MetSign: a computational platform for high-resolution mass spectrometry-based metabolomics

Data analysis in metabolomics is currently a major challenge, particularly when large sample sets are analyzed. Herein, we present a novel computational platform entitled MetSign for high-resolution mass spectrometry-based metabolomics. By converting the instrument raw data into mzXML format as its input data, MetSign provides a suite of bioinformatics tools to perform raw data deconvolution, metabolite putative assignment, peak list alignment, normalization, statistical significance tests, unsupervised pattern recognition, and time course analysis. MetSign uses a modular design and an interactive visual data mining approach to enable efficient extraction of useful patterns from data sets. Analysis steps, designed as containers, are presented with a wizard for the user to follow analyses. Each analysis step might contain multiple analysis procedures and/or methods and serves as a pausing point where users can interact with the system to review the results, to shape the next steps, and to return to previous steps to repeat them with different methods or parameter settings. Analysis of metabolite extract of mouse liver with spiked-in acid standards shows that MetSign outperforms the existing publically available software packages. MetSign has also been successfully applied to investigate the regulation and time course trajectory of metabolites in hepatic liver.     

Algorithms and tools for the preprocessing of LC-MS metabolomics data

Metabolomics encompasses the study of small molecules in a biological sample. Liquid Chromatography coupled with Mass Spectrometry (LC-MS) profiling is an important approach for the identification and quantification of metabolites from complex biological samples. The amount and complexity of data produced in an LC-MS profiling experiment demand automatic tools for the preprocessing, analysis, and extraction of useful biological information. Data preprocessing—a topic that covers noise filtering, peak detection, deisotoping, alignment, identification, and normalization—is thus an active area of metabolomics research. Recent years have witnessed development of many software for data preprocessing, and still there is a need for further improvement of the data preprocessing pipeline. This review presents an overview of selected software tools for preprocessing LC-MS based metabolomics data and tries to provide future directions.     

Between-person comparison of metabolite fitting for NMR-based quantitative metabolomics

Nuclear magnetic resonance (NMR) spectroscopy is widely used as an analytical platform for metabolomics. Many studies make use of 1D spectra, which have the advantages of relative simplicity and rapid acquisition times. The spectral data can then be analyzed either with a chemometric workflow or by an initial deconvolution or fitting step to generate a list of identified metabolites and associated sample concentrations. Various software tools exist to simplify the fitting process, but at least for 1D spectra, this still requires a degree of skilled operator input. It is of critical importance that we know how much person-to-person variability affects the results, in order to be able to judge between different studies. Here we tested a commercially available software package (Chenomx' NMR Suite) for fitting metabolites to a set of NMR spectra of yeast extracts and compared the output of five different people for both metabolite identification and quantitation. An initial comparison showed good agreement for a restricted set of common metabolites with characteristic well-resolved resonances but wide divergence in the overall identities and number of compounds fitted; refitting according to an agreed set of metabolites and spectral processing approach increased the total number of metabolites fitted but did not dramatically increase the quality of the metabolites that could be fitted without prior knowledge about peak identity. Hence, robust peak assignments are required in advance of manual deconvolution, when the widest range of metabolites is desired. However, very low concentration metabolites still had high coefficients of variation even with shared information on peak assignment. Overall, the effect of the person was less than the experimental group (in this case, sampling method) for almost all of the metabolites.     

Arc Crossing Minimization in Graphs with GRASP

Graphs are commonly used to represent information in many fields of science and engineering. Automatic drawing tools generate comprehensible graphs from data, taking into account a variety of properties, enabling users to see important relationships in the data. The goal of limiting the number of arc crossings is a well-admitted criterion for a good drawing. In this paper, we present a Greedy Randomized Adaptive Search Procedure (GRASP) for the problem of minimizing are crossings in graphs. Computational experiments with 200 graphs with up to 350 vertices are presented to assess the merit of the method. We show that simple heuristics are very fast but result in inferior solutions, while high-quality solutions have been found with complex meta-heuristics but demand an impractical amount of computer time. The proposed GRASP algorithm is better in speed than complex meta-heuristics and in quality than simple heuristics. Thus, it is a clear candidate for practical implementations in graph drawing software systems.     

XCMS2: processing tandem mass spectrometry data for metabolite identification and structural characterization

Mass spectrometry based metabolomics represents a new area for bioinformatics technology development. While the computational tools currently available such as XCMS statistically assess and rank LC-MS features, they do not provide information about their structural identity. XCMS(2) is an open source software package which has been developed to automatically search tandem mass spectrometry (MS/MS) data against high quality experimental MS/MS data from known metabolites contained in a reference library (METLIN). Scoring of hits is based on a "shared peak count" method that identifies masses of fragment ions shared between the analytical and reference MS/MS spectra. Another functional component of XCMS(2) is the capability of providing structural information for unknown metabolites, which are not in the METLIN database. This "similarity search" algorithm has been developed to detect possible structural motifs in the unknown metabolite which may produce characteristic fragment ions and neutral losses to related reference compounds contained in METLIN, even if the precursor masses are not the same.     

ADAP-GC 2.0: Deconvolution of Coeluting Metabolites from GC/TOF-MS Data for Metabolomics Studies

ADAP-GC 2.0 has been developed to deconvolute coeluting metabolites that frequently exist in real biological samples of metabolomics studies. Deconvolution is based on a chromatographic model peak approach that combines five metrics of peak qualities for constructing/selecting model peak features. Prior to deconvolution, ADAP-GC 2.0 takes raw mass spectral data as input, extracts ion chromatograms for all the observed masses, and detects chromatographic peak features. After deconvolution, it aligns components across samples and exports the qualitative and quantitative information of all of the observed components. Centered on the deconvolution, the entire data analysis workflow is fully automated. ADAP-GC 2.0 has been tested using three different types of samples. The testing results demonstrate significant improvements of ADAP-GC 2.0, compared to the previous ADAP 1.0, to identify and quantify metabolites from gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS) data in untargeted metabolomics studies.     

XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification

Metabolite profiling in biomarker discovery, enzyme substrate assignment, drug activity/specificity determination, and basic metabolic research requires new data preprocessing approaches to correlate specific metabolites to their biological origin. Here we introduce an LC/MS-based data analysis approach, XCMS, which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, and peak matching. Without using internal standards, the method dynamically identifies hundreds of endogenous metabolites for use as standards, calculating a nonlinear retention time correction profile for each sample. Following retention time correction, the relative metabolite ion intensities are directly compared to identify changes in specific endogenous metabolites, such as potential biomarkers. The software is demonstrated using data sets from a previously reported enzyme knockout study and a large-scale study of plasma samples. XCMS is freely available under an open-source license at http://metlin.scripps.edu/download/.     

Liquid chromatography-mass spectrometry and 15N metabolic labeling for quantitative metabolic profiling

Metabolomics, i.e., the global analysis of cellular metabolites, is becoming a powerful tool for gaining insights into biological functions in the postgenomic context. However, absolute quantitation of endogenous metabolites in biological media remains an issue, and available technologies for the analysis of metabolome still lack robustness and accuracy. We describe here a new method based on liquid chromatography-mass spectrometry and (15)N uniform metabolic labeling of Saccharomyces cerevisiae for accurate and absolute quantitation of nitrogen-containing cell metabolites in metabolic profiling experiments. As a proof of concept study, eight sulfur metabolites involved in the glutathione biosynthesis pathway (i.e., cysteine, homocysteine, methionine, gamma-glutamylcysteine, cystathionine, reduced and oxidized forms of glutathione, and S-adenosylhomocysteine) were simultaneously quantified. The analytical method has been validated by studies of stability, selectivity, precision, and linearity and by the determination of the limits of detection and quantification. It was then applied to the analysis of extracts from cadmium-treated yeasts. In these conditions, the intracellular concentrations of most of the metabolites involved in the glutathione biosynthesis pathway were increased when compared to control extracts. These data correlate with previous proteomic results and also underline the importance of glutathione in cadmium detoxication.     

Evaluation of coupling reversed phase, aqueous normal phase, and hydrophilic interaction liquid chromatography with Orbitrap mass spectrometry for metabolomic studies of human urine

In this study, we assessed three liquid chromatographic platforms: reversed phase (RP), aqueous normal phase (ANP), and hydrophilic interaction (HILIC) for the analysis of polar metabolite standard mixtures and for their coverage of urinary metabolites. The two zwitterionic HILIC columns showed high-quality chromatographic performance for metabolite standards, improved separation for isomers, and the greatest coverage of polar metabolites in urine. In contrast, on the reversed phase column, most metabolites eluted very rapidly with little or no separation. Using an Exactive Orbitrap mass spectrometer with a HILIC liquid chromatographic platform, approximately 970 metabolite signals with repeatable peak areas (relative standard deviation (RSD) ≤ 25%) could be putatively identified in human urine, by elemental composition assignment within a 3 ppm mass error. The ability of the methodology for the verification of nonmolecular ions, which arise from adduct formation, and the possibility of distinguishing isomers could also be demonstrated. Careful examination of the raw data and the use of masses for predicted metabolites produced an extension of the metabolite list for human urine.     

Pattern recognition approaches and computational systems tools for ultra performance liquid chromatography-mass spectrometry-based comprehensive metabolomic profiling and pathways analysis of biological data sets

Metabolomics represents an emerging and powerful discipline that provides an accurate and dynamic picture of the phenotype of biosystems through the study of potential metabolites that could be used for therapeutic targets and discovery of new drugs. Metabolomic network construction has led to the integration of metabolites associated with the caused perturbation of multiple pathways. Herein, we present a method for the construction of efficient networks with regard to that Jujuboside B (JuB) protects against insomnia as a case study. UPLC/ESI-SYNAPT-HDMS coupled with pattern recognition methods including PCA, PLS-DA, OPLS-DA, and computational systems analysis were integrated to obtain comprehensive metabolomic profiling and pathways of the large biological data sets. Among the regulated pathways, twelve biomarkers were identified and tryptophan metabolism, phenylalanine, tyrosine, tryptophan biosynthesis, arachidonic acid metabolism, and phenylalanine metabolism related network were acutely perturbed. Results not only supplied a systematic view of the development and progression of insomnia but also were used to analyze the therapeutic effects of JuB, a widely used anti-insomina medicine in clinics. The results showed that JuB administration could provide satisfactory effects on insomnia through partially regulating the perturbed pathway. We have constructed a metabolomic feature network of JuB to protect against insomnia. The most promising use in the near future would be to clarify pathways for the drugs and get biomarkers for these pathways, to help guide testable predictions, provide insights into drug action mechanisms, and enable us to increase research productivity toward metabolomic drug discovery.     

Second-order peak detection for multicomponent high-resolution LC/MS data

The first step when analyzing multicomponent LC/MS data from complex samples such as biofluid metabolic profiles is to separate the data into information and noise via, for example, peak detection. Due to the complex nature of this type of data, with problems such as alternating backgrounds and differing peak shapes, this can be a very complex task. This paper presents and evaluates a two-dimensional peak detection algorithm based on raw vector-represented LC/MS data. The algorithm exploits the fact that in high-resolution centroid data chromatographic peaks emerge flanked with data voids in the corresponding mass axis. According to the proposed method, only 4 per thousand of the total amount of data from a urine sample is defined as chromatographic peaks; however, 94% of the raw data variance is captured within these peaks. Compared to bucketed data, results show that essentially the same features that an experienced analyst would define as peaks can automatically be extracted with a minimum of noise and background. The method is simple and requires a priori knowledge of only the minimum chromatographic peak width-a system-dependent parameter that is easily assessed. Additional meta parameters are estimated from the data themselves. The result is well-defined chromatographic peaks that are consistently arranged in a matrix at their corresponding m/z values. In the context of automated analysis, the method thus provides an alternative to the traditional approach of bucketing the data followed by denoising and/or one-dimensional peak detection. The software implementation of the proposed algorithm is available at http://www.anchem.su.se/peakd as compiled code for Matlab.     

QuaMeter: multivendor performance metrics for LC-MS/MS proteomics instrumentation

LC-MS/MS-based proteomics studies rely on stable analytical system performance that can be evaluated by objective criteria. The National Institute of Standards and Technology (NIST) introduced the MSQC software to compute diverse metrics from experimental LC-MS/MS data, enabling quality analysis and quality control (QA/QC) of proteomics instrumentation. In practice, however, several attributes of the MSQC software prevent its use for routine instrument monitoring. Here, we present QuaMeter, an open-source tool that improves MSQC in several aspects. QuaMeter can directly read raw data from instruments manufactured by different vendors. The software can work with a wide variety of peptide identification software for improved reliability and flexibility. Finally, QC metrics implemented in QuaMeter are rigorously defined and tested. The source code and binary versions of QuaMeter are available under Apache 2.0 License at http://fenchurch.mc.vanderbilt.edu.     

Computer‐aided tolerance synthesis with statistical method and optimization techniques

Most tolerance design optimization problems have focused on developing exact methods to reduce manufacturing cost or to increase product quality. The inherent assumption with this approach is that assembly functions are known before a tolerance design problem is analyzed. With the current development of CAD (Computer‐Aided Design) software, design engineers can address the tolerance design problem without knowing assembly functions in advance. In this study, VSA‐3D/Pro software, which contains a set of simulation tools, is employed to generate experimental assembly data. These computer experimental data will be converted into other forms such as total cost and Process Capability Index. Total cost consists of tolerance cost and quality loss. Then, empirical equations representing two variables can be obtained through a statistical regression method. After that, mathematical optimization and sensitivity analysis are performed within the constrained ‘desired design and process’ space. Consequently, tolerance design via computer experiments enables engineers to optimize design tolerance and manufacturing variation to achieve the highest quality at the most cost effective price during the design and planning stage. Copyright © 2001 John Wiley & Sons, Ltd.     

PeakML/mzMatch: a file format, Java library, R library, and tool-chain for mass spectrometry data analysis

The recent proliferation of high-resolution mass spectrometers has generated a wealth of new data analysis methods. However, flexible integration of these methods into configurations best suited to the research question is hampered by heterogeneous file formats and monolithic software development. The mzXML, mzData, and mzML file formats have enabled uniform access to unprocessed raw data. In this paper we present our efforts to produce an equally simple and powerful format, PeakML, to uniformly exchange processed intermediary and result data. To demonstrate the versatility of PeakML, we have developed an open source Java toolkit for processing, filtering, and annotating mass spectra in a customizable pipeline (mzMatch), as well as a user-friendly data visualization environment (PeakML Viewer). The PeakML format in particular enables the flexible exchange of processed data between software created by different groups or companies, as we illustrate by providing a PeakML-based integration of the widely used XCMS package with mzMatch data processing tools. As an added advantage, downstream analysis can benefit from direct access to the full mass trace information underlying summarized mass spectrometry results, providing the user with the means to rapidly verify results. The PeakML/mzMatch software is freely available at http://mzmatch.sourceforge.net, with documentation, tutorials, and a community forum.     

Traffic fatalities, Peltzman's model, and directed graphs

We show how statistical methods based on directed graphs may be useful in modeling traffic fatalities by comparing models specified using directed graphs to a model originally developed by Peltzman. The comparison uses Peltzman's original data, as well as up-dated data (and coefficients) through 1993. Out-of-sample forecasts of traffic fatalities from Peltzman's model are compared with those from a model constructed using directed graphs over data for the more recent period. The directed graphs model outperforms Peltzman's model in root mean squared forecast error.     

Integration of two-dimensional LC-MS with multivariate statistics for comparative analysis of proteomic samples

LC-MS-based proteomics requires methods with high peak capacity and a high degree of automation, integrated with data-handling tools able to cope with the massive data produced and able to quantitatively compare them. This paper describes an off-line two-dimensional (2D) LC-MS method and its integration with software tools for data preprocessing and multivariate statistical analysis. The 2D LC-MS method was optimized in order to minimize peptide loss prior to sample injection and during the collection step after the first LC dimension, thus minimizing errors from off-column sample handling. The second dimension was run in fully automated mode, injecting onto a nanoscale LC-MS system a series of more than 100 samples, representing fractions collected in the first dimension (8 fractions/sample). As a model study, the method was applied to finding biomarkers for the antiinflammatory properties of zilpaterol, which are coupled to the beta2-adrenergic receptor. Secreted proteomes from U937 macrophages exposed to lipopolysaccharide in the presence or absence of propanolol or zilpaterol were analysed. Multivariate statistical analysis of 2D LC-MS data, based on principal component analysis, and subsequent targeted LC-MS/MS identification of peptides of interest demonstrated the applicability of the approach.     

基于信息流转的ISO 9001:2000质量管理信息系统架构

针对当前企业在ISO 9001:2000贯标过程中存在的效率低、易出错、缺乏规范性、过场化等一系列质量管理问题,将质量体系的贯彻实施落实到质量体系文件的管理上,从系统开发目的、开发工具、系统总体设计和模块划分,以及产品实现信息流的分析对ISO 9001:2000质量管理信息系统进行了深入分析,给出了系统的模块划分图以及质量手册数据流图和质量记录数据流图.最后总结归纳了质量管理信息系统的特色.     

An integrated system for optical imaging of ice cores

An end-to-end system for optical image acquisition and data processing for ice cores has been developed for the United States National Ice Core Laboratory (NICL). The components of this system include highly integrated, automated methods for image capture in the cold-room environment and subsequent analysis by scientists. These components seamlessly manage the various aspects of physical scanning, metadata capture, image processing tests for data quality assurance, database integration and file management, processing of raw data to standard products, data distribution, and image processing and annotation tools for end-users in the ice core science community. The system has been tested operationally on cores retrieved from the West Antarctic Ice Sheet Divide drilling project during the core processing lines at NICL in 2006 and 2007.     

Total precision inspection of machine tools with virtual metrology

The technology of virtual metrology (VM) has been applied in the semiconductor industry to convert sampling inspection with metrology delay into real time and online total inspection. The purpose of this study is trying to apply VM for inspecting machining precision of machine tools. However, machining processes will cause severe vibrations that make process data collection, data cleaning, and feature extraction difficult to handle. Thus, the tasks of how to accurately segment essential parts of the raw process data from the original numerical-control file, how to effectively handle raw process/sensor data with low signal-to-noise ratios, and how to properly extract significant features from the segmented and clean raw process data are challenging issues for successfully applying VM to machine tools. These issues are judiciously addressed and successfully resolved in this paper. Testing results of machining standard workpieces and cellphone shells of two three-axis CNC machines show that the proposed approach of applying VM to accomplish total precision inspection of machine tools is promising.     

Deconvolution of two-dimensional NMR spectra by fast maximum likelihood reconstruction: application to quantitative metabolomics

We have developed an algorithm called fast maximum likelihood reconstruction (FMLR) that performs spectral deconvolution of 1D-2D NMR spectra for the purpose of accurate signal quantification. FMLR constructs the simplest time-domain model (e.g., the model with the fewest number of signals and parameters) whose frequency spectrum matches the visible regions of the spectrum obtained from identical Fourier processing of the acquired data. We describe the application of FMLR to quantitative metabolomics and demonstrate the accuracy of the method by analysis of complex, synthetic mixtures of metabolites and liver extracts. The algorithm demonstrates greater accuracy (0.5-5.0% error) than peak height analysis and peak integral analysis with greatly reduced operator intervention. FMLR has been implemented in a Java-based framework that is available for download on multiple platforms and is interoperable with popular NMR display and processing software. Two-dimensional (1)H-(13)C spectra of mixtures can be acquired with acquisition times of 15 min and analyzed by FMLR in the range of 2-5 min per spectrum to identify and quantify constituents present at concentrations of 0.2 mM or greater.