Browse
Submit Data
Databases
API
Help

Dataset Information

10 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Separation of Chromatographic Co-Eluted Compounds by Clustering and by Functional Data Analysis.

ABSTRACT: Peak overlapping is a common problem in chromatography, mainly in the case of complex biological mixtures, i.e., metabolites. Due to the existence of the phenomenon of co-elution of different compounds with similar chromatographic properties, peak separation becomes challenging. In this paper, two computational methods of separating peaks, applied, for the first time, to large chromatographic datasets, are described, compared, and experimentally validated. The methods lead from raw observations to data that can form inputs for statistical analysis. First, in both methods, data are normalized by the mass of sample, the baseline is removed, retention time alignment is conducted, and detection of peaks is performed. Then, in the first method, clustering is used to separate overlapping peaks, whereas in the second method, functional principal component analysis (FPCA) is applied for the same purpose. Simulated data and experimental results are used as examples to present both methods and to compare them. Real data were obtained in a study of metabolomic changes in barley (Hordeum vulgare) leaves under drought stress. The results suggest that both methods are suitable for separation of overlapping peaks, but the additional advantage of the FPCA is the possibility to assess the variability of individual compounds present within the same peaks of different chromatograms.

SUBMITTER: Sawikowska A

PROVIDER: S-EPMC8065729 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Liquid Chromatographic Strategies for Separation of Bioactive Compounds in Food Matrices.

Project description:Nowadays, there is an increasing attention for nutraceuticals and, in general, bioactive compounds naturally present in food. Indeed, the possibility of preserving human health and preventing disease (e.g., cardiovascular diseases, cancer etc.) by the intake of healthy food is attractive for both consumers and food industries. In turn, research in this field was also prompted significantly, with the aim of characterizing these bioactive compounds and ascribe to them a specific activity. The bioactive compounds can belong to several chemical classes. However, their chemical diversity and presence in complex matrices, such as food, make it challenging both their isolation and characterization. To tackle this issue, efficient separation systems are needed, which are mainly based on chromatography. In this context, this mini-review aims to provide the reader with an overview of the most relevant and recent approaches for the separation of the most common bioactive compounds in food, in particular polyphenols, phenols, carotenoids, and peptides, by liquid chromatography approaches.

| S-EPMC6320936 | biostudies-other

HetFCM: functional co-module discovery by heterogeneous network co-clustering.

Project description:Functional molecular module (i.e., gene-miRNA co-modules and gene-miRNA-lncRNA triple-layer modules) analysis can dissect complex regulations underlying etiology or phenotypes. However, current module detection methods lack an appropriate usage and effective model of multi-omics data and cross-layer regulations of heterogeneous molecules, causing the loss of critical genetic information and corrupting the detection performance. In this study, we propose a heterogeneous network co-clustering framework (HetFCM) to detect functional co-modules. HetFCM introduces an attributed heterogeneous network to jointly model interplays and multi-type attributes of different molecules, and applies multiple variational graph autoencoders on the network to generate cross-layer association matrices, then it performs adaptive weighted co-clustering on association matrices and attribute data to identify co-modules of heterogeneous molecules. Empirical study on Human and Maize datasets reveals that HetFCM can find out co-modules characterized with denser topology and more significant functions, which are associated with human breast cancer (subtypes) and maize phenotypes (i.e., lipid storage, drought tolerance and oil content). HetFCM is a useful tool to detect co-modules and can be applied to multi-layer functional modules, yielding novel insights for analyzing molecular mechanisms. We also developed a user-friendly module detection and analysis tool and shared it at http://www.sdu-idea.cn/FMDTool.

| S-EPMC10853805 | biostudies-literature

Detection of co-eluted peptides using database search methods.

Project description:BackgroundCurrent experimental techniques, especially those applying liquid chromatography mass spectrometry, have made high-throughput proteomic studies possible. The increase in throughput however also raises concerns on the accuracy of identification or quantification. Most experimental procedures select in a given MS scan only a few relatively most intense parent ions, each to be fragmented (MS2) separately, and most other minor co-eluted peptides that have similar chromatographic retention times are ignored and their information lost.ResultsWe have computationally investigated the possibility of enhancing the information retrieval during a given LC/MS experiment by selecting the two or three most intense parent ions for simultaneous fragmentation. A set of spectra is created via superimposing a number of MS2 spectra, each can be identified by all search methods tested with high confidence, to mimick the spectra of co-eluted peptides. The generated convoluted spectra were used to evaluate the capability of several database search methods - SEQUEST, Mascot, X!Tandem, OMSSA, and RAId_DbS - in identifying true peptides from superimposed spectra of co-eluted peptides. We show that using these simulated spectra, all the database search methods will gain eventually in the number of true peptides identified by using the compound spectra of co-eluted peptides.Open peer reviewReviewed by Vlad Petyuk (nominated by Arcady Mushegian), King Jordan and Shamil Sunyaev. For the full reviews, please go to the Reviewers' comments section.

| S-EPMC2483259 | biostudies-literature

Gas chromatographic separation of methoxylated polychlorinated biphenyl atropisomers.

Project description:Several polychlorinated biphenyls (PCBs) and their hydroxylated metabolites display axial chirality. Here we describe an enantioselective, gas chromatographic separation of methylated derivatives of hydroxylated (OH-)PCB atropisomers (MeO-PCB) using a chemically bonded beta-cyclodextrin column (Chirasil-Dex). The atropisomers of several MeO-PCBs could be separated on this column with resolutions ranging from 0.42 to 0.87 under isothermal or temperature-programmed conditions. In addition, the enantiomeric fraction of OH-PCB 136 metabolites was determined in male and female rats treated with racemic PCB 136. The methylated derivatives of two OH-PCB 136 metabolites showed an enantiomeric enrichment in liver tissue, whereas PCB 136 itself was near racemic.

| S-EPMC2579784 | biostudies-literature

Functional clustering of mouse ultrasonic vocalization data.

Project description:Mouse ultrasonic vocalizations (USVs) are studied in many fields of science. However, various noise and varied USV patterns in observed signals make complete automatic analysis difficult. We improve several methods to reduce noise, detect USV calls and automatically cluster USV calls. After reduction of noise and detection of USV calls, we consider USV calls as functional data and characterize them as USV functions with B-spline basis functions. For discontinuous USV calls, breakpoints in the USV functions are defined using multiple knots in the construction of the B-spline basis functions, and a hierarchical method is used to cluster the USV functions by shape. We finally show the performance of the proposed methods with USV data recorded for laboratory mice.

| S-EPMC5942836 | biostudies-literature

Mining Chromatographic Enantioseparation Data Using Matched Molecular Pair Analysis.

Project description:We apply matched molecular pair (MMP) analysis to data from ChirBase, which contains literature reports of chromatographic enantioseparations. For the 19 chiral stationary phases we examined, we were able to identify 289 sets of pairs where there is a statistically significant and consistent difference in enantioseparation due to a small chemical change. In many cases these changes highlight enantioselectivity differences between pairs or small families of closely related molecules that have for many years been used to probe the mechanisms of chromatographic chiral recognition; for example, the comparison of N-H vs. N-Me analytes to determine the criticality of an N-H hydrogen bond in chiral molecular recognition. In other cases, statistically significant MMPs surfaced by the analysis are less familiar or somewhat puzzling, sparking a need to generate and test hypotheses to more fully understand. Consequently, mining of appropriate datasets using MMP analysis provides an important new approach for studying and understanding the process of chromatographic enantioseparation.

| S-EPMC6273938 | biostudies-literature

Iridoid glycoside permethylation enhances chromatographic separation and chemical ionization.

Project description:While natural products isolated from medicinal plants can serve as a rich source of biologically active metabolites, mixtures of structurally related compounds of a polar nature are often difficult to chemically resolve by traditional separation techniques. Chemical derivatization to reduce metabolite polarity combined with liquid chromatography (LC) is the strategy presented here to resolve a mixture of structurally related natural product glycosides solvent extracted from the medicinal herb Teucrium polium for mass spectrometric characterization.The partially purified plant extract was subjected to chemical derivatization and electrospray ionization mass spectrometry (ESI-MS) fragmentation pattern analysis allowed for structural characterization of iridoid and secoiridoid glycosides. Selected ions were subjected to tandem mass spectrometric (MS/MS) analysis with a relatively higher-energy collision dissociation to assist in structural elucidation.Permethylation replaced all protons from free hydroxyl and amino groups with methyls and resulted in increases in both hydrophobicity, for facilitated chromatographic separation, and proton affinity, for enhanced chemical ionization. Protonated and/or sodiated adducts were observed for the six compounds detected in positive-ion mode ESI-MS with a mass accuracy of less than 2 ppm.Permethylation combined with LC/MS analysis is shown here to be an effective chemical practice for separating and characterizing iridoid glucosinolates and is expected to be well suited for the chemical characterization of other polar natural-product mixtures of closely related compounds. Copyright © 2016 John Wiley & Sons, Ltd.

| S-EPMC5017310 | biostudies-literature

Entropy subspace separation-based clustering for noise reduction (ENCORE) of scRNA-seq data.

Project description:Single-cell RNA sequencing enables us to characterize the cellular heterogeneity in single cell resolution with the help of cell type identification algorithms. However, the noise inherent in single-cell RNA-sequencing data severely disturbs the accuracy of cell clustering, marker identification and visualization. We propose that clustering based on feature density profiles can distinguish informative features from noise. We named such strategy as 'entropy subspace' separation and designed a cell clustering algorithm called ENtropy subspace separation-based Clustering for nOise REduction (ENCORE) by integrating the 'entropy subspace' separation strategy with a consensus clustering method. We demonstrate that ENCORE performs superiorly on cell clustering and generates high-resolution visualization across 12 standard datasets. More importantly, ENCORE enables identification of group markers with biological significance from a hard-to-separate dataset. With the advantages of effective feature selection, improved clustering, accurate marker identification and high-resolution visualization, we present ENCORE to the community as an important tool for scRNA-seq data analysis to study cellular heterogeneity and discover group markers.

| S-EPMC7897472 | biostudies-literature

Spatially weighted functional clustering of river network data.

Project description:Incorporating spatial covariance into clustering has previously been considered for functional data to identify groups of functions which are similar across space. However, in the majority of situations that have been considered until now the most appropriate metric has been Euclidean distance. Directed networks present additional challenges in terms of estimating spatial covariance due to their complex structure. Although suitable river network covariance models have been proposed for use with stream distance, where distance is computed along the stream network, these models have not been extended for contexts where the data are functional, as is often the case with environmental data. The paper develops a method of calculating spatial covariance between functions from sites along a river network and applies the measure as a weight within functional hierarchical clustering. Levels of nitrate pollution on the River Tweed in Scotland are considered with the aim of identifying groups of monitoring stations which display similar spatiotemporal characteristics.

| S-EPMC4407953 | biostudies-literature

Comprehensive ultra-performance liquid chromatographic separation and mass spectrometric analysis of eicosanoid metabolites in human samples.

Project description:Over the past decade, the number of known eicosanoids has expanded immensely and we have now developed an ultra-performance liquid chromatography-electrospray ionization triple quadrupole mass spectrometric (UPLC-QTRAP/MS/MS) method to monitor and quantify numerous eicosanoids. The UPLC-QTRAP/MS/MS approach utilizes scheduled multiple reaction monitoring (MRM) to optimize sensitivity, number of metabolites that can be analyzed and the time requirement of the analysis. A total of 184 eicosanoids including 26 deuterated internal standards can be separated and monitored in a single 5min UPLC run. To demonstrate a practical application, human plasma samples were analyzed following solid-phase extraction (SPE) and the recovery rate and matrix effects were determined for the 26 deuterated internal standards added to the plasma. The method was validated and shown to be sensitive with the limit of quantitation at pg levels for most compounds, accurate with recovery rates of 70-120%, and precise with a CV<30 for all compounds. Also, the method showed a linear response over a range spanning several orders of magnitude. In a QC human plasma sample, we identified and rigorously quantified over 120 eicosanoids.

| S-EPMC4592635 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data