Accurate LC peak boundary detection for ¹⁶O/¹⁸O labeled LC-MS data.
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ABSTRACT:
SUBMITTER: Cui J
PROVIDER: S-EPMC3792097 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Peak detection is a key step in the preprocessing of untargeted metabolomics data generated from high-resolution liquid chromatography-mass spectrometry (LC/MS). The common practice is to use filters with predetermined parameters to select peaks in the LC/MS profile. This rigid approach can cause suboptimal performance when the choice of peak model and parameters do not suit the data characteristics.Here we present a method that learns directly from various data features of the extracted ion chromatograms (EICs) to differentiate between true peak regions from noise regions in the LC/MS profile. It utilizes the knowledge of known metabolites, as well as robust machine learning approaches. Unlike currently available methods, this new approach does not assume a parametric peak shape model and allows maximum flexibility. We demonstrate the superiority of the new approach using real data. Because matching to known metabolites entails uncertainties and cannot be considered a gold standard, we also developed a probabilistic receiver-operating characteristic (pROC) approach that can incorporate uncertainties.The new peak detection approach is implemented as part of the apLCMS package available at http://web1.sph.emory.edu/apLCMS/ CONTACT: tyu8@emory.eduSupplementary data are available at Bioinformatics online.
| S-EPMC4184266 | biostudies-literature
Project description:IntroductionDespite the availability of several pre-processing software, poor peak integration remains a prevalent problem in untargeted metabolomics data generated using liquid chromatography high-resolution mass spectrometry (LC-MS). As a result, the output of these pre-processing software may retain incorrectly calculated metabolite abundances that can perpetuate in downstream analyses.ObjectivesTo address this problem, we propose a computational methodology that combines machine learning and peak quality metrics to filter out low quality peaks.MethodsSpecifically, we comprehensively and systematically compared the performance of 24 different classifiers generated by combining eight classification algorithms and three sets of peak quality metrics on the task of distinguishing reliably integrated peaks from poorly integrated ones. These classifiers were compared to using a residual standard deviation (RSD) cut-off in pooled quality-control (QC) samples, which aims to remove peaks with analytical error.ResultsThe best performing classifier was found to be a combination of the AdaBoost algorithm and a set of 11 peak quality metrics previously explored in untargeted metabolomics and proteomics studies. As a complementary approach, applying our framework to peaks retained after filtering by 30% RSD across pooled QC samples was able to further distinguish poorly integrated peaks that were not removed from filtering alone. An R implementation of these classifiers and the overall computational approach is available as the MetaClean package at https://CRAN.R-project.org/package=MetaClean .ConclusionOur work represents an important step forward in developing an automated tool for filtering out unreliable peak integrations in untargeted LC-MS metabolomics data.
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Project description:A data dependent peak model (DDPM) based spectrum deconvolution method was developed for analysis of high resolution LC-MS data. To construct the selected ion chromatogram (XIC), a clustering method, the density based spatial clustering of applications with noise (DBSCAN), is applied to all m/z values of an LC-MS data set to group the m/z values into each XIC. The DBSCAN constructs XICs without the need for a user defined m/z variation window. After the XIC construction, the peaks of molecular ions in each XIC are detected using both the first and the second derivative tests, followed by an optimized chromatographic peak model selection method for peak deconvolution. A total of six chromatographic peak models are considered, including Gaussian, log-normal, Poisson, gamma, exponentially modified Gaussian, and hybrid of exponential and Gaussian models. The abundant nonoverlapping peaks are chosen to find the optimal peak models that are both data- and retention-time-dependent. Analysis of 18 spiked-in LC-MS data demonstrates that the proposed DDPM spectrum deconvolution method outperforms the traditional method. On average, the DDPM approach not only detected 58 more chromatographic peaks from each of the testing LC-MS data but also improved the retention time and peak area 3% and 6%, respectively.
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Project description:Untargeted metabolomics can detect more than 10 000 peaks in a single LC-MS run. The correspondence between these peaks and metabolites, however, remains unclear. Here, we introduce a Peak Annotation and Verification Engine (PAVE) for annotating untargeted microbial metabolomics data. The workflow involves growing cells in 13C and 15N isotope-labeled media to identify peaks from biological compounds and their carbon and nitrogen atom counts. Improved deisotoping and deadducting are enabled by algorithms that integrate positive mode, negative mode, and labeling data. To distinguish metabolites and their fragments, PAVE experimentally measures the response of each peak to weak in-source collision induced dissociation, which increases the peak intensity for fragments while decreasing it for their parent ions. The molecular formulas of the putative metabolites are then assigned based on database searching using both m/ z and C/N atom counts. Application of this procedure to Saccharomyces cerevisiae and Escherichia coli revealed that more than 80% of peaks do not label, i.e., are environmental contaminants. More than 70% of the biological peaks are isotopic variants, adducts, fragments, or mass spectrometry artifacts yielding ∼2000 apparent metabolites across the two organisms. About 650 match to a known metabolite formula based on m/ z and C/N atom counts, with 220 assigned structures based on MS/MS and/or retention time to match to authenticated standards. Thus, PAVE enables systematic annotation of LC-MS metabolomics data with only ∼4% of peaks annotated as apparent metabolites.
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Project description:Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics provides a wealth of information about proteins present in biological samples. In bottom-up LC-MS/MS-based proteomics, proteins are enzymatically digested into peptides prior to query by LC-MS/MS. Thus, the information directly available from the LC-MS/MS data is at the peptide level. If a protein-level analysis is desired, the peptide-level information must be rolled up into protein-level information. We propose a principal component analysis-based statistical method, ProPCA, for efficiently estimating relative protein abundance from bottom-up label-free LC-MS/MS data that incorporates both spectral count information and LC-MS peptide ion peak attributes, such as peak area, volume, or height. ProPCA may be used effectively with a variety of quantification platforms and is easily implemented. We show that ProPCA outperformed existing quantitative methods for peptide-protein roll-up, including spectral counting methods and other methods for combining LC-MS peptide peak attributes. The performance of ProPCA was validated using a data set derived from the LC-MS/MS analysis of a mixture of protein standards (the UPS2 proteomic dynamic range standard introduced by The Association of Biomolecular Resource Facilities Proteomics Standards Research Group in 2006). Finally, we applied ProPCA to a comparative LC-MS/MS analysis of digested total cell lysates prepared for LC-MS/MS analysis by alternative lysis methods and show that ProPCA identified more differentially abundant proteins than competing methods.
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Project description:In liquid chromatography-mass spectrometry/tandem mass spectrometry (LC-MS/MS), it is necessary to link tandem MS-identified peptide peaks so that protein expression changes between the two runs can be tracked. However, only a small number of peptides can be identified and linked by tandem MS in two runs, and it becomes necessary to link peptide peaks with tandem identification in one run to their corresponding ones in another run without identification. In the past, peptide peaks are linked based on similarities in retention time (rt), mass or peak shape after rt alignment, which corrects mean rt shifts between runs. However, the accuracy in linking is still limited especially for complex samples collected from different conditions. Consequently, large-scale proteomics studies that require comparison of protein expression profiles of hundreds of patients can not be carried out effectively.In this article, we consider the problem of linking peptides from a pair of LC-MS/MS runs and propose a new method, PeakLink (PL), which uses information in both the time and frequency domain as inputs to a non-linear support vector machine (SVM) classifier. The PL algorithm first uses a threshold on an rt likelihood ratio score to remove candidate corresponding peaks with excessively large elution time shifts, then PL calculates the correlation between a pair of candidate peaks after reducing noise through wavelet transformation. After converting rt and peak shape correlation to statistical scores, an SVM classifier is trained and applied for differentiating corresponding and non-corresponding peptide peaks.PL is tested in multiple challenging cases, in which LC-MS/MS samples are collected from different disease states, different instruments and different laboratories. Testing results show significant improvement in linking accuracy compared with other algorithms.M files for the PL alignment method are available at http://compgenomics.utsa.edu/zgroup/PeakLink.Supplementary data are available at Bioinformatics online.
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Project description:Although phototherapy (PT) is a standard treatment for neonatal jaundice, no validated clinical methods for determination of bilirubin phototherapy products are available. Thus, the aim of our study was to establish a such method for clinical use. To achieve this aim, a LC-MS/MS assay for simultaneous determination of Z-lumirubin (LR) and unconjugated bilirubin (UCB) was conducted. LR was purified after irradiation of UCB at 460 nm. The assay was tested on human sera from PT-treated neonates. Samples were separated on a HPLC system with a triple quadrupole mass spectrometer detector. The instrument response was linear up to 5.8 and 23.4 mg/dL for LR and UCB, respectively, with submicromolar limits of detection and validity parameters relevant for use in clinical medicine. Exposure of newborns to PT raised serum LR concentrations three-fold (p < 0.01), but the absolute concentrations were low (0.37 ± 0.16 mg/dL), despite a dramatic decrease of serum UCB concentrations (13.6 ± 2.2 vs. 10.3 ± 3.3 mg/dL, p < 0.01). A LC-MS/MS method for the simultaneous determination of LR and UCB in human serum was established and validated for clinical use. This method should help to monitor neonates on PT, as well as to improve our understanding of both the kinetics and biology of bilirubin phototherapy products.
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Project description:In the field of quantitative proteomics, the Isobaric Tags for Relative and Absolute Quantitation (iTRAQ) technology has demonstrated efficacy for proteome monitoring despite its lack of a consensus for data handling. In the present study, after peptide and protein identification, we compared the widespread quantitation method based on the calculation of MS/MS reporter ion peaks areas ratios (ProteinPilot) to the alternative method based on the calculation of ratios of the sum of peak intensities (jTRAQx [Quant]) and we processed output data with the in-house Customizable iTRAQ Ratios Calculator (CiR-C) algorithm. Quantitation based on peak area ratios displayed no significant linear correlation with Western blot quantitation. In contrast, quantitation based on the sum of peak intensities displayed a significant linear association with Western blot quantitation (non-zero slope; Pearson correlation coefficient test, r = 0.296, P=0.010**) with an average bias of 0.087 ± 0.500 and 95% Limits of Agreement from -0.893 to 1.068. We proposed the Mascot-jTRAQx-CiR-C strategy as a simple yet powerful data processing adjunct to the iTRAQ technology.
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Project description:Feature detection is a critical step in the preprocessing of liquid chromatography-mass spectrometry (LC-MS) metabolomics data. Currently, the predominant approach is to detect features using noise filters and peak shape models based on the data at hand alone. Databases of known metabolites and historical data contain information that could help boost the sensitivity of feature detection, especially for low-concentration metabolites. However, utilizing such information in targeted feature detection may cause large number of false positives because of the high levels of noise in LC-MS data. With high-resolution mass spectrometry such as liquid chromatograph-Fourier transform mass spectrometry (LC-FTMS), high-confidence matching of peaks to known features is feasible. Here we describe a computational approach that serves two purposes. First it boosts feature detection sensitivity by using a hybrid procedure of both untargeted and targeted peak detection. New algorithms are designed to reduce the chance of false-positives by nonparametric local peak detection and filtering. Second, it can accumulate information on the concentration variation of metabolites over large number of samples, which can help find rare features and/or features with uncommon concentration in future studies. Information can be accumulated on features that are consistently found in real data even before their identities are found. We demonstrate the value of the approach in a proof-of-concept study. The method is implemented as part of the R package apLCMS at http://www.sph.emory.edu/apLCMS/ .
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Project description:Heavy-labelled internal standard (IS) compounds are commonly used in liquid chromatography-tandem mass spectrometry (LC-MS/MS) assays to control for stochastic and systematic variation. Identifying samples that suffer from unwanted variation is critically important in order to avoid factitiously inaccurate results. Current approaches for outlier detection typically employ arbitrary thresholds and ignore systematic drift. To improve this, we applied robust linear mixed-effects models (LMMs) to capture the within- and between-run variability in IS signal and generate data-driven acceptance ranges for routine use. Data from in-house LC-MS/MS assays for 25-hydroxyvitamin D3 and D2 and prednisolone were retrospectively collected. The variation in the percentage deviation of the internal standard area from the mean of the calibrators was modelled through the use of robust LMMs. The fitted LMMs revealed significant positive drift in IS signal over the analytical runs for vitamin D, with slope coefficients of 0.118 (95% CI: 0.098, 0.138) and 0.192 (0.168, 0.215) for D3 and D2, respectively. In contrast, the models for prednisolone demonstrated a significant negative drift in IS signal, with a slope coefficient of -0.164 (-0.297, -0.036). Non-parametric, cluster bootstrap resampling enabled us to define acceptance ranges for use in future assays. Here, we have described a computational approach to extensively characterise the variation in IS signal in routinely-performed LC-MS/MS assays. This approach facilitates a robust quality assessment of IS outliers in routine practice and thus has the potential to improve patient safety. Importantly, this approach is applicable to other MS assays where linear variation in IS signal is observed.
| S-EPMC8600994 | biostudies-literature