently, the installation and evaluation of the "correct" parameter settings can be quite a time-consuming task, and the success of a particular method is still highly dependent on the experience of the user. Therefore, we propose to continue and extend this type of study to a community-wide competition. All data as well as our evaluation scripts are available at http://msbi.ipb-halle.de/msbi/caap.

SUBMITTER: Lange E

PROVIDER: S-EPMC2570366 | biostudies-literature | 2008 Sep

REPOSITORIES: biostudies-literature

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Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements.

Lange Eva E Tautenhahn Ralf R Neumann Steffen S Gröpl Clemens C

BMC bioinformatics 20080915

<h4>Background</h4>Liquid chromatography coupled to mass spectrometry (LC-MS) has become a prominent tool for the analysis of complex proteomics and metabolomics samples. In many applications multiple LC-MS measurements need to be compared, e. g. to improve reliability or to combine results from different samples in a statistical comparative analysis. As in all physical experiments, LC-MS data are affected by uncertainties, and variability of retention time is encountered in all data sets. It is ...[more]

PMID: 18793413

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Project description:BackgroundLiquid chromatography-mass spectrometry is widely used in untargeted metabolomics for composition profiling. In multi-run analysis scenarios, features of each run are aligned into consensus features by feature alignment algorithms to observe the intensity variations across runs. However, most of the existing feature alignment methods focus more on accurate retention time correction, while underestimating the importance of feature matching. None of the existing methods can comprehensively consider feature correspondences among all runs and achieve optimal matching.ResultsTo comprehensively analyze feature correspondences among runs, we propose G-Aligner, a graph-based feature alignment method for untargeted LC-MS data. In the feature matching stage, G-Aligner treats features and potential correspondences as nodes and edges in a multipartite graph, considers the multi-run feature matching problem an unbalanced multidimensional assignment problem, and provides three combinatorial optimization algorithms to find optimal matching solutions. In comparison with the feature alignment methods in OpenMS, MZmine2 and XCMS on three public metabolomics benchmark datasets, G-Aligner achieved the best feature alignment performance on all the three datasets with up to 9.8% and 26.6% increase in accurately aligned features and analytes, and helped all comparison software obtain more accurate results on their self-extracted features by integrating G-Aligner to their analysis workflow. G-Aligner is open-source and freely available at https://github.com/CSi-Studio/G-Aligner under a permissive license. Benchmark datasets, manual annotation results, evaluation methods and results are available at https://doi.org/10.5281/zenodo.8313034 CONCLUSIONS: In this study, we proposed G-Aligner to improve feature matching accuracy for untargeted metabolomics LC-MS data. G-Aligner comprehensively considered potential feature correspondences between all runs, converting the feature matching problem as a multidimensional assignment problem (MAP). In evaluations on three public metabolomics benchmark datasets, G-Aligner achieved the highest alignment accuracy on manual annotated and popular software extracted features, proving the effectiveness and robustness of the algorithm.

Project description:BackgroundImmunoglobulin A nephropathy (IgAN), a globally common primary chronic glomerulopathy, is one of the leading causes of end-stage renal disease. However, the underlying mechanisms of IgAN have yet to be demonstrated. There were no adequate and reliable plasma biomarkers for clinical diagnosis, especially at the early stage. In the present study, integrative proteomics and metabolomics were aimed at exploring the mechanism of IgAN and identifying potential biomarkers.MethodsPlasma from IgAN and healthy individuals were collected and analyzed in a randomized controlled manner. Data-independent acquisition quantification proteomics and mass spectrometry based untargeted metabolomics techniques were used to profile the differentially expressed proteins (DEPs) and differentially abundant metabolites (DAMs) between two groups and identify potential biomarkers for IgAN from health at the early stage. Disease-related pathways were screened out by clustering and function enrichment analyses of DEPs and DAMs. And the potential biomarkers for IgAN were identified through the machine learning approach. Additionally, an independent cohort was used to validate the priority candidates by enzyme-linked immunosorbent assay (ELISA).ResultsProteomic and metabolomic analyses of IgAN plasma showed that the complement and the immune system were activated, while the energy and amino acid metabolism were disordered in the IgAN patients. PRKAR2A, IL6ST, SOS1, and palmitoleic acid have been identified as potential biomarkers. Based on the AUC value for the training and test sets, the classification performance was 0.994 and 0.977, respectively. The AUC of the external validation of the four biomarkers was 0.91.ConclusionIn this study, we combined proteomics and metabolomics techniques to analyze the plasma of IgAN patients and healthy individuals, constructing a biomarker panel, which could provide new insights and provide potential novel molecular diagnoses for IgAN.

Dataset Information

Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements.

Publications

Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements.

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