Science.gov

Sample records for metabolome annotation quality

  1. Quality of Computationally Inferred Gene Ontology Annotations

    PubMed Central

    Škunca, Nives; Altenhoff, Adrian; Dessimoz, Christophe

    2012-01-01

    Gene Ontology (GO) has established itself as the undisputed standard for protein function annotation. Most annotations are inferred electronically, i.e. without individual curator supervision, but they are widely considered unreliable. At the same time, we crucially depend on those automated annotations, as most newly sequenced genomes are non-model organisms. Here, we introduce a methodology to systematically and quantitatively evaluate electronic annotations. By exploiting changes in successive releases of the UniProt Gene Ontology Annotation database, we assessed the quality of electronic annotations in terms of specificity, reliability, and coverage. Overall, we not only found that electronic annotations have significantly improved in recent years, but also that their reliability now rivals that of annotations inferred by curators when they use evidence other than experiments from primary literature. This work provides the means to identify the subset of electronic annotations that can be relied upon—an important outcome given that >98% of all annotations are inferred without direct curation. PMID:22693439

  2. BioSpider: a web server for automating metabolome annotations.

    PubMed

    Knox, Craig; Shrivastava, Savita; Stothard, Paul; Eisner, Roman; Wishart, David S

    2007-01-01

    One of the growing challenges in life science research lies in finding useful, descriptive or quantitative data about newly reported biomolecules (genes, proteins, metabolites and drugs). An even greater challenge is finding information that connects these genes, proteins, drugs or metabolites to each other. Much of this information is scattered through hundreds of different databases, abstracts or books and almost none of it is particularly well integrated. While some efforts are being undertaken at the NCBI and EBI to integrate many different databases together, this still falls short of the goal of having some kind of human-readable synopsis that summarizes the state of knowledge about a given biomolecule - especially small molecules. To address this shortfall, we have developed BioSpider. BioSpider is essentially an automated report generator designed specifically to tabulate and summarize data on biomolecules - both large and small. Specifically, BioSpider allows users to type in almost any kind of biological or chemical identifier (protein/gene name, sequence, accession number, chemical name, brand name, SMILES string, InCHI string, CAS number, etc.) and it returns an in-depth synoptic report (approximately 3-30 pages in length) about that biomolecule and any other biomolecule it may target. This summary includes physico-chemical parameters, images, models, data files, descriptions and predictions concerning the query molecule. BioSpider uses a web-crawler to scan through dozens of public databases and employs a variety of specially developed text mining tools and locally developed prediction tools to find, extract and assemble data for its reports. Because of its breadth, depth and comprehensiveness, we believe BioSpider will prove to be a particularly valuable tool for researchers in metabolomics. BioSpider is available at: www.biospider.ca PMID:17990488

  3. ProbMetab: an R package for Bayesian probabilistic annotation of LC-MS-based metabolomics.

    PubMed

    Silva, Ricardo R; Jourdan, Fabien; Salvanha, Diego M; Letisse, Fabien; Jamin, Emilien L; Guidetti-Gonzalez, Simone; Labate, Carlos A; Vêncio, Ricardo Z N

    2014-05-01

    We present ProbMetab, an R package that promotes substantial improvement in automatic probabilistic liquid chromatography-mass spectrometry-based metabolome annotation. The inference engine core is based on a Bayesian model implemented to (i) allow diverse source of experimental data and metadata to be systematically incorporated into the model with alternative ways to calculate the likelihood function and (ii) allow sensitive selection of biologically meaningful biochemical reaction databases as Dirichlet-categorical prior distribution. Additionally, to ensure result interpretation by system biologists, we display the annotation in a network where observed mass peaks are connected if their candidate metabolites are substrate/product of known biochemical reactions. This graph can be overlaid with other graph-based analysis, such as partial correlation networks, in a visualization scheme exported to Cytoscape, with web and stand-alone versions. PMID:24443383

  4. ProbMetab: an R package for Bayesian probabilistic annotation of LC–MS-based metabolomics

    PubMed Central

    Silva, Ricardo R.; Jourdan, Fabien; Salvanha, Diego M.; Letisse, Fabien; Jamin, Emilien L.; Guidetti-Gonzalez, Simone; Labate, Carlos A.; Vêncio, Ricardo Z. N.

    2014-01-01

    Summary: We present ProbMetab, an R package that promotes substantial improvement in automatic probabilistic liquid chromatography–mass spectrometry-based metabolome annotation. The inference engine core is based on a Bayesian model implemented to (i) allow diverse source of experimental data and metadata to be systematically incorporated into the model with alternative ways to calculate the likelihood function and (ii) allow sensitive selection of biologically meaningful biochemical reaction databases as Dirichlet-categorical prior distribution. Additionally, to ensure result interpretation by system biologists, we display the annotation in a network where observed mass peaks are connected if their candidate metabolites are substrate/product of known biochemical reactions. This graph can be overlaid with other graph-based analysis, such as partial correlation networks, in a visualization scheme exported to Cytoscape, with web and stand-alone versions. Availability and implementation: ProbMetab was implemented in a modular manner to fit together with established upstream (xcms, CAMERA, AStream, mzMatch.R, etc) and downstream R package tools (GeneNet, RCytoscape, DiffCorr, etc). ProbMetab, along with extensive documentation and case studies, is freely available under GNU license at: http://labpib.fmrp.usp.br/methods/probmetab/. Contact: rvencio@usp.br Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24443383

  5. MeRy-B: a web knowledgebase for the storage, visualization, analysis and annotation of plant NMR metabolomic profiles

    PubMed Central

    2011-01-01

    Background Improvements in the techniques for metabolomics analyses and growing interest in metabolomic approaches are resulting in the generation of increasing numbers of metabolomic profiles. Platforms are required for profile management, as a function of experimental design, and for metabolite identification, to facilitate the mining of the corresponding data. Various databases have been created, including organism-specific knowledgebases and analytical technique-specific spectral databases. However, there is currently no platform meeting the requirements for both profile management and metabolite identification for nuclear magnetic resonance (NMR) experiments. Description MeRy-B, the first platform for plant 1H-NMR metabolomic profiles, is designed (i) to provide a knowledgebase of curated plant profiles and metabolites obtained by NMR, together with the corresponding experimental and analytical metadata, (ii) for queries and visualization of the data, (iii) to discriminate between profiles with spectrum visualization tools and statistical analysis, (iv) to facilitate compound identification. It contains lists of plant metabolites and unknown compounds, with information about experimental conditions, the factors studied and metabolite concentrations for several plant species, compiled from more than one thousand annotated NMR profiles for various organs or tissues. Conclusion MeRy-B manages all the data generated by NMR-based plant metabolomics experiments, from description of the biological source to identification of the metabolites and determinations of their concentrations. It is the first database allowing the display and overlay of NMR metabolomic profiles selected through queries on data or metadata. MeRy-B is available from http://www.cbib.u-bordeaux2.fr/MERYB/index.php. PMID:21668943

  6. Gene Ontology annotation quality analysis in model eukaryotes

    PubMed Central

    Buza, Teresia J.; McCarthy, Fiona M.; Wang, Nan; Bridges, Susan M.; Burgess, Shane C.

    2008-01-01

    Functional analysis using the Gene Ontology (GO) is crucial for array analysis, but it is often difficult for researchers to assess the amount and quality of GO annotations associated with different sets of gene products. In many cases the source of the GO annotations and the date the GO annotations were last updated is not apparent, further complicating a researchers’ ability to assess the quality of the GO data provided. Moreover, GO biocurators need to ensure that the GO quality is maintained and optimal for the functional processes that are most relevant for their research community. We report the GO Annotation Quality (GAQ) score, a quantitative measure of GO quality that includes breadth of GO annotation, the level of detail of annotation and the type of evidence used to make the annotation. As a case study, we apply the GAQ scoring method to a set of diverse eukaryotes and demonstrate how the GAQ score can be used to track changes in GO annotations over time and to assess the quality of GO annotations available for specific biological processes. The GAQ score also allows researchers to quantitatively assess the functional data available for their experimental systems (arrays or databases). PMID:18187504

  7. Experimental Strategies for Functional Annotation and Metabolism Discovery: Targeted Screening of Solute Binding Proteins and Unbiased Panning of Metabolomes

    PubMed Central

    2015-01-01

    The rate at which genome sequencing data is accruing demands enhanced methods for functional annotation and metabolism discovery. Solute binding proteins (SBPs) facilitate the transport of the first reactant in a metabolic pathway, thereby constraining the regions of chemical space and the chemistries that must be considered for pathway reconstruction. We describe high-throughput protein production and differential scanning fluorimetry platforms, which enabled the screening of 158 SBPs against a 189 component library specifically tailored for this class of proteins. Like all screening efforts, this approach is limited by the practical constraints imposed by construction of the library, i.e., we can study only those metabolites that are known to exist and which can be made in sufficient quantities for experimentation. To move beyond these inherent limitations, we illustrate the promise of crystallographic- and mass spectrometric-based approaches for the unbiased use of entire metabolomes as screening libraries. Together, our approaches identified 40 new SBP ligands, generated experiment-based annotations for 2084 SBPs in 71 isofunctional clusters, and defined numerous metabolic pathways, including novel catabolic pathways for the utilization of ethanolamine as sole nitrogen source and the use of d-Ala-d-Ala as sole carbon source. These efforts begin to define an integrated strategy for realizing the full value of amassing genome sequence data. PMID:25540822

  8. Metabolomics and proteomics annotate therapeutic properties of geniposide: targeting and regulating multiple perturbed pathways.

    PubMed

    Wang, Xijun; Zhang, Aihua; Yan, Guangli; Sun, Wenjun; Han, Ying; Sun, Hui

    2013-01-01

    Geniposide is an important constituent of Gardenia jasminoides Ellis, a famous Chinese medicinal plant, and has displayed bright prospects in prevention and therapy of hepatic injury (HI). Unfortunately, the working mechanisms of this compound are difficult to determine and thus remain unknown. To determine the mechanisms that underlie this compound, we conducted a systematic analysis of the therapeutic effects of geniposide using biochemistry, metabolomics and proteomics. Geniposide significantly intensified the therapeutic efficacy as indicated by our modern biochemical analysis. Metabolomics results indicate 9 ions in the positive mode as differentiating metabolites which were associated with perturbations in primary bile acid biosynthesis, butanoate metabolism, citrate cycle (TCA cycle), alanine, aspartate and glutamate metabolism. Of note, geniposide has potential pharmacological effect through regulating multiple perturbed pathways to normal state. In an attempt to address the benefits of geniposide based on the proteomics approaches, the protein-interacting networks were constructed to aid identifying the drug targets of geniposide. Six identified differential proteins appear to be involved in antioxidation and signal transduction, energy production, immunity, metabolism, chaperoning. These proteins were closely related in the protein-protein interaction network and the modulation of multiple vital physiological pathways. These data will help to understand the molecular therapeutic mechanisms of geniposide on hepatic damage rats. We also conclude that metabolomics and proteomics are powerful and versatile tools for both biomarker discovery and exploring the complex relationships between biological pathways and drug response, highlighting insights into drug discovery. PMID:23967205

  9. Metabolomics annotates ABHD3 as a physiologic regulator of medium-chain phospholipids

    PubMed Central

    Long, Jonathan Z.; Cisar, Justin S.; Milliken, David; Niessen, Sherry; Wang, Chu; Trauger, Sunia A.; Siuzdak, Gary; Cravatt, Benjamin F.

    2011-01-01

    All organisms, including humans, possess a huge number of uncharacterized enzymes. Here, we describe a general cell-based screen for enzyme substrate discovery by untargeted metabolomics and its application to identify α/β-hydrolase domain-containing 3 (ABHD3) as a lipase that selectively cleaves medium-chain and oxidatively-truncated phospholipids. Abhd3−/− mice possess elevated myristoyl (C14)-phospholipids, including the bioactive lipid C14-lysophosphatidylcholine, confirming the physiological relevance of our substrate assignments. PMID:21926997

  10. Metabolomic Technologies for Improving the Quality of Food: Practice and Promise.

    PubMed

    Johanningsmeier, Suzanne D; Harris, G Keith; Klevorn, Claire M

    2016-01-01

    It is now well documented that the diet has a significant impact on human health and well-being. However, the complete set of small molecule metabolites present in foods that make up the human diet and the role of food production systems in altering this food metabolome are still largely unknown. Metabolomic platforms that rely on nuclear magnetic resonance (NMR) and mass spectrometry (MS) analytical technologies are being employed to study the impact of agricultural practices, processing, and storage on the global chemical composition of food; to identify novel bioactive compounds; and for authentication and region-of-origin classifications. This review provides an overview of the current terminology, analytical methods, and compounds associated with metabolomic studies, and provides insight into the application of metabolomics to generate new knowledge that enables us to produce, preserve, and distribute high-quality foods for health promotion. PMID:26772413

  11. Automated LC-HRMS(/MS) Approach for the Annotation of Fragment Ions Derived from Stable Isotope Labeling-Assisted Untargeted Metabolomics

    PubMed Central

    2014-01-01

    Structure elucidation of biological compounds is still a major bottleneck of untargeted LC-HRMS approaches in metabolomics research. The aim of the present study was to combine stable isotope labeling and tandem mass spectrometry for the automated interpretation of the elemental composition of fragment ions and thereby facilitate the structural characterization of metabolites. The software tool FragExtract was developed and evaluated with LC-HRMS/MS spectra of both native 12C- and uniformly 13C (U-13C)-labeled analytical standards of 10 fungal substances in pure solvent and spiked into fungal culture filtrate of Fusarium graminearum respectively. Furthermore, the developed approach is exemplified with nine unknown biochemical compounds contained in F. graminearum samples derived from an untargeted metabolomics experiment. The mass difference between the corresponding fragment ions present in the MS/MS spectra of the native and U-13C-labeled compound enabled the assignment of the number of carbon atoms to each fragment signal and allowed the generation of meaningful putative molecular formulas for each fragment ion, which in turn also helped determine the elemental composition of the precursor ion. Compared to laborious manual analysis of the MS/MS spectra, the presented algorithm marks an important step toward efficient fragment signal elucidation and structure annotation of metabolites in future untargeted metabolomics studies. Moreover, as demonstrated for a fungal culture sample, FragExtract also assists the characterization of unknown metabolites, which are not contained in databases, and thus exhibits a significant contribution to untargeted metabolomics research. PMID:24965664

  12. Different Statistical Approaches to Investigate Porcine Muscle Metabolome Profiles to Highlight New Biomarkers for Pork Quality Assessment

    PubMed Central

    Welzenbach, Julia; Neuhoff, Christiane; Looft, Christian; Schellander, Karl; Tholen, Ernst; Große-Brinkhaus, Christine

    2016-01-01

    The aim of this study was to elucidate the underlying biochemical processes to identify potential key molecules of meat quality traits drip loss, pH of meat 1 h post-mortem (pH1), pH in meat 24 h post-mortem (pH24) and meat color. An untargeted metabolomics approach detected the profiles of 393 annotated and 1,600 unknown metabolites in 97 Duroc × Pietrain pigs. Despite obvious differences regarding the statistical approaches, the four applied methods, namely correlation analysis, principal component analysis, weighted network analysis (WNA) and random forest regression (RFR), revealed mainly concordant results. Our findings lead to the conclusion that meat quality traits pH1, pH24 and color are strongly influenced by processes of post-mortem energy metabolism like glycolysis and pentose phosphate pathway, whereas drip loss is significantly associated with metabolites of lipid metabolism. In case of drip loss, RFR was the most suitable method to identify reliable biomarkers and to predict the phenotype based on metabolites. On the other hand, WNA provides the best parameters to investigate the metabolite interactions and to clarify the complex molecular background of meat quality traits. In summary, it was possible to attain findings on the interaction of meat quality traits and their underlying biochemical processes. The detected key metabolites might be better indicators of meat quality especially of drip loss than the measured phenotype itself and potentially might be used as bio indicators. PMID:26919205

  13. Different Statistical Approaches to Investigate Porcine Muscle Metabolome Profiles to Highlight New Biomarkers for Pork Quality Assessment.

    PubMed

    Welzenbach, Julia; Neuhoff, Christiane; Looft, Christian; Schellander, Karl; Tholen, Ernst; Große-Brinkhaus, Christine

    2016-01-01

    The aim of this study was to elucidate the underlying biochemical processes to identify potential key molecules of meat quality traits drip loss, pH of meat 1 h post-mortem (pH1), pH in meat 24 h post-mortem (pH24) and meat color. An untargeted metabolomics approach detected the profiles of 393 annotated and 1,600 unknown metabolites in 97 Duroc × Pietrain pigs. Despite obvious differences regarding the statistical approaches, the four applied methods, namely correlation analysis, principal component analysis, weighted network analysis (WNA) and random forest regression (RFR), revealed mainly concordant results. Our findings lead to the conclusion that meat quality traits pH1, pH24 and color are strongly influenced by processes of post-mortem energy metabolism like glycolysis and pentose phosphate pathway, whereas drip loss is significantly associated with metabolites of lipid metabolism. In case of drip loss, RFR was the most suitable method to identify reliable biomarkers and to predict the phenotype based on metabolites. On the other hand, WNA provides the best parameters to investigate the metabolite interactions and to clarify the complex molecular background of meat quality traits. In summary, it was possible to attain findings on the interaction of meat quality traits and their underlying biochemical processes. The detected key metabolites might be better indicators of meat quality especially of drip loss than the measured phenotype itself and potentially might be used as bio indicators. PMID:26919205

  14. A Novel Quality Measure and Correction Procedure for the Annotation of Microbial Translation Initiation Sites

    PubMed Central

    Overmars, Lex; Siezen, Roland J.; Francke, Christof

    2015-01-01

    The identification of translation initiation sites (TISs) constitutes an important aspect of sequence-based genome analysis. An erroneous TIS annotation can impair the identification of regulatory elements and N-terminal signal peptides, and also may flaw the determination of descent, for any particular gene. We have formulated a reference-free method to score the TIS annotation quality. The method is based on a comparison of the observed and expected distribution of all TISs in a particular genome given prior gene-calling. We have assessed the TIS annotations for all available NCBI RefSeq microbial genomes and found that approximately 87% is of appropriate quality, whereas 13% needs substantial improvement. We have analyzed a number of factors that could affect TIS annotation quality such as GC-content, taxonomy, the fraction of genes with a Shine-Dalgarno sequence and the year of publication. The analysis showed that only the first factor has a clear effect. We have then formulated a straightforward Principle Component Analysis-based TIS identification strategy to self-organize and score potential TISs. The strategy is independent of reference data and a priori calculations. A representative set of 277 genomes was subjected to the analysis and we found a clear increase in TIS annotation quality for the genomes with a low quality score. The PCA-based annotation was also compared with annotation with the current tool of reference, Prodigal. The comparison for the model genome of Escherichia coli K12 showed that both methods supplement each other and that prediction agreement can be used as an indicator of a correct TIS annotation. Importantly, the data suggest that the addition of a PCA-based strategy to a Prodigal prediction can be used to ‘flag’ TIS annotations for re-evaluation and in addition can be used to evaluate a given annotation in case a Prodigal annotation is lacking. PMID:26204119

  15. Assessing the quality of annotations in asthma gene expression experiments

    PubMed Central

    2010-01-01

    Background The amount of data deposited in the Gene Expression Omnibus (GEO) has expanded significantly. It is important to ensure that these data are properly annotated with clinical data and descriptions of experimental conditions so that they can be useful for future analysis. This study assesses the adequacy of documented asthma markers in GEO. Three objective measures (coverage, consistency and association) were used for evaluation of annotations contained in 17 asthma studies. Results There were 918 asthma samples with 20,640 annotated markers. Of these markers, only 10,419 had documented values (50% coverage). In one study carefully examined for consistency, there were discrepancies in drug name usage, with brand name and generic name used in different sections to refer to the same drug. Annotated markers showed adequate association with other relevant variables (i.e. the use of medication only when its corresponding disease state was present). Conclusions There is inadequate variable coverage within GEO and usage of terms lacks consistency. Association between relevant variables, however, was adequate. PMID:21044366

  16. Diagnostic Metabolomic Blood Tests for Endoluminal Gastrointestinal Cancer--A Systematic Review and Assessment of Quality.

    PubMed

    Antonowicz, Stefan; Kumar, Sacheen; Wiggins, Tom; Markar, Sheraz R; Hanna, George B

    2016-01-01

    Advances in analytics have resulted in metabolomic blood tests being developed for the detection of cancer. This systematic review aims to assess the diagnostic accuracy of blood-based metabolomic biomarkers for endoluminal gastrointestinal (GI) cancer. Using endoscopic diagnosis as a reference standard, methodologic and reporting quality was assessed using validated tools, in addition to pathway-based informatics to biologically contextualize discriminant features. Twenty-nine studies (15 colorectal, 9 esophageal, 3 gastric, and 2 mixed) with data from 10,835 participants were included. All reported significant differences in hematologic metabolites. In pooled analysis, 246 metabolites were found to be significantly different after multiplicity correction. Incremental metabolic flux with disease progression was frequently reported. Two promising candidates have been validated in independent populations (both colorectal biomarkers), and one has been approved for clinical use. Networks analysis suggested modulation of elements of up to half of Edinburgh Human Metabolic Network subdivisions, and that the poor clinical applicability of commonly modulated metabolites could be due to extensive molecular interconnectivity. Methodologic and reporting quality was assessed as moderate-to-poor. Serum metabolomics holds promise for GI cancer diagnostics; however, future efforts must adhere to consensus standardization initiatives, utilize high-resolution discovery analytics, and compare candidate biomarkers with peer nonendoscopic alternatives. PMID:26598534

  17. Metabolomic database annotations via query of elemental compositions: Mass accuracy is insufficient even at less than 1 ppm

    PubMed Central

    Kind, Tobias; Fiehn, Oliver

    2006-01-01

    Background Metabolomic studies are targeted at identifying and quantifying all metabolites in a given biological context. Among the tools used for metabolomic research, mass spectrometry is one of the most powerful tools. However, metabolomics by mass spectrometry always reveals a high number of unknown compounds which complicate in depth mechanistic or biochemical understanding. In principle, mass spectrometry can be utilized within strategies of de novo structure elucidation of small molecules, starting with the computation of the elemental composition of an unknown metabolite using accurate masses with errors <5 ppm (parts per million). However even with very high mass accuracy (<1 ppm) many chemically possible formulae are obtained in higher mass regions. In automatic routines an additional orthogonal filter therefore needs to be applied in order to reduce the number of potential elemental compositions. This report demonstrates the necessity of isotope abundance information by mathematical confirmation of the concept. Results High mass accuracy (<1 ppm) alone is not enough to exclude enough candidates with complex elemental compositions (C, H, N, S, O, P, and potentially F, Cl, Br and Si). Use of isotopic abundance patterns as a single further constraint removes >95% of false candidates. This orthogonal filter can condense several thousand candidates down to only a small number of molecular formulas. Example calculations for 10, 5, 3, 1 and 0.1 ppm mass accuracy are given. Corresponding software scripts can be downloaded from . A comparison of eight chemical databases revealed that PubChem and the Dictionary of Natural Products can be recommended for automatic queries using molecular formulae. Conclusion More than 1.6 million molecular formulae in the range 0–500 Da were generated in an exhaustive manner under strict observation of mathematical and chemical rules. Assuming that ion species are fully resolved (either by chromatography or by high resolution

  18. Told through the wine: A liquid chromatography-mass spectrometry interplatform comparison reveals the influence of the global approach on the final annotated metabolites in non-targeted metabolomics.

    PubMed

    Díaz, Ramon; Gallart-Ayala, Hector; Sancho, Juan V; Nuñez, Oscar; Zamora, Tatiana; Martins, Claudia P B; Hernández, Félix; Hernández-Cassou, Santiago; Saurina, Javier; Checa, Antonio

    2016-02-12

    This work focuses on the influence of the selected LC-HRMS platform on the final annotated compounds in non-targeted metabolomics. Two platforms that differed in columns, mobile phases, gradients, chromatographs, mass spectrometers (Orbitrap [Platform#1] and Q-TOF [Platform#2]), data processing and marker selection protocols were compared. A total of 42 wines samples from three different protected denomination of origin (PDO) were analyzed. At the feature level, good (O)PLS-DA models were obtained for both platforms (Q(2)[Platform#1]=0.89, 0.83 and 0.72; Q(2)[Platform#2]=0.86, 0.86 and 0.77 for Penedes, Ribera del Duero and Rioja wines respectively) with 100% correctly classified samples in all cases. At the annotated metabolite level, platforms proposed 9 and 8 annotated metabolites respectively which were identified by matching standards or the MS/MS spectra of the compounds. At this stage, there was no coincidence among platforms regarding the suggested metabolites. When screened on the raw data, 6 and 5 of these compounds were detected on the other platform with a similar trend. Some of the detected metabolites showed complimentary information when integrated on biological pathways. Through the use of some examples at the annotated metabolite level, possible explanations of this initial divergence on the results are presented. This work shows the complications that may arise on the comparison of non-targeted metabolomics platforms even when metabolite focused approaches are used in the identification. PMID:26795279

  19. Integration of high-content screening and untargeted metabolomics for comprehensive functional annotation of natural product libraries

    PubMed Central

    Kurita, Kenji L.; Glassey, Emerson; Linington, Roger G.

    2015-01-01

    Traditional natural products discovery using a combination of live/dead screening followed by iterative bioassay-guided fractionation affords no information about compound structure or mode of action until late in the discovery process. This leads to high rates of rediscovery and low probabilities of finding compounds with unique biological and/or chemical properties. By integrating image-based phenotypic screening in HeLa cells with high-resolution untargeted metabolomics analysis, we have developed a new platform, termed Compound Activity Mapping, that is capable of directly predicting the identities and modes of action of bioactive constituents for any complex natural product extract library. This new tool can be used to rapidly identify novel bioactive constituents and provide predictions of compound modes of action directly from primary screening data. This approach inverts the natural products discovery process from the existing ‟grind and find” model to a targeted, hypothesis-driven discovery model where the chemical features and biological function of bioactive metabolites are known early in the screening workflow, and lead compounds can be rationally selected based on biological and/or chemical novelty. We demonstrate the utility of the Compound Activity Mapping platform by combining 10,977 mass spectral features and 58,032 biological measurements from a library of 234 natural products extracts and integrating these two datasets to identify 13 clusters of fractions containing 11 known compound families and four new compounds. Using Compound Activity Mapping we discovered the quinocinnolinomycins, a new family of natural products with a unique carbon skeleton that cause endoplasmic reticulum stress. PMID:26371303

  20. Metabolomics Provides Quality Characterization of Commercial Gochujang (Fermented Pepper Paste).

    PubMed

    Lee, Gyu Min; Suh, Dong Ho; Jung, Eun Sung; Lee, Choong Hwan

    2016-01-01

    To identify the major factors contributing to the quality of commercial gochujang (fermented red pepper paste), metabolites were profiled by mass spectrometry. In principal component analysis, cereal type (wheat, brown rice, and white rice) and species of hot pepper (Capsicum annuum, C. annuum cv. Chung-yang, and C. frutescens) affected clustering patterns. Relative amino acid and citric acid levels were significantly higher in wheat gochujang than in rice gochujang. Sucrose, linoleic acid, oleic acid, and lysophospholipid levels were high in brown-rice gochujang, whereas glucose, maltose, and γ-aminobutyric acid levels were high in white-rice gochujang. The relative capsaicinoid and luteolin derivative contents in gochujang were affected by the hot pepper species used. Gochujang containing C. annuum cv. Chung-yang and C. frutescens showed high capsaicinoid levels. The luteolin derivative level was high in gochujang containing C. frutescens. These metabolite variations in commercial gochujang may be related to different physicochemical phenotypes and antioxidant activity. PMID:27428946

  1. Framing Quality: Annotated Video-Based Portfolios of Classroom Practice by Pre-Service Teachers

    ERIC Educational Resources Information Center

    Joseph, Gail E.; Brennan, Carolyn

    2013-01-01

    In this paper we describe the use of peer learning teams creating annotated video-based portfolios to improve the quality of teacher-child interactions of undergraduate majors in early childhood and family studies. We used the intentional teaching framework (Hamre et al. in "Handbook of Early Education." Guilford Publications, New York,…

  2. Early Learning: Program Quality in Early Childhood Education. Annotated Bibliography

    ERIC Educational Resources Information Center

    Southern Regional Education Board (SREB), 2014

    2014-01-01

    Overwhelmingly, research shows that program quality is a major determinant in the achievement gains for young children who participate in early education programs. Teacher quality, in particular, is closely related to positive educational outcomes for preschool participants. Research shows that children in programs whose lead and supporting…

  3. SMART: Statistical Metabolomics Analysis-An R Tool.

    PubMed

    Liang, Yu-Jen; Lin, Yu-Ting; Chen, Chia-Wei; Lin, Chien-Wei; Chao, Kun-Mao; Pan, Wen-Harn; Yang, Hsin-Chou

    2016-06-21

    Metabolomics data provide unprecedented opportunities to decipher metabolic mechanisms by analyzing hundreds to thousands of metabolites. Data quality concerns and complex batch effects in metabolomics must be appropriately addressed through statistical analysis. This study developed an integrated analysis tool for metabolomics studies to streamline the complete analysis flow from initial data preprocessing to downstream association analysis. We developed Statistical Metabolomics Analysis-An R Tool (SMART), which can analyze input files with different formats, visually represent various types of data features, implement peak alignment and annotation, conduct quality control for samples and peaks, explore batch effects, and perform association analysis. A pharmacometabolomics study of antihypertensive medication was conducted and data were analyzed using SMART. Neuromedin N was identified as a metabolite significantly associated with angiotensin-converting-enzyme inhibitors in our metabolome-wide association analysis (p = 1.56 × 10(-4) in an analysis of covariance (ANCOVA) with an adjustment for unknown latent groups and p = 1.02 × 10(-4) in an ANCOVA with an adjustment for hidden substructures). This endogenous neuropeptide is highly related to neurotensin and neuromedin U, which are involved in blood pressure regulation and smooth muscle contraction. The SMART software, a user guide, and example data can be downloaded from http://www.stat.sinica.edu.tw/hsinchou/metabolomics/SMART.htm . PMID:27248514

  4. Versatile annotation and publication quality visualization of protein complexes using POLYVIEW-3D

    PubMed Central

    Porollo, Aleksey; Meller, Jaroslaw

    2007-01-01

    Background Macromolecular visualization as well as automated structural and functional annotation tools play an increasingly important role in the post-genomic era, contributing significantly towards the understanding of molecular systems and processes. For example, three dimensional (3D) models help in exploring protein active sites and functional hot spots that can be targeted in drug design. Automated annotation and visualization pipelines can also reveal other functionally important attributes of macromolecules. These goals are dependent on the availability of advanced tools that integrate better the existing databases, annotation servers and other resources with state-of-the-art rendering programs. Results We present a new tool for protein structure analysis, with the focus on annotation and visualization of protein complexes, which is an extension of our previously developed POLYVIEW web server. By integrating the web technology with state-of-the-art software for macromolecular visualization, such as the PyMol program, POLYVIEW-3D enables combining versatile structural and functional annotations with a simple web-based interface for creating publication quality structure rendering, as well as animated images for Powerpoint™, web sites and other electronic resources. The service is platform independent and no plug-ins are required. Several examples of how POLYVIEW-3D can be used for structural and functional analysis in the context of protein-protein interactions are presented to illustrate the available annotation options. Conclusion POLYVIEW-3D server features the PyMol image rendering that provides detailed and high quality presentation of macromolecular structures, with an easy to use web-based interface. POLYVIEW-3D also provides a wide array of options for automated structural and functional analysis of proteins and their complexes. Thus, the POLYVIEW-3D server may become an important resource for researches and educators in the fields of protein

  5. Automatic ECG quality scoring methodology: mimicking human annotators.

    PubMed

    Johannesen, Lars; Galeotti, Loriano

    2012-09-01

    An algorithm to determine the quality of electrocardiograms (ECGs) can enable inexperienced nurses and paramedics to record ECGs of sufficient diagnostic quality. Previously, we proposed an algorithm for determining if ECG recordings are of acceptable quality, which was entered in the PhysioNet Challenge 2011. In the present work, we propose an improved two-step algorithm, which first rejects ECGs with macroscopic errors (signal absent, large voltage shifts or saturation) and subsequently quantifies the noise (baseline, powerline or muscular noise) on a continuous scale. The performance of the improved algorithm was evaluated using the PhysioNet Challenge database (1500 ECGs rated by humans for signal quality). We achieved a classification accuracy of 92.3% on the training set and 90.0% on the test set. The improved algorithm is capable of detecting ECGs with macroscopic errors and giving the user a score of the overall quality. This allows the user to assess the degree of noise and decide if it is acceptable depending on the purpose of the recording. PMID:22902927

  6. Comparative mass spectrometry & nuclear magnetic resonance metabolomic approaches for nutraceuticals quality control analysis: a brief review.

    PubMed

    Farag, Mohamed A

    2014-01-01

    The number of botanical dietary supplements in the market has recently increased primarily due to increased health awareness. Standardization and quality control of the constituents of these plant extracts is an important topic, particularly when such ingredients are used long term as dietary supplements, or in cases where higher doses are marketed as drugs. The development of fast, comprehensive, and effective untargeted analytical methods for plant extracts is of high interest. Nuclear magnetic resonance spectroscopy and mass spectrometry are the most informative tools, each of which enables high-throughput and global analysis of hundreds of metabolites in a single step. Although only one of the two techniques is utilized in the majority of plant metabolomics applications, there is a growing interest in combining the data from both platforms to effectively unravel the complexity of plant samples. The application of combined MS and NMR in the quality control of nutraceuticals forms the major part of this review. Finally I will look at the future developments and perspectives of these two technologies for the quality control of herbal materials. PMID:24354527

  7. Filtered Push: Annotating Distributed Data for Quality Control and Fitness for Use Analysis

    NASA Astrophysics Data System (ADS)

    Morris, P. J.; Kelly, M. A.; Lowery, D. B.; Macklin, J. A.; Morris, R. A.; Tremonte, D.; Wang, Z.

    2009-12-01

    The single greatest problem with the federation of scientific data is the assessment of the quality and validity of the aggregated data in the context of particular research problems, that is, its fitness for use. There are three critical data quality issues in networks of distributed natural science collections data, as in all scientific data: identifying and correcting errors, maintaining currency, and assessing fitness for use. To this end, we have designed and implemented a prototype network in the domain of natural science collections. This prototype is built over the open source Map-Reduce platform Hadoop with a network client in the open source collections management system Specify 6. We call this network “Filtered Push” as, at its core, annotations are pushed from the network edges to relevant authoritative repositories, where humans and software filter the annotations before accepting them as changes to the authoritative data. The Filtered Push software is a domain-neutral framework for originating, distributing, and analyzing record-level annotations. Network participants can subscribe to notifications arising from ontology-based analyses of new annotations or of purpose-built queries against the network's global history of annotations. Quality and fitness for use of distributed natural science collections data can be addressed with Filtered Push software by implementing a network that allows data providers and consumers to define potential errors in data, develop metrics for those errors, specify workflows to analyze distributed data to detect potential errors, and to close the quality management cycle by providing a network architecture to pushing assertions about data quality such as corrections back to the curators of the participating data sets. Quality issues in distributed scientific data have several things in common: (1) Statements about data quality should be regarded as hypotheses about inconsistencies between perhaps several records, data

  8. Exploring molecular backgrounds of quality traits in rice by predictive models based on high-coverage metabolomics

    PubMed Central

    2011-01-01

    Background Increasing awareness of limitations to natural resources has set high expectations for plant science to deliver efficient crops with increased yields, improved stress tolerance, and tailored composition. Collections of representative varieties are a valuable resource for compiling broad breeding germplasms that can satisfy these diverse needs. Results Here we show that the untargeted high-coverage metabolomic characterization of such core collections is a powerful approach for studying the molecular backgrounds of quality traits and for constructing predictive metabolome-trait models. We profiled the metabolic composition of kernels from field-grown plants of the rice diversity research set using 4 complementary analytical platforms. We found that the metabolite profiles were correlated with both the overall population structure and fine-grained genetic diversity. Multivariate regression analysis showed that 10 of the 17 studied quality traits could be predicted from the metabolic composition independently of the population structure. Furthermore, the model of amylose ratio could be validated using external varieties grown in an independent experiment. Conclusions Our results demonstrate the utility of metabolomics for linking traits with quantitative molecular data. This opens up new opportunities for trait prediction and construction of tailored germplasms to support modern plant breeding. PMID:22034874

  9. Ranking Biomedical Annotations with Annotator's Semantic Relevancy

    PubMed Central

    2014-01-01

    Biomedical annotation is a common and affective artifact for researchers to discuss, show opinion, and share discoveries. It becomes increasing popular in many online research communities, and implies much useful information. Ranking biomedical annotations is a critical problem for data user to efficiently get information. As the annotator's knowledge about the annotated entity normally determines quality of the annotations, we evaluate the knowledge, that is, semantic relationship between them, in two ways. The first is extracting relational information from credible websites by mining association rules between an annotator and a biomedical entity. The second way is frequent pattern mining from historical annotations, which reveals common features of biomedical entities that an annotator can annotate with high quality. We propose a weighted and concept-extended RDF model to represent an annotator, a biomedical entity, and their background attributes and merge information from the two ways as the context of an annotator. Based on that, we present a method to rank the annotations by evaluating their correctness according to user's vote and the semantic relevancy between the annotator and the annotated entity. The experimental results show that the approach is applicable and efficient even when data set is large. PMID:24899918

  10. Ranking biomedical annotations with annotator's semantic relevancy.

    PubMed

    Wu, Aihua

    2014-01-01

    Biomedical annotation is a common and affective artifact for researchers to discuss, show opinion, and share discoveries. It becomes increasing popular in many online research communities, and implies much useful information. Ranking biomedical annotations is a critical problem for data user to efficiently get information. As the annotator's knowledge about the annotated entity normally determines quality of the annotations, we evaluate the knowledge, that is, semantic relationship between them, in two ways. The first is extracting relational information from credible websites by mining association rules between an annotator and a biomedical entity. The second way is frequent pattern mining from historical annotations, which reveals common features of biomedical entities that an annotator can annotate with high quality. We propose a weighted and concept-extended RDF model to represent an annotator, a biomedical entity, and their background attributes and merge information from the two ways as the context of an annotator. Based on that, we present a method to rank the annotations by evaluating their correctness according to user's vote and the semantic relevancy between the annotator and the annotated entity. The experimental results show that the approach is applicable and efficient even when data set is large. PMID:24899918

  11. Method for the Compound Annotation of Conjugates in Nontargeted Metabolomics Using Accurate Mass Spectrometry, Multistage Product Ion Spectra and Compound Database Searching

    PubMed Central

    Ogura, Tairo; Bamba, Takeshi; Tai, Akihiro; Fukusaki, Eiichiro

    2015-01-01

    Owing to biotransformation, xenobiotics are often found in conjugated form in biological samples such as urine and plasma. Liquid chromatography coupled with accurate mass spectrometry with multistage collision-induced dissociation provides spectral information concerning these metabolites in complex materials. Unfortunately, compound databases typically do not contain a sufficient number of records for such conjugates. We report here on the development of a novel protocol, referred to as ChemProphet, to annotate compounds, including conjugates, using compound databases such as PubChem and ChemSpider. The annotation of conjugates involves three steps: 1. Recognition of the type and number of conjugates in the sample; 2. Compound search and annotation of the deconjugated form; and 3. In silico evaluation of the candidate conjugate. ChemProphet assigns a spectrum to each candidate by automatically exploring the substructures corresponding to the observed product ion spectrum. When finished, it annotates the candidates assigning a rank for each candidate based on the calculated score that ranks its relative likelihood. We assessed our protocol by annotating a benchmark dataset by including the product ion spectra for 102 compounds, annotating the commercially available standard for quercetin 3-glucuronide, and by conducting a model experiment using urine from mice that had been administered a green tea extract. The results show that by using the ChemProphet approach, it is possible to annotate not only the deconjugated molecules but also the conjugated molecules using an automatic interpretation method based on deconjugation that involves multistage collision-induced dissociation and in silico calculated conjugation. PMID:26819907

  12. Metabolomic technologies for improving the quality of food: Practice and promise

    Technology Transfer Automated Retrieval System (TEKTRAN)

    It is now well documented that the diet has a significant impact on human health and well-being. However, the complete set of small molecule metabolites present in foods that make up the human diet and the role of food production systems in altering this food metabolome are still largely unknown. Me...

  13. Metabolomic analysis of the effect of shade treatment on the nutritional and sensory qualities of green tea.

    PubMed

    Lee, Lan-Sook; Choi, Ji Hea; Son, Nari; Kim, Sang-Hee; Park, Jong-Dae; Jang, Dae-Ja; Jeong, Yoonhwa; Kim, Hyun-Jin

    2013-01-16

    We analyzed metabolites from a 50% aqueous methanol extract of green teas treated with different shade periods (0, 15, 18, and 20 days) to investigate the effect of low light on their nutritional and sensory qualities. The shaded groups could be clearly distinguished from the control (0 day), and the 20 day group was separated from the 15 and 18 day groups. The shade treatment increased quercetin-galactosylrutinoside, kaempferol-glucosylrutinoside, epicatechin gallate, epigallocatechin gallate, tryptophan, phenylalanine, theanine, glutamine, glutamate, and caffeine levels but decreased quercetin-glucosylrutinoside, kaempferol-glucoside, gallocatechin, and epigallocatechin levels. Further studies on the nutritional benefits of these metabolites are needed. However, this result, along with the sensory evaluation and color measurement data, suggests that shade treatment improves the nutritional and sensory quality of green tea. Thus, we proposed a metabolomic pathway related to the effect of low light, which could elucidate the relationship between low light and tea quality. PMID:23256790

  14. Microbial Metabolomics

    PubMed Central

    Tang, Jane

    2011-01-01

    Microbial metabolomics constitutes an integrated component of systems biology. By studying the complete set of metabolites within a microorganism and monitoring the global outcome of interactions between its development processes and the environment, metabolomics can potentially provide a more accurate snap shot of the actual physiological state of the cell. Recent advancement of technologies and post-genomic developments enable the study and analysis of metabolome. This unique contribution resulted in many scientific disciplines incorporating metabolomics as one of their “omics” platforms. This review focuses on metabolomics in microorganisms and utilizes selected topics to illustrate its impact on the understanding of systems microbiology. PMID:22379393

  15. Metabolomic profiling of urine samples from mice exposed to protons reveals radiation quality and dose specific differences.

    PubMed

    Laiakis, Evagelia C; Trani, Daniela; Moon, Bo-Hyun; Strawn, Steven J; Fornace, Albert J

    2015-04-01

    As space travel is expanding to include private tourism and travel beyond low-Earth orbit, so is the risk of exposure to space radiation. Galactic cosmic rays and solar particle events have the potential to expose space travelers to significant doses of radiation that can lead to increased cancer risk and other adverse health consequences. Metabolomics has the potential to assess an individual's risk by exploring the metabolic perturbations in a biofluid or tissue. In this study, C57BL/6 mice were exposed to 0.5 and 2 Gy of 1 GeV/nucleon of protons and the levels of metabolites were evaluated in urine at 4 h after radiation exposure through liquid chromatography coupled to time-of-flight mass spectrometry. Significant differences were identified in metabolites that map to the tricarboxylic acid (TCA) cycle and fatty acid metabolism, suggesting that energy metabolism is severely impacted after exposure to protons. Additionally, various pathways of amino acid metabolism (tryptophan, tyrosine, arginine and proline and phenylalanine) were affected with potential implications for DNA damage repair and cognitive impairment. Finally, presence of products of purine and pyrimidine metabolism points to direct DNA damage or increased apoptosis. Comparison of these metabolomic data to previously published data from our laboratory with gamma radiation strongly suggests a more pronounced effect on metabolism with protons. This is the first metabolomics study with space radiation in an easily accessible biofluid such as urine that further investigates and exemplifies the biological differences at early time points after exposure to different radiation qualities. PMID:25768838

  16. Metabolomic Profiling of Urine Samples from Mice Exposed to Protons Reveals Radiation Quality and Dose Specific Differences

    PubMed Central

    Laiakis, Evagelia C.; Trani, Daniela; Moon, Bo-Hyun; Strawn, Steven J.; Fornace, Albert J.

    2015-01-01

    As space travel is expanding to include private tourism and travel beyond low-Earth orbit, so is the risk of exposure to space radiation. Galactic cosmic rays and solar particle events have the potential to expose space travelers to significant doses of radiation that can lead to increased cancer risk and other adverse health consequences. Metabolomics has the potential to assess an individual’s risk by exploring the metabolic perturbations in a biofluid or tissue. In this study, C57BL/6 mice were exposed to 0.5 and 2 Gy of 1 GeV/nucleon of protons and the levels of metabolites were evaluated in urine at 4 h after radiation exposure through liquid chromatography coupled to time-of-flight mass spectrometry. Significant differences were identified in metabolites that map to the tricarboxylic acid (TCA) cycle and fatty acid metabolism, suggesting that energy metabolism is severely impacted after exposure to protons. Additionally, various pathways of amino acid metabolism (tryptophan, tyrosine, arginine and proline and phenylalanine) were affected with potential implications for DNA damage repair and cognitive impairment. Finally, presence of products of purine and pyrimidine metabolism points to direct DNA damage or increased apoptosis. Comparison of these metabolomic data to previously published data from our laboratory with gamma radiation strongly suggests a more pronounced effect on metabolism with protons. This is the first metabolomics study with space radiation in an easily accessible biofluid such as urine that further investigates and exemplifies the biological differences at early time points after exposure to different radiation qualities. PMID:25768838

  17. Digital pathology and image analysis augment biospecimen annotation and biobank quality assurance harmonization

    PubMed Central

    Wei, Bih-Rong; Simpson, R. Mark

    2015-01-01

    Standardization of biorepository best practices will enhance the quality of translational biomedical research utilizing patient-derived biobank specimens. Harmonization of pathology quality assurance procedures for biobank accessions has lagged behind other avenues of biospecimen research and biobank development. Comprehension of the cellular content of biorepository specimens is important for discovery of tissue-specific clinically relevant biomarkers for diagnosis and treatment. While rapidly emerging technologies in molecular analyses and data mining create focus on appropriate measures for minimizing pre-analytic artifact-inducing variables, less attention gets paid to annotating the constituent make up of biospecimens for more effective specimen selection by biobank clients. Both pre-analytic tissue processing and a specimen's composition influence acquisition of relevant macromolecules for downstream assays. Pathologist review of biorepository submissions, particularly tissues as part of quality assurance procedures, helps to ensure that the intended target cells are present and in sufficient quantity in accessioned specimens. This manual procedure can be tedious and subjective. Incorporating digital pathology into biobank quality assurance procedures, using automated pattern recognition morphometric image analysis to quantify tissue feature areas in digital whole slide images of tissue sections, can minimize variability and subjectivity associated with routine pathologic evaluations in biorepositories. Whole-slide images and pathologist-reviewed morphometric analyses can be provided to researchers to guide specimen selection. Harmonization of pathology quality assurance methods that minimize subjectivity and improve reproducibility among collections would facilitate research-relevant specimen selection by investigators and could facilitate information sharing in an integrated network approach to biobanking. PMID:24362266

  18. Quantitative performance of E-Scribe warehouse in detecting quality issues with digital annotated ECG data from healthy subjects.

    PubMed

    Sarapa, Nenad; Mortara, Justin L; Brown, Barry D; Isola, Lamberto; Badilini, Fabio

    2008-05-01

    The US Food and Drug Administration recommends submission of digital electrocardiograms in the standard HL7 XML format into the electrocardiogram warehouse to support preapproval review of new drug applications. The Food and Drug Administration scrutinizes electrocardiogram quality by viewing the annotated waveforms and scoring electrocardiogram quality by the warehouse algorithms. Part of the Food and Drug Administration warehouse is commercially available to sponsors as the E-Scribe Warehouse. The authors tested the performance of E-Scribe Warehouse algorithms by quantifying electrocardiogram acquisition quality, adherence to QT annotation protocol, and T-wave signal strength in 2 data sets: "reference" (104 digital electrocardiograms from a phase I study with sotalol in 26 healthy subjects with QT annotations by computer-assisted manual adjustment) and "test" (the same electrocardiograms with an intentionally introduced predefined number of quality issues). The E-Scribe Warehouse correctly detected differences between the 2 sets expected from the number and pattern of errors in the "test" set (except for 1 subject with QT misannotated in different leads of serial electrocardiograms) and confirmed the absence of differences where none was expected. E-Scribe Warehouse scores below the threshold value identified individual electrocardiograms with questionable T-wave signal strength. The E-Scribe Warehouse showed satisfactory performance in detecting electrocardiogram quality issues that may impair reliability of QTc assessment in clinical trials in healthy subjects. PMID:18353997

  19. MAKER-P: A Tool Kit for the Rapid Creation, Management, and Quality Control of Plant Genome Annotations1[W][OPEN

    PubMed Central

    Campbell, Michael S.; Law, MeiYee; Holt, Carson; Stein, Joshua C.; Moghe, Gaurav D.; Hufnagel, David E.; Lei, Jikai; Achawanantakun, Rujira; Jiao, Dian; Lawrence, Carolyn J.; Ware, Doreen; Shiu, Shin-Han; Childs, Kevin L.; Sun, Yanni; Jiang, Ning; Yandell, Mark

    2014-01-01

    We have optimized and extended the widely used annotation engine MAKER in order to better support plant genome annotation efforts. New features include better parallelization for large repeat-rich plant genomes, noncoding RNA annotation capabilities, and support for pseudogene identification. We have benchmarked the resulting software tool kit, MAKER-P, using the Arabidopsis (Arabidopsis thaliana) and maize (Zea mays) genomes. Here, we demonstrate the ability of the MAKER-P tool kit to automatically update, extend, and revise the Arabidopsis annotations in light of newly available data and to annotate pseudogenes and noncoding RNAs absent from The Arabidopsis Informatics Resource 10 build. Our results demonstrate that MAKER-P can be used to manage and improve the annotations of even Arabidopsis, perhaps the best-annotated plant genome. We have also installed and benchmarked MAKER-P on the Texas Advanced Computing Center. We show that this public resource can de novo annotate the entire Arabidopsis and maize genomes in less than 3 h and produce annotations of comparable quality to those of the current The Arabidopsis Information Resource 10 and maize V2 annotation builds. PMID:24306534

  20. Metabolomics as a Powerful Tool for Molecular Quality Assessment of the Fish Sparus aurata

    PubMed Central

    Picone, Gianfranco; Engelsen, Søren Balling; Savorani, Francesco; Testi, Silvia; Badiani, Anna; Capozzi, Francesco

    2011-01-01

    The molecular profiles of perchloric acid solutions extracted from the flesh of Sparus aurata fish specimens, produced according to different aquaculture systems, have been investigated. The 1H-NMR spectra of aqueous extracts are indicative of differences in the metabolite content of fish reared under different conditions that are already distinguishable at their capture, and substantially maintain the same differences in their molecular profiles after sixteen days of storage under ice. The fish metabolic profiles are studied by top-down chemometric analysis. The results of this exploratory investigation show that the fish metabolome accurately reflects the rearing conditions. The level of many metabolites co-vary with the rearing conditions and a few metabolites are quantified including glycogen (stress indicator), histidine, alanine and glycine which all display significant changes dependent on the aquaculture system and on the storage times. PMID:22254093

  1. Tools and databases of the KOMICS web portal for preprocessing, mining, and dissemination of metabolomics data.

    PubMed

    Sakurai, Nozomu; Ara, Takeshi; Enomoto, Mitsuo; Motegi, Takeshi; Morishita, Yoshihiko; Kurabayashi, Atsushi; Iijima, Yoko; Ogata, Yoshiyuki; Nakajima, Daisuke; Suzuki, Hideyuki; Shibata, Daisuke

    2014-01-01

    A metabolome--the collection of comprehensive quantitative data on metabolites in an organism--has been increasingly utilized for applications such as data-intensive systems biology, disease diagnostics, biomarker discovery, and assessment of food quality. A considerable number of tools and databases have been developed to date for the analysis of data generated by various combinations of chromatography and mass spectrometry. We report here a web portal named KOMICS (The Kazusa Metabolomics Portal), where the tools and databases that we developed are available for free to academic users. KOMICS includes the tools and databases for preprocessing, mining, visualization, and publication of metabolomics data. Improvements in the annotation of unknown metabolites and dissemination of comprehensive metabolomic data are the primary aims behind the development of this portal. For this purpose, PowerGet and FragmentAlign include a manual curation function for the results of metabolite feature alignments. A metadata-specific wiki-based database, Metabolonote, functions as a hub of web resources related to the submitters' work. This feature is expected to increase citation of the submitters' work, thereby promoting data publication. As an example of the practical use of KOMICS, a workflow for a study on Jatropha curcas is presented. The tools and databases available at KOMICS should contribute to enhanced production, interpretation, and utilization of metabolomic Big Data. PMID:24949426

  2. Semantic Annotation of Mutable Data

    PubMed Central

    Morris, Robert A.; Dou, Lei; Hanken, James; Kelly, Maureen; Lowery, David B.; Ludäscher, Bertram; Macklin, James A.; Morris, Paul J.

    2013-01-01

    Electronic annotation of scientific data is very similar to annotation of documents. Both types of annotation amplify the original object, add related knowledge to it, and dispute or support assertions in it. In each case, annotation is a framework for discourse about the original object, and, in each case, an annotation needs to clearly identify its scope and its own terminology. However, electronic annotation of data differs from annotation of documents: the content of the annotations, including expectations and supporting evidence, is more often shared among members of networks. Any consequent actions taken by the holders of the annotated data could be shared as well. But even those current annotation systems that admit data as their subject often make it difficult or impossible to annotate at fine-enough granularity to use the results in this way for data quality control. We address these kinds of issues by offering simple extensions to an existing annotation ontology and describe how the results support an interest-based distribution of annotations. We are using the result to design and deploy a platform that supports annotation services overlaid on networks of distributed data, with particular application to data quality control. Our initial instance supports a set of natural science collection metadata services. An important application is the support for data quality control and provision of missing data. A previous proof of concept demonstrated such use based on data annotations modeled with XML-Schema. PMID:24223697

  3. Metabolomic profiling and sensorial quality of 'Golden Delicious', 'Liberty', 'Santana', and 'Topaz' apples grown using organic and integrated production systems.

    PubMed

    Vanzo, Andreja; Jenko, Mojca; Vrhovsek, Urska; Stopar, Matej

    2013-07-01

    Apple quality was investigated in the scab-resistant 'Liberty', 'Santana', and 'Topaz' cultivars and the scab-susceptible 'Golden Delicious' cultivar. Trees subjected to the same crop load were cultivated using either an organic (ORG) or an integrated production (IP) system. Physicochemical properties, phenolic content, and sensorial quality of fruit from both systems were compared. There were no significant differences in fruit mass, starch, and total soluble solid content (the latter was higher in ORG 'Liberty') between ORG and IP fruit, whereas significantly higher flesh firmness was found in ORG fruit (except no difference in 'Golden Delicious'). Significantly higher total phenolic content in ORG fruit was found in 'Golden Delicious', whereas differences in other cultivars were not significant. Targeted metabolomic profiling of multiple classes of phenolics confirmed the impact of the production system on the 'Golden Delicious' phenolic profile as higher levels of 4-hydroxybenzoic acid, neo- and chlorogenic acids, phloridzin, procyanidin B2+B4, -3-O-glucoside and -3-O-galactoside of quercetin, kaempferol-3-O-rutinoside, and rutin being found in ORG fruit. The results obtained suggested that scab resistance influenced the phenolic biosynthesis in relation to the agricultural system. Sensorial evaluation indicated significantly better flavor (except for 'Topaz') and better appearance of IP fruit. PMID:23745580

  4. The Quest for Environmental Quality, Federal and State Action, 1969-70, Annotated Bibliography.

    ERIC Educational Resources Information Center

    Stanfield, Rochelle L.

    This report and annotated bilbiography is issued by the Advisory Commission on Intergovernmental Relations as part of its function to provide information on emerging issues with intergovernmental implications. Legislative and administrative environmental action of the Federal government during 1969-70 is reported. This covers policy and…

  5. The Human Urine Metabolome

    PubMed Central

    Bouatra, Souhaila; Aziat, Farid; Mandal, Rupasri; Guo, An Chi; Wilson, Michael R.; Knox, Craig; Bjorndahl, Trent C.; Krishnamurthy, Ramanarayan; Saleem, Fozia; Liu, Philip; Dame, Zerihun T.; Poelzer, Jenna; Huynh, Jessica; Yallou, Faizath S.; Psychogios, Nick; Dong, Edison; Bogumil, Ralf; Roehring, Cornelia; Wishart, David S.

    2013-01-01

    Urine has long been a “favored” biofluid among metabolomics researchers. It is sterile, easy-to-obtain in large volumes, largely free from interfering proteins or lipids and chemically complex. However, this chemical complexity has also made urine a particularly difficult substrate to fully understand. As a biological waste material, urine typically contains metabolic breakdown products from a wide range of foods, drinks, drugs, environmental contaminants, endogenous waste metabolites and bacterial by-products. Many of these compounds are poorly characterized and poorly understood. In an effort to improve our understanding of this biofluid we have undertaken a comprehensive, quantitative, metabolome-wide characterization of human urine. This involved both computer-aided literature mining and comprehensive, quantitative experimental assessment/validation. The experimental portion employed NMR spectroscopy, gas chromatography mass spectrometry (GC-MS), direct flow injection mass spectrometry (DFI/LC-MS/MS), inductively coupled plasma mass spectrometry (ICP-MS) and high performance liquid chromatography (HPLC) experiments performed on multiple human urine samples. This multi-platform metabolomic analysis allowed us to identify 445 and quantify 378 unique urine metabolites or metabolite species. The different analytical platforms were able to identify (quantify) a total of: 209 (209) by NMR, 179 (85) by GC-MS, 127 (127) by DFI/LC-MS/MS, 40 (40) by ICP-MS and 10 (10) by HPLC. Our use of multiple metabolomics platforms and technologies allowed us to identify several previously unknown urine metabolites and to substantially enhance the level of metabolome coverage. It also allowed us to critically assess the relative strengths and weaknesses of different platforms or technologies. The literature review led to the identification and annotation of another 2206 urinary compounds and was used to help guide the subsequent experimental studies. An online database containing

  6. YMDB: the Yeast Metabolome Database.

    PubMed

    Jewison, Timothy; Knox, Craig; Neveu, Vanessa; Djoumbou, Yannick; Guo, An Chi; Lee, Jacqueline; Liu, Philip; Mandal, Rupasri; Krishnamurthy, Ram; Sinelnikov, Igor; Wilson, Michael; Wishart, David S

    2012-01-01

    The Yeast Metabolome Database (YMDB, http://www.ymdb.ca) is a richly annotated 'metabolomic' database containing detailed information about the metabolome of Saccharomyces cerevisiae. Modeled closely after the Human Metabolome Database, the YMDB contains >2000 metabolites with links to 995 different genes/proteins, including enzymes and transporters. The information in YMDB has been gathered from hundreds of books, journal articles and electronic databases. In addition to its comprehensive literature-derived data, the YMDB also contains an extensive collection of experimental intracellular and extracellular metabolite concentration data compiled from detailed Mass Spectrometry (MS) and Nuclear Magnetic Resonance (NMR) metabolomic analyses performed in our lab. This is further supplemented with thousands of NMR and MS spectra collected on pure, reference yeast metabolites. Each metabolite entry in the YMDB contains an average of 80 separate data fields including comprehensive compound description, names and synonyms, structural information, physico-chemical data, reference NMR and MS spectra, intracellular/extracellular concentrations, growth conditions and substrates, pathway information, enzyme data, gene/protein sequence data, as well as numerous hyperlinks to images, references and other public databases. Extensive searching, relational querying and data browsing tools are also provided that support text, chemical structure, spectral, molecular weight and gene/protein sequence queries. Because of S. cervesiae's importance as a model organism for biologists and as a biofactory for industry, we believe this kind of database could have considerable appeal not only to metabolomics researchers, but also to yeast biologists, systems biologists, the industrial fermentation industry, as well as the beer, wine and spirit industry. PMID:22064855

  7. Metabolomic analysis of three Mollicute species.

    PubMed

    Vanyushkina, Anna A; Fisunov, Gleb Y; Gorbachev, Alexey Y; Kamashev, Dmitri E; Govorun, Vadim M

    2014-01-01

    We present a systematic study of three bacterial species that belong to the class Mollicutes, the smallest and simplest bacteria, Spiroplasma melliferum, Mycoplasma gallisepticum, and Acholeplasma laidlawii. To understand the difference in the basic principles of metabolism regulation and adaptation to environmental conditions in the three species, we analyzed the metabolome of these bacteria. Metabolic pathways were reconstructed using the proteogenomic annotation data provided by our lab. The results of metabolome, proteome and genome profiling suggest a fundamental difference in the adaptation of the three closely related Mollicute species to stress conditions. As the transaldolase is not annotated in Mollicutes, we propose variants of the pentose phosphate pathway catalyzed by annotated enzymes for three species. For metabolite detection we employed high performance liquid chromatography coupled with mass spectrometry. We used liquid chromatography method - hydrophilic interaction chromatography with silica column - as it effectively separates highly polar cellular metabolites prior to their detection by mass spectrometer. PMID:24595068

  8. Metabolomics by Gas Chromatography-Mass Spectrometry: Combined Targeted and Untargeted Profiling.

    PubMed

    Fiehn, Oliver

    2016-01-01

    Gas chromatography-mass spectrometry (GC-MS)-based metabolomics is ideal for identifying and quantitating small-molecule metabolites (<650 Da), including small acids, alcohols, hydroxyl acids, amino acids, sugars, fatty acids, sterols, catecholamines, drugs, and toxins, often using chemical derivatization to make these compounds sufficiently volatile for gas chromatography. This unit shows how GC-MS-based metabolomics allows integration of targeted assays for absolute quantification of specific metabolites with untargeted metabolomics to discover novel compounds. Complemented by database annotations using large spectral libraries and validated standard operating procedures, GC-MS can identify and semiquantify over 200 compounds from human body fluids (e.g., plasma, urine, or stool) per study. Deconvolution software enables detection of more than 300 additional unidentified signals that can be annotated through accurate mass instruments with appropriate data processing workflows, similar to untargeted profiling using liquid chromatography-mass spectrometry. GC-MS is a mature technology that uses not only classic detectors (quadrupole) but also target mass spectrometers (triple quadrupole) and accurate mass instruments (quadrupole-time of flight). This unit covers sample preparation from mammalian samples, data acquisition, quality control, and data processing. © 2016 by John Wiley & Sons, Inc. PMID:27038389

  9. Tools and Databases of the KOMICS Web Portal for Preprocessing, Mining, and Dissemination of Metabolomics Data

    PubMed Central

    Enomoto, Mitsuo; Morishita, Yoshihiko; Kurabayashi, Atsushi; Iijima, Yoko; Ogata, Yoshiyuki; Nakajima, Daisuke; Suzuki, Hideyuki; Shibata, Daisuke

    2014-01-01

    A metabolome—the collection of comprehensive quantitative data on metabolites in an organism—has been increasingly utilized for applications such as data-intensive systems biology, disease diagnostics, biomarker discovery, and assessment of food quality. A considerable number of tools and databases have been developed to date for the analysis of data generated by various combinations of chromatography and mass spectrometry. We report here a web portal named KOMICS (The Kazusa Metabolomics Portal), where the tools and databases that we developed are available for free to academic users. KOMICS includes the tools and databases for preprocessing, mining, visualization, and publication of metabolomics data. Improvements in the annotation of unknown metabolites and dissemination of comprehensive metabolomic data are the primary aims behind the development of this portal. For this purpose, PowerGet and FragmentAlign include a manual curation function for the results of metabolite feature alignments. A metadata-specific wiki-based database, Metabolonote, functions as a hub of web resources related to the submitters' work. This feature is expected to increase citation of the submitters' work, thereby promoting data publication. As an example of the practical use of KOMICS, a workflow for a study on Jatropha curcas is presented. The tools and databases available at KOMICS should contribute to enhanced production, interpretation, and utilization of metabolomic Big Data. PMID:24949426

  10. Community gene annotation in practice

    PubMed Central

    Loveland, Jane E.; Gilbert, James G.R.; Griffiths, Ed; Harrow, Jennifer L.

    2012-01-01

    Manual annotation of genomic data is extremely valuable to produce an accurate reference gene set but is expensive compared with automatic methods and so has been limited to model organisms. Annotation tools that have been developed at the Wellcome Trust Sanger Institute (WTSI, http://www.sanger.ac.uk/.) are being used to fill that gap, as they can be used remotely and so open up viable community annotation collaborations. We introduce the ‘Blessed’ annotator and ‘Gatekeeper’ approach to Community Annotation using the Otterlace/ZMap genome annotation tool. We also describe the strategies adopted for annotation consistency, quality control and viewing of the annotation. Database URL: http://vega.sanger.ac.uk/index.html PMID:22434843

  11. Metabolomics in transfusion medicine.

    PubMed

    Nemkov, Travis; Hansen, Kirk C; Dumont, Larry J; D'Alessandro, Angelo

    2016-04-01

    Biochemical investigations on the regulatory mechanisms of red blood cell (RBC) and platelet (PLT) metabolism have fostered a century of advances in the field of transfusion medicine. Owing to these advances, storage of RBCs and PLT concentrates has become a lifesaving practice in clinical and military settings. There, however, remains room for improvement, especially with regard to the introduction of novel storage and/or rejuvenation solutions, alternative cell processing strategies (e.g., pathogen inactivation technologies), and quality testing (e.g., evaluation of novel containers with alternative plasticizers). Recent advancements in mass spectrometry-based metabolomics and systems biology, the bioinformatics integration of omics data, promise to speed up the design and testing of innovative storage strategies developed to improve the quality, safety, and effectiveness of blood products. Here we review the currently available metabolomics technologies and briefly describe the routine workflow for transfusion medicine-relevant studies. The goal is to provide transfusion medicine experts with adequate tools to navigate through the otherwise overwhelming amount of metabolomics data burgeoning in the field during the past few years. Descriptive metabolomics data have represented the first step omics researchers have taken into the field of transfusion medicine. However, to up the ante, clinical and omics experts will need to merge their expertise to investigate correlative and mechanistic relationships among metabolic variables and transfusion-relevant variables, such as 24-hour in vivo recovery for transfused RBCs. Integration with systems biology models will potentially allow for in silico prediction of metabolic phenotypes, thus streamlining the design and testing of alternative storage strategies and/or solutions. PMID:26662506

  12. Automated update, revision, and quality control of the maize genome annotations using MAKER-P improves the B73 RefGen_v3 gene models and identifies new genes

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The large size and relative complexity of many plant genomes make creation, quality control, and dissemination of high-quality gene structure annotations challenging. In response, we have developed MAKER-P, a fast and easy-to-use genome annotation engine for plants. Here, we report the use of MAKER-...

  13. Metabolomic Analyses Reveal Distinct Change of Metabolites and Quality of Green Tea during the Short Duration of a Single Spring Season.

    PubMed

    Liu, Jianwei; Zhang, Qunfeng; Liu, Meiya; Ma, Lifeng; Shi, Yuanzhi; Ruan, Jianyun

    2016-04-27

    The sensory quality of green tea changes greatly within a single spring season, but the mechanism is not clearly elucidated. Young shoots of the early, middle, and late spring season were subjected to metabolite profiling using gas chromatography-time-of-flight mass spectrometry (TOF/MS) and ultraperformance liquid chromatography-quadrupole-TOF/MS. Multivariate analyses revealed largely different metabolite phenotypes in young shoots among different periods. The contents of amino acids decreased, whereas carbohydrates, flavonoids and their glycosides, tricarboxylic acid cycle, and photorespiration pathways were strongly reinforced in the late spring season, which were well reflected in the sensory quality of made teas. Metabolomic analyses further demonstrated distinct variations of metabolite phenotypes in mature leaves. The results suggested that the fluctuation of green tea quality in the spring season was caused by changes of metabolite phenotypes in young shoots, which was likely related to the remobilization of carbon and nitrogen reserves from mature leaves. PMID:27052744

  14. Automated Update, Revision, and Quality Control of the Maize Genome Annotations Using MAKER-P Improves the B73 RefGen_v3 Gene Models and Identifies New Genes1[OPEN

    PubMed Central

    Law, MeiYee; Childs, Kevin L.; Campbell, Michael S.; Stein, Joshua C.; Olson, Andrew J.; Holt, Carson; Panchy, Nicholas; Lei, Jikai; Jiao, Dian; Andorf, Carson M.; Lawrence, Carolyn J.; Ware, Doreen; Shiu, Shin-Han; Sun, Yanni; Jiang, Ning; Yandell, Mark

    2015-01-01

    The large size and relative complexity of many plant genomes make creation, quality control, and dissemination of high-quality gene structure annotations challenging. In response, we have developed MAKER-P, a fast and easy-to-use genome annotation engine for plants. Here, we report the use of MAKER-P to update and revise the maize (Zea mays) B73 RefGen_v3 annotation build (5b+) in less than 3 h using the iPlant Cyberinfrastructure. MAKER-P identified and annotated 4,466 additional, well-supported protein-coding genes not present in the 5b+ annotation build, added additional untranslated regions to 1,393 5b+ gene models, identified 2,647 5b+ gene models that lack any supporting evidence (despite the use of large and diverse evidence data sets), identified 104,215 pseudogene fragments, and created an additional 2,522 noncoding gene annotations. We also describe a method for de novo training of MAKER-P for the annotation of newly sequenced grass genomes. Collectively, these results lead to the 6a maize genome annotation and demonstrate the utility of MAKER-P for rapid annotation, management, and quality control of grasses and other difficult-to-annotate plant genomes. PMID:25384563

  15. ECMDB: the E. coli Metabolome Database.

    PubMed

    Guo, An Chi; Jewison, Timothy; Wilson, Michael; Liu, Yifeng; Knox, Craig; Djoumbou, Yannick; Lo, Patrick; Mandal, Rupasri; Krishnamurthy, Ram; Wishart, David S

    2013-01-01

    The Escherichia coli Metabolome Database (ECMDB, http://www.ecmdb.ca) is a comprehensively annotated metabolomic database containing detailed information about the metabolome of E. coli (K-12). Modelled closely on the Human and Yeast Metabolome Databases, the ECMDB contains >2600 metabolites with links to ∼1500 different genes and proteins, including enzymes and transporters. The information in the ECMDB has been collected from dozens of textbooks, journal articles and electronic databases. Each metabolite entry in the ECMDB contains an average of 75 separate data fields, including comprehensive compound descriptions, names and synonyms, chemical taxonomy, compound structural and physicochemical data, bacterial growth conditions and substrates, reactions, pathway information, enzyme data, gene/protein sequence data and numerous hyperlinks to images, references and other public databases. The ECMDB also includes an extensive collection of intracellular metabolite concentration data compiled from our own work as well as other published metabolomic studies. This information is further supplemented with thousands of fully assigned reference nuclear magnetic resonance and mass spectrometry spectra obtained from pure E. coli metabolites that we (and others) have collected. Extensive searching, relational querying and data browsing tools are also provided that support text, chemical structure, spectral, molecular weight and gene/protein sequence queries. Because of E. coli's importance as a model organism for biologists and as a biofactory for industry, we believe this kind of database could have considerable appeal not only to metabolomics researchers but also to molecular biologists, systems biologists and individuals in the biotechnology industry. PMID:23109553

  16. Validation of a two-step quality control approach for a large-scale human urine metabolomic study conducted in seven experimental batches with LC/QTOF-MS.

    PubMed

    Demetrowitsch, Tobias J; Petersen, Beate; Keppler, Julia K; Koch, Andreas; Schreiber, Stefan; Laudes, Matthias; Schwarz, Karin

    2015-01-01

    After his study of food science at the Rheinische Friedrich-Wilhelms University of Bonn, Tobias J Demetrowitsch obtained his doctoral degree in the research field of metabolomics at the Christian-Albrechts-University of Kiel. The present paper is part of his doctoral thesis and describes an extended strategy to evaluate and verify complex or large-scale experiments and data sets. Large-scale studies result in high sample numbers, requiring the analysis of samples in different batches. So far, the verification of such LC-MS-based metabolomics studies is difficult. Common approaches have not provided a reliable validation procedure to date. This article shows a novel verification process for a large-scale human urine study (analyzed by a LC/QToF-MS system) using a two-step validation procedure. The first step comprises a targeted approach that aims to examine and exclude statistical outliers. The second step consists of a principle component analysis, with the aim of a tight cluster of all quality controls and a second for all volunteer samples. The applied study design provides a reliable two-step validation procedure for large-scale studies and additionally contains an inhouse verification procedure. PMID:25558939

  17. Quality Assurance for Alcohol, Drug Abuse, and Mental Health Services: An Annotated Bibliography.

    ERIC Educational Resources Information Center

    Towery, O. B.; And Others

    This is a comprehensive bibliography for all those in the alcohol, drug abuse and mental health fields who are developing and implementing programs for assuring quality in the services they provide. A major problem is the newness of the language and the unfamilarity with procedures required by the government and others seeking accountability from…

  18. Integrated metabolomic and transcriptome analyses reveal finishing forage affects metabolic pathways related to beef quality and animal welfare

    PubMed Central

    Carrillo, José A.; He, Yanghua; Li, Yaokun; Liu, Jianan; Erdman, Richard A.; Sonstegard, Tad S.; Song, Jiuzhou

    2016-01-01

    Beef represents a major dietary component and source of protein in many countries. With an increasing demand for beef, the industry is currently undergoing changes towards naturally produced beef. However, the true differences between the feeding systems, especially the biochemical and nutritional aspects, are still unclear. Using transcriptome and metabolome profiles, we identified biological pathways related to the differences between grass- and grain-fed Angus steers. In the latissimus dorsi muscle, we have recognized 241 differentially expressed genes (FDR < 0.1). The metabolome examinations of muscle and blood revealed 163 and 179 altered compounds in each tissue (P < 0.05), respectively. Accordingly, alterations in glucose metabolism, divergences in free fatty acids and carnitine conjugated lipid levels, and altered β-oxidation have been observed. The anti-inflammatory n3 polyunsaturated fatty acids are enriched in grass finished beef, while higher levels of n6 PUFAs in grain finished animals may promote inflammation and oxidative stress. Furthermore, grass-fed animals produce tender beef with lower total fat and a higher omega3/omega6 ratio than grain-fed ones, which could potentially benefit consumer health. Most importantly, blood cortisol levels strongly indicate that grass-fed animals may experience less stress than the grain-fed individuals. These results will provide deeper insights into the merits and mechanisms of muscle development. PMID:27185157

  19. Integrated metabolomic and transcriptome analyses reveal finishing forage affects metabolic pathways related to beef quality and animal welfare.

    PubMed

    Carrillo, José A; He, Yanghua; Li, Yaokun; Liu, Jianan; Erdman, Richard A; Sonstegard, Tad S; Song, Jiuzhou

    2016-01-01

    Beef represents a major dietary component and source of protein in many countries. With an increasing demand for beef, the industry is currently undergoing changes towards naturally produced beef. However, the true differences between the feeding systems, especially the biochemical and nutritional aspects, are still unclear. Using transcriptome and metabolome profiles, we identified biological pathways related to the differences between grass- and grain-fed Angus steers. In the latissimus dorsi muscle, we have recognized 241 differentially expressed genes (FDR < 0.1). The metabolome examinations of muscle and blood revealed 163 and 179 altered compounds in each tissue (P < 0.05), respectively. Accordingly, alterations in glucose metabolism, divergences in free fatty acids and carnitine conjugated lipid levels, and altered β-oxidation have been observed. The anti-inflammatory n3 polyunsaturated fatty acids are enriched in grass finished beef, while higher levels of n6 PUFAs in grain finished animals may promote inflammation and oxidative stress. Furthermore, grass-fed animals produce tender beef with lower total fat and a higher omega3/omega6 ratio than grain-fed ones, which could potentially benefit consumer health. Most importantly, blood cortisol levels strongly indicate that grass-fed animals may experience less stress than the grain-fed individuals. These results will provide deeper insights into the merits and mechanisms of muscle development. PMID:27185157

  20. Evaluating plant immunity using mass spectrometry-based metabolomics workflows

    PubMed Central

    Heuberger, Adam L.; Robison, Faith M.; Lyons, Sarah Marie A.; Broeckling, Corey D.; Prenni, Jessica E.

    2014-01-01

    Metabolic processes in plants are key components of physiological and biochemical disease resistance. Metabolomics, the analysis of a broad range of small molecule compounds in a biological system, has been used to provide a systems-wide overview of plant metabolism associated with defense responses. Plant immunity has been examined using multiple metabolomics workflows that vary in methods of detection, annotation, and interpretation, and the choice of workflow can significantly impact the conclusions inferred from a metabolomics investigation. The broad range of metabolites involved in plant defense often requires multiple chemical detection platforms and implementation of a non-targeted approach. A review of the current literature reveals a wide range of workflows that are currently used in plant metabolomics, and new methods for analyzing and reporting mass spectrometry (MS) data can improve the ability to translate investigative findings among different plant-pathogen systems. PMID:25009545

  1. Genome Annotation and Curation Using MAKER and MAKER-P

    PubMed Central

    Campbell, Michael S.; Holt, Carson; Moore, Barry; Yandell, Mark

    2014-01-01

    This unit describes how to use the genome annotation and curation tools MAKER and MAKER-P to annotate protein coding and non-coding RNA genes in newly assembled genomes, update/combine legacy annotations in light of new evidence, add quality metrics to annotations from other pipelines, and map existing annotations to a new assembly. MAKER and MAKER-P can rapidly annotate genomes of any size, and scale to match available computational resources. PMID:25501943

  2. MAKER-P: a tool-kit for the creation, management, and quality control of plant genome annotations

    Technology Transfer Automated Retrieval System (TEKTRAN)

    We have optimized and extended the widely used annotation-engine MAKER for use on plant genomes. We have benchmarked the resulting software, MAKER-P, using the A. thaliana genome and the TAIR10 gene models. Here we demonstrate the ability of the MAKER-P toolkit to generate de novo repeat databases, ...

  3. HMDB: a knowledgebase for the human metabolome.

    PubMed

    Wishart, David S; Knox, Craig; Guo, An Chi; Eisner, Roman; Young, Nelson; Gautam, Bijaya; Hau, David D; Psychogios, Nick; Dong, Edison; Bouatra, Souhaila; Mandal, Rupasri; Sinelnikov, Igor; Xia, Jianguo; Jia, Leslie; Cruz, Joseph A; Lim, Emilia; Sobsey, Constance A; Shrivastava, Savita; Huang, Paul; Liu, Philip; Fang, Lydia; Peng, Jun; Fradette, Ryan; Cheng, Dean; Tzur, Dan; Clements, Melisa; Lewis, Avalyn; De Souza, Andrea; Zuniga, Azaret; Dawe, Margot; Xiong, Yeping; Clive, Derrick; Greiner, Russ; Nazyrova, Alsu; Shaykhutdinov, Rustem; Li, Liang; Vogel, Hans J; Forsythe, Ian

    2009-01-01

    The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. Since its first release in 2007, the HMDB has been used to facilitate the research for nearly 100 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 2.0) has been significantly expanded and enhanced over the previous release (version 1.0). In particular, the number of fully annotated metabolite entries has grown from 2180 to more than 6800 (a 300% increase), while the number of metabolites with biofluid or tissue concentration data has grown by a factor of five (from 883 to 4413). Similarly, the number of purified compounds with reference to NMR, LC-MS and GC-MS spectra has more than doubled (from 380 to more than 790 compounds). In addition to this significant expansion in database size, many new database searching tools and new data content has been added or enhanced. These include better algorithms for spectral searching and matching, more powerful chemical substructure searches, faster text searching software, as well as dedicated pathway searching tools and customized, clickable metabolic maps. Changes to the user-interface have also been implemented to accommodate future expansion and to make database navigation much easier. These improvements should make the HMDB much more useful to a much wider community of users. PMID:18953024

  4. HMDB: a knowledgebase for the human metabolome

    PubMed Central

    Wishart, David S.; Knox, Craig; Guo, An Chi; Eisner, Roman; Young, Nelson; Gautam, Bijaya; Hau, David D.; Psychogios, Nick; Dong, Edison; Bouatra, Souhaila; Mandal, Rupasri; Sinelnikov, Igor; Xia, Jianguo; Jia, Leslie; Cruz, Joseph A.; Lim, Emilia; Sobsey, Constance A.; Shrivastava, Savita; Huang, Paul; Liu, Philip; Fang, Lydia; Peng, Jun; Fradette, Ryan; Cheng, Dean; Tzur, Dan; Clements, Melisa; Lewis, Avalyn; De Souza, Andrea; Zuniga, Azaret; Dawe, Margot; Xiong, Yeping; Clive, Derrick; Greiner, Russ; Nazyrova, Alsu; Shaykhutdinov, Rustem; Li, Liang; Vogel, Hans J.; Forsythe, Ian

    2009-01-01

    The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. Since its first release in 2007, the HMDB has been used to facilitate the research for nearly 100 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 2.0) has been significantly expanded and enhanced over the previous release (version 1.0). In particular, the number of fully annotated metabolite entries has grown from 2180 to more than 6800 (a 300% increase), while the number of metabolites with biofluid or tissue concentration data has grown by a factor of five (from 883 to 4413). Similarly, the number of purified compounds with reference to NMR, LC-MS and GC-MS spectra has more than doubled (from 380 to more than 790 compounds). In addition to this significant expansion in database size, many new database searching tools and new data content has been added or enhanced. These include better algorithms for spectral searching and matching, more powerful chemical substructure searches, faster text searching software, as well as dedicated pathway searching tools and customized, clickable metabolic maps. Changes to the user-interface have also been implemented to accommodate future expansion and to make database navigation much easier. These improvements should make the HMDB much more useful to a much wider community of users. PMID:18953024

  5. Metabolomics and Epidemiology Working Group

    Cancer.gov

    The Metabolomics and Epidemiology (MetEpi) Working Group promotes metabolomics analyses in population-based studies, as well as advancement in the field of metabolomics for broader biomedical and public health research.

  6. Gene Ontology annotations and resources.

    PubMed

    Blake, J A; Dolan, M; Drabkin, H; Hill, D P; Li, Ni; Sitnikov, D; Bridges, S; Burgess, S; Buza, T; McCarthy, F; Peddinti, D; Pillai, L; Carbon, S; Dietze, H; Ireland, A; Lewis, S E; Mungall, C J; Gaudet, P; Chrisholm, R L; Fey, P; Kibbe, W A; Basu, S; Siegele, D A; McIntosh, B K; Renfro, D P; Zweifel, A E; Hu, J C; Brown, N H; Tweedie, S; Alam-Faruque, Y; Apweiler, R; Auchinchloss, A; Axelsen, K; Bely, B; Blatter, M -C; Bonilla, C; Bouguerleret, L; Boutet, E; Breuza, L; Bridge, A; Chan, W M; Chavali, G; Coudert, E; Dimmer, E; Estreicher, A; Famiglietti, L; Feuermann, M; Gos, A; Gruaz-Gumowski, N; Hieta, R; Hinz, C; Hulo, C; Huntley, R; James, J; Jungo, F; Keller, G; Laiho, K; Legge, D; Lemercier, P; Lieberherr, D; Magrane, M; Martin, M J; Masson, P; Mutowo-Muellenet, P; O'Donovan, C; Pedruzzi, I; Pichler, K; Poggioli, D; Porras Millán, P; Poux, S; Rivoire, C; Roechert, B; Sawford, T; Schneider, M; Stutz, A; Sundaram, S; Tognolli, M; Xenarios, I; Foulgar, R; Lomax, J; Roncaglia, P; Khodiyar, V K; Lovering, R C; Talmud, P J; Chibucos, M; Giglio, M Gwinn; Chang, H -Y; Hunter, S; McAnulla, C; Mitchell, A; Sangrador, A; Stephan, R; Harris, M A; Oliver, S G; Rutherford, K; Wood, V; Bahler, J; Lock, A; Kersey, P J; McDowall, D M; Staines, D M; Dwinell, M; Shimoyama, M; Laulederkind, S; Hayman, T; Wang, S -J; Petri, V; Lowry, T; D'Eustachio, P; Matthews, L; Balakrishnan, R; Binkley, G; Cherry, J M; Costanzo, M C; Dwight, S S; Engel, S R; Fisk, D G; Hitz, B C; Hong, E L; Karra, K; Miyasato, S R; Nash, R S; Park, J; Skrzypek, M S; Weng, S; Wong, E D; Berardini, T Z; Huala, E; Mi, H; Thomas, P D; Chan, J; Kishore, R; Sternberg, P; Van Auken, K; Howe, D; Westerfield, M

    2013-01-01

    The Gene Ontology (GO) Consortium (GOC, http://www.geneontology.org) is a community-based bioinformatics resource that classifies gene product function through the use of structured, controlled vocabularies. Over the past year, the GOC has implemented several processes to increase the quantity, quality and specificity of GO annotations. First, the number of manual, literature-based annotations has grown at an increasing rate. Second, as a result of a new 'phylogenetic annotation' process, manually reviewed, homology-based annotations are becoming available for a broad range of species. Third, the quality of GO annotations has been improved through a streamlined process for, and automated quality checks of, GO annotations deposited by different annotation groups. Fourth, the consistency and correctness of the ontology itself has increased by using automated reasoning tools. Finally, the GO has been expanded not only to cover new areas of biology through focused interaction with experts, but also to capture greater specificity in all areas of the ontology using tools for adding new combinatorial terms. The GOC works closely with other ontology developers to support integrated use of terminologies. The GOC supports its user community through the use of e-mail lists, social media and web-based resources. PMID:23161678

  7. GC/MS based metabolomics: development of a data mining system for metabolite identification by using soft independent modeling of class analogy (SIMCA)

    PubMed Central

    2011-01-01

    Background The goal of metabolomics analyses is a comprehensive and systematic understanding of all metabolites in biological samples. Many useful platforms have been developed to achieve this goal. Gas chromatography coupled to mass spectrometry (GC/MS) is a well-established analytical method in metabolomics study, and 200 to 500 peaks are routinely observed with one biological sample. However, only ~100 metabolites can be identified, and the remaining peaks are left as "unknowns". Result We present an algorithm that acquires more extensive metabolite information. Pearson's product-moment correlation coefficient and the Soft Independent Modeling of Class Analogy (SIMCA) method were combined to automatically identify and annotate unknown peaks, which tend to be missed in routine studies that employ manual processing. Conclusions Our data mining system can offer a wealth of metabolite information quickly and easily, and it provides new insights, particularly into food quality evaluation and prediction. PMID:21542920

  8. Training in metabolomics research. I. Designing the experiment, collecting and extracting samples and generating metabolomics data.

    PubMed

    Barnes, Stephen; Benton, H Paul; Casazza, Krista; Cooper, Sara J; Cui, Xiangqin; Du, Xiuxia; Engler, Jeffrey; Kabarowski, Janusz H; Li, Shuzhao; Pathmasiri, Wimal; Prasain, Jeevan K; Renfrow, Matthew B; Tiwari, Hemant K

    2016-07-01

    The study of metabolism has had a long history. Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. The National Institutes of Health Common Fund Metabolomics Program was established in 2012 to stimulate interest in the approaches and technologies of metabolomics. To deliver one of the program's goals, the University of Alabama at Birmingham has hosted an annual 4-day short course in metabolomics for faculty, postdoctoral fellows and graduate students from national and international institutions. This paper is the first part of a summary of the training materials presented in the course to be used as a resource for all those embarking on metabolomics research. The complete set of training materials including slide sets and videos can be viewed at http://www.uab.edu/proteomics/metabolomics/workshop/workshop_june_2015.php. Copyright © 2016 John Wiley & Sons, Ltd. PMID:27434804

  9. Cancer Metabolomics and the Human Metabolome Database

    PubMed Central

    Wishart, David S.; Mandal, Rupasri; Stanislaus, Avalyn; Ramirez-Gaona, Miguel

    2016-01-01

    The application of metabolomics towards cancer research has led to a renewed appreciation of metabolism in cancer development and progression. It has also led to the discovery of metabolite cancer biomarkers and the identification of a number of novel cancer causing metabolites. The rapid growth of metabolomics in cancer research is also leading to challenges. In particular, with so many cancer-associate metabolites being identified, it is often difficult to keep track of which compounds are associated with which cancers. It is also challenging to track down information on the specific pathways that particular metabolites, drugs or drug metabolites may be affecting. Even more frustrating are the difficulties associated with identifying metabolites from NMR or MS spectra. Fortunately, a number of metabolomics databases are emerging that are designed to address these challenges. One such database is the Human Metabolome Database (HMDB). The HMDB is currently the world’s largest and most comprehensive, organism-specific metabolomics database. It contains more than 40,000 metabolite entries, thousands of metabolite concentrations, >700 metabolic and disease-associated pathways, as well as information on dozens of cancer biomarkers. This review is intended to provide a brief summary of the HMDB and to offer some guidance on how it can be used in metabolomic studies of cancer. PMID:26950159

  10. ALLocator: An Interactive Web Platform for the Analysis of Metabolomic LC-ESI-MS Datasets, Enabling Semi-Automated, User-Revised Compound Annotation and Mass Isotopomer Ratio Analysis

    PubMed Central

    Persicke, Marcus; Albaum, Stefan P.; Kalinowski, Jörn; Goesmann, Alexander; Niehaus, Karsten; Nattkemper, Tim W.

    2014-01-01

    Adduct formation, fragmentation events and matrix effects impose special challenges to the identification and quantitation of metabolites in LC-ESI-MS datasets. An important step in compound identification is the deconvolution of mass signals. During this processing step, peaks representing adducts, fragments, and isotopologues of the same analyte are allocated to a distinct group, in order to separate peaks from coeluting compounds. From these peak groups, neutral masses and pseudo spectra are derived and used for metabolite identification via mass decomposition and database matching. Quantitation of metabolites is hampered by matrix effects and nonlinear responses in LC-ESI-MS measurements. A common approach to correct for these effects is the addition of a U-13C-labeled internal standard and the calculation of mass isotopomer ratios for each metabolite. Here we present a new web-platform for the analysis of LC-ESI-MS experiments. ALLocator covers the workflow from raw data processing to metabolite identification and mass isotopomer ratio analysis. The integrated processing pipeline for spectra deconvolution “ALLocatorSD” generates pseudo spectra and automatically identifies peaks emerging from the U-13C-labeled internal standard. Information from the latter improves mass decomposition and annotation of neutral losses. ALLocator provides an interactive and dynamic interface to explore and enhance the results in depth. Pseudo spectra of identified metabolites can be stored in user- and method-specific reference lists that can be applied on succeeding datasets. The potential of the software is exemplified in an experiment, in which abundance fold-changes of metabolites of the l-arginine biosynthesis in C. glutamicum type strain ATCC 13032 and l-arginine producing strain ATCC 21831 are compared. Furthermore, the capability for detection and annotation of uncommon large neutral losses is shown by the identification of (γ-)glutamyl dipeptides in the same strains

  11. Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models

    DOE PAGESBeta

    Benedict, Matthew N.; Mundy, Michael B.; Henry, Christopher S.; Chia, Nicholas; Price, Nathan D.; Maranas, Costas D.

    2014-10-16

    Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genesmore » and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary

  12. Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models

    SciTech Connect

    Benedict, Matthew N.; Mundy, Michael B.; Henry, Christopher S.; Chia, Nicholas; Price, Nathan D.; Maranas, Costas D.

    2014-10-16

    Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to

  13. Gene Ontology Annotations and Resources

    PubMed Central

    2013-01-01

    The Gene Ontology (GO) Consortium (GOC, http://www.geneontology.org) is a community-based bioinformatics resource that classifies gene product function through the use of structured, controlled vocabularies. Over the past year, the GOC has implemented several processes to increase the quantity, quality and specificity of GO annotations. First, the number of manual, literature-based annotations has grown at an increasing rate. Second, as a result of a new ‘phylogenetic annotation’ process, manually reviewed, homology-based annotations are becoming available for a broad range of species. Third, the quality of GO annotations has been improved through a streamlined process for, and automated quality checks of, GO annotations deposited by different annotation groups. Fourth, the consistency and correctness of the ontology itself has increased by using automated reasoning tools. Finally, the GO has been expanded not only to cover new areas of biology through focused interaction with experts, but also to capture greater specificity in all areas of the ontology using tools for adding new combinatorial terms. The GOC works closely with other ontology developers to support integrated use of terminologies. The GOC supports its user community through the use of e-mail lists, social media and web-based resources. PMID:23161678

  14. MetaDB a Data Processing Workflow in Untargeted MS-Based Metabolomics Experiments.

    PubMed

    Franceschi, Pietro; Mylonas, Roman; Shahaf, Nir; Scholz, Matthias; Arapitsas, Panagiotis; Masuero, Domenico; Weingart, Georg; Carlin, Silvia; Vrhovsek, Urska; Mattivi, Fulvio; Wehrens, Ron

    2014-01-01

    Due to their sensitivity and speed, mass-spectrometry based analytical technologies are widely used to in metabolomics to characterize biological phenomena. To address issues like metadata organization, quality assessment, data processing, data storage, and, finally, submission to public repositories, bioinformatic pipelines of a non-interactive nature are often employed, complementing the interactive software used for initial inspection and visualization of the data. These pipelines often are created as open-source software allowing the complete and exhaustive documentation of each step, ensuring the reproducibility of the analysis of extensive and often expensive experiments. In this paper, we will review the major steps which constitute such a data processing pipeline, discussing them in the context of an open-source software for untargeted MS-based metabolomics experiments recently developed at our institute. The software has been developed by integrating our metaMS R package with a user-friendly web-based application written in Grails. MetaMS takes care of data pre-processing and annotation, while the interface deals with the creation of the sample lists, the organization of the data storage, and the generation of survey plots for quality assessment. Experimental and biological metadata are stored in the ISA-Tab format making the proposed pipeline fully integrated with the Metabolights framework. PMID:25566535

  15. MetaDB a Data Processing Workflow in Untargeted MS-Based Metabolomics Experiments

    PubMed Central

    Franceschi, Pietro; Mylonas, Roman; Shahaf, Nir; Scholz, Matthias; Arapitsas, Panagiotis; Masuero, Domenico; Weingart, Georg; Carlin, Silvia; Vrhovsek, Urska; Mattivi, Fulvio; Wehrens, Ron

    2014-01-01

    Due to their sensitivity and speed, mass-spectrometry based analytical technologies are widely used to in metabolomics to characterize biological phenomena. To address issues like metadata organization, quality assessment, data processing, data storage, and, finally, submission to public repositories, bioinformatic pipelines of a non-interactive nature are often employed, complementing the interactive software used for initial inspection and visualization of the data. These pipelines often are created as open-source software allowing the complete and exhaustive documentation of each step, ensuring the reproducibility of the analysis of extensive and often expensive experiments. In this paper, we will review the major steps which constitute such a data processing pipeline, discussing them in the context of an open-source software for untargeted MS-based metabolomics experiments recently developed at our institute. The software has been developed by integrating our metaMS R package with a user-friendly web-based application written in Grails. MetaMS takes care of data pre-processing and annotation, while the interface deals with the creation of the sample lists, the organization of the data storage, and the generation of survey plots for quality assessment. Experimental and biological metadata are stored in the ISA-Tab format making the proposed pipeline fully integrated with the Metabolights framework. PMID:25566535

  16. Training in metabolomics research. I. Designing the experiment, collecting and extracting samples and generating metabolomics data

    PubMed Central

    Barnes, Stephen; Benton, H. Paul; Casazza, Krista; Cooper, Sara J.; Cui, Xiangqin; Du, Xiuxia; Engler, Jeffrey; Kabarowski, Janusz H.; Li, Shuzhao; Pathmasiri, Wimal; Prasain, Jeevan K.; Renfrow, Matthew B.; Tiwari, Hemant K.

    2016-01-01

    The study of metabolism has had a long history. Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. The National Institutes of Health Common Fund Metabolomics Program was established in 2012 to stimulate interest in the approaches and technologies of metabolomics. To deliver one of the program’s goals, the University of Alabama at Birmingham has hosted an annual 4-day short course in metabolomics for faculty, postdoctoral fellows and graduate students from national and international institutions. This paper is the first part of a summary of the training materials presented in the course to be used as a resource for all those embarking on metabolomics research. PMID:27434804

  17. The Human Serum Metabolome

    PubMed Central

    Psychogios, Nikolaos; Hau, David D.; Peng, Jun; Guo, An Chi; Mandal, Rupasri; Bouatra, Souhaila; Sinelnikov, Igor; Krishnamurthy, Ramanarayan; Eisner, Roman; Gautam, Bijaya; Young, Nelson; Xia, Jianguo; Knox, Craig; Dong, Edison; Huang, Paul; Hollander, Zsuzsanna; Pedersen, Theresa L.; Smith, Steven R.; Bamforth, Fiona; Greiner, Russ; McManus, Bruce; Newman, John W.; Goodfriend, Theodore; Wishart, David S.

    2011-01-01

    Continuing improvements in analytical technology along with an increased interest in performing comprehensive, quantitative metabolic profiling, is leading to increased interest pressures within the metabolomics community to develop centralized metabolite reference resources for certain clinically important biofluids, such as cerebrospinal fluid, urine and blood. As part of an ongoing effort to systematically characterize the human metabolome through the Human Metabolome Project, we have undertaken the task of characterizing the human serum metabolome. In doing so, we have combined targeted and non-targeted NMR, GC-MS and LC-MS methods with computer-aided literature mining to identify and quantify a comprehensive, if not absolutely complete, set of metabolites commonly detected and quantified (with today's technology) in the human serum metabolome. Our use of multiple metabolomics platforms and technologies allowed us to substantially enhance the level of metabolome coverage while critically assessing the relative strengths and weaknesses of these platforms or technologies. Tables containing the complete set of 4229 confirmed and highly probable human serum compounds, their concentrations, related literature references and links to their known disease associations are freely available at http://www.serummetabolome.ca. PMID:21359215

  18. TriAnnot: A Versatile and High Performance Pipeline for the Automated Annotation of Plant Genomes.

    PubMed

    Leroy, Philippe; Guilhot, Nicolas; Sakai, Hiroaki; Bernard, Aurélien; Choulet, Frédéric; Theil, Sébastien; Reboux, Sébastien; Amano, Naoki; Flutre, Timothée; Pelegrin, Céline; Ohyanagi, Hajime; Seidel, Michael; Giacomoni, Franck; Reichstadt, Mathieu; Alaux, Michael; Gicquello, Emmanuelle; Legeai, Fabrice; Cerutti, Lorenzo; Numa, Hisataka; Tanaka, Tsuyoshi; Mayer, Klaus; Itoh, Takeshi; Quesneville, Hadi; Feuillet, Catherine

    2012-01-01

    In support of the international effort to obtain a reference sequence of the bread wheat genome and to provide plant communities dealing with large and complex genomes with a versatile, easy-to-use online automated tool for annotation, we have developed the TriAnnot pipeline. Its modular architecture allows for the annotation and masking of transposable elements, the structural, and functional annotation of protein-coding genes with an evidence-based quality indexing, and the identification of conserved non-coding sequences and molecular markers. The TriAnnot pipeline is parallelized on a 712 CPU computing cluster that can run a 1-Gb sequence annotation in less than 5 days. It is accessible through a web interface for small scale analyses or through a server for large scale annotations. The performance of TriAnnot was evaluated in terms of sensitivity, specificity, and general fitness using curated reference sequence sets from rice and wheat. In less than 8 h, TriAnnot was able to predict more than 83% of the 3,748 CDS from rice chromosome 1 with a fitness of 67.4%. On a set of 12 reference Mb-sized contigs from wheat chromosome 3B, TriAnnot predicted and annotated 93.3% of the genes among which 54% were perfectly identified in accordance with the reference annotation. It also allowed the curation of 12 genes based on new biological evidences, increasing the percentage of perfect gene prediction to 63%. TriAnnot systematically showed a higher fitness than other annotation pipelines that are not improved for wheat. As it is easily adaptable to the annotation of other plant genomes, TriAnnot should become a useful resource for the annotation of large and complex genomes in the future. PMID:22645565

  19. Discovering Regulated Metabolite Families in Untargeted Metabolomics Studies.

    PubMed

    Treutler, Hendrik; Tsugawa, Hiroshi; Porzel, Andrea; Gorzolka, Karin; Tissier, Alain; Neumann, Steffen; Balcke, Gerd Ulrich

    2016-08-16

    The identification of metabolites by mass spectrometry constitutes a major bottleneck which considerably limits the throughput of metabolomics studies in biomedical or plant research. Here, we present a novel approach to analyze metabolomics data from untargeted, data-independent LC-MS/MS measurements. By integrated analysis of MS(1) abundances and MS/MS spectra, the identification of regulated metabolite families is achieved. This approach offers a global view on metabolic regulation in comparative metabolomics. We implemented our approach in the web application "MetFamily", which is freely available at http://msbi.ipb-halle.de/MetFamily/ . MetFamily provides a dynamic link between the patterns based on MS(1)-signal intensity and the corresponding structural similarity at the MS/MS level. Structurally related metabolites are annotated as metabolite families based on a hierarchical cluster analysis of measured MS/MS spectra. Joint examination with principal component analysis of MS(1) patterns, where this annotation is preserved in the loadings, facilitates the interpretation of comparative metabolomics data at the level of metabolite families. As a proof of concept, we identified two trichome-specific metabolite families from wild-type tomato Solanum habrochaites LA1777 in a fully unsupervised manner and validated our findings based on earlier publications and with NMR. PMID:27452369

  20. Metabolomics in multiple sclerosis.

    PubMed

    Bhargava, Pavan; Calabresi, Peter A

    2016-04-01

    Multiple sclerosis (MS) is a chronic demyelinating disorder of the central nervous system with inflammatory and degenerative components. The cause of MS remains unknown although genetic and environmental factors appear to play a role in its etiopathogenesis. Metabolomics is a new "omics" technology that aims at measuring small molecules in various biological matrices and can provide information that is not readily obtained from genomics, transcriptomics, or proteomics. Currently, several different analytical platforms exist for metabolomics, and both untargeted and targeted approaches are being employed. Methods of analysis of metabolomics data are also being developed and no consensus currently exists on the optimal approach to analysis and interpretation of these data. Metabolomics has the potential to provide putative biomarkers, insights into the pathophysiology of the disease, and to aid in precision medicine for patients with MS. PMID:26754801

  1. Metabolomics in dyslipidemia.

    PubMed

    Chen, Hua; Miao, Hua; Feng, Ya-Long; Zhao, Ying-Yong; Lin, Rui-Chao

    2014-01-01

    Hyperlipidemia is an important public health problem with increased incidence and prevalence worldwide. Current clinical biomarkers, triglyceride, total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol lack the necessary specificity and sensitivity and only increase significantly after serious dyslipidemia. Therefore, sensitive biomarkers are needed for hyperlipidemia. Hyperlipidemia-specific biomarkers would improve clinical diagnosis and therapeutic treatment at early disease stages. The aim of metabolomics is to identify untargeted and global small-molecule metabolite profiles from cells, biofluids, and tissues. This method offers the potential for a holistic approach to improve disease diagnoses and our understanding of underlying pathologic mechanisms. This review summarizes analytical techniques, data collection and analysis for metabolomics, and metabolomics in hyperlipidemia animal models and clinical studies. Mechanisms of hypolipemia and antilipemic drug therapy are also discussed. Metabolomics provides a new opportunity to gain insight into metabolic profiling and pathophysiologic mechanisms of hyperlipidemia. PMID:25344987

  2. Multivariate Analysis in Metabolomics

    PubMed Central

    Worley, Bradley; Powers, Robert

    2015-01-01

    Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis (PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions. PMID:26078916

  3. The human serum metabolome

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Continuing improvements in analytical technology along with an increased interest in performing comprehensive, quantitative metabolic profiling, is leading to increased interest pressures within the metabolomics community to develop centralized metabolite reference resources for certain clinically i...

  4. Enhanced Acylcarnitine Annotation in High-Resolution Mass Spectrometry Data: Fragmentation Analysis for the Classification and Annotation of Acylcarnitines

    PubMed Central

    van der Hooft, Justin J. J.; Ridder, Lars; Barrett, Michael P.; Burgess, Karl E. V.

    2015-01-01

    Metabolite annotation and identification are primary challenges in untargeted metabolomics experiments. Rigorous workflows for reliable annotation of mass features with chemical structures or compound classes are needed to enhance the power of untargeted mass spectrometry. High-resolution mass spectrometry considerably improves the confidence in assigning elemental formulas to mass features in comparison to nominal mass spectrometry, and embedding of fragmentation methods enables more reliable metabolite annotations and facilitates metabolite classification. However, the analysis of mass fragmentation spectra can be a time-consuming step and requires expert knowledge. This study demonstrates how characteristic fragmentations, specific to compound classes, can be used to systematically analyze their presence in complex biological extracts like urine that have undergone untargeted mass spectrometry combined with data dependent or targeted fragmentation. Human urine extracts were analyzed using normal phase liquid chromatography (hydrophilic interaction chromatography) coupled to an Ion Trap-Orbitrap hybrid instrument. Subsequently, mass chromatograms and collision-induced dissociation and higher-energy collisional dissociation (HCD) fragments were annotated using the freely available MAGMa software1. Acylcarnitines play a central role in energy metabolism by transporting fatty acids into the mitochondrial matrix. By filtering on a combination of a mass fragment and neutral loss designed based on the MAGMa fragment annotations, we were able to classify and annotate 50 acylcarnitines in human urine extracts, based on high-resolution mass spectrometry HCD fragmentation spectra at different energies for all of them. Of these annotated acylcarnitines, 31 are not described in HMDB yet and for only 4 annotated acylcarnitines the fragmentation spectra could be matched to reference spectra. Therefore, we conclude that the use of mass fragmentation filters within the context

  5. Quality classification of Spanish olive oils by untargeted gas chromatography coupled to hybrid quadrupole-time of flight mass spectrometry with atmospheric pressure chemical ionization and metabolomics-based statistical approach.

    PubMed

    Sales, C; Cervera, M I; Gil, R; Portolés, T; Pitarch, E; Beltran, J

    2017-02-01

    The novel atmospheric pressure chemical ionization (APCI) source has been used in combination with gas chromatography (GC) coupled to hybrid quadrupole time-of-flight (QTOF) mass spectrometry (MS) for determination of volatile components of olive oil, enhancing its potential for classification of olive oil samples according to their quality using a metabolomics-based approach. The full-spectrum acquisition has allowed the detection of volatile organic compounds (VOCs) in olive oil samples, including Extra Virgin, Virgin and Lampante qualities. A dynamic headspace extraction with cartridge solvent elution was applied. The metabolomics strategy consisted of three different steps: a full mass spectral alignment of GC-MS data using MzMine 2.0, a multivariate analysis using Ez-Info and the creation of the statistical model with combinations of responses for molecular fragments. The model was finally validated using blind samples, obtaining an accuracy in oil classification of 70%, taking the official established method, "PANEL TEST", as reference. PMID:27596432

  6. COnsortium of METabolomics Studies (COMETS)

    Cancer.gov

    The COnsortium of METabolomics Studies (COMETS) is an extramural-intramural partnership that promotes collaboration among prospective cohort studies that follow participants for a range of outcomes and perform metabolomic profiling of individuals.

  7. Metabolomics of forage plants: a review

    PubMed Central

    Rasmussen, Susanne; Parsons, Anthony J.; Jones, Christopher S.

    2012-01-01

    Background Forage plant breeding is under increasing pressure to deliver new cultivars with improved yield, quality and persistence to the pastoral industry. New innovations in DNA sequencing technologies mean that quantitative trait loci analysis and marker-assisted selection approaches are becoming faster and cheaper, and are increasingly used in the breeding process with the aim to speed it up and improve its precision. High-throughput phenotyping is currently a major bottle neck and emerging technologies such as metabolomics are being developed to bridge the gap between genotype and phenotype; metabolomics studies on forages are reviewed in this article. Scope Major challenges for pasture production arise from the reduced availability of resources, mainly water, nitrogen and phosphorus, and metabolomics studies on metabolic responses to these abiotic stresses in Lolium perenne and Lotus species will be discussed here. Many forage plants can be associated with symbiotic microorganisms such as legumes with nitrogen fixing rhizobia, grasses and legumes with phosphorus-solubilizing arbuscular mycorrhizal fungi, and cool temperate grasses with fungal anti-herbivorous alkaloid-producing Neotyphodium endophytes and metabolomics studies have shown that these associations can significantly affect the metabolic composition of forage plants. The combination of genetics and metabolomics, also known as genetical metabolomics can be a powerful tool to identify genetic regions related to specific metabolites or metabolic profiles, but this approach has not been widely adopted for forages yet, and we argue here that more studies are needed to improve our chances of success in forage breeding. Conclusions Metabolomics combined with other ‘-omics’ technologies and genome sequencing can be invaluable tools for large-scale geno- and phenotyping of breeding populations, although the implementation of these approaches in forage breeding programmes still lags behind. The majority

  8. Improving the quality of biomarker candidates in untargeted metabolomics via peak table-based alignment of comprehensive two-dimensional gas chromatography-mass spectrometry data.

    PubMed

    Bean, Heather D; Hill, Jane E; Dimandja, Jean-Marie D

    2015-05-15

    The potential of high-resolution analytical technologies like GC×GC/TOF MS in untargeted metabolomics and biomarker discovery has been limited by the development of fully automated software that can efficiently align and extract information from multiple chromatographic data sets. In this work we report the first investigation on a peak-by-peak basis of the chromatographic factors that impact GC×GC data alignment. A representative set of 16 compounds of different chromatographic characteristics were followed through the alignment of 63 GC×GC chromatograms. We found that varying the mass spectral match parameter had a significant influence on the alignment for poorly-resolved peaks, especially those at the extremes of the detector linear range, and no influence on well-chromatographed peaks. Therefore, optimized chromatography is required for proper GC×GC data alignment. Based on these observations, a workflow is presented for the conservative selection of biomarker candidates from untargeted metabolomics analyses. PMID:25857541

  9. Improving the quality of biomarker candidates in untargeted metabolomics via peak table-based alignment of comprehensive two-dimensional gas chromatography-mass spectrometry data

    PubMed Central

    Bean, Heather D.; Hill, Jane E.; Dimandja, Jean-Marie D.

    2015-01-01

    The potential of high-resolution analytical technologies like GC×GC/TOF MS in untargeted metabolomics and biomarker discovery has been limited by the development of fully automated software that can efficiently align and extract information from multiple chromatographic data sets. In this work we report the first investigation on a peak-by-peak basis of the chromatographic factors that impact GC×GC data alignment. A representative set of 16 compounds of different chromatographic characteristics were followed through the alignment of 63 GC×GC chromatograms. We found that varying the mass spectral match parameter had a significant influence on the alignment for poorly- resolved peaks, especially those at the extremes of the detector linear range, and no influence on well- chromatographed peaks. Therefore, optimized chromatography is required for proper GC×GC data alignment. Based on these observations, a workflow is presented for the conservative selection of biomarker candidates from untargeted metabolomics analyses. PMID:25857541

  10. Corpus annotation for mining biomedical events from literature

    PubMed Central

    Kim, Jin-Dong; Ohta, Tomoko; Tsujii, Jun'ichi

    2008-01-01

    Background Advanced Text Mining (TM) such as semantic enrichment of papers, event or relation extraction, and intelligent Question Answering have increasingly attracted attention in the bio-medical domain. For such attempts to succeed, text annotation from the biological point of view is indispensable. However, due to the complexity of the task, semantic annotation has never been tried on a large scale, apart from relatively simple term annotation. Results We have completed a new type of semantic annotation, event annotation, which is an addition to the existing annotations in the GENIA corpus. The corpus has already been annotated with POS (Parts of Speech), syntactic trees, terms, etc. The new annotation was made on half of the GENIA corpus, consisting of 1,000 Medline abstracts. It contains 9,372 sentences in which 36,114 events are identified. The major challenges during event annotation were (1) to design a scheme of annotation which meets specific requirements of text annotation, (2) to achieve biology-oriented annotation which reflect biologists' interpretation of text, and (3) to ensure the homogeneity of annotation quality across annotators. To meet these challenges, we introduced new concepts such as Single-facet Annotation and Semantic Typing, which have collectively contributed to successful completion of a large scale annotation. Conclusion The resulting event-annotated corpus is the largest and one of the best in quality among similar annotation efforts. We expect it to become a valuable resource for NLP (Natural Language Processing)-based TM in the bio-medical domain. PMID:18182099

  11. Siderophore biosynthesis coordinately modulated the virulence-associated interactive metabolome of uropathogenic Escherichia coli and human urine.

    PubMed

    Su, Qiao; Guan, Tianbing; Lv, Haitao

    2016-01-01

    Uropathogenic Escherichia coli (UPEC) growth in women's bladders during urinary tract infection (UTI) incurs substantial chemical exchange, termed the "interactive metabolome", which primarily accounts for the metabolic costs (utilized metabolome) and metabolic donations (excreted metabolome) between UPEC and human urine. Here, we attempted to identify the individualized interactive metabolome between UPEC and human urine. We were able to distinguish UPEC from non-UPEC by employing a combination of metabolomics and genetics. Our results revealed that the interactive metabolome between UPEC and human urine was markedly different from that between non-UPEC and human urine, and that UPEC triggered much stronger perturbations in the interactive metabolome in human urine. Furthermore, siderophore biosynthesis coordinately modulated the individualized interactive metabolome, which we found to be a critical component of UPEC virulence. The individualized virulence-associated interactive metabolome contained 31 different metabolites and 17 central metabolic pathways that were annotated to host these different metabolites, including energetic metabolism, amino acid metabolism, and gut microbe metabolism. Changes in the activities of these pathways mechanistically pinpointed the virulent capability of siderophore biosynthesis. Together, our findings provide novel insights into UPEC virulence, and we propose that siderophores are potential targets for further discovery of drugs to treat UPEC-induced UTI. PMID:27076285

  12. Metabolomics in Newborns.

    PubMed

    Noto, Antonio; Fanos, Vassilios; Dessì, Angelica

    2016-01-01

    Metabolomics is the quantitative analysis of a large number of low molecular weight metabolites that are intermediate or final products of all the metabolic pathways in a living organism. Any metabolic profiles detectable in a human biological fluid are caused by the interaction between gene expression and the environment. The metabolomics approach offers the possibility to identify variations in metabolite profile that can be used to discriminate disease. This is particularly important for neonatal and pediatric studies especially for severe ill patient diagnosis and early identification. This property is of a great clinical importance in view of the newer definitions of health and disease. This review emphasizes the workflow of a typical metabolomics study and summarizes the latest results obtained in neonatal studies with particular interest in prematurity, intrauterine growth retardation, inborn errors of metabolism, perinatal asphyxia, sepsis, necrotizing enterocolitis, kidney disease, bronchopulmonary dysplasia, and cardiac malformation and dysfunction. PMID:27117660

  13. Establishing Substantial Equivalence: Metabolomics

    NASA Astrophysics Data System (ADS)

    Beale, Michael H.; Ward, Jane L.; Baker, John M.

    Modern ‘metabolomic’ methods allow us to compare levels of many structurally diverse compounds in an automated fashion across a large number of samples. This technology is ideally suited to screening of populations of plants, including trials where the aim is the determination of unintended effects introduced by GM. A number of metabolomic methods have been devised for the determination of substantial equivalence. We have developed a methodology, using [1H]-NMR fingerprinting, for metabolomic screening of plants and have applied it to the study of substantial equivalence of field-grown GM wheat. We describe here the principles and detail of that protocol as applied to the analysis of flour generated from field plots of wheat. Particular emphasis is given to the downstream data processing and comparison of spectra by multivariate analysis, from which conclusions regarding metabolome changes due to the GM can be assessed against the background of natural variation due to environment.

  14. Metabolomic strategies for the identification of new enzyme functions and metabolic pathways.

    PubMed

    Prosser, Gareth A; Larrouy-Maumus, Gerald; de Carvalho, Luiz Pedro S

    2014-06-01

    Recent technological advances in accurate mass spectrometry and data analysis have revolutionized metabolomics experimentation. Activity-based and global metabolomic profiling methods allow simultaneous and rapid screening of hundreds of metabolites from a variety of chemical classes, making them useful tools for the discovery of novel enzymatic activities and metabolic pathways. By using the metabolome of the relevant organism or close species, these methods capitalize on biological relevance, avoiding the assignment of artificial and non-physiological functions. This review discusses state-of-the-art metabolomic approaches and highlights recent examples of their use for enzyme annotation, discovery of new metabolic pathways, and gene assignment of orphan metabolic activities across diverse biological sources. PMID:24829223

  15. Metabolomic strategies for the identification of new enzyme functions and metabolic pathways

    PubMed Central

    Prosser, Gareth A; Larrouy-Maumus, Gerald; de Carvalho, Luiz Pedro S

    2014-01-01

    Recent technological advances in accurate mass spectrometry and data analysis have revolutionized metabolomics experimentation. Activity-based and global metabolomic profiling methods allow simultaneous and rapid screening of hundreds of metabolites from a variety of chemical classes, making them useful tools for the discovery of novel enzymatic activities and metabolic pathways. By using the metabolome of the relevant organism or close species, these methods capitalize on biological relevance, avoiding the assignment of artificial and non-physiological functions. This review discusses state-of-the-art metabolomic approaches and highlights recent examples of their use for enzyme annotation, discovery of new metabolic pathways, and gene assignment of orphan metabolic activities across diverse biological sources. PMID:24829223

  16. Metabolomics and Renal Disease

    PubMed Central

    Rhee, Eugene P.

    2015-01-01

    Purpose of review This review summarizes recent metabolomics studies of renal disease, outlining some of the limitations of the literature to date. Recent findings The application of metabolomics in nephrology research has expanded from initial analyses of uremia to include both cross-sectional and longitudinal studies of earlier stages of kidney disease. Although these studies have nominated several potential markers of incident CKD and CKD progression, lack of overlap in metabolite coverage has limited the ability to synthesize results across groups. Further, direct examination of renal metabolite handling has underscored the substantial impact kidney function has on these potential markers (and many other circulating metabolites). In experimental studies, metabolomics has been used to identify a signature of decreased mitochondrial function in diabetic nephropathy and a preference for aerobic glucose metabolism in PKD; in each case, these studies have outlined novel therapeutic opportunities. Finally, as a complement to the longstanding interest in renal metabolite clearance, the microbiome has been increasingly recognized as the source of many plasma metabolites, including some with potential functional relevance to CKD and its complications. Summary The high-throughput, high-resolution phenotyping enabled by metabolomics technologies has begun to provide insight on renal disease in clinical, physiologic, and experimental contexts. PMID:26050125

  17. The Otter Annotation System

    PubMed Central

    Searle, Stephen M.J.; Gilbert, James; Iyer, Vivek; Clamp, Michele

    2004-01-01

    With the completion of the human genome sequence and genome sequence available for other vertebrate genomes, the task of manual annotation at the large genome scale has become a priority. Possibly even more important, is the requirement to curate and improve this annotation in the light of future data. For this to be possible, there is a need for tools to access and manage the annotation. Ensembl provides an excellent means for storing gene structures, genome features, and sequence, but it does not support the extra textual data necessary for manual annotation. We have extended Ensembl to create the Otter manual annotation system. This comprises a relational database schema for storing the manual annotation data, an application-programming interface (API) to access it, an extensible markup language (XML) format to allow transfer of the data, and a server to allow multiuser/multimachine access to the data. We have also written a data-adaptor plugin for the Apollo Browser/Editor to enable it to utilize an Otter server. The otter database is currently used by the Vertebrate Genome Annotation (VEGA) site (http://vega.sanger.ac.uk), which provides access to manually curated human chromosomes. Support is also being developed for using the AceDB annotation editor, FMap, via a perl wrapper called Lace. The Human and Vertebrate Annotation (HAVANA) group annotators at the Sanger center are using this to annotate human chromosomes 1 and 20. PMID:15123593

  18. Plant metabolomics: from holistic hope, to hype, to hot topic.

    PubMed

    Hall, Robert D

    2006-01-01

    In a short time, plant metabolomics has gone from being just an ambitious concept to being a rapidly growing, valuable technology applied in the stride to gain a more global picture of the molecular organization of multicellular organisms. The combination of improved analytical capabilities with newly designed, dedicated statistical, bioinformatics and data mining strategies, is beginning to broaden the horizons of our understanding of how plants are organized and how metabolism is both controlled but highly flexible. Metabolomics is predicted to play a significant, if not indispensable role in bridging the phenotype-genotype gap and thus in assisting us in our desire for full genome sequence annotation as part of the quest to link gene to function. Plants are a fabulously rich source of diverse functional biochemicals and metabolomics is also already proving valuable in an applied context. By creating unique opportunities for us to interrogate plant systems and characterize their biochemical composition, metabolomics will greatly assist in identifying and defining much of the still unexploited biodiversity available today. PMID:16411949

  19. Automated annotation of microbial proteomes in SWISS-PROT.

    PubMed

    Gattiker, Alexandre; Michoud, Karine; Rivoire, Catherine; Auchincloss, Andrea H; Coudert, Elisabeth; Lima, Tania; Kersey, Paul; Pagni, Marco; Sigrist, Christian J A; Lachaize, Corinne; Veuthey, Anne Lise; Gasteiger, Elisabeth; Bairoch, Amos

    2003-02-01

    Large-scale sequencing of prokaryotic genomes demands the automation of certain annotation tasks currently manually performed in the production of the SWISS-PROT protein knowledgebase. The HAMAP project, or 'High-quality Automated and Manual Annotation of microbial Proteomes', aims to integrate manual and automatic annotation methods in order to enhance the speed of the curation process while preserving the quality of the database annotation. Automatic annotation is only applied to entries that belong to manually defined orthologous families and to entries with no identifiable similarities (ORFans). Many checks are enforced in order to prevent the propagation of wrong annotation and to spot problematic cases, which are channelled to manual curation. The results of this annotation are integrated in SWISS-PROT, and a website is provided at http://www.expasy.org/sprot/hamap/. PMID:12798039

  20. Siderophore biosynthesis coordinately modulated the virulence-associated interactive metabolome of uropathogenic Escherichia coli and human urine

    PubMed Central

    Su, Qiao; Guan, Tianbing; Lv, Haitao

    2016-01-01

    Uropathogenic Escherichia coli (UPEC) growth in women’s bladders during urinary tract infection (UTI) incurs substantial chemical exchange, termed the “interactive metabolome”, which primarily accounts for the metabolic costs (utilized metabolome) and metabolic donations (excreted metabolome) between UPEC and human urine. Here, we attempted to identify the individualized interactive metabolome between UPEC and human urine. We were able to distinguish UPEC from non-UPEC by employing a combination of metabolomics and genetics. Our results revealed that the interactive metabolome between UPEC and human urine was markedly different from that between non-UPEC and human urine, and that UPEC triggered much stronger perturbations in the interactive metabolome in human urine. Furthermore, siderophore biosynthesis coordinately modulated the individualized interactive metabolome, which we found to be a critical component of UPEC virulence. The individualized virulence-associated interactive metabolome contained 31 different metabolites and 17 central metabolic pathways that were annotated to host these different metabolites, including energetic metabolism, amino acid metabolism, and gut microbe metabolism. Changes in the activities of these pathways mechanistically pinpointed the virulent capability of siderophore biosynthesis. Together, our findings provide novel insights into UPEC virulence, and we propose that siderophores are potential targets for further discovery of drugs to treat UPEC-induced UTI. PMID:27076285

  1. Concept annotation in the CRAFT corpus

    PubMed Central

    2012-01-01

    Background Manually annotated corpora are critical for the training and evaluation of automated methods to identify concepts in biomedical text. Results This paper presents the concept annotations of the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of 97 full-length, open-access biomedical journal articles that have been annotated both semantically and syntactically to serve as a research resource for the biomedical natural-language-processing (NLP) community. CRAFT identifies all mentions of nearly all concepts from nine prominent biomedical ontologies and terminologies: the Cell Type Ontology, the Chemical Entities of Biological Interest ontology, the NCBI Taxonomy, the Protein Ontology, the Sequence Ontology, the entries of the Entrez Gene database, and the three subontologies of the Gene Ontology. The first public release includes the annotations for 67 of the 97 articles, reserving two sets of 15 articles for future text-mining competitions (after which these too will be released). Concept annotations were created based on a single set of guidelines, which has enabled us to achieve consistently high interannotator agreement. Conclusions As the initial 67-article release contains more than 560,000 tokens (and the full set more than 790,000 tokens), our corpus is among the largest gold-standard annotated biomedical corpora. Unlike most others, the journal articles that comprise the corpus are drawn from diverse biomedical disciplines and are marked up in their entirety. Additionally, with a concept-annotation count of nearly 100,000 in the 67-article subset (and more than 140,000 in the full collection), the scale of conceptual markup is also among the largest of comparable corpora. The concept annotations of the CRAFT Corpus have the potential to significantly advance biomedical text mining by providing a high-quality gold standard for NLP systems. The corpus, annotation guidelines, and other associated resources are freely available at http

  2. Facilitating functional annotation of chicken microarray data

    PubMed Central

    2009-01-01

    Background Modeling results from chicken microarray studies is challenging for researchers due to little functional annotation associated with these arrays. The Affymetrix GenChip chicken genome array, one of the biggest arrays that serve as a key research tool for the study of chicken functional genomics, is among the few arrays that link gene products to Gene Ontology (GO). However the GO annotation data presented by Affymetrix is incomplete, for example, they do not show references linked to manually annotated functions. In addition, there is no tool that facilitates microarray researchers to directly retrieve functional annotations for their datasets from the annotated arrays. This costs researchers amount of time in searching multiple GO databases for functional information. Results We have improved the breadth of functional annotations of the gene products associated with probesets on the Affymetrix chicken genome array by 45% and the quality of annotation by 14%. We have also identified the most significant diseases and disorders, different types of genes, and known drug targets represented on Affymetrix chicken genome array. To facilitate functional annotation of other arrays and microarray experimental datasets we developed an Array GO Mapper (AGOM) tool to help researchers to quickly retrieve corresponding functional information for their dataset. Conclusion Results from this study will directly facilitate annotation of other chicken arrays and microarray experimental datasets. Researchers will be able to quickly model their microarray dataset into more reliable biological functional information by using AGOM tool. The disease, disorders, gene types and drug targets revealed in the study will allow researchers to learn more about how genes function in complex biological systems and may lead to new drug discovery and development of therapies. The GO annotation data generated will be available for public use via AgBase website and will be updated on regular

  3. Computing human image annotation.

    PubMed

    Channin, David S; Mongkolwat, Pattanasak; Kleper, Vladimir; Rubin, Daniel L

    2009-01-01

    An image annotation is the explanatory or descriptive information about the pixel data of an image that is generated by a human (or machine) observer. An image markup is the graphical symbols placed over the image to depict an annotation. In the majority of current, clinical and research imaging practice, markup is captured in proprietary formats and annotations are referenced only in free text radiology reports. This makes these annotations difficult to query, retrieve and compute upon, hampering their integration into other data mining and analysis efforts. This paper describes the National Cancer Institute's Cancer Biomedical Informatics Grid's (caBIG) Annotation and Image Markup (AIM) project, focusing on how to use AIM to query for annotations. The AIM project delivers an information model for image annotation and markup. The model uses controlled terminologies for important concepts. All of the classes and attributes of the model have been harmonized with the other models and common data elements in use at the National Cancer Institute. The project also delivers XML schemata necessary to instantiate AIMs in XML as well as a software application for translating AIM XML into DICOM S/R and HL7 CDA. Large collections of AIM annotations can be built and then queried as Grid or Web services. Using the tools of the AIM project, image annotations and their markup can be captured and stored in human and machine readable formats. This enables the inclusion of human image observation and inference as part of larger data mining and analysis activities. PMID:19964202

  4. Maize - GO annotation methods, evaluation, and review (Maize-GAMER)

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Making a genome sequence accessible and useful involves three basic steps: genome assembly, structural annotation, and functional annotation. The quality of data generated at each step influences the accuracy of inferences that can be made, with high-quality analyses produce better datasets resultin...

  5. MetaboLights: An Open-Access Database Repository for Metabolomics Data.

    PubMed

    Kale, Namrata S; Haug, Kenneth; Conesa, Pablo; Jayseelan, Kalaivani; Moreno, Pablo; Rocca-Serra, Philippe; Nainala, Venkata Chandrasekhar; Spicer, Rachel A; Williams, Mark; Li, Xuefei; Salek, Reza M; Griffin, Julian L; Steinbeck, Christoph

    2016-01-01

    MetaboLights is the first general purpose, open-access database repository for cross-platform and cross-species metabolomics research at the European Bioinformatics Institute (EMBL-EBI). Based upon the open-source ISA framework, MetaboLights provides Metabolomics Standard Initiative (MSI) compliant metadata and raw experimental data associated with metabolomics experiments. Users can upload their study datasets into the MetaboLights Repository. These studies are then automatically assigned a stable and unique identifier (e.g., MTBLS1) that can be used for publication reference. The MetaboLights Reference Layer associates metabolites with metabolomics studies in the archive and is extensively annotated with data fields such as structural and chemical information, NMR and MS spectra, target species, metabolic pathways, and reactions. The database is manually curated with no specific release schedules. MetaboLights is also recommended by journals for metabolomics data deposition. This unit provides a guide to using MetaboLights, downloading experimental data, and depositing metabolomics datasets using user-friendly submission tools. PMID:27010336

  6. Plant Metabolomics: An Indispensable System Biology Tool for Plant Science

    PubMed Central

    Hong, Jun; Yang, Litao; Zhang, Dabing; Shi, Jianxin

    2016-01-01

    As genomes of many plant species have been sequenced, demand for functional genomics has dramatically accelerated the improvement of other omics including metabolomics. Despite a large amount of metabolites still remaining to be identified, metabolomics has contributed significantly not only to the understanding of plant physiology and biology from the view of small chemical molecules that reflect the end point of biological activities, but also in past decades to the attempts to improve plant behavior under both normal and stressed conditions. Hereby, we summarize the current knowledge on the genetic and biochemical mechanisms underlying plant growth, development, and stress responses, focusing further on the contributions of metabolomics to practical applications in crop quality improvement and food safety assessment, as well as plant metabolic engineering. We also highlight the current challenges and future perspectives in this inspiring area, with the aim to stimulate further studies leading to better crop improvement of yield and quality. PMID:27258266

  7. Plant Metabolomics: An Indispensable System Biology Tool for Plant Science.

    PubMed

    Hong, Jun; Yang, Litao; Zhang, Dabing; Shi, Jianxin

    2016-01-01

    As genomes of many plant species have been sequenced, demand for functional genomics has dramatically accelerated the improvement of other omics including metabolomics. Despite a large amount of metabolites still remaining to be identified, metabolomics has contributed significantly not only to the understanding of plant physiology and biology from the view of small chemical molecules that reflect the end point of biological activities, but also in past decades to the attempts to improve plant behavior under both normal and stressed conditions. Hereby, we summarize the current knowledge on the genetic and biochemical mechanisms underlying plant growth, development, and stress responses, focusing further on the contributions of metabolomics to practical applications in crop quality improvement and food safety assessment, as well as plant metabolic engineering. We also highlight the current challenges and future perspectives in this inspiring area, with the aim to stimulate further studies leading to better crop improvement of yield and quality. PMID:27258266

  8. Metabolite Profiling in the Pursuit of Biomarkers for IVF Outcome: The Case for Metabolomics Studies

    PubMed Central

    McRae, C.; Sharma, V.; Fisher, J.

    2013-01-01

    Background. This paper presents the literature on biomarkers of in vitro fertilisation (IVF) outcome, demonstrating the progression of these studies towards metabolite profiling, specifically metabolomics. The need for more, and improved, metabolomics studies in the field of assisted conception is discussed. Methods. Searches were performed on ISI Web of Knowledge SM for literature associated with biomarkers of oocyte and embryo quality, and biomarkers of IVF outcome in embryo culture medium, follicular fluid (FF), and blood plasma in female mammals. Results. Metabolomics in the field of female reproduction is still in its infancy. Metabolomics investigations of embryo culture medium for embryo selection have been the most common, but only within the last five years. Only in 2012 has the first metabolomics investigation of FF for biomarkers of oocyte quality been reported. The only metabolomics studies of human blood plasma in this context have been aimed at identifying women with polycystic ovary syndrome (PCOS). Conclusions. Metabolomics is becoming more established in the field of assisted conception, but the studies performed so far have been preliminary and not all potential applications have yet been explored. With further improved metabolomics studies, the possibility of identifying a method for predicting IVF outcome may become a reality. PMID:25763388

  9. Cadec: A corpus of adverse drug event annotations.

    PubMed

    Karimi, Sarvnaz; Metke-Jimenez, Alejandro; Kemp, Madonna; Wang, Chen

    2015-06-01

    CSIRO Adverse Drug Event Corpus (Cadec) is a new rich annotated corpus of medical forum posts on patient-reported Adverse Drug Events (ADEs). The corpus is sourced from posts on social media, and contains text that is largely written in colloquial language and often deviates from formal English grammar and punctuation rules. Annotations contain mentions of concepts such as drugs, adverse effects, symptoms, and diseases linked to their corresponding concepts in controlled vocabularies, i.e., SNOMED Clinical Terms and MedDRA. The quality of the annotations is ensured by annotation guidelines, multi-stage annotations, measuring inter-annotator agreement, and final review of the annotations by a clinical terminologist. This corpus is useful for studies in the area of information extraction, or more generally text mining, from social media to detect possible adverse drug reactions from direct patient reports. The corpus is publicly available at https://data.csiro.au.(1). PMID:25817970

  10. MASS SPECTROMETRY-BASED METABOLOMICS

    PubMed Central

    Dettmer, Katja; Aronov, Pavel A.; Hammock, Bruce D.

    2007-01-01

    This review presents an overview of the dynamically developing field of mass spectrometry-based metabolomics. Metabolomics aims at the comprehensive and quantitative analysis of wide arrays of metabolites in biological samples. These numerous analytes have very diverse physico-chemical properties and occur at different abundance levels. Consequently, comprehensive metabolomics investigations are primarily a challenge for analytical chemistry and specifically mass spectrometry has vast potential as a tool for this type of investigation. Metabolomics require special approaches for sample preparation, separation, and mass spectrometric analysis. Current examples of those approaches are described in this review. It primarily focuses on metabolic fingerprinting, a technique that analyzes all detectable analytes in a given sample with subsequent classification of samples and identification of differentially expressed metabolites, which define the sample classes. To perform this complex task, data analysis tools, metabolite libraries, and databases are required. Therefore, recent advances in metabolomics bioinformatics are also discussed. PMID:16921475

  11. LC-MS-based metabolomics

    PubMed Central

    Zhou, Bin; Xiao, Jun Feng; Tuli, Leepika

    2013-01-01

    Metabolomics aims at identification and quantitation of small molecules involved in metabolic reactions. LC-MS has enjoyed a growing popularity as the platform for metabolomic studies due to its high throughput, soft ionization, and good coverage of metabolites. The success of LC-MS-based metabolomic study often depends on multiple experimental, analytical, and computational steps. This review presents a workflow of a typical LC-MS-based metabolomic analysis for identification and quantitation of metabolites indicative of biological/environmental perturbations. Challenges and current solutions in each step of the workflow are reviewed. The review intends to help investigators understand the challenges in metabolomic studies and to determine appropriate experimental, analytical, and computational methods to address these challenges. PMID:22041788

  12. Expansion of DSSTox: Leveraging public data to create a semantic cheminformatics resource with quality annotations for support of U.S. EPA applications. (American Chemical Society)

    EPA Science Inventory

    The expansion of chemical-bioassay data in the public domain is a boon to science; however, the difficulty in establishing accurate linkages from CAS registry number (CASRN) to structure, or for properly annotating names and synonyms for a particular structure is well known. DSS...

  13. Integration of metabolomics data into metabolic networks

    PubMed Central

    Töpfer, Nadine; Kleessen, Sabrina; Nikoloski, Zoran

    2015-01-01

    Metabolite levels together with their corresponding metabolic fluxes are integrative outcomes of biochemical transformations and regulatory processes and they can be used to characterize the response of biological systems to genetic and/or environmental changes. However, while changes in transcript or to some extent protein levels can usually be traced back to one or several responsible genes, changes in fluxes and particularly changes in metabolite levels do not follow such rationale and are often the outcome of complex interactions of several components. The increasing quality and coverage of metabolomics technologies have fostered the development of computational approaches for integrating metabolic read-outs with large-scale models to predict the physiological state of a system. Constraint-based approaches, relying on the stoichiometry of the considered reactions, provide a modeling framework amenable to analyses of large-scale systems and to the integration of high-throughput data. Here we review the existing approaches that integrate metabolomics data in variants of constrained-based approaches to refine model reconstructions, to constrain flux predictions in metabolic models, and to relate network structural properties to metabolite levels. Finally, we discuss the challenges and perspectives in the developments of constraint-based modeling approaches driven by metabolomics data. PMID:25741348

  14. SEED Software Annotations.

    ERIC Educational Resources Information Center

    Bethke, Dee; And Others

    This document provides a composite index of the first five sets of software annotations produced by Project SEED. The software has been indexed by title, subject area, and grade level, and it covers sets of annotations distributed in September 1986, April 1987, September 1987, November 1987, and February 1988. The date column in the index…

  15. Annotation extension through protein family annotation coherence metrics

    PubMed Central

    Bastos, Hugo P.; Clarke, Luka A.; Couto, Francisco M.

    2013-01-01

    Protein functional annotation consists in associating proteins with textual descriptors elucidating their biological roles. The bulk of annotation is done via automated procedures that ultimately rely on annotation transfer. Despite a large number of existing protein annotation procedures the ever growing protein space is never completely annotated. One of the facets of annotation incompleteness derives from annotation uncertainty. Often when protein function cannot be predicted with enough specificity it is instead conservatively annotated with more generic terms. In a scenario of protein families or functionally related (or even dissimilar) sets this leads to a more difficult task of using annotations to compare the extent of functional relatedness among all family or set members. However, we postulate that identifying sub-sets of functionally coherent proteins annotated at a very specific level, can help the annotation extension of other incompletely annotated proteins within the same family or functionally related set. As an example we analyse the status of annotation of a set of CAZy families belonging to the Polysaccharide Lyase class. We show that through the use of visualization methods and semantic similarity based metrics it is possible to identify families and respective annotation terms within them that are suitable for possible annotation extension. Based on our analysis we then propose a semi-automatic methodology leading to the extension of single annotation terms within these partially annotated protein sets or families. PMID:24130572

  16. CAMERA: An integrated strategy for compound spectra extraction and annotation of LC/MS data sets

    PubMed Central

    Kuhl, Carsten; Tautenhahn, Ralf; Böttcher, Christoph; Larson, Tony R.; Neumann, Steffen

    2013-01-01

    Liquid chromatography coupled to mass spectrometry is routinely used for metabolomics experiments. In contrast to the fairly routine and automated data acquisition steps, subsequent compound annotation and identification require extensive manual analysis and thus form a major bottle neck in data interpretation. Here we present CAMERA, a Bioconductor package integrating algorithms to extract compound spectra, annotate isotope and adduct peaks, and propose the accurate compound mass even in highly complex data. To evaluate the algorithms, we compared the annotation of CAMERA against a manually defined annotation for a mixture of known compounds spiked into a complex matrix at different concentrations. CAMERA successfully extracted accurate masses for 89.7% and 90.3% of the annotatable compounds in positive and negative ion mode, respectively. Furthermore, we present a novel annotation approach that combines spectral information of data acquired in opposite ion modes to further improve the annotation rate. We demonstrate the utility of CAMERA in two different, easily adoptable plant metabolomics experiments, where the application of CAMERA drastically reduced the amount of manual analysis. PMID:22111785

  17. Ion trace detection algorithm to extract pure ion chromatograms to improve untargeted peak detection quality for liquid chromatography/time-of-flight mass spectrometry-based metabolomics data.

    PubMed

    Wang, San-Yuan; Kuo, Ching-Hua; Tseng, Yufeng J

    2015-03-01

    Able to detect known and unknown metabolites, untargeted metabolomics has shown great potential in identifying novel biomarkers. However, elucidating all possible liquid chromatography/time-of-flight mass spectrometry (LC/TOF-MS) ion signals in a complex biological sample remains challenging since many ions are not the products of metabolites. Methods of reducing ions not related to metabolites or simply directly detecting metabolite related (pure) ions are important. In this work, we describe PITracer, a novel algorithm that accurately detects the pure ions of a LC/TOF-MS profile to extract pure ion chromatograms and detect chromatographic peaks. PITracer estimates the relative mass difference tolerance of ions and calibrates the mass over charge (m/z) values for peak detection algorithms with an additional option to further mass correction with respect to a user-specified metabolite. PITracer was evaluated using two data sets containing 373 human metabolite standards, including 5 saturated standards considered to be split peaks resultant from huge m/z fluctuation, and 12 urine samples spiked with 50 forensic drugs of varying concentrations. Analysis of these data sets show that PITracer correctly outperformed existing state-of-art algorithm and extracted the pure ion chromatograms of the 5 saturated standards without generating split peaks and detected the forensic drugs with high recall, precision, and F-score and small mass error. PMID:25622715

  18. Omic Relief for the Biotically Stressed: Metabolomics of Plant Biotic Interactions.

    PubMed

    Tenenboim, Hezi; Brotman, Yariv

    2016-09-01

    Many aspects of the way plants protect themselves against pathogen attack, or react upon such an attack, are realized by metabolites. The ambitious aim of metabolomics, namely the identification and annotation of the entire cellular metabolome, still poses a considerable challenge due to the high diversity of the metabolites in the cell. Recent advances in analytical methods and data analysis have resulted in improved sensitivity, accuracy, and capacity, allowing the analysis of several hundreds or even thousands of compounds within one sample. Investigators have only recently begun to acknowledge and harness the power of metabolomics to elucidate key questions in the study of plant biotic interactions; we review trends and developments in the field. PMID:27185334

  19. Metabolite identification and quantitation in LC-MS/MS-based metabolomics

    PubMed Central

    Xiao, Jun Feng; Zhou, Bin; Ressom, Habtom W.

    2011-01-01

    Metabolomics aims at detection and quantitation of all metabolites in biological samples. The presence of metabolites with a wide variety of physicochemical properties and different levels of abundance challenges existing analytical platforms used for identification and quantitation of metabolites. Significant efforts have been made to improve analytical and computational methods for metabolomics studies. This review focuses on the use of liquid chromatography with tandem mass spectrometry (LC-MS/MS) for quantitative and qualitative metabolomics studies. It illustrates recent developments in computational methods for metabolite identification, including ion annotation, spectral interpretation and spectral matching. We also review selected reaction monitoring and high-resolution MS for metabolite quantitation. We discuss current challenges in metabolite identification and quantitation as well as potential solutions. PMID:22345829

  20. Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics

    PubMed Central

    Vaniya, Arpana

    2015-01-01

    Identification of unknown metabolites is the bottleneck in advancing metabolomics, leaving interpretation of metabolomics results ambiguous. The chemical diversity of metabolism is vast, making structure identification arduous and time consuming. Currently, comprehensive analysis of mass spectra in metabolomics is limited to library matching, but tandem mass spectral libraries are small compared to the large number of compounds found in the biosphere, including xenobiotics. Resolving this bottleneck requires richer data acquisition and better computational tools. Multi-stage mass spectrometry (MSn) trees show promise to aid in this regard. Fragmentation trees explore the fragmentation process, generate fragmentation rules and aid in sub-structure identification, while mass spectral trees delineate the dependencies in multi-stage MS of collision-induced dissociations. This review covers advancements over the past 10 years as a tool for metabolite identification, including algorithms, software and databases used to build and to implement fragmentation trees and mass spectral annotations. PMID:26213431

  1. Nuclear magnetic resonance metabolomics of iron deficiency in soybean leaves

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Iron (Fe) deficiency is an important agricultural concern leading to lower yields and crop quality. A better understanding of the condition, at the metabolome level, could contribute to the design of strategies to ameliorate Fe deficiency problems. Fe-sufficient and Fe-deficient soybean leaf extract...

  2. Metabolomics of genetically modified crops.

    PubMed

    Simó, Carolina; Ibáñez, Clara; Valdés, Alberto; Cifuentes, Alejandro; García-Cañas, Virginia

    2014-01-01

    Metabolomic-based approaches are increasingly applied to analyse genetically modified organisms (GMOs) making it possible to obtain broader and deeper information on the composition of GMOs compared to that obtained from traditional analytical approaches. The combination in metabolomics of advanced analytical methods and bioinformatics tools provides wide chemical compositional data that contributes to corroborate (or not) the substantial equivalence and occurrence of unintended changes resulting from genetic transformation. This review provides insight into recent progress in metabolomics studies on transgenic crops focusing mainly in papers published in the last decade. PMID:25334064

  3. Metabolomics of Genetically Modified Crops

    PubMed Central

    Simó, Carolina; Ibáñez, Clara; Valdés, Alberto; Cifuentes, Alejandro; García-Cañas, Virginia

    2014-01-01

    Metabolomic-based approaches are increasingly applied to analyse genetically modified organisms (GMOs) making it possible to obtain broader and deeper information on the composition of GMOs compared to that obtained from traditional analytical approaches. The combination in metabolomics of advanced analytical methods and bioinformatics tools provides wide chemical compositional data that contributes to corroborate (or not) the substantial equivalence and occurrence of unintended changes resulting from genetic transformation. This review provides insight into recent progress in metabolomics studies on transgenic crops focusing mainly in papers published in the last decade. PMID:25334064

  4. Metabolomics in diabetic complications.

    PubMed

    Filla, Laura A; Edwards, James L

    2016-04-22

    With a global prevalence of 9%, diabetes is the direct cause of millions of deaths each year and is quickly becoming a health crisis. Major long-term complications of diabetes arise from persistent oxidative stress and dysfunction in multiple metabolic pathways. The most serious complications involve vascular damage and include cardiovascular disease as well as microvascular disorders such as nephropathy, neuropathy, and retinopathy. Current clinical analyses like glycated hemoglobin and plasma glucose measurements hold some value as prognostic indicators of the severity of complications, but investigations into the underlying pathophysiology are still lacking. Advancements in biotechnology hold the key to uncovering new pathways and establishing therapeutic targets. Metabolomics, the study of small endogenous molecules, is a powerful toolset for studying pathophysiological processes and has been used to elucidate metabolic signatures of diabetes in various biological systems. Current challenges in the field involve correlating these biomarkers to specific complications to provide a better prediction of future risk and disease progression. This review will highlight the progress that has been made in the field of metabolomics including technological advancements, the identification of potential biomarkers, and metabolic pathways relevant to macro- and microvascular diabetic complications. PMID:26891794

  5. JGI Plant Genomics Gene Annotation Pipeline

    SciTech Connect

    Shu, Shengqiang; Rokhsar, Dan; Goodstein, David; Hayes, David; Mitros, Therese

    2014-07-14

    Plant genomes vary in size and are highly complex with a high amount of repeats, genome duplication and tandem duplication. Gene encodes a wealth of information useful in studying organism and it is critical to have high quality and stable gene annotation. Thanks to advancement of sequencing technology, many plant species genomes have been sequenced and transcriptomes are also sequenced. To use these vastly large amounts of sequence data to make gene annotation or re-annotation in a timely fashion, an automatic pipeline is needed. JGI plant genomics gene annotation pipeline, called integrated gene call (IGC), is our effort toward this aim with aid of a RNA-seq transcriptome assembly pipeline. It utilizes several gene predictors based on homolog peptides and transcript ORFs. See Methods for detail. Here we present genome annotation of JGI flagship green plants produced by this pipeline plus Arabidopsis and rice except for chlamy which is done by a third party. The genome annotations of these species and others are used in our gene family build pipeline and accessible via JGI Phytozome portal whose URL and front page snapshot are shown below.

  6. An annotated energy bibliography

    NASA Technical Reports Server (NTRS)

    Blow, S. J.

    1979-01-01

    Comprehensive annotated compilation of books, journals, periodicals, and reports on energy and energy related topics, contains approximately 10,0000 tehcnical and nontechnical references from bibliographic and other sources dated January 1975 through May 1977.

  7. Computational approaches for systems metabolomics.

    PubMed

    Krumsiek, Jan; Bartel, Jörg; Theis, Fabian J

    2016-06-01

    Systems genetics is defined as the simultaneous assessment and analysis of multi-omics datasets. In the past few years, metabolomics has been established as a robust tool describing an important functional layer in this approach. The metabolome of a biological system represents an integrated state of genetic and environmental factors and has been referred to as a 'link between genotype and phenotype'. In this review, we summarize recent progresses in statistical analysis methods for metabolomics data in combination with other omics layers. We put a special focus on complex, multivariate statistical approaches as well as pathway-based and network-based analysis methods. Moreover, we outline current challenges and pitfalls of metabolomics-focused multi-omics analyses and discuss future steps for the field. PMID:27135552

  8. Metabolomic fingerprinting of plant extracts.

    PubMed

    Mattoli, L; Cangi, F; Maidecchi, A; Ghiara, C; Ragazzi, E; Tubaro, M; Stella, L; Tisato, F; Traldi, P

    2006-12-01

    The standardization and quality control of plant extracts is an important topic, in particular, when such extracts are used for medicinal purposes. Consequently, the development of fast and effective analytical methods for metabolomic fingerprinting of plant extracts is of high interest. In this investigation, electrospray mass spectrometry (ESI-MS) and (1)H NMR techniques were employed with further statistical analyses of the acquired data. The results showed that negative ion mode ESI-MS is particularly effective for characterization of plant extracts. Different samples of the same species appear well-clustered and separated from the other species. To verify the effectiveness of the method, two other batches of extracts from a species, in which the principal components were already identified (Cynara scolymus), were analyzed, and the components that were verified by the principal component analysis (PCA) were found to be within the region identified as characteristic of Cynara Scolymus extracts. The data from extracts of the other species were well separated from those pertaining to the species previously characterized. Only the case of a species that was strictly correlated from a botanical point of view, with extracts that were previously analyzed, showed overlapping. PMID:17051519

  9. Understanding Metabolomics in Biomedical Research

    PubMed Central

    Kim, Su Jung; Kim, Su Hee; Kim, Ji Hyun; Hwang, Shin

    2016-01-01

    The term "omics" refers to any type of specific study that provides collective information on a biological system. Representative omics includes genomics, proteomics, and metabolomics, and new omics is constantly being added, such as lipidomics or glycomics. Each omics technique is crucial to the understanding of various biological systems and complements the information provided by the other approaches. The main strengths of metabolomics are that metabolites are closely related to the phenotypes of living organisms and provide information on biochemical activities by reflecting the substrates and products of cellular metabolism. The transcriptome does not always correlate with the proteome, and the translated proteome might not be functionally active. Therefore, their changes do not always result in phenotypic alterations. Unlike the genome or proteome, the metabolome is often called the molecular phenotype of living organisms and is easily translated into biological conditions and disease states. Here, we review the general strategies of mass spectrometry-based metabolomics. Targeted metabolome or lipidome analysis is discussed, as well as nontargeted approaches, with a brief explanation of the advantages and disadvantages of each platform. Biomedical applications that use mass spectrometry-based metabolomics are briefly introduced. PMID:26676338

  10. A Novel Approach to Semantic and Coreference Annotation at LLNL

    SciTech Connect

    Firpo, M

    2005-02-04

    A case is made for the importance of high quality semantic and coreference annotation. The challenges of providing such annotation are described. Asperger's Syndrome is introduced, and the connections are drawn between the needs of text annotation and the abilities of persons with Asperger's Syndrome to meet those needs. Finally, a pilot program is recommended wherein semantic annotation is performed by people with Asperger's Syndrome. The primary points embodied in this paper are as follows: (1) Document annotation is essential to the Natural Language Processing (NLP) projects at Lawrence Livermore National Laboratory (LLNL); (2) LLNL does not currently have a system in place to meet its need for text annotation; (3) Text annotation is challenging for a variety of reasons, many related to its very rote nature; (4) Persons with Asperger's Syndrome are particularly skilled at rote verbal tasks, and behavioral experts agree that they would excel at text annotation; and (6) A pilot study is recommend in which two to three people with Asperger's Syndrome annotate documents and then the quality and throughput of their work is evaluated relative to that of their neuro-typical peers.

  11. Algal functional annotation tool

    Energy Science and Technology Software Center (ESTSC)

    2012-07-12

    Abstract BACKGROUND: Progress in genome sequencing is proceeding at an exponential pace, and several new algal genomes are becoming available every year. One of the challenges facing the community is the association of protein sequences encoded in the genomes with biological function. While most genome assembly projects generate annotations for predicted protein sequences, they are usually limited and integrate functional terms from a limited number of databases. Another challenge is the use of annotations tomore » interpret large lists of 'interesting' genes generated by genome-scale datasets. Previously, these gene lists had to be analyzed across several independent biological databases, often on a gene-by-gene basis. In contrast, several annotation databases, such as DAVID, integrate data from multiple functional databases and reveal underlying biological themes of large gene lists. While several such databases have been constructed for animals, none is currently available for the study of algae. Due to renewed interest in algae as potential sources of biofuels and the emergence of multiple algal genome sequences, a significant need has arisen for such a database to process the growing compendiums of algal genomic data. DESCRIPTION: The Algal Functional Annotation Tool is a web-based comprehensive analysis suite integrating annotation data from several pathway, ontology, and protein family databases. The current version provides annotation for the model alga Chlamydomonas reinhardtii, and in the future will include additional genomes. The site allows users to interpret large gene lists by identifying associated functional terms, and their enrichment. Additionally, expression data for several experimental conditions were compiled and analyzed to provide an expression-based enrichment search. A tool to search for functionally-related genes based on gene expression across these conditions is also provided. Other features include dynamic visualization of genes on

  12. Algal functional annotation tool

    SciTech Connect

    2012-07-12

    Abstract BACKGROUND: Progress in genome sequencing is proceeding at an exponential pace, and several new algal genomes are becoming available every year. One of the challenges facing the community is the association of protein sequences encoded in the genomes with biological function. While most genome assembly projects generate annotations for predicted protein sequences, they are usually limited and integrate functional terms from a limited number of databases. Another challenge is the use of annotations to interpret large lists of 'interesting' genes generated by genome-scale datasets. Previously, these gene lists had to be analyzed across several independent biological databases, often on a gene-by-gene basis. In contrast, several annotation databases, such as DAVID, integrate data from multiple functional databases and reveal underlying biological themes of large gene lists. While several such databases have been constructed for animals, none is currently available for the study of algae. Due to renewed interest in algae as potential sources of biofuels and the emergence of multiple algal genome sequences, a significant need has arisen for such a database to process the growing compendiums of algal genomic data. DESCRIPTION: The Algal Functional Annotation Tool is a web-based comprehensive analysis suite integrating annotation data from several pathway, ontology, and protein family databases. The current version provides annotation for the model alga Chlamydomonas reinhardtii, and in the future will include additional genomes. The site allows users to interpret large gene lists by identifying associated functional terms, and their enrichment. Additionally, expression data for several experimental conditions were compiled and analyzed to provide an expression-based enrichment search. A tool to search for functionally-related genes based on gene expression across these conditions is also provided. Other features include dynamic visualization of genes on KEGG

  13. Human Genome Annotation

    NASA Astrophysics Data System (ADS)

    Gerstein, Mark

    A central problem for 21st century science is annotating the human genome and making this annotation useful for the interpretation of personal genomes. My talk will focus on annotating the 99% of the genome that does not code for canonical genes, concentrating on intergenic features such as structural variants (SVs), pseudogenes (protein fossils), binding sites, and novel transcribed RNAs (ncRNAs). In particular, I will describe how we identify regulatory sites and variable blocks (SVs) based on processing next-generation sequencing experiments. I will further explain how we cluster together groups of sites to create larger annotations. Next, I will discuss a comprehensive pseudogene identification pipeline, which has enabled us to identify >10K pseudogenes in the genome and analyze their distribution with respect to age, protein family, and chromosomal location. Throughout, I will try to introduce some of the computational algorithms and approaches that are required for genome annotation. Much of this work has been carried out in the framework of the ENCODE, modENCODE, and 1000 genomes projects.

  14. Algal functional annotation tool

    SciTech Connect

    Lopez, D.; Casero, D.; Cokus, S. J.; Merchant, S. S.; Pellegrini, M.

    2012-07-01

    The Algal Functional Annotation Tool is a web-based comprehensive analysis suite integrating annotation data from several pathway, ontology, and protein family databases. The current version provides annotation for the model alga Chlamydomonas reinhardtii, and in the future will include additional genomes. The site allows users to interpret large gene lists by identifying associated functional terms, and their enrichment. Additionally, expression data for several experimental conditions were compiled and analyzed to provide an expression-based enrichment search. A tool to search for functionally-related genes based on gene expression across these conditions is also provided. Other features include dynamic visualization of genes on KEGG pathway maps and batch gene identifier conversion.

  15. Metabolomics combined with standard quality measures of ‘Honeycrisp’ apple fruit reveals aspects of metabolism related to flavor, crispness, maturity, and storability

    Technology Transfer Automated Retrieval System (TEKTRAN)

    ‘Honeycrisp’ is popular dessert apple cultivar. As acreage of planted ‘Honeycrisp’ increases, reliably assessing optimum storage duration is becoming increasingly important. Apple fruit quality is typically assessed by measuring titratible acidity, internal ethylene concentration, firmness, solubl...

  16. The UniProt-GO Annotation database in 2011

    PubMed Central

    Dimmer, Emily C.; Huntley, Rachael P.; Alam-Faruque, Yasmin; Sawford, Tony; O'Donovan, Claire; Martin, Maria J.; Bely, Benoit; Browne, Paul; Mun Chan, Wei; Eberhardt, Ruth; Gardner, Michael; Laiho, Kati; Legge, Duncan; Magrane, Michele; Pichler, Klemens; Poggioli, Diego; Sehra, Harminder; Auchincloss, Andrea; Axelsen, Kristian; Blatter, Marie-Claude; Boutet, Emmanuel; Braconi-Quintaje, Silvia; Breuza, Lionel; Bridge, Alan; Coudert, Elizabeth; Estreicher, Anne; Famiglietti, Livia; Ferro-Rojas, Serenella; Feuermann, Marc; Gos, Arnaud; Gruaz-Gumowski, Nadine; Hinz, Ursula; Hulo, Chantal; James, Janet; Jimenez, Silvia; Jungo, Florence; Keller, Guillaume; Lemercier, Phillippe; Lieberherr, Damien; Masson, Patrick; Moinat, Madelaine; Pedruzzi, Ivo; Poux, Sylvain; Rivoire, Catherine; Roechert, Bernd; Schneider, Michael; Stutz, Andre; Sundaram, Shyamala; Tognolli, Michael; Bougueleret, Lydie; Argoud-Puy, Ghislaine; Cusin, Isabelle; Duek- Roggli, Paula; Xenarios, Ioannis; Apweiler, Rolf

    2012-01-01

    The GO annotation dataset provided by the UniProt Consortium (GOA: http://www.ebi.ac.uk/GOA) is a comprehensive set of evidenced-based associations between terms from the Gene Ontology resource and UniProtKB proteins. Currently supplying over 100 million annotations to 11 million proteins in more than 360 000 taxa, this resource has increased 2-fold over the last 2 years and has benefited from a wealth of checks to improve annotation correctness and consistency as well as now supplying a greater information content enabled by GO Consortium annotation format developments. Detailed, manual GO annotations obtained from the curation of peer-reviewed papers are directly contributed by all UniProt curators and supplemented with manual and electronic annotations from 36 model organism and domain-focused scientific resources. The inclusion of high-quality, automatic annotation predictions ensures the UniProt GO annotation dataset supplies functional information to a wide range of proteins, including those from poorly characterized, non-model organism species. UniProt GO annotations are freely available in a range of formats accessible by both file downloads and web-based views. In addition, the introduction of a new, normalized file format in 2010 has made for easier handling of the complete UniProt-GOA data set. PMID:22123736

  17. The UniProt-GO Annotation database in 2011.

    PubMed

    Dimmer, Emily C; Huntley, Rachael P; Alam-Faruque, Yasmin; Sawford, Tony; O'Donovan, Claire; Martin, Maria J; Bely, Benoit; Browne, Paul; Mun Chan, Wei; Eberhardt, Ruth; Gardner, Michael; Laiho, Kati; Legge, Duncan; Magrane, Michele; Pichler, Klemens; Poggioli, Diego; Sehra, Harminder; Auchincloss, Andrea; Axelsen, Kristian; Blatter, Marie-Claude; Boutet, Emmanuel; Braconi-Quintaje, Silvia; Breuza, Lionel; Bridge, Alan; Coudert, Elizabeth; Estreicher, Anne; Famiglietti, Livia; Ferro-Rojas, Serenella; Feuermann, Marc; Gos, Arnaud; Gruaz-Gumowski, Nadine; Hinz, Ursula; Hulo, Chantal; James, Janet; Jimenez, Silvia; Jungo, Florence; Keller, Guillaume; Lemercier, Phillippe; Lieberherr, Damien; Masson, Patrick; Moinat, Madelaine; Pedruzzi, Ivo; Poux, Sylvain; Rivoire, Catherine; Roechert, Bernd; Schneider, Michael; Stutz, Andre; Sundaram, Shyamala; Tognolli, Michael; Bougueleret, Lydie; Argoud-Puy, Ghislaine; Cusin, Isabelle; Duek-Roggli, Paula; Xenarios, Ioannis; Apweiler, Rolf

    2012-01-01

    The GO annotation dataset provided by the UniProt Consortium (GOA: http://www.ebi.ac.uk/GOA) is a comprehensive set of evidenced-based associations between terms from the Gene Ontology resource and UniProtKB proteins. Currently supplying over 100 million annotations to 11 million proteins in more than 360,000 taxa, this resource has increased 2-fold over the last 2 years and has benefited from a wealth of checks to improve annotation correctness and consistency as well as now supplying a greater information content enabled by GO Consortium annotation format developments. Detailed, manual GO annotations obtained from the curation of peer-reviewed papers are directly contributed by all UniProt curators and supplemented with manual and electronic annotations from 36 model organism and domain-focused scientific resources. The inclusion of high-quality, automatic annotation predictions ensures the UniProt GO annotation dataset supplies functional information to a wide range of proteins, including those from poorly characterized, non-model organism species. UniProt GO annotations are freely available in a range of formats accessible by both file downloads and web-based views. In addition, the introduction of a new, normalized file format in 2010 has made for easier handling of the complete UniProt-GOA data set. PMID:22123736

  18. Geothermal wetlands: an annotated bibliography of pertinent literature

    SciTech Connect

    Stanley, N.E.; Thurow, T.L.; Russell, B.F.; Sullivan, J.F.

    1980-05-01

    This annotated bibliography covers the following topics: algae, wetland ecosystems; institutional aspects; macrophytes - general, production rates, and mineral absorption; trace metal absorption; wetland soils; water quality; and other aspects of marsh ecosystems. (MHR)

  19. Metabolomics: moving towards personalized medicine

    PubMed Central

    Baraldi, Eugenio; Carraro, Silvia; Giordano, Giuseppe; Reniero, Fabiano; Perilongo, Giorgio; Zacchello, Franco

    2009-01-01

    In many fields of medicine there is a growing interest in characterizing diseases at molecular level with a view to developing an individually tailored therapeutic approach. Metabolomics is a novel area that promises to contribute significantly to the characterization of various disease phenotypes and to the identification of personal metabolic features that can predict response to therapies. Based on analytical platforms such as mass spectrometry or NMR-based spectroscopy, the metabolomic approach enables a comprehensive overview of the metabolites, leading to the characterization of the metabolic fingerprint of a given sample. These metabolic fingerprints can then be used to distinguish between different disease phenotypes and to predict a drug's effectiveness and/or toxicity. Several studies published in the last few years applied the metabolomic approach in the field of pediatric medicine. Being a highly informative technique that can be used on samples collected non-invasively (e.g. urine or exhaled breath condensate), metabolomics has appeal for the study of pediatric diseases. Here we present and discuss the pediatric clinical studies that have taken the metabolomic approach. PMID:19852788

  20. Omics data management and annotation.

    PubMed

    Harel, Arye; Dalah, Irina; Pietrokovski, Shmuel; Safran, Marilyn; Lancet, Doron

    2011-01-01

    Technological Omics breakthroughs, including next generation sequencing, bring avalanches of data which need to undergo effective data management to ensure integrity, security, and maximal knowledge-gleaning. Data management system requirements include flexible input formats, diverse data entry mechanisms and views, user friendliness, attention to standards, hardware and software platform definition, as well as robustness. Relevant solutions elaborated by the scientific community include Laboratory Information Management Systems (LIMS) and standardization protocols facilitating data sharing and managing. In project planning, special consideration has to be made when choosing relevant Omics annotation sources, since many of them overlap and require sophisticated integration heuristics. The data modeling step defines and categorizes the data into objects (e.g., genes, articles, disorders) and creates an application flow. A data storage/warehouse mechanism must be selected, such as file-based systems and relational databases, the latter typically used for larger projects. Omics project life cycle considerations must include the definition and deployment of new versions, incorporating either full or partial updates. Finally, quality assurance (QA) procedures must validate data and feature integrity, as well as system performance expectations. We illustrate these data management principles with examples from the life cycle of the GeneCards Omics project (http://www.genecards.org), a comprehensive, widely used compendium of annotative information about human genes. For example, the GeneCards infrastructure has recently been changed from text files to a relational database, enabling better organization and views of the growing data. Omics data handling benefits from the wealth of Web-based information, the vast amount of public domain software, increasingly affordable hardware, and effective use of data management and annotation principles as outlined in this chapter

  1. Gene and alternative splicing annotation with AIR

    PubMed Central

    Florea, Liliana; Di Francesco, Valentina; Miller, Jason; Turner, Russell; Yao, Alison; Harris, Michael; Walenz, Brian; Mobarry, Clark; Merkulov, Gennady V.; Charlab, Rosane; Dew, Ian; Deng, Zuoming; Istrail, Sorin; Li, Peter; Sutton, Granger

    2005-01-01

    Designing effective and accurate tools for identifying the functional and structural elements in a genome remains at the frontier of genome annotation owing to incompleteness and inaccuracy of the data, limitations in the computational models, and shifting paradigms in genomics, such as alternative splicing. We present a methodology for the automated annotation of genes and their alternatively spliced mRNA transcripts based on existing cDNA and protein sequence evidence from the same species or projected from a related species using syntenic mapping information. At the core of the method is the splice graph, a compact representation of a gene, its exons, introns, and alternatively spliced isoforms. The putative transcripts are enumerated from the graph and assigned confidence scores based on the strength of sequence evidence, and a subset of the high-scoring candidates are selected and promoted into the annotation. The method is highly selective, eliminating the unlikely candidates while retaining 98% of the high-quality mRNA evidence in well-formed transcripts, and produces annotation that is measurably more accurate than some evidence-based gene sets. The process is fast, accurate, and fully automated, and combines the traditionally distinct gene annotation and alternative splicing detection processes in a comprehensive and systematic way, thus considerably aiding in the ensuing manual curation efforts. PMID:15632090

  2. Pathway Analysis Software: Annotation Errors and Solutions

    PubMed Central

    Henderson-MacLennan, Nicole K.; Papp, Jeanette C.; Talbot, C. Conover; McCabe, Edward R.B.; Presson, Angela P.

    2010-01-01

    Genetic databases contain a variety of annotation errors that often go unnoticed due to the large size of modern genetic data sets. Interpretation of these data sets requires bioinformatics tools that may contribute to this problem. While providing gene symbol annotations for identifiers (IDs) such as microarray probeset, RefSeq, GenBank and Entrez Gene is seemingly trivial, the accuracy is fundamental to any subsequent conclusions. We examine gene symbol annotations and results from three commercial pathway analysis software (PAS) packages: Ingenuity Pathways Analysis, GeneGO and Pathway Studio. We compare gene symbol annotations and canonical pathway results over time and among different input ID types. We find that PAS results can be affected by variation in gene symbol annotations across software releases and the input ID type analyzed. As a result, we offer suggestions for using commercial PAS and reporting microarray results to improve research quality. We propose a wiki type website to facilitate communication of bioinformatics software problems within the scientific community. PMID:20663702

  3. Annotation: The Savant Syndrome

    ERIC Educational Resources Information Center

    Heaton, Pamela; Wallace, Gregory L.

    2004-01-01

    Background: Whilst interest has focused on the origin and nature of the savant syndrome for over a century, it is only within the past two decades that empirical group studies have been carried out. Methods: The following annotation briefly reviews relevant research and also attempts to address outstanding issues in this research area.…

  4. Collaborative Movie Annotation

    NASA Astrophysics Data System (ADS)

    Zad, Damon Daylamani; Agius, Harry

    In this paper, we focus on metadata for self-created movies like those found on YouTube and Google Video, the duration of which are increasing in line with falling upload restrictions. While simple tags may have been sufficient for most purposes for traditionally very short video footage that contains a relatively small amount of semantic content, this is not the case for movies of longer duration which embody more intricate semantics. Creating metadata is a time-consuming process that takes a great deal of individual effort; however, this effort can be greatly reduced by harnessing the power of Web 2.0 communities to create, update and maintain it. Consequently, we consider the annotation of movies within Web 2.0 environments, such that users create and share that metadata collaboratively and propose an architecture for collaborative movie annotation. This architecture arises from the results of an empirical experiment where metadata creation tools, YouTube and an MPEG-7 modelling tool, were used by users to create movie metadata. The next section discusses related work in the areas of collaborative retrieval and tagging. Then, we describe the experiments that were undertaken on a sample of 50 users. Next, the results are presented which provide some insight into how users interact with existing tools and systems for annotating movies. Based on these results, the paper then develops an architecture for collaborative movie annotation.

  5. Annotated Bibliography. First Edition.

    ERIC Educational Resources Information Center

    Haring, Norris G.

    An annotated bibliography which presents approximately 300 references from 1951 to 1973 on the education of severely/profoundly handicapped persons. Citations are grouped alphabetically by author's name within the following categories: characteristics and treatment, gross motor development, sensory and motor development, physical therapy for the…

  6. Ghostwriting: An Annotated Bibliography.

    ERIC Educational Resources Information Center

    Simmons, Donald B.

    Drawn from communication journals, historical and news magazines, business and industrial magazines, political science and world affairs journals, general interest periodicals, and literary and political review magazines, the approximately 90 entries in this annotated bibliography discuss ghostwriting as practiced through the ages and reveal the…

  7. Metabolomics techniques in nanotoxicology studies.

    PubMed

    Schnackenberg, Laura K; Sun, Jinchun; Beger, Richard D

    2012-01-01

    The rapid growth in the development of nanoparticles for uses in a variety of applications including targeted drug delivery, cancer therapy, imaging, and as biological sensors has led to questions about potential toxicity of such particles to humans. High-throughput methods are necessary to evaluate the potential toxicity of nanoparticles. The omics technologies are particularly well suited to evaluate toxicity in both in vitro and in vivo systems. Metabolomics, specifically, can rapidly screen for biomarkers related to predefined pathways or processes in biofluids and tissues. Specifically, oxidative stress has been implicated as a potential mechanism of toxicity in nanoparticles and is generally difficult to measure by conventional methods. Furthermore, metabolomics can provide mechanistic insight into nanotoxicity. This chapter focuses on the application of both LC/MS and NMR-based metabolomics approaches to study the potential toxicity of nanoparticles. PMID:22975962

  8. Methods used to increase the comprehensive coverage of urinary and plasma metabolomes by MS.

    PubMed

    Chen, Yanhua; Xu, Jing; Zhang, Ruiping; Abliz, Zeper

    2016-05-01

    Metabolomics, focusing on comprehensive analysis of all the metabolites in a biological system, provides a direct signature of biochemical activity. Using emerging technologies in MS, it is possible to simultaneously and rapidly analyze thousands of metabolites. However, due to the chemical and physical diversity of metabolites, it is difficult to acquire a comprehensive and reliable profiling of the whole metabolome. Here, we summarize the state of the art in metabolomics research, focusing on efforts to provide a more comprehensive metabolome coverage via improvements in two fundamental processes: sample preparation and MS analysis. Additionally, the reliable analysis is also highlighted via the combinations of multiple methods (e.g., targeted and untargeted approaches), and analytical quality control and calibration methods. PMID:27079429

  9. Metabolomic quality control of commercial Asian ginseng, and cultivated and wild American ginseng using (1)H NMR and multi-step PCA.

    PubMed

    Zhao, Huiying; Xu, Jin; Ghebrezadik, Helen; Hylands, Peter J

    2015-10-10

    Ginseng, mainly Asian ginseng and American ginseng, is the most widely consumed herbal product in the world . However, the existing quality control method is not adequate: adulteration is often seen in the market. In this study, 31 batches of ginseng from Chinese stores were analyzed using (1)H NMR metabolite profiles together with multi-step principal component analysis. The most abundant metabolites, sugars, were excluded from the NMR spectra after the first principal component analysis, in order to reveal differences contributed by less abundant metabolites. For the first time, robust, distinctive and representative differences of Asian ginseng from American ginseng were found and the key metabolites responsible were identified as sucrose, glucose, arginine, choline, and 2-oxoglutarate and malate. Differences between wild and cultivated ginseng were identified as ginsenosides. A substitute cultivated American ginseng was noticed. These results demonstrated that the combination of (1)H NMR and PCA is effective in quality control of ginseng. PMID:26037159

  10. [UPLC/Q-TOF MS and NMR plant metabolomics approach in studying the effect of growth year on the quality of Polygala tenuifolia].

    PubMed

    Xue, Ying; Li, Xiao-wei; Li, Zhen-yu; Zeng, Zu-ping; Zhang, Fu-sheng; Li, Ai-ping; Qin, Xue-mei; Peng, Bing

    2015-03-01

    Growth year is one of the important factors for the quality of Polygala tenufolia. In this study, primary metabolites and secondary metabolites were compared in 1, 2 and 3 years old P. tenufolia cultivated in Shaanxi Heyang. The samples were subjected to ultra-high performance liquid chromatography (UPLC)-quadrupole time-of-flight mass spectrometry (Q-TOF MS) and nuclear magnetic resonance (NMR) analysis, and the obtained data were analyzed using principal component analysis (PCA) and other statistical analysis methods. In addition, content and correlation of different metabolites were also calculated. The results showed no significance between main component contents in 2 year-old and 3 year-old P. Tenufolia, but 1 year-old was statistically different. The contents of primary metabolites, such as fructose, sucrose, and choline increased as time goes on, while glycine and raffinose decreased. The contents of secondary metabolites, such as onjisaponin Fg, polygalasaponin XXVIII, polygalasaponin XXXII increased, while polygalaxanthone III and parts of oligosaccharide multi-ester including tenuifoliose A, tenuifoliose C, tenuifoliose C2 and tenuifoliose H decreased with the extension of the growth years. Growth years has important impact on the quality of P. tenuifolia and the existing growing years of commodity P. tenuifolia have its scientific evidence. This study supplied a new method for the quality evaluation of Chinese medicinal materials. PMID:26118115

  11. The MetaboLights repository: curation challenges in metabolomics.

    PubMed

    Salek, Reza M; Haug, Kenneth; Conesa, Pablo; Hastings, Janna; Williams, Mark; Mahendraker, Tejasvi; Maguire, Eamonn; González-Beltrán, Alejandra N; Rocca-Serra, Philippe; Sansone, Susanna-Assunta; Steinbeck, Christoph

    2013-01-01

    MetaboLights is the first general-purpose open-access curated repository for metabolomic studies, their raw experimental data and associated metadata, maintained by one of the major open-access data providers in molecular biology. Increases in the number of depositions, number of samples per study and the file size of data submitted to MetaboLights present a challenge for the objective of ensuring high-quality and standardized data in the context of diverse metabolomic workflows and data representations. Here, we describe the MetaboLights curation pipeline, its challenges and its practical application in quality control of complex data depositions. Database URL: http://www.ebi.ac.uk/metabolights. PMID:23630246

  12. The MetaboLights repository: curation challenges in metabolomics

    PubMed Central

    Salek, Reza M.; Haug, Kenneth; Conesa, Pablo; Hastings, Janna; Williams, Mark; Mahendraker, Tejasvi; Maguire, Eamonn; González-Beltrán, Alejandra N.; Rocca-Serra, Philippe; Sansone, Susanna-Assunta; Steinbeck, Christoph

    2013-01-01

    MetaboLights is the first general-purpose open-access curated repository for metabolomic studies, their raw experimental data and associated metadata, maintained by one of the major open-access data providers in molecular biology. Increases in the number of depositions, number of samples per study and the file size of data submitted to MetaboLights present a challenge for the objective of ensuring high-quality and standardized data in the context of diverse metabolomic workflows and data representations. Here, we describe the MetaboLights curation pipeline, its challenges and its practical application in quality control of complex data depositions. Database URL: http://www.ebi.ac.uk/metabolights PMID:23630246

  13. Semantator: annotating clinical narratives with semantic web ontologies.

    PubMed

    Song, Dezhao; Chute, Christopher G; Tao, Cui

    2012-01-01

    To facilitate clinical research, clinical data needs to be stored in a machine processable and understandable way. Manual annotating clinical data is time consuming. Automatic approaches (e.g., Natural Language Processing systems) have been adopted to convert such data into structured formats; however, the quality of such automatically extracted data may not always be satisfying. In this paper, we propose Semantator, a semi-automatic tool for document annotation with Semantic Web ontologies. With a loaded free text document and an ontology, Semantator supports the creation/deletion of ontology instances for any document fragment, linking/disconnecting instances with the properties in the ontology, and also enables automatic annotation by connecting to the NCBO annotator and cTAKES. By representing annotations in Semantic Web standards, Semantator supports reasoning based upon the underlying semantics of the owl:disjointWith and owl:equivalentClass predicates. We present discussions based on user experiences of using Semantator. PMID:22779043

  14. Semantator: Annotating Clinical Narratives with Semantic Web Ontologies

    PubMed Central

    Song, Dezhao; Chute, Christopher G.; Tao, Cui

    2012-01-01

    To facilitate clinical research, clinical data needs to be stored in a machine processable and understandable way. Manual annotating clinical data is time consuming. Automatic approaches (e.g., Natural Language Processing systems) have been adopted to convert such data into structured formats; however, the quality of such automatically extracted data may not always be satisfying. In this paper, we propose Semantator, a semi-automatic tool for document annotation with Semantic Web ontologies. With a loaded free text document and an ontology, Semantator supports the creation/deletion of ontology instances for any document fragment, linking/disconnecting instances with the properties in the ontology, and also enables automatic annotation by connecting to the NCBO annotator and cTAKES. By representing annotations in Semantic Web standards, Semantator supports reasoning based upon the underlying semantics of the owl:disjointWith and owl:equivalentClass predicates. We present discussions based on user experiences of using Semantator. PMID:22779043

  15. Apollo: a sequence annotation editor

    PubMed Central

    Lewis, SE; Searle, SMJ; Harris, N; Gibson, M; Iyer, V; Richter, J; Wiel, C; Bayraktaroglu, L; Birney, E; Crosby, MA; Kaminker, JS; Matthews, BB; Prochnik, SE; Smith, CD; Tupy, JL; Rubin, GM; Misra, S; Mungall, CJ; Clamp, ME

    2002-01-01

    The well-established inaccuracy of purely computational methods for annotating genome sequences necessitates an interactive tool to allow biological experts to refine these approximations by viewing and independently evaluating the data supporting each annotation. Apollo was developed to meet this need, enabling curators to inspect genome annotations closely and edit them. FlyBase biologists successfully used Apollo to annotate the Drosophila melanogaster genome and it is increasingly being used as a starting point for the development of customized annotation editing tools for other genome projects. PMID:12537571

  16. Re-Annotation Is an Essential Step in Systems Biology Modeling of Functional Genomics Data

    PubMed Central

    van den Berg, Bart H. J.; McCarthy, Fiona M.; Lamont, Susan J.; Burgess, Shane C.

    2010-01-01

    One motivation of systems biology research is to understand gene functions and interactions from functional genomics data such as that derived from microarrays. Up-to-date structural and functional annotations of genes are an essential foundation of systems biology modeling. We propose that the first essential step in any systems biology modeling of functional genomics data, especially for species with recently sequenced genomes, is gene structural and functional re-annotation. To demonstrate the impact of such re-annotation, we structurally and functionally re-annotated a microarray developed, and previously used, as a tool for disease research. We quantified the impact of this re-annotation on the array based on the total numbers of structural- and functional-annotations, the Gene Annotation Quality (GAQ) score, and canonical pathway coverage. We next quantified the impact of re-annotation on systems biology modeling using a previously published experiment that used this microarray. We show that re-annotation improves the quantity and quality of structural- and functional-annotations, allows a more comprehensive Gene Ontology based modeling, and improves pathway coverage for both the whole array and a differentially expressed mRNA subset. Our results also demonstrate that re-annotation can result in a different knowledge outcome derived from previous published research findings. We propose that, because of this, re-annotation should be considered to be an essential first step for deriving value from functional genomics data. PMID:20498845

  17. The RAST server : rapid annotations using subsystems technology.

    SciTech Connect

    Aziz, R. K.; Bartels, D.; Best, A. A.; DeJongh, M.; Disz, T.; Edwards, R. A.; Formsma, K.; Gerdes, S.; Glass, E. M.; Kubal, M.; Meyer, F.; Olsen, G. J.; Olson, R.; Osterman, A. L.; Overbeek, R. A.; McNeil, L. K.; Paarmann, D.; Paczian, T.; Parrello, B.; Pusch, G. D.; Reich, C.; Stevens, R.; Vassieva, O.; Vonstein, V.; Wilke, A.; Zagnitko, O.; Mathematics and Computer Science; Fellowship for Interpretation of Genomes; Univ. of Chicago; Univ. of Illinois; The Burnham Inst.; Hope Coll.; Univ. of Tenn.; Cairo Univ.

    2008-02-08

    The number of prokaryotic genome sequences becoming available is growing steadily and is growing faster than our ability to accurately annotate them. We describe a fully automated service for annotating bacterial and archaeal genomes. The service identifies protein-encoding, rRNA and tRNA genes, assigns functions to the genes, predicts which subsystems are represented in the genome, uses this information to reconstruct the metabolic network and makes the output easily downloadable for the user. In addition, the annotated genome can be browsed in an environment that supports comparative analysis with the annotated genomes maintained in the SEED environment. The service normally makes the annotated genome available within 12-24 hours of submission, but ultimately the quality of such a service will be judged in terms of accuracy, consistency, and completeness of the produced annotations. We summarize our attempts to address these issues and discuss plans for incrementally enhancing the service. By providing accurate, rapid annotation freely to the community we have created an important community resource. The service has now been utilized by over 120 external users annotating over 350 distinct genomes.

  18. Considerations to improve functional annotations in biological databases.

    PubMed

    Benítez-Páez, Alfonso

    2009-12-01

    Despite the great effort to design efficient systems allowing the electronic indexation of information concerning genes, proteins, structures, and interactions published daily in scientific journals, some problems are still observed in specific tasks such as functional annotation. The annotation of function is a critical issue for bioinformatic routines, such as for instance, in functional genomics and the further prediction of unknown protein function, which are highly dependent of the quality of existing annotations. Some information management systems evolve to efficiently incorporate information from large-scale projects, but often, annotation of single records from the literature is difficult and slow. In this short report, functional characterizations of a representative sample of the entire set of uncharacterized proteins from Escherichia coli K12 was compiled from Swiss-Prot, PubMed, and EcoCyc and demonstrate a functional annotation deficit in biological databases. Some issues are postulated as causes of the lack of annotation, and different solutions are evaluated and proposed to avoid them. The hope is that as a consequence of these observations, there will be new impetus to improve the speed and quality of functional annotation and ultimately provide updated, reliable information to the scientific community. PMID:20050264

  19. Mulligan Concept manual therapy: standardizing annotation.

    PubMed

    McDowell, Jillian Marie; Johnson, Gillian Margaret; Hetherington, Barbara Helen

    2014-10-01

    Quality technique documentation is integral to the practice of manual therapy, ensuring uniform application and reproducibility of treatment. Manual therapy techniques are described by annotations utilizing a range of acronyms, abbreviations and universal terminology based on biomechanical and anatomical concepts. The various combinations of therapist and patient generated forces utilized in a variety of weight-bearing positions, which are synonymous with Mulligan Concept, challenge practitioners existing annotational skills. An annotation framework with recording rules adapted to the Mulligan Concept is proposed in which the abbreviations incorporate established manual therapy tenets and are detailed in the following sequence of; starting position, side, joint/s, method of application, glide/s, Mulligan technique, movement (or function), whether an assistant is used, overpressure (and by whom) and numbers of repetitions or time and sets. Therapist or patient application of overpressure and utilization of treatment belts or manual techniques must be recorded to capture the complete description. The adoption of the Mulligan Concept annotation framework in this way for documentation purposes will provide uniformity and clarity of information transfer for the future purposes of teaching, clinical practice and audit for its practitioners. PMID:24491791

  20. Statistical mechanics of ontology based annotations

    NASA Astrophysics Data System (ADS)

    Hoyle, David C.; Brass, Andrew

    2016-01-01

    We present a statistical mechanical theory of the process of annotating an object with terms selected from an ontology. The term selection process is formulated as an ideal lattice gas model, but in a highly structured inhomogeneous field. The model enables us to explain patterns recently observed in real-world annotation data sets, in terms of the underlying graph structure of the ontology. By relating the external field strengths to the information content of each node in the ontology graph, the statistical mechanical model also allows us to propose a number of practical metrics for assessing the quality of both the ontology, and the annotations that arise from its use. Using the statistical mechanical formalism we also study an ensemble of ontologies of differing size and complexity; an analysis not readily performed using real data alone. Focusing on regular tree ontology graphs we uncover a rich set of scaling laws describing the growth in the optimal ontology size as the number of objects being annotated increases. In doing so we provide a further possible measure for assessment of ontologies.

  1. Metabolomics: a state-of-the-art technology for better understanding of male infertility.

    PubMed

    Minai-Tehrani, A; Jafarzadeh, N; Gilany, K

    2016-08-01

    Male factor infertility affects approximately half of the infertile couples, in spite of many years of research on male infertility treatment and diagnosis; several outstanding questions remain to be addressed. In this regard, metabolomics as a novel field of omics has been suggested to be applied for male infertility problems. A variety of terms associated with metabolite quantity and quality have been established to demonstrate mixtures of metabolites. Despite metabolomics and metabolite analyses have been around more than decades, a limited number of studies concerning male infertility have been carried out. In this review, we summarised the latest finding in metabolomics techniques and metabolomics biomarkers correlated with male infertility. The rapid progress of a variety of metabolomics platforms, such as nonoptical and optical spectroscopy, could ease separation, recognition, classification and quantification of several metabolites and their metabolic pathways. Here, we recommend that the novel biomarkers determined in the course of metabolomics analysis may stand for potential application of treatment and future clinical practice. PMID:26608970

  2. Contributions from metabolomics to fish research.

    PubMed

    Samuelsson, Linda M; Larsson, D G Joakim

    2008-10-01

    Metabolomics is a systems approach to studying the small, endogenous metabolites in an organ, biofluid or whole organism. It can be used as a screening tool for metabolite profiling, or to detect changes in the metabolome brought on by external or internal stressors. The purpose of this review is to summarize and evaluate the information obtained from the application of metabolomics in fish research and to discuss its future potential. It is already clear that metabolomics has contributed new knowledge about fish in areas such as basic physiology and development, disease, water pollution and aspects concerning fish as foodstuffs. PMID:19082135

  3. Solar Tutorial and Annotation Resource (STAR)

    NASA Astrophysics Data System (ADS)

    Showalter, C.; Rex, R.; Hurlburt, N. E.; Zita, E. J.

    2009-12-01

    We have written a software suite designed to facilitate solar data analysis by scientists, students, and the public, anticipating enormous datasets from future instruments. Our “STAR" suite includes an interactive learning section explaining 15 classes of solar events. Users learn software tools that exploit humans’ superior ability (over computers) to identify many events. Annotation tools include time slice generation to quantify loop oscillations, the interpolation of event shapes using natural cubic splines (for loops, sigmoids, and filaments) and closed cubic splines (for coronal holes). Learning these tools in an environment where examples are provided prepares new users to comfortably utilize annotation software with new data. Upon completion of our tutorial, users are presented with media of various solar events and asked to identify and annotate the images, to test their mastery of the system. Goals of the project include public input into the data analysis of very large datasets from future solar satellites, and increased public interest and knowledge about the Sun. In 2010, the Solar Dynamics Observatory (SDO) will be launched into orbit. SDO’s advancements in solar telescope technology will generate a terabyte per day of high-quality data, requiring innovation in data management. While major projects develop automated feature recognition software, so that computers can complete much of the initial event tagging and analysis, still, that software cannot annotate features such as sigmoids, coronal magnetic loops, coronal dimming, etc., due to large amounts of data concentrated in relatively small areas. Previously, solar physicists manually annotated these features, but with the imminent influx of data it is unrealistic to expect specialized researchers to examine every image that computers cannot fully process. A new approach is needed to efficiently process these data. Providing analysis tools and data access to students and the public have proven

  4. Multi-Platform Metabolomic Analyses of Ergosterol-Induced Dynamic Changes in Nicotiana tabacum Cells

    PubMed Central

    Tugizimana, Fidele; Steenkamp, Paul A.; Piater, Lizelle A.; Dubery, Ian A.

    2014-01-01

    Metabolomics is providing new dimensions into understanding the intracellular adaptive responses in plants to external stimuli. In this study, a multi-technology-metabolomic approach was used to investigate the effect of the fungal sterol, ergosterol, on the metabolome of cultured tobacco cells. Cell suspensions were treated with different concentrations (0–1000 nM) of ergosterol and incubated for different time periods (0–24 h). Intracellular metabolites were extracted with two methods: a selective dispersive liquid-liquid micro-extraction and a general methanol extraction. Chromatographic techniques (GC-FID, GC-MS, GC×GC-TOF-MS, UHPLC-MS) and 1H NMR spectroscopy were used for quantitative and qualitative analyses. Multivariate data analyses (PCA and OPLS-DA models) were used to extract interpretable information from the multidimensional data generated from the analytical techniques. The results showed that ergosterol triggered differential changes in the metabolome of the cells, leading to variation in the biosynthesis of secondary metabolites. PCA scores plots revealed dose- and time-dependent metabolic variations, with optimal treatment conditions being found to be 300 nM ergosterol and an 18 h incubation period. The observed ergosterol-induced metabolic changes were correlated with changes in defence-related metabolites. The ‘defensome’ involved increases in terpenoid metabolites with five antimicrobial compounds (the bicyclic sesquiterpenoid phytoalexins: phytuberin, solavetivone, capsidiol, lubimin and rishitin) and other metabolites (abscisic acid and phytosterols) putatively identified. In addition, various phenylpropanoid precursors, cinnamic acid derivatives and - conjugates, coumarins and lignin monomers were annotated. These annotated metabolites revealed a dynamic reprogramming of metabolic networks that are functionally correlated, with a high complexity in their regulation. PMID:24498209

  5. Multi-platform metabolomic analyses of ergosterol-induced dynamic changes in Nicotiana tabacum cells.

    PubMed

    Tugizimana, Fidele; Steenkamp, Paul A; Piater, Lizelle A; Dubery, Ian A

    2014-01-01

    Metabolomics is providing new dimensions into understanding the intracellular adaptive responses in plants to external stimuli. In this study, a multi-technology-metabolomic approach was used to investigate the effect of the fungal sterol, ergosterol, on the metabolome of cultured tobacco cells. Cell suspensions were treated with different concentrations (0-1000 nM) of ergosterol and incubated for different time periods (0-24 h). Intracellular metabolites were extracted with two methods: a selective dispersive liquid-liquid micro-extraction and a general methanol extraction. Chromatographic techniques (GC-FID, GC-MS, GC × GC-TOF-MS, UHPLC-MS) and (1)H NMR spectroscopy were used for quantitative and qualitative analyses. Multivariate data analyses (PCA and OPLS-DA models) were used to extract interpretable information from the multidimensional data generated from the analytical techniques. The results showed that ergosterol triggered differential changes in the metabolome of the cells, leading to variation in the biosynthesis of secondary metabolites. PCA scores plots revealed dose- and time-dependent metabolic variations, with optimal treatment conditions being found to be 300 nM ergosterol and an 18 h incubation period. The observed ergosterol-induced metabolic changes were correlated with changes in defence-related metabolites. The 'defensome' involved increases in terpenoid metabolites with five antimicrobial compounds (the bicyclic sesquiterpenoid phytoalexins: phytuberin, solavetivone, capsidiol, lubimin and rishitin) and other metabolites (abscisic acid and phytosterols) putatively identified. In addition, various phenylpropanoid precursors, cinnamic acid derivatives and - conjugates, coumarins and lignin monomers were annotated. These annotated metabolites revealed a dynamic reprogramming of metabolic networks that are functionally correlated, with a high complexity in their regulation. PMID:24498209

  6. Metabolomic Profiling of 13 Diatom Cultures and Their Adaptation to Nitrate-Limited Growth Conditions

    PubMed Central

    Bromke, Mariusz A.; Sabir, Jamal S.; Alfassi, Fahad A.; Hajarah, Nahid H.; Kabli, Saleh A.; Al-Malki, Abdulrahman L.; Ashworth, Matt P.; Méret, Michaël; Jansen, Robert K.; Willmitzer, Lothar

    2015-01-01

    Diatoms are very efficient in their use of available nutrients. Changes in nutrient availability influence the metabolism and the composition of the cell constituents. Since diatoms are valuable candidates to search for oil producing algae, measurements of diatom-produced compounds can be very useful for biotechnology. In order to explore the diversity of lipophilic compounds produced by diatoms, we describe the results from an analysis of 13 diatom strains. With the help of a lipidomics platform, which combines an UPLC separation with a high resolution/high mass accuracy mass spectrometer, we were able to measure and annotate 142 lipid species. Out of these, 32 were present in all 13 cultures. The annotated lipid features belong to six classes of glycerolipids. The data obtained from the measurements were used to create lipidomic profiles. The metabolomic overview of analysed cultures is amended by the measurement of 96 polar compounds. To further increase the lipid diversity and gain insight into metabolomic adaptation to nitrogen limitation, diatoms were cultured in media with high and low concentrations of nitrate. The growth in nitrogen-deplete or nitrogen-replete conditions affects metabolite accumulation but has no major influence on the species-specific metabolomic profile. Thus, the genetic component is stronger in determining metabolic patterns than nitrogen levels. Therefore, lipid profiling is powerful enough to be used as a molecular fingerprint for diatom cultures. Furthermore, an increase of triacylglycerol (TAG) accumulation was observed in low nitrogen samples, although this trend was not consistent across all 13 diatom strains. Overall, our results expand the current understanding of metabolomics diversity in diatoms and confirm their potential value for producing lipids for either bioenergy or as feed stock. PMID:26440112

  7. The Ensembl gene annotation system.

    PubMed

    Aken, Bronwen L; Ayling, Sarah; Barrell, Daniel; Clarke, Laura; Curwen, Valery; Fairley, Susan; Fernandez Banet, Julio; Billis, Konstantinos; García Girón, Carlos; Hourlier, Thibaut; Howe, Kevin; Kähäri, Andreas; Kokocinski, Felix; Martin, Fergal J; Murphy, Daniel N; Nag, Rishi; Ruffier, Magali; Schuster, Michael; Tang, Y Amy; Vogel, Jan-Hinnerk; White, Simon; Zadissa, Amonida; Flicek, Paul; Searle, Stephen M J

    2016-01-01

    The Ensembl gene annotation system has been used to annotate over 70 different vertebrate species across a wide range of genome projects. Furthermore, it generates the automatic alignment-based annotation for the human and mouse GENCODE gene sets. The system is based on the alignment of biological sequences, including cDNAs, proteins and RNA-seq reads, to the target genome in order to construct candidate transcript models. Careful assessment and filtering of these candidate transcripts ultimately leads to the final gene set, which is made available on the Ensembl website. Here, we describe the annotation process in detail.Database URL: http://www.ensembl.org/index.html. PMID:27337980

  8. The Ensembl gene annotation system

    PubMed Central

    Aken, Bronwen L.; Ayling, Sarah; Barrell, Daniel; Clarke, Laura; Curwen, Valery; Fairley, Susan; Fernandez Banet, Julio; Billis, Konstantinos; García Girón, Carlos; Hourlier, Thibaut; Howe, Kevin; Kähäri, Andreas; Kokocinski, Felix; Martin, Fergal J.; Murphy, Daniel N.; Nag, Rishi; Ruffier, Magali; Schuster, Michael; Tang, Y. Amy; Vogel, Jan-Hinnerk; White, Simon; Zadissa, Amonida; Flicek, Paul

    2016-01-01

    The Ensembl gene annotation system has been used to annotate over 70 different vertebrate species across a wide range of genome projects. Furthermore, it generates the automatic alignment-based annotation for the human and mouse GENCODE gene sets. The system is based on the alignment of biological sequences, including cDNAs, proteins and RNA-seq reads, to the target genome in order to construct candidate transcript models. Careful assessment and filtering of these candidate transcripts ultimately leads to the final gene set, which is made available on the Ensembl website. Here, we describe the annotation process in detail. Database URL: http://www.ensembl.org/index.html PMID:27337980

  9. Detection of gene annotations and protein-protein interaction associated disorders through transitive relationships between integrated annotations

    PubMed Central

    2015-01-01

    Background Increasingly high amounts of heterogeneous and valuable controlled biomolecular annotations are available, but far from exhaustive and scattered in many databases. Several annotation integration and prediction approaches have been proposed, but these issues are still unsolved. We previously created a Genomic and Proteomic Knowledge Base (GPKB) that efficiently integrates many distributed biomolecular annotation and interaction data of several organisms, including 32,956,102 gene annotations, 273,522,470 protein annotations and 277,095 protein-protein interactions (PPIs). Results By comprehensively leveraging transitive relationships defined by the numerous association data integrated in GPKB, we developed a software procedure that effectively detects and supplement consistent biomolecular annotations not present in the integrated sources. According to some defined logic rules, it does so only when the semantic type of data and of their relationships, as well as the cardinality of the relationships, allow identifying molecular biology compliant annotations. Thanks to controlled consistency and quality enforced on data integrated in GPKB, and to the procedures used to avoid error propagation during their automatic processing, we could reliably identify many annotations, which we integrated in GPKB. They comprise 3,144 gene to pathway and 21,942 gene to biological function annotations of many organisms, and 1,027 candidate associations between 317 genetic disorders and 782 human PPIs. Overall estimated recall and precision of our approach were 90.56 % and 96.61 %, respectively. Co-functional evaluation of genes with known function showed high functional similarity between genes with new detected and known annotation to the same pathway; considering also the new detected gene functional annotations enhanced such functional similarity, which resembled the one existing between genes known to be annotated to the same pathway. Strong evidence was also found in

  10. Widowed Persons Service: Selected Annotated Bibliography.

    ERIC Educational Resources Information Center

    Bressler, Dawn, Comp.; And Others

    This document presents an annotated bibliography of books and articles on topics relevant to widowhood. These annotations are included: (1) 21 annotations on the grief process; (2) 11 annotations on personal observations about widowhood; (3) 16 annotations on practical problems surrounding widowhood, including legal and financial problems and job…

  11. MEETING: Chlamydomonas Annotation Jamboree - October 2003

    SciTech Connect

    Grossman, Arthur R

    2007-04-13

    Shotgun sequencing of the nuclear genome of Chlamydomonas reinhardtii (Chlamydomonas throughout) was performed at an approximate 10X coverage by JGI. Roughly half of the genome is now contained on 26 scaffolds, all of which are at least 1.6 Mb, and the coverage of the genome is ~95%. There are now over 200,000 cDNA sequence reads that we have generated as part of the Chlamydomonas genome project (Grossman, 2003; Shrager et al., 2003; Grossman et al. 2007; Merchant et al., 2007); other sequences have also been generated by the Kasuza sequence group (Asamizu et al., 1999; Asamizu et al., 2000) or individual laboratories that have focused on specific genes. Shrager et al. (2003) placed the reads into distinct contigs (an assemblage of reads with overlapping nucleotide sequences), and contigs that group together as part of the same genes have been designated ACEs (assembly of contigs generated from EST information). All of the reads have also been mapped to the Chlamydomonas nuclear genome and the cDNAs and their corresponding genomic sequences have been reassembled, and the resulting assemblage is called an ACEG (an Assembly of contiguous EST sequences supported by genomic sequence) (Jain et al., 2007). Most of the unique genes or ACEGs are also represented by gene models that have been generated by the Joint Genome Institute (JGI, Walnut Creek, CA). These gene models have been placed onto the DNA scaffolds and are presented as a track on the Chlamydomonas genome browser associated with the genome portal (http://genome.jgi-psf.org/Chlre3/Chlre3.home.html). Ultimately, the meeting grant awarded by DOE has helped enormously in the development of an annotation pipeline (a set of guidelines used in the annotation of genes) and resulted in high quality annotation of over 4,000 genes; the annotators were from both Europe and the USA. Some of the people who led the annotation initiative were Arthur Grossman, Olivier Vallon, and Sabeeha Merchant (with many individual

  12. HMDB: the Human Metabolome Database.

    PubMed

    Wishart, David S; Tzur, Dan; Knox, Craig; Eisner, Roman; Guo, An Chi; Young, Nelson; Cheng, Dean; Jewell, Kevin; Arndt, David; Sawhney, Summit; Fung, Chris; Nikolai, Lisa; Lewis, Mike; Coutouly, Marie-Aude; Forsythe, Ian; Tang, Peter; Shrivastava, Savita; Jeroncic, Kevin; Stothard, Paul; Amegbey, Godwin; Block, David; Hau, David D; Wagner, James; Miniaci, Jessica; Clements, Melisa; Gebremedhin, Mulu; Guo, Natalie; Zhang, Ying; Duggan, Gavin E; Macinnis, Glen D; Weljie, Alim M; Dowlatabadi, Reza; Bamforth, Fiona; Clive, Derrick; Greiner, Russ; Li, Liang; Marrie, Tom; Sykes, Brian D; Vogel, Hans J; Querengesser, Lori

    2007-01-01

    The Human Metabolome Database (HMDB) is currently the most complete and comprehensive curated collection of human metabolite and human metabolism data in the world. It contains records for more than 2180 endogenous metabolites with information gathered from thousands of books, journal articles and electronic databases. In addition to its comprehensive literature-derived data, the HMDB also contains an extensive collection of experimental metabolite concentration data compiled from hundreds of mass spectra (MS) and Nuclear Magnetic resonance (NMR) metabolomic analyses performed on urine, blood and cerebrospinal fluid samples. This is further supplemented with thousands of NMR and MS spectra collected on purified, reference metabolites. Each metabolite entry in the HMDB contains an average of 90 separate data fields including a comprehensive compound description, names and synonyms, structural information, physico-chemical data, reference NMR and MS spectra, biofluid concentrations, disease associations, pathway information, enzyme data, gene sequence data, SNP and mutation data as well as extensive links to images, references and other public databases. Extensive searching, relational querying and data browsing tools are also provided. The HMDB is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. The HMDB is available at: www.hmdb.ca. PMID:17202168

  13. Metabolomic Fingerprinting: Challenges and Opportunities

    PubMed Central

    Kosmides, Alyssa K.; Kamisoglu, Kubra; Calvano, Steve E.; Corbett, Siobhan A.; Androulakis, Ioannis P.

    2014-01-01

    Systems biology has primarily focused on studying genomics, transcriptomics, and proteomics and their dynamic interactions. These, however, represent only the potential for a biological outcome since the ultimate phenotype at the level of the eventually produced metabolites is not taken into consideration. The emerging field of metabolomics provides complementary guidance toward an integrated approach to this problem: It allows global profiling of the metabolites of a cell, tissue, or host and presents information on the actual end points of a response. A wide range of data collection methods are currently used and allow the extraction of global or tissue-specific metabolic profiles. The great amount and complexity of data that are collected require multivariate analysis techniques, but the increasing amount of work in this field has made easy-to-use analysis programs readily available. Metabolomics has already shown great potential in drug toxicity studies, disease modeling, and diagnostics and may be integrated with genomic and proteomic data in the future to provide in-depth understanding of systems, pathways, and their functionally dynamic interactions. In this review we discuss the current state of the art of metabolomics, its applications, and future potential. PMID:24579644

  14. Metabolomic assessment of embryo viability.

    PubMed

    Uyar, Asli; Seli, Emre

    2014-03-01

    Preimplantation embryo metabolism demonstrates distinctive characteristics associated with the developmental potential of embryos. On this basis, metabolite content of culture media was hypothesized to reflect the implantation potential of individual embryos. This hypothesis was tested in consecutive studies reporting a significant association between culture media metabolites and embryo development or clinical pregnancy. The need for a noninvasive, reliable, and rapid embryo assessment strategy promoted metabolomics studies in vitro fertilization (IVF) in an effort to increase success rates of single embryo transfers. With the advance of analytical techniques and bioinformatics, commercial instruments were developed to predict embryo viability using spectroscopic analysis of surplus culture media. However, despite the initial promising results from proof-of-principal studies, recent randomized controlled trials using commercial instruments failed to show a consistent benefit in improving pregnancy rates when metabolomics is used as an adjunct to morphology. At present, the application of metabolomics technology in clinical IVF laboratory requires the elimination of factors underlying inconsistent findings, when possible, and development of reliable predictive models accounting for all possible sources of bias throughout the embryo selection process. PMID:24515909

  15. HMDB: the Human Metabolome Database

    PubMed Central

    Wishart, David S.; Tzur, Dan; Knox, Craig; Eisner, Roman; Guo, An Chi; Young, Nelson; Cheng, Dean; Jewell, Kevin; Arndt, David; Sawhney, Summit; Fung, Chris; Nikolai, Lisa; Lewis, Mike; Coutouly, Marie-Aude; Forsythe, Ian; Tang, Peter; Shrivastava, Savita; Jeroncic, Kevin; Stothard, Paul; Amegbey, Godwin; Block, David; Hau, David. D.; Wagner, James; Miniaci, Jessica; Clements, Melisa; Gebremedhin, Mulu; Guo, Natalie; Zhang, Ying; Duggan, Gavin E.; MacInnis, Glen D.; Weljie, Alim M.; Dowlatabadi, Reza; Bamforth, Fiona; Clive, Derrick; Greiner, Russ; Li, Liang; Marrie, Tom; Sykes, Brian D.; Vogel, Hans J.; Querengesser, Lori

    2007-01-01

    The Human Metabolome Database (HMDB) is currently the most complete and comprehensive curated collection of human metabolite and human metabolism data in the world. It contains records for more than 2180 endogenous metabolites with information gathered from thousands of books, journal articles and electronic databases. In addition to its comprehensive literature-derived data, the HMDB also contains an extensive collection of experimental metabolite concentration data compiled from hundreds of mass spectra (MS) and Nuclear Magnetic resonance (NMR) metabolomic analyses performed on urine, blood and cerebrospinal fluid samples. This is further supplemented with thousands of NMR and MS spectra collected on purified, reference metabolites. Each metabolite entry in the HMDB contains an average of 90 separate data fields including a comprehensive compound description, names and synonyms, structural information, physico-chemical data, reference NMR and MS spectra, biofluid concentrations, disease associations, pathway information, enzyme data, gene sequence data, SNP and mutation data as well as extensive links to images, references and other public databases. Extensive searching, relational querying and data browsing tools are also provided. The HMDB is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. The HMDB is available at: PMID:17202168

  16. An integrated computational pipeline and database to support whole-genome sequence annotation

    PubMed Central

    Mungall, CJ; Misra, S; Berman, BP; Carlson, J; Frise, E; Harris, N; Marshall, B; Shu, S; Kaminker, JS; Prochnik, SE; Smith, CD; Smith, E; Tupy, JL; Wiel, C; Rubin, GM; Lewis, SE

    2002-01-01

    We describe here our experience in annotating the Drosophila melanogaster genome sequence, in the course of which we developed several new open-source software tools and a database schema to support large-scale genome annotation. We have developed these into an integrated and reusable software system for whole-genome annotation. The key contributions to overall annotation quality are the marshalling of high-quality sequences for alignments and the design of a system with an adaptable and expandable flexible architecture. PMID:12537570

  17. Communication and Gender: Annotated Bibliography

    ERIC Educational Resources Information Center

    Todd-Mancillas, William R.; Krug, Linda

    Focusing on the similarities and differences in men's and women's verbal and nonverbal communication behavior, this 33-item annotated bibliography presents a sample of articles appearing in speech communication publications on the subject. Categories of the annotated bibliography are books, sexism and sexual harassment in academia, theoretic…

  18. Drug Education: An Annotated Bibliography.

    ERIC Educational Resources Information Center

    Mathieson, Moira B.

    This bibliography consists of a total of 215 entries dealing with drug education, including curriculum guides, and drawn from documents in the ERIC system. There are two sections, the first containing 130 annotated citations of documents and journal articles, and the second containing 85 citations of journal articles without annotations, but with…

  19. Women in Communication: Annotated Bibliography.

    ERIC Educational Resources Information Center

    Mills, Carol A.

    This annotated bibliography is designed to survey the field of women in communication. The bibliography is centered on a specific context: who are and who were the women who worked in the communication field, and specifically, what were their writings like? The 56 annotations date from 1949 through 1990 and deal mostly with books (especially…

  20. Effects of pre-analytical processes on blood samples used in metabolomics studies.

    PubMed

    Yin, Peiyuan; Lehmann, Rainer; Xu, Guowang

    2015-07-01

    Every day, analytical and bio-analytical chemists make sustained efforts to improve the sensitivity, specificity, robustness, and reproducibility of their methods. Especially in targeted and non-targeted profiling approaches, including metabolomics analysis, these objectives are not easy to achieve; however, robust and reproducible measurements and low coefficients of variation (CV) are crucial for successful metabolomics approaches. Nevertheless, all efforts from the analysts are in vain if the sample quality is poor, i.e. if preanalytical errors are made by the partner during sample collection. Preanalytical risks and errors are more common than expected, even when standard operating procedures (SOP) are used. This risk is particularly high in clinical studies, and poor sample quality may heavily bias the CV of the final analytical results, leading to disappointing outcomes of the study and consequently, although unjustified, to critical questions about the analytical performance of the approach from the partner who provided the samples. This review focuses on the preanalytical phase of liquid chromatography-mass spectrometry-driven metabolomics analysis of body fluids. Several important preanalytical factors that may seriously affect the profile of the investigated metabolome in body fluids, including factors before sample collection, blood drawing, subsequent handling of the whole blood (transportation), processing of plasma and serum, and inadequate conditions for sample storage, will be discussed. In addition, a detailed description of latent effects on the stability of the blood metabolome and a suggestion for a practical procedure to circumvent risks in the preanalytical phase will be given. PMID:25736245

  1. Model and Interoperability using Meta Data Annotations

    NASA Astrophysics Data System (ADS)

    David, O.

    2011-12-01

    modeling components are not directly bound to framework by the use of specific APIs and/or data types they can more easily be reused both within the framework as well as outside. While providing all those capabilities, a significant reduction in the size of the model source code was achieved. To support the benefit of annotations for a modeler, studies were conducted to evaluate the effectiveness of an annotation based framework approach with other modeling frameworks and libraries, a framework-invasiveness study was conducted to evaluate the effects of framework design on model code quality. A typical hydrological model was implemented across several modeling frameworks and several software metrics were collected. The metrics selected were measures of non-invasive design methods for modeling frameworks from a software engineering perspective. It appears that the use of annotations positively impacts several software quality measures. Experience to date has demonstrated the multi-purpose value of using annotations. Annotations are also a feasible and practical method to enable interoperability among models and modeling frameworks.

  2. Development of a universal metabolome-standard method for long-term LC-MS metabolome profiling and its application for bladder cancer urine-metabolite-biomarker discovery.

    PubMed

    Peng, Jun; Chen, Yi-Ting; Chen, Chien-Lun; Li, Liang

    2014-07-01

    Large-scale metabolomics study requires a quantitative method to generate metabolome data over an extended period with high technical reproducibility. We report a universal metabolome-standard (UMS) method, in conjunction with chemical isotope labeling liquid chromatography-mass spectrometry (LC-MS), to provide long-term analytical reproducibility and facilitate metabolome comparison among different data sets. In this method, UMS of a specific type of sample labeled by an isotope reagent is prepared a priori. The UMS is spiked into any individual samples labeled by another form of the isotope reagent in a metabolomics study. The resultant mixture is analyzed by LC-MS to provide relative quantification of the individual sample metabolome to UMS. UMS is independent of a study undertaking as well as the time of analysis and useful for profiling the same type of samples in multiple studies. In this work, the UMS method was developed and applied for a urine metabolomics study of bladder cancer. UMS of human urine was prepared by (13)C2-dansyl labeling of a pooled sample from 20 healthy individuals. This method was first used to profile the discovery samples to generate a list of putative biomarkers potentially useful for bladder cancer detection and then used to analyze the verification samples about one year later. Within the discovery sample set, three-month technical reproducibility was examined using a quality control sample and found a mean CV of 13.9% and median CV of 9.4% for all the quantified metabolites. Statistical analysis of the urine metabolome data showed a clear separation between the bladder cancer group and the control group from the discovery samples, which was confirmed by the verification samples. Receiver operating characteristic (ROC) test showed that the area under the curve (AUC) was 0.956 in the discovery data set and 0.935 in the verification data set. These results demonstrated the utility of the UMS method for long-term metabolomics and

  3. A semantic analysis of the annotations of the human genome.

    PubMed

    Khatri, Purvesh; Done, Bogdan; Rao, Archana; Done, Arina; Draghici, Sorin

    2005-08-15

    The correct interpretation of any biological experiment depends in an essential way on the accuracy and consistency of the existing annotation databases. Such databases are ubiquitous and used by all life scientists in most experiments. However, it is well known that such databases are incomplete and many annotations may also be incorrect. In this paper we describe a technique that can be used to analyze the semantic content of such annotation databases. Our approach is able to extract implicit semantic relationships between genes and functions. This ability allows us to discover novel functions for known genes. This approach is able to identify missing and inaccurate annotations in existing annotation databases, and thus help improve their accuracy. We used our technique to analyze the current annotations of the human genome. From this body of annotations, we were able to predict 212 additional gene-function assignments. A subsequent literature search found that 138 of these gene-functions assignments are supported by existing peer-reviewed papers. An additional 23 assignments have been confirmed in the meantime by the addition of the respective annotations in later releases of the Gene Ontology database. Overall, the 161 confirmed assignments represent 75.95% of the proposed gene-function assignments. Only one of our predictions (0.4%) was contradicted by the existing literature. We could not find any relevant articles for 50 of our predictions (23.58%). The method is independent of the organism and can be used to analyze and improve the quality of the data of any public or private annotation database. PMID:15955782

  4. Alterations in Human Liver Metabolome during Prolonged Cryostorage.

    PubMed

    Abuja, Peter M; Ehrhart, Friederike; Schoen, Uwe; Schmidt, Tomm; Stracke, Frank; Dallmann, Guido; Friedrich, Torben; Zimmermann, Heiko; Zatloukal, Kurt

    2015-07-01

    Tissue metabolomics requires high sample quality that crucially depends on the biobanking storage protocol. Hence, we systematically analyzed the influence of realistic storage scenarios on the liver metabolome with different storage temperatures and repeated transfer of samples between storage and retrieval environments, simulating the repeated temperature changes affecting unrelated samples stored in the same container as the sample that is to be retrieved. By cycling between storage (-80 °C freezer, liquid nitrogen, cold nitrogen gas) and retrieval (room temperature, -80 °C), assuming three cycles per day and sample, we simulated biobank storage between 3 months and 10 years. Liver tissue metabolome was analyzed by liquid chromatography/mass spectrometry. Most metabolite concentrations changed <5% for the first "year" of time-compressed biobanking simulation, predominantly due to hydrolysis of peptides and lipids. Interestingly, storage temperature affected metabolite concentrations only little, while there was a linear dependence on the number of temperature change cycles. Elevated sample temperature during (prolonged) retrieval time led to a distinctly different signature of metabolite changes that were induced by cycling. Our findings allow giving recommendations for optimized storage protocols and provide signatures that allow detection of deviations from protocol. PMID:26036795

  5. Current Advances in the Metabolomics Study on Lotus Seeds

    PubMed Central

    Zhu, Mingzhi; Liu, Ting; Guo, Mingquan

    2016-01-01

    Lotus (Nelumbo nucifera), which is distributed widely throughout Asia, Australia and North America, is an aquatic perennial that has been cultivated for over 2,000 years. It is very stimulating that almost all parts of lotus have been consumed as vegetable as well as food, especially the seeds. Except for the nutritive values of lotus, there has been increasing interest in its potential as functional food due to its rich secondary metabolites, such as flavonoids and alkaloids. Not only have these metabolites greatly contributed to the biological process of lotus seeds, but also have been reported to possess multiple health-promoting effects, including antioxidant, anti-amnesic, anti-inflammatory, and anti-tumor activities. Thus, comprehensive metabolomic profiling of these metabolites is of key importance to help understand their biological activities, and other chemical biology features. In this context, this review will provide an update on the current technological platforms, and workflow associated with metabolomic studies on lotus seeds, as well as insights into the application of metabolomics for the improvement of food safety and quality, assisting breeding, and promotion of the study of metabolism and pharmacokinetics of lotus seeds; meanwhile it will also help explore new perspectives and outline future challenges in this fast-growing research subject. PMID:27379154

  6. Current Advances in the Metabolomics Study on Lotus Seeds.

    PubMed

    Zhu, Mingzhi; Liu, Ting; Guo, Mingquan

    2016-01-01

    Lotus (Nelumbo nucifera), which is distributed widely throughout Asia, Australia and North America, is an aquatic perennial that has been cultivated for over 2,000 years. It is very stimulating that almost all parts of lotus have been consumed as vegetable as well as food, especially the seeds. Except for the nutritive values of lotus, there has been increasing interest in its potential as functional food due to its rich secondary metabolites, such as flavonoids and alkaloids. Not only have these metabolites greatly contributed to the biological process of lotus seeds, but also have been reported to possess multiple health-promoting effects, including antioxidant, anti-amnesic, anti-inflammatory, and anti-tumor activities. Thus, comprehensive metabolomic profiling of these metabolites is of key importance to help understand their biological activities, and other chemical biology features. In this context, this review will provide an update on the current technological platforms, and workflow associated with metabolomic studies on lotus seeds, as well as insights into the application of metabolomics for the improvement of food safety and quality, assisting breeding, and promotion of the study of metabolism and pharmacokinetics of lotus seeds; meanwhile it will also help explore new perspectives and outline future challenges in this fast-growing research subject. PMID:27379154

  7. Establishment of Protocols for Global Metabolomics by LC-MS for Biomarker Discovery.

    PubMed

    Saigusa, Daisuke; Okamura, Yasunobu; Motoike, Ikuko N; Katoh, Yasutake; Kurosawa, Yasuhiro; Saijyo, Reina; Koshiba, Seizo; Yasuda, Jun; Motohashi, Hozumi; Sugawara, Junichi; Tanabe, Osamu; Kinoshita, Kengo; Yamamoto, Masayuki

    2016-01-01

    Metabolomics is a promising avenue for biomarker discovery. Although the quality of metabolomic analyses, especially global metabolomics (G-Met) using mass spectrometry (MS), largely depends on the instrumentation, potential bottlenecks still exist at several basic levels in the metabolomics workflow. Therefore, we established a precise protocol initially for the G-Met analyses of human blood plasma to overcome some these difficulties. In our protocol, samples are deproteinized in a 96-well plate using an automated liquid-handling system, and conducted either using a UHPLC-QTOF/MS system equipped with a reverse phase column or a LC-FTMS system equipped with a normal phase column. A normalization protocol of G-Met data was also developed to compensate for intra- and inter-batch differences, and the variations were significantly reduced along with our normalization, especially for the UHPLC-QTOF/MS data with a C18 reverse-phase column for positive ions. Secondly, we examined the changes in metabolomic profiles caused by the storage of EDTA-blood specimens to identify quality markers for the evaluation of the specimens' pre-analytical conditions. Forty quality markers, including lysophospholipids, dipeptides, fatty acids, succinic acid, amino acids, glucose, and uric acid were identified by G-Met for the evaluation of plasma sample quality and established the equation of calculating the quality score. We applied our quality markers to a small-scale study to evaluate the quality of clinical samples. The G-Met protocols and quality markers established here should prove useful for the discovery and development of biomarkers for a wider range of diseases. PMID:27579980

  8. Tissue-Based Metabolomics to Analyze the Breast Cancer Metabolome.

    PubMed

    Budczies, Jan; Denkert, Carsten

    2016-01-01

    Mass spectrometry and nuclear magnetic resonance-based metabolomics have been developed into mature technologies that can be utilized to analyze hundreds of biological samples in a high-throughput manner. Over the past few years, both technologies were utilized to analyze large cohorts of fresh frozen breast cancer tissues. Metabolite biomarkers were shown to separate breast cancer tissues from normal breast tissues with high sensitivity and specificity. Furthermore, the metabolome differed between hormone receptor positive (HR+) and hormone receptor negative (HR-) breast cancer, but was unchanged in HER2+ tumors compared to HER2- tumors. New metabolism-related biomarkers were discovered including the 4-aminobutyrate aminotransferase ABAT, where low mRNA expression led to an accumulation of beta-alanine and shortened relapse-free survival. The glutamate-to-glutamine ratio (GGR) represents another new biomarker that was increased in 88 % of HR- tumors and 56 % of HR+ tumors compared to normal breast tissues. The GGR might help to stratify patients for the treatment with specific glutaminase inhibitors that were recently developed and are currently being tested in phase I clinical studies. Surprisingly, 2-hydroxyglutarate (2-HG), initially found to accumulate in isocitrate dehydrogenase (IDH) mutated gliomas and leukemias and described as an oncometabolite, was detected to be drastically increased in several breast carcinomas in the absence of IDH mutations. In summary, metabolomics analysis of breast cancer tissues is a reliable method and has produced many new biological insights that may impact breast cancer diagnostics and treatment over the coming years. PMID:27557538

  9. Sma3s: a three-step modular annotator for large sequence datasets.

    PubMed

    Muñoz-Mérida, Antonio; Viguera, Enrique; Claros, M Gonzalo; Trelles, Oswaldo; Pérez-Pulido, Antonio J

    2014-08-01

    Automatic sequence annotation is an essential component of modern 'omics' studies, which aim to extract information from large collections of sequence data. Most existing tools use sequence homology to establish evolutionary relationships and assign putative functions to sequences. However, it can be difficult to define a similarity threshold that achieves sufficient coverage without sacrificing annotation quality. Defining the correct configuration is critical and can be challenging for non-specialist users. Thus, the development of robust automatic annotation techniques that generate high-quality annotations without needing expert knowledge would be very valuable for the research community. We present Sma3s, a tool for automatically annotating very large collections of biological sequences from any kind of gene library or genome. Sma3s is composed of three modules that progressively annotate query sequences using either: (i) very similar homologues, (ii) orthologous sequences or (iii) terms enriched in groups of homologous sequences. We trained the system using several random sets of known sequences, demonstrating average sensitivity and specificity values of ~85%. In conclusion, Sma3s is a versatile tool for high-throughput annotation of a wide variety of sequence datasets that outperforms the accuracy of other well-established annotation algorithms, and it can enrich existing database annotations and uncover previously hidden features. Importantly, Sma3s has already been used in the functional annotation of two published transcriptomes. PMID:24501397

  10. Sma3s: A Three-Step Modular Annotator for Large Sequence Datasets

    PubMed Central

    Muñoz-Mérida, Antonio; Viguera, Enrique; Claros, M. Gonzalo; Trelles, Oswaldo; Pérez-Pulido, Antonio J.

    2014-01-01

    Automatic sequence annotation is an essential component of modern ‘omics’ studies, which aim to extract information from large collections of sequence data. Most existing tools use sequence homology to establish evolutionary relationships and assign putative functions to sequences. However, it can be difficult to define a similarity threshold that achieves sufficient coverage without sacrificing annotation quality. Defining the correct configuration is critical and can be challenging for non-specialist users. Thus, the development of robust automatic annotation techniques that generate high-quality annotations without needing expert knowledge would be very valuable for the research community. We present Sma3s, a tool for automatically annotating very large collections of biological sequences from any kind of gene library or genome. Sma3s is composed of three modules that progressively annotate query sequences using either: (i) very similar homologues, (ii) orthologous sequences or (iii) terms enriched in groups of homologous sequences. We trained the system using several random sets of known sequences, demonstrating average sensitivity and specificity values of ∼85%. In conclusion, Sma3s is a versatile tool for high-throughput annotation of a wide variety of sequence datasets that outperforms the accuracy of other well-established annotation algorithms, and it can enrich existing database annotations and uncover previously hidden features. Importantly, Sma3s has already been used in the functional annotation of two published transcriptomes. PMID:24501397

  11. Automated Eukaryotic Gene Structure Annotation Using EVidenceModeler and the Program to Assemble Spliced Alignments

    SciTech Connect

    Haas, B J; Salzberg, S L; Zhu, W; Pertea, M; Allen, J E; Orvis, J; White, O; Buell, C R; Wortman, J R

    2007-12-10

    EVidenceModeler (EVM) is presented as an automated eukaryotic gene structure annotation tool that reports eukaryotic gene structures as a weighted consensus of all available evidence. EVM, when combined with the Program to Assemble Spliced Alignments (PASA), yields a comprehensive, configurable annotation system that predicts protein-coding genes and alternatively spliced isoforms. Our experiments on both rice and human genome sequences demonstrate that EVM produces automated gene structure annotation approaching the quality of manual curation.

  12. Global LC/MS Metabolomics Profiling of Calcium Stressed and Immunosuppressant Drug Treated Saccharomyces cerevisiae.

    PubMed

    Jenkins, Stefan; Fischer, Steven M; Chen, Lily; Sana, Theodore R

    2013-01-01

    Previous studies have shown that calcium stressed Saccharomyces cerevisiae, challenged with immunosuppressant drugs FK506 and Cyclosporin A, responds with comprehensive gene expression changes and attenuation of the generalized calcium stress response. Here, we describe a global metabolomics workflow for investigating the utility of tracking corresponding phenotypic changes. This was achieved by efficiently analyzing relative abundance differences between intracellular metabolite pools from wild-type and calcium stressed cultures, with and without prior immunosuppressant drugs exposure. We used pathway database content from WikiPathways and YeastCyc to facilitate the projection of our metabolomics profiling results onto biological pathways. A key challenge was to increase the coverage of the detected metabolites. This was achieved by applying both reverse phase (RP) and aqueous normal phase (ANP) chromatographic separations, as well as electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) sources for detection in both ion polarities. Unsupervised principle component analysis (PCA) and ANOVA results revealed differentiation between wild-type controls, calcium stressed and immunosuppressant/calcium challenged cells. Untargeted data mining resulted in 247 differentially expressed, annotated metabolites, across at least one pair of conditions. A separate, targeted data mining strategy identified 187 differential, annotated metabolites. All annotated metabolites were subsequently mapped onto curated pathways from YeastCyc and WikiPathways for interactive pathway analysis and visualization. Dozens of pathways showed differential responses to stress conditions based on one or more matches to the list of annotated metabolites or to metabolites that had been identified further by MS/MS. The purine salvage, pantothenate and sulfur amino acid pathways were flagged as being enriched, which is consistent with previously published literature for

  13. Global LC/MS Metabolomics Profiling of Calcium Stressed and Immunosuppressant Drug Treated Saccharomyces cerevisiae

    PubMed Central

    Jenkins, Stefan; Fischer, Steven M.; Chen, Lily; Sana, Theodore R.

    2013-01-01

    Previous studies have shown that calcium stressed Saccharomyces cerevisiae, challenged with immunosuppressant drugs FK506 and Cyclosporin A, responds with comprehensive gene expression changes and attenuation of the generalized calcium stress response. Here, we describe a global metabolomics workflow for investigating the utility of tracking corresponding phenotypic changes. This was achieved by efficiently analyzing relative abundance differences between intracellular metabolite pools from wild-type and calcium stressed cultures, with and without prior immunosuppressant drugs exposure. We used pathway database content from WikiPathways and YeastCyc to facilitate the projection of our metabolomics profiling results onto biological pathways. A key challenge was to increase the coverage of the detected metabolites. This was achieved by applying both reverse phase (RP) and aqueous normal phase (ANP) chromatographic separations, as well as electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) sources for detection in both ion polarities. Unsupervised principle component analysis (PCA) and ANOVA results revealed differentiation between wild-type controls, calcium stressed and immunosuppressant/calcium challenged cells. Untargeted data mining resulted in 247 differentially expressed, annotated metabolites, across at least one pair of conditions. A separate, targeted data mining strategy identified 187 differential, annotated metabolites. All annotated metabolites were subsequently mapped onto curated pathways from YeastCyc and WikiPathways for interactive pathway analysis and visualization. Dozens of pathways showed differential responses to stress conditions based on one or more matches to the list of annotated metabolites or to metabolites that had been identified further by MS/MS. The purine salvage, pantothenate and sulfur amino acid pathways were flagged as being enriched, which is consistent with previously published literature for

  14. Video data annotation, archiving, and access

    NASA Astrophysics Data System (ADS)

    Wilkin, D.; Connor, J.; Stout, N. J.; Walz, K.; Schlining, K.; Graybeal, J.

    2002-12-01

    Scientifically useful, high-quality video data can be challenging to integrate with other data, and to analyze and archive for use in ocean science. The Monterey Bay Aquarium Research Institute (MBARI) uses high-resolution video equipment to record over 300 remotely operated vehicle dives per year. Over the past 14 years, 13,000 videotapes have been archived and maintained as a centralized institutional resource. MBARI has developed a set of software applications to annotate and access video data. Users can identify the location of video sequences using a data query component; complex queries can be made by constraining temporal, spatial, or physical parameters (e.g., season, location, or depth). The applications reference a knowledge base of over 3,000 biological, geological and technical terms, providing consistent hierarchical information about objects and associated descriptions for annotating video at sea or on shore. The annotation, knowledge base, and query components together provide a comprehensive video archive software system that can be applied to a variety of scientific disciplines. Also in development, using the XML data format, is an interactive reference interface to explore MBARI's deep-sea knowledge base. When complete, the full software system will be disseminated to the research community via the web or CD, to help meet the challenges inherent in archiving video data.

  15. Plant tissue extraction for metabolomics.

    PubMed

    Roessner, Ute; Dias, Daniel Anthony

    2013-01-01

    Plants are not only important producers of foods and energy storages (e.g., sugars, carbohydrates, proteins, and fats) in the form of grains, fruits, and vegetables, they also provide many valuable products to human existence including wood, fibers, oils, resins, pigments, antioxidants, and sources of medicine. Most importantly in light of this book, plants have been a source of therapeutic and health promoting compounds throughout history. This chapter describes several essential considerations for the extraction process when aiming to study plant metabolism or to characterize the chemical composition of plant originated samples using metabolomics technologies. PMID:23963900

  16. Metabolomics for Biomarker Discovery in Gastroenterological Cancer

    PubMed Central

    Nishiumi, Shin; Suzuki, Makoto; Kobayashi, Takashi; Matsubara, Atsuki; Azuma, Takeshi; Yoshida, Masaru

    2014-01-01

    The study of the omics cascade, which involves comprehensive investigations based on genomics, transcriptomics, proteomics, metabolomics, etc., has developed rapidly and now plays an important role in life science research. Among such analyses, metabolome analysis, in which the concentrations of low molecular weight metabolites are comprehensively analyzed, has rapidly developed along with improvements in analytical technology, and hence, has been applied to a variety of research fields including the clinical, cell biology, and plant/food science fields. The metabolome represents the endpoint of the omics cascade and is also the closest point in the cascade to the phenotype. Moreover, it is affected by variations in not only the expression but also the enzymatic activity of several proteins. Therefore, metabolome analysis can be a useful approach for finding effective diagnostic markers and examining unknown pathological conditions. The number of studies involving metabolome analysis has recently been increasing year-on-year. Here, we describe the findings of studies that used metabolome analysis to attempt to discover biomarker candidates for gastroenterological cancer and discuss metabolome analysis-based disease diagnosis. PMID:25003943

  17. Effective function annotation through catalytic residue conservation.

    PubMed

    George, Richard A; Spriggs, Ruth V; Bartlett, Gail J; Gutteridge, Alex; MacArthur, Malcolm W; Porter, Craig T; Al-Lazikani, Bissan; Thornton, Janet M; Swindells, Mark B

    2005-08-30

    Because of the extreme impact of genome sequencing projects, protein sequences without accompanying experimental data now dominate public databases. Homology searches, by providing an opportunity to transfer functional information between related proteins, have become the de facto way to address this. Although a single, well annotated, close relationship will often facilitate sufficient annotation, this situation is not always the case, particularly if mutations are present in important functional residues. When only distant relationships are available, the transfer of function information is more tenuous, and the likelihood of encountering several well annotated proteins with different functions is increased. The consequence for a researcher is a range of candidate functions with little way of knowing which, if any, are correct. Here, we address the problem directly by introducing a computational approach to accurately identify and segregate related proteins into those with a functional similarity and those where function differs. This approach should find a wide range of applications, including the interpretation of genomics/proteomics data and the prioritization of targets for high-throughput structure determination. The method is generic, but here we concentrate on enzymes and apply high-quality catalytic site data. In addition to providing a series of comprehensive benchmarks to show the overall performance of our approach, we illustrate its utility with specific examples that include the correct identification of haptoglobin as a nonenzymatic relative of trypsin, discrimination of acid-d-amino acid ligases from a much larger ligase pool, and the successful annotation of BioH, a structural genomics target. PMID:16037208

  18. [The use of metabolomics in medicine - some examples of oncological and metabolic diseases].

    PubMed

    Zimny, Dominika; Szatkowska, Marta; Połubok, Joanna; Maciaszek, Julian; Machaj, Mikołaj; Barg, Ewa

    2015-01-01

    Metabolomics is a new field of medicine focused on examining and analyzing metabolites produced in biological cells. Biological fluids primarily used in this method include: plasma, cerebrospinal fluid, saliva and urine. The most common methods of evaluating the composition involve nuclear magnetic resonance (NMR) and magnetic resonance (MR) with addition of gas chromatography (GC-MS) or liquid chromatography (LC-MS). Metabolomics is used in a wide variety of medicine disciplines. The variability of biochemical processes in tumor cells in comparison to normal cells is the starting point of such studies. The metabolomic changes are observed not only in solid tumors, like the mammary tumor, ovarian cancer, prostate cancer but also in tumors of the hematopoietic and lymphoid tissues. Nowadays, the aim of studies is to find biomarkers which would help to diagnose a disease quickly, assess its progression, and implement effective treatment. Metabolomics is also widely applied in metabolic diseases, mainly the diabetes. The list of examined metabolites gives promising chances for a successful prognosis, diagnosis and comprehensive monitoring of the progression of this civilization disease. The development of metabolomics will also contribute to the individualization of treatment, proper drugs adjustment, which will make a therapy more successful, cause less side effects and improve the quality of patient's life. PMID:26615014

  19. Separating the Wheat from the Chaff: Internet Quality.

    ERIC Educational Resources Information Center

    Skov, Annette

    1998-01-01

    Examines guidelines for conducting quality Internet searches, highlighting subject catalogs, annotated directories, annotated directories with ratings or reviews, subject directories with ratings, subject guides, and information gateways. Discusses variable quality; evaluation criteria: metainformation, credibility, timeliness; quality assurance;…

  20. The guard cell metabolome: functions in stomatal movement and global food security

    PubMed Central

    Misra, Biswapriya B.; Acharya, Biswa R.; Granot, David; Assmann, Sarah M.; Chen, Sixue

    2015-01-01

    Guard cells represent a unique single cell-type system for the study of cellular responses to abiotic and biotic perturbations that affect stomatal movement. Decades of effort through both classical physiological and functional genomics approaches have generated an enormous amount of information on the roles of individual metabolites in stomatal guard cell function and physiology. Recent application of metabolomics methods has produced a substantial amount of new information on metabolome control of stomatal movement. In conjunction with other “omics” approaches, the knowledge-base is growing to reach a systems-level description of this single cell-type. Here we summarize current knowledge of the guard cell metabolome and highlight critical metabolites that bear significant impact on future engineering and breeding efforts to generate plants/crops that are resistant to environmental challenges and produce high yield and quality products for food and energy security. PMID:26042131

  1. Metagenomic gene annotation by a homology-independent approach

    SciTech Connect

    Froula, Jeff; Zhang, Tao; Salmeen, Annette; Hess, Matthias; Kerfeld, Cheryl A.; Wang, Zhong; Du, Changbin

    2011-06-02

    Fully understanding the genetic potential of a microbial community requires functional annotation of all the genes it encodes. The recently developed deep metagenome sequencing approach has enabled rapid identification of millions of genes from a complex microbial community without cultivation. Current homology-based gene annotation fails to detect distantly-related or structural homologs. Furthermore, homology searches with millions of genes are very computational intensive. To overcome these limitations, we developed rhModeller, a homology-independent software pipeline to efficiently annotate genes from metagenomic sequencing projects. Using cellulases and carbonic anhydrases as two independent test cases, we demonstrated that rhModeller is much faster than HMMER but with comparable accuracy, at 94.5percent and 99.9percent accuracy, respectively. More importantly, rhModeller has the ability to detect novel proteins that do not share significant homology to any known protein families. As {approx}50percent of the 2 million genes derived from the cow rumen metagenome failed to be annotated based on sequence homology, we tested whether rhModeller could be used to annotate these genes. Preliminary results suggest that rhModeller is robust in the presence of missense and frameshift mutations, two common errors in metagenomic genes. Applying the pipeline to the cow rumen genes identified 4,990 novel cellulases candidates and 8,196 novel carbonic anhydrase candidates.In summary, we expect rhModeller to dramatically increase the speed and quality of metagnomic gene annotation.

  2. Metabolomics applied to the pancreatic islet

    PubMed Central

    Gooding, Jessica R.; Jensen, Mette V.; Newgard, Christopher B.

    2016-01-01

    Metabolomics, the characterization of the set of small molecules in a biological system, is advancing research in multiple areas of islet biology. Measuring a breadth of metabolites simultaneously provides a broad perspective on metabolic changes as the islets respond dynamically to metabolic fuels, hormones, or environmental stressors. As a result, metabolomics has the potential to provide new mechanistic insights into islet physiology and pathophysiology. Here we summarize advances in our understanding of islet physiology and the etiologies of type-1 and type-2 diabetes gained from metabolomics studies. PMID:26116790

  3. Metabolomics applied to the pancreatic islet.

    PubMed

    Gooding, Jessica R; Jensen, Mette V; Newgard, Christopher B

    2016-01-01

    Metabolomics, the characterization of the set of small molecules in a biological system, is advancing research in multiple areas of islet biology. Measuring a breadth of metabolites simultaneously provides a broad perspective on metabolic changes as the islets respond dynamically to metabolic fuels, hormones, or environmental stressors. As a result, metabolomics has the potential to provide new mechanistic insights into islet physiology and pathophysiology. Here we summarize advances in our understanding of islet physiology and the etiologies of type-1 and type-2 diabetes gained from metabolomics studies. PMID:26116790

  4. Patient Education: An Annotated Bibliography.

    ERIC Educational Resources Information Center

    Simmons, Jeannette

    Topics included in this annotated bibliography on patient education are (1) background on development of patient education programs, (2) patient education interventions, (3) references for health professionals, and (4) research and evaluation in patient education. (TA)

  5. Revisiting Protocols for the NMR Analysis of Bacterial Metabolomes

    PubMed Central

    Halouska, Steven; Zhang, Bo; Gaupp, Rosmarie; Lei, Shulei; Snell, Emily; Fenton, Robert J.; Barletta, Raul G.; Somerville, Greg A.; Powers, Robert

    2015-01-01

    Over the past decade, metabolomics has emerged as an important technique for systems biology. Measuring all the metabolites in a biological system provides an invaluable source of information to explore various cellular processes, and to investigate the impact of environmental factors and genetic modifications. Nuclear magnetic resonance (NMR) spectroscopy is an important method routinely employed in metabolomics. NMR provides comprehensive structural and quantitative information useful for metabolomics fingerprinting, chemometric analysis, metabolite identification and metabolic pathway construction. A successful metabolomics study relies on proper experimental protocols for the collection, handling, processing and analysis of metabolomics data. Critically, these protocols should eliminate or avoid biologically-irrelevant changes to the metabolome. We provide a comprehensive description of our NMR-based metabolomics procedures optimized for the analysis of bacterial metabolomes. The technical details described within this manuscript should provide a useful guide to reliably apply our NMR-based metabolomics methodology to systems biology studies. PMID:26078915

  6. PhenoMeter: A Metabolome Database Search Tool Using Statistical Similarity Matching of Metabolic Phenotypes for High-Confidence Detection of Functional Links

    PubMed Central

    Carroll, Adam J.; Zhang, Peng; Whitehead, Lynne; Kaines, Sarah; Tcherkez, Guillaume; Badger, Murray R.

    2015-01-01

    This article describes PhenoMeter (PM), a new type of metabolomics database search that accepts metabolite response patterns as queries and searches the MetaPhen database of reference patterns for responses that are statistically significantly similar or inverse for the purposes of detecting functional links. To identify a similarity measure that would detect functional links as reliably as possible, we compared the performance of four statistics in correctly top-matching metabolic phenotypes of Arabidopsis thaliana metabolism mutants affected in different steps of the photorespiration metabolic pathway to reference phenotypes of mutants affected in the same enzymes by independent mutations. The best performing statistic, the PM score, was a function of both Pearson correlation and Fisher’s Exact Test of directional overlap. This statistic outperformed Pearson correlation, biweight midcorrelation and Fisher’s Exact Test used alone. To demonstrate general applicability, we show that the PM reliably retrieved the most closely functionally linked response in the database when queried with responses to a wide variety of environmental and genetic perturbations. Attempts to match metabolic phenotypes between independent studies were met with varying success and possible reasons for this are discussed. Overall, our results suggest that integration of pattern-based search tools into metabolomics databases will aid functional annotation of newly recorded metabolic phenotypes analogously to the way sequence similarity search algorithms have aided the functional annotation of genes and proteins. PM is freely available at MetabolomeExpress (https://www.metabolome-express.org/phenometer.php). PMID:26284240

  7. How Large Is the Metabolome? A Critical Analysis of Data Exchange Practices in Chemistry

    PubMed Central

    Kind, Tobias; Scholz, Martin; Fiehn, Oliver

    2009-01-01

    Background Calculating the metabolome size of species by genome-guided reconstruction of metabolic pathways misses all products from orphan genes and from enzymes lacking annotated genes. Hence, metabolomes need to be determined experimentally. Annotations by mass spectrometry would greatly benefit if peer-reviewed public databases could be queried to compile target lists of structures that already have been reported for a given species. We detail current obstacles to compile such a knowledge base of metabolites. Results As an example, results are presented for rice. Two rice (oryza sativa) subspecies have been fully sequenced, oryza japonica and oryza indica. Several major small molecule databases were compared for listing known rice metabolites comprising PubChem, Chemical Abstracts, Beilstein, Patent databases, Dictionary of Natural Products, SetupX/BinBase, KNApSAcK DB, and finally those databases which were obtained by computational approaches, i.e. RiceCyc, KEGG, and Reactome. More than 5,000 small molecules were retrieved when searching these databases. Unfortunately, most often, genuine rice metabolites were retrieved together with non-metabolite database entries such as pesticides. Overlaps from database compound lists were very difficult to compare because structures were either not encoded in machine-readable format or because compound identifiers were not cross-referenced between databases. Conclusions We conclude that present databases are not capable of comprehensively retrieving all known metabolites. Metabolome lists are yet mostly restricted to genome-reconstructed pathways. We suggest that providers of (bio)chemical databases enrich their database identifiers to PubChem IDs and InChIKeys to enable cross-database queries. In addition, peer-reviewed journal repositories need to mandate submission of structures and spectra in machine readable format to allow automated semantic annotation of articles containing chemical structures. Such changes in

  8. NCBI prokaryotic genome annotation pipeline.

    PubMed

    Tatusova, Tatiana; DiCuccio, Michael; Badretdin, Azat; Chetvernin, Vyacheslav; Nawrocki, Eric P; Zaslavsky, Leonid; Lomsadze, Alexandre; Pruitt, Kim D; Borodovsky, Mark; Ostell, James

    2016-08-19

    Recent technological advances have opened unprecedented opportunities for large-scale sequencing and analysis of populations of pathogenic species in disease outbreaks, as well as for large-scale diversity studies aimed at expanding our knowledge across the whole domain of prokaryotes. To meet the challenge of timely interpretation of structure, function and meaning of this vast genetic information, a comprehensive approach to automatic genome annotation is critically needed. In collaboration with Georgia Tech, NCBI has developed a new approach to genome annotation that combines alignment based methods with methods of predicting protein-coding and RNA genes and other functional elements directly from sequence. A new gene finding tool, GeneMarkS+, uses the combined evidence of protein and RNA placement by homology as an initial map of annotation to generate and modify ab initio gene predictions across the whole genome. Thus, the new NCBI's Prokaryotic Genome Annotation Pipeline (PGAP) relies more on sequence similarity when confident comparative data are available, while it relies more on statistical predictions in the absence of external evidence. The pipeline provides a framework for generation and analysis of annotation on the full breadth of prokaryotic taxonomy. For additional information on PGAP see https://www.ncbi.nlm.nih.gov/genome/annotation_prok/ and the NCBI Handbook, https://www.ncbi.nlm.nih.gov/books/NBK174280/. PMID:27342282

  9. Emerging applications of metabolomics in drug discovery and precision medicine.

    PubMed

    Wishart, David S

    2016-07-01

    Metabolomics is an emerging 'omics' science involving the comprehensive characterization of metabolites and metabolism in biological systems. Recent advances in metabolomics technologies are leading to a growing number of mainstream biomedical applications. In particular, metabolomics is increasingly being used to diagnose disease, understand disease mechanisms, identify novel drug targets, customize drug treatments and monitor therapeutic outcomes. This Review discusses some of the latest technological advances in metabolomics, focusing on the application of metabolomics towards uncovering the underlying causes of complex diseases (such as atherosclerosis, cancer and diabetes), the growing role of metabolomics in drug discovery and its potential effect on precision medicine. PMID:26965202

  10. ORegAnno 3.0: a community-driven resource for curated regulatory annotation.

    PubMed

    Lesurf, Robert; Cotto, Kelsy C; Wang, Grace; Griffith, Malachi; Kasaian, Katayoon; Jones, Steven J M; Montgomery, Stephen B; Griffith, Obi L

    2016-01-01

    The Open Regulatory Annotation database (ORegAnno) is a resource for curated regulatory annotation. It contains information about regulatory regions, transcription factor binding sites, RNA binding sites, regulatory variants, haplotypes, and other regulatory elements. ORegAnno differentiates itself from other regulatory resources by facilitating crowd-sourced interpretation and annotation of regulatory observations from the literature and highly curated resources. It contains a comprehensive annotation scheme that aims to describe both the elements and outcomes of regulatory events. Moreover, ORegAnno assembles these disparate data sources and annotations into a single, high quality catalogue of curated regulatory information. The current release is an update of the database previously featured in the NAR Database Issue, and now contains 1 948 307 records, across 18 species, with a combined coverage of 334 215 080 bp. Complete records, annotation, and other associated data are available for browsing and download at http://www.oreganno.org/. PMID:26578589

  11. ORegAnno 3.0: a community-driven resource for curated regulatory annotation

    PubMed Central

    Lesurf, Robert; Cotto, Kelsy C.; Wang, Grace; Griffith, Malachi; Kasaian, Katayoon; Jones, Steven J. M.; Montgomery, Stephen B.; Griffith, Obi L.

    2016-01-01

    The Open Regulatory Annotation database (ORegAnno) is a resource for curated regulatory annotation. It contains information about regulatory regions, transcription factor binding sites, RNA binding sites, regulatory variants, haplotypes, and other regulatory elements. ORegAnno differentiates itself from other regulatory resources by facilitating crowd-sourced interpretation and annotation of regulatory observations from the literature and highly curated resources. It contains a comprehensive annotation scheme that aims to describe both the elements and outcomes of regulatory events. Moreover, ORegAnno assembles these disparate data sources and annotations into a single, high quality catalogue of curated regulatory information. The current release is an update of the database previously featured in the NAR Database Issue, and now contains 1 948 307 records, across 18 species, with a combined coverage of 334 215 080 bp. Complete records, annotation, and other associated data are available for browsing and download at http://www.oreganno.org/. PMID:26578589

  12. Metabolomic studies of human gastric cancer: review.

    PubMed

    Jayavelu, Naresh Doni; Bar, Nadav S

    2014-07-01

    Metabolomics is a field of study in systems biology that involves the identification and quantification of metabolites present in a biological system. Analyzing metabolic differences between unperturbed and perturbed networks, such as cancerous and non-cancerous samples, can provide insight into underlying disease pathology, disease prognosis and diagnosis. Despite the large number of review articles concerning metabolomics and its application in cancer research, biomarker and drug discovery, these reviews do not focus on a specific type of cancer. Metabolomics may provide biomarkers useful for identification of early stage gastric cancer, potentially addressing an important clinical need. Here, we present a short review on metabolomics as a tool for biomarker discovery in human gastric cancer, with a primary focus on its use as a predictor of anticancer drug chemosensitivity, diagnosis, prognosis, and metastasis. PMID:25009381

  13. Applications of Metabolomics for Kidney Disease Research

    PubMed Central

    Wettersten, Hiromi I.; Weiss, Robert H.

    2013-01-01

    Metabolomics is one of the relative newcomers of the omics techniques and is likely the one most closely related to actual real-time disease pathophysiology. Hence, it has the power to yield not only specific biomarkers but also insight into the pathophysiology of disease. Despite this power, metabolomics as applied to kidney disease is still in its early adolescence and has not yet reached the mature stage of clinical application, i.e., specific biomarker and therapeutic target discovery. On the other hand, the insight gained from hints into what makes these diseases tick, as is evident from the metabolomics pathways which have been found to be altered in kidney cancer, are now beginning to bear fruit in leading to potential therapeutic targets. It is quite likely that, with greater numbers of clinical materials and with more investigators jumping into the field, metabolomics may well change the course of kidney disease research. PMID:23538740

  14. Metabolomic profiling of tumor-bearing mice.

    PubMed

    Wettersten, Hiromi I; Ganti, Sheila; Weiss, Robert H

    2014-01-01

    Metabolomics is one of the newcomers among the "omics" techniques, perhaps also constituting the most relevant for the study of pathophysiological conditions. Metabolomics may indeed yield not only disease-specific biomarkers but also profound insights into the etiology and progression of a variety of human disorders. Various metabolomic approaches are currently available to study oncogenesis and tumor progression in vivo, in murine tumor models. Many of these models rely on the xenograft of human cancer cells into immunocompromised mice. Understanding how the metabolism of these cells evolves in vivo is critical to evaluate the actual pertinence of xenograft models to human pathology. Here, we discuss various tumor xenograft models and methods for their metabolomic profiling to provide a short guide to investigators interested in this field of research. PMID:24924138

  15. Metabolomics in Population-Based Research

    Cancer.gov

    Metabolomics is the study of small molecules of both endogenous and exogenous origin, such as metabolic substrates and their products, lipids, small peptides, vitamins and other protein cofactors generated by metabolism, which are downstream from genes.

  16. MEGAnnotator: a user-friendly pipeline for microbial genomes assembly and annotation.

    PubMed

    Lugli, Gabriele Andrea; Milani, Christian; Mancabelli, Leonardo; van Sinderen, Douwe; Ventura, Marco

    2016-04-01

    Genome annotation is one of the key actions that must be undertaken in order to decipher the genetic blueprint of organisms. Thus, a correct and reliable annotation is essential in rendering genomic data valuable. Here, we describe a bioinformatics pipeline based on freely available software programs coordinated by a multithreaded script named MEGAnnotator (Multithreaded Enhanced prokaryotic Genome Annotator). This pipeline allows the generation of multiple annotated formats fulfilling the NCBI guidelines for assembled microbial genome submission, based on DNA shotgun sequencing reads, and minimizes manual intervention, while also reducing waiting times between software program executions and improving final quality of both assembly and annotation outputs. MEGAnnotator provides an efficient way to pre-arrange the assembly and annotation work required to process NGS genome sequence data. The script improves the final quality of microbial genome annotation by reducing ambiguous annotations. Moreover, the MEGAnnotator platform allows the user to perform a partial annotation of pre-assembled genomes and includes an option to accomplish metagenomic data set assemblies. MEGAnnotator platform will be useful for microbiologists interested in genome analyses of bacteria as well as those investigating the complexity of microbial communities that do not possess the necessary skills to prepare their own bioinformatics pipeline. PMID:26936607

  17. Clinical impact of human breast milk metabolomics.

    PubMed

    Cesare Marincola, Flaminia; Dessì, Angelica; Corbu, Sara; Reali, Alessandra; Fanos, Vassilios

    2015-12-01

    Metabolomics is a research field concerned with the analysis of metabolome, the complete set of metabolites in a given cell, tissue, or biological sample. Being able to provide a molecular snapshot of biological systems, metabolomics has emerged as a functional methodology in a wide range of research areas such as toxicology, pharmacology, food technology, nutrition, microbial biotechnology, systems biology, and plant biotechnology. In this review, we emphasize the applications of metabolomics in investigating the human breast milk (HBM) metabolome. HBM is the recommended source of nutrition for infants since it contains the optimal balance of nutrients for developing babies, and it provides a range of benefits for growth, immunity, and development. The molecular mechanisms beyond the inter- and intra-variability of HBM that make its composition unique are yet to be well-characterized. Although still in its infancy, the study of HBM metabolome has already proven itself to be of great value in providing insights into this biochemical variability in relation to mother phenotype, diet, disease, and lifestyle. The results of these investigations lay the foundation for further developments useful to identify normal and aberrant biochemical changes as well as to develop strategies to promote healthy infant feeding practices. PMID:25689794

  18. Sample normalization methods in quantitative metabolomics.

    PubMed

    Wu, Yiman; Li, Liang

    2016-01-22

    To reveal metabolomic changes caused by a biological event in quantitative metabolomics, it is critical to use an analytical tool that can perform accurate and precise quantification to examine the true concentration differences of individual metabolites found in different samples. A number of steps are involved in metabolomic analysis including pre-analytical work (e.g., sample collection and storage), analytical work (e.g., sample analysis) and data analysis (e.g., feature extraction and quantification). Each one of them can influence the quantitative results significantly and thus should be performed with great care. Among them, the total sample amount or concentration of metabolites can be significantly different from one sample to another. Thus, it is critical to reduce or eliminate the effect of total sample amount variation on quantification of individual metabolites. In this review, we describe the importance of sample normalization in the analytical workflow with a focus on mass spectrometry (MS)-based platforms, discuss a number of methods recently reported in the literature and comment on their applicability in real world metabolomics applications. Sample normalization has been sometimes ignored in metabolomics, partially due to the lack of a convenient means of performing sample normalization. We show that several methods are now available and sample normalization should be performed in quantitative metabolomics where the analyzed samples have significant variations in total sample amounts. PMID:26763302

  19. Metabolomics Analysis of Metabolic Effects of Nicotinamide Phosphoribosyltransferase (NAMPT) Inhibition on Human Cancer Cells

    PubMed Central

    Tolstikov, Vladimir; Nikolayev, Alexander; Dong, Sucai; Zhao, Genshi; Kuo, Ming-Shang

    2014-01-01

    Nicotinamide phosphoribosyltransferase (NAMPT) plays an important role in cellular bioenergetics. It is responsible for converting nicotinamide to nicotinamide adenine dinucleotide, an essential molecule in cellular metabolism. NAMPT has been extensively studied over the past decade due to its role as a key regulator of nicotinamide adenine dinucleotide–consuming enzymes. NAMPT is also known as a potential target for therapeutic intervention due to its involvement in disease. In the current study, we used a global mass spectrometry–based metabolomic approach to investigate the effects of FK866, a small molecule inhibitor of NAMPT currently in clinical trials, on metabolic perturbations in human cancer cells. We treated A2780 (ovarian cancer) and HCT-116 (colorectal cancer) cell lines with FK866 in the presence and absence of nicotinic acid. Significant changes were observed in the amino acids metabolism and the purine and pyrimidine metabolism. We also observed metabolic alterations in glycolysis, the citric acid cycle (TCA), and the pentose phosphate pathway. To expand the range of the detected polar metabolites and improve data confidence, we applied a global metabolomics profiling platform by using both non-targeted and targeted hydrophilic (HILIC)-LC-MS and GC-MS analysis. We used Ingenuity Knowledge Base to facilitate the projection of metabolomics data onto metabolic pathways. Several metabolic pathways showed differential responses to FK866 based on several matches to the list of annotated metabolites. This study suggests that global metabolomics can be a useful tool in pharmacological studies of the mechanism of action of drugs at a cellular level. PMID:25486521

  20. Metabolomics analysis of metabolic effects of nicotinamide phosphoribosyltransferase (NAMPT) inhibition on human cancer cells.

    PubMed

    Tolstikov, Vladimir; Nikolayev, Alexander; Dong, Sucai; Zhao, Genshi; Kuo, Ming-Shang

    2014-01-01

    Nicotinamide phosphoribosyltransferase (NAMPT) plays an important role in cellular bioenergetics. It is responsible for converting nicotinamide to nicotinamide adenine dinucleotide, an essential molecule in cellular metabolism. NAMPT has been extensively studied over the past decade due to its role as a key regulator of nicotinamide adenine dinucleotide-consuming enzymes. NAMPT is also known as a potential target for therapeutic intervention due to its involvement in disease. In the current study, we used a global mass spectrometry-based metabolomic approach to investigate the effects of FK866, a small molecule inhibitor of NAMPT currently in clinical trials, on metabolic perturbations in human cancer cells. We treated A2780 (ovarian cancer) and HCT-116 (colorectal cancer) cell lines with FK866 in the presence and absence of nicotinic acid. Significant changes were observed in the amino acids metabolism and the purine and pyrimidine metabolism. We also observed metabolic alterations in glycolysis, the citric acid cycle (TCA), and the pentose phosphate pathway. To expand the range of the detected polar metabolites and improve data confidence, we applied a global metabolomics profiling platform by using both non-targeted and targeted hydrophilic (HILIC)-LC-MS and GC-MS analysis. We used Ingenuity Knowledge Base to facilitate the projection of metabolomics data onto metabolic pathways. Several metabolic pathways showed differential responses to FK866 based on several matches to the list of annotated metabolites. This study suggests that global metabolomics can be a useful tool in pharmacological studies of the mechanism of action of drugs at a cellular level. PMID:25486521

  1. High Metabolomic Microdiversity within Co-Occurring Isolates of the Extremely Halophilic Bacterium Salinibacter ruber

    PubMed Central

    Antón, Josefa; Lucio, Marianna; Peña, Arantxa; Cifuentes, Ana; Brito-Echeverría, Jocelyn; Moritz, Franco; Tziotis, Dimitrios; López, Cristina; Urdiain, Mercedes; Schmitt-Kopplin, Philippe; Rosselló-Móra, Ramon

    2013-01-01

    Salinibacter ruber is an extremely halophilic member of the Bacteroidetes that thrives in crystallizer ponds worldwide. Here, we have analyzed two sets of 22 and 35 co-occurring S. ruber strains, newly isolated respectively, from 100 microliters water samples from crystalizer ponds in Santa Pola and Mallorca, located in coastal and inland Mediterranean Spain and 350 km apart from each other. A set of old strains isolated from the same setting were included in the analysis. Genomic and taxonomy relatedness of the strains were analyzed by means of PFGE and MALDI-TOF, respectively, while their metabolomic potential was explored with high resolution ion cyclotron resonance Fourier transform mass spectrometry (ICR-FT/MS). Overall our results show a phylogenetically very homogeneous species expressing a very diverse metabolomic pool. The combination of MALDI-TOF and PFGE provides, for the newly isolated strains, the same scenario presented by the previous studies of intra-specific diversity of S. ruber using a more restricted number of strains: the species seems to be very homogeneous at the ribosomal level while the genomic diversity encountered was rather high since no identical genome patterns could be retrieved from each of the samples. The high analytical mass resolution of ICR-FT/MS enabled the description of thousands of putative metabolites from which to date only few can be annotated in databases. Some metabolomic differences, mainly related to lipid metabolism and antibiotic-related compounds, provided enough specificity to delineate different clusters within the co-occurring strains. In addition, metabolomic differences were found between old and new strains isolated from the same ponds that could be related to extended exposure to laboratory conditions. PMID:23741374

  2. Chicano Perspectives in Literature--A Critical and Annotated Bibliography.

    ERIC Educational Resources Information Center

    Lomeli, Francisco A.; Urioste, Donaldo W.

    As an effort to define and explore the horizons of Chicano literature, this annotated and critical bibliography provides bibliographical data and critical evaluations and judgments regarding the quality, importance, and impact of 131 literary works by Chicanos. The commentaries are intended to be taken as opinions with the objective for promoting…

  3. Collective dynamics of social annotation

    PubMed Central

    Cattuto, Ciro; Barrat, Alain; Baldassarri, Andrea; Schehr, Gregory; Loreto, Vittorio

    2009-01-01

    The enormous increase of popularity and use of the worldwide web has led in the recent years to important changes in the ways people communicate. An interesting example of this fact is provided by the now very popular social annotation systems, through which users annotate resources (such as web pages or digital photographs) with keywords known as “tags.” Understanding the rich emergent structures resulting from the uncoordinated actions of users calls for an interdisciplinary effort. In particular concepts borrowed from statistical physics, such as random walks (RWs), and complex networks theory, can effectively contribute to the mathematical modeling of social annotation systems. Here, we show that the process of social annotation can be seen as a collective but uncoordinated exploration of an underlying semantic space, pictured as a graph, through a series of RWs. This modeling framework reproduces several aspects, thus far unexplained, of social annotation, among which are the peculiar growth of the size of the vocabulary used by the community and its complex network structure that represents an externalization of semantic structures grounded in cognition and that are typically hard to access. PMID:19506244

  4. Collective dynamics of social annotation.

    PubMed

    Cattuto, Ciro; Barrat, Alain; Baldassarri, Andrea; Schehr, Gregory; Loreto, Vittorio

    2009-06-30

    The enormous increase of popularity and use of the worldwide web has led in the recent years to important changes in the ways people communicate. An interesting example of this fact is provided by the now very popular social annotation systems, through which users annotate resources (such as web pages or digital photographs) with keywords known as "tags." Understanding the rich emergent structures resulting from the uncoordinated actions of users calls for an interdisciplinary effort. In particular concepts borrowed from statistical physics, such as random walks (RWs), and complex networks theory, can effectively contribute to the mathematical modeling of social annotation systems. Here, we show that the process of social annotation can be seen as a collective but uncoordinated exploration of an underlying semantic space, pictured as a graph, through a series of RWs. This modeling framework reproduces several aspects, thus far unexplained, of social annotation, among which are the peculiar growth of the size of the vocabulary used by the community and its complex network structure that represents an externalization of semantic structures grounded in cognition and that are typically hard to access. PMID:19506244

  5. METABOLOMICS IN SMALL FISH TOXICOLOGY AND OTHER ENVIRONMENTAL APPLICATIONS

    EPA Science Inventory

    Although lagging behind applications targeted to human endpoints, metabolomics offers great potential in environmental applications, including ecotoxicology. Indeed, the advantages of metabolomics (relative to other 'omic techniques) may be more tangible in ecotoxicology because...

  6. NMR-based Metabolomics Applications in Biological and Environmental Science

    EPA Science Inventory

    As a complimentary tool to other omics platforms, metabolomics is increasingly being used bybiologists to study the dynamic response of biological systems (cells, tissues, or wholeorganisms) under diverse physiological or pathological conditions. Metabolomics deals with the quali...

  7. Chemical annotation of small and peptide-like molecules at the Protein Data Bank.

    PubMed

    Young, Jasmine Y; Feng, Zukang; Dimitropoulos, Dimitris; Sala, Raul; Westbrook, John; Zhuravleva, Marina; Shao, Chenghua; Quesada, Martha; Peisach, Ezra; Berman, Helen M

    2013-01-01

    Over the past decade, the number of polymers and their complexes with small molecules in the Protein Data Bank archive (PDB) has continued to increase significantly. To support scientific advancements and ensure the best quality and completeness of the data files over the next 10 years and beyond, the Worldwide PDB partnership that manages the PDB archive is developing a new deposition and annotation system. This system focuses on efficient data capture across all supported experimental methods. The new deposition and annotation system is composed of four major modules that together support all of the processing requirements for a PDB entry. In this article, we describe one such module called the Chemical Component Annotation Tool. This tool uses information from both the Chemical Component Dictionary and Biologically Interesting molecule Reference Dictionary to aid in annotation. Benchmark studies have shown that the Chemical Component Annotation Tool provides significant improvements in processing efficiency and data quality. Database URL: http://wwpdb.org. PMID:24291661

  8. Metabolomic unveiling of a diverse range of green tea (Camellia sinensis) metabolites dependent on geography.

    PubMed

    Lee, Jang-Eun; Lee, Bum-Jin; Chung, Jin-Oh; Kim, Hak-Nam; Kim, Eun-Hee; Jung, Sungheuk; Lee, Hyosang; Lee, Sang-Jun; Hong, Young-Shick

    2015-05-01

    Numerous factors such as geographical origin, cultivar, climate, cultural practices, and manufacturing processes influence the chemical compositions of tea, in the same way as growing conditions and grape variety affect wine quality. However, the relationships between these factors and tea chemical compositions are not well understood. In this study, a new approach for non-targeted or global analysis, i.e., metabolomics, which is highly reproducible and statistically effective in analysing a diverse range of compounds, was used to better understand the metabolome of Camellia sinensis and determine the influence of environmental factors, including geography, climate, and cultural practices, on tea-making. We found a strong correlation between environmental factors and the metabolome of green, white, and oolong teas from China, Japan, and South Korea. In particular, multivariate statistical analysis revealed strong inter-country and inter-city relationships in the levels of theanine and catechin derivatives found in green and white teas. This information might be useful for assessing tea quality or producing distinct tea products across different locations, and highlights simultaneous identification of diverse tea metabolites through an NMR-based metabolomics approach. PMID:25529705

  9. Whiplash: a selective annotated bibliography

    PubMed Central

    Smith, Brad MT; Adams, Alan

    1997-01-01

    Objective: To review the literature on whiplash injury including an overview, collision mechanics, pathophysiology, neurobehavioral, imaging, treatment/management, prognosis, outcomes, and litigation. Design: An annotated bibliography. Methods: A literature search of MEDLINE from 1987 to 1995 and CHIROLARS from 1900 to 1996, with emphasis on the last ten years, was performed. Conference proceedings and the personal files of the authors were searched for relevant citations. Key words utilized in the search were whiplash injury, acceleration/deceleration injury, neck pain, head pain, cognitive impairment, treatment, imaging, prognosis and litigation. Results: This annotated bibliography identifies key studies and potential models for future research. Conclusions: There is currently a lack of clinical consensus both in practice and in the literature regarding the evaluation and management of an episode of whiplash injury. This annotated bibliography has been developed in an attempt to provide an overview of the literature regarding various issues surrounding an episode of whiplash injury.

  10. Vcfanno: fast, flexible annotation of genetic variants.

    PubMed

    Pedersen, Brent S; Layer, Ryan M; Quinlan, Aaron R

    2016-01-01

    The integration of genome annotations is critical to the identification of genetic variants that are relevant to studies of disease or other traits. However, comprehensive variant annotation with diverse file formats is difficult with existing methods. Here we describe vcfanno, which flexibly extracts and summarizes attributes from multiple annotation files and integrates the annotations within the INFO column of the original VCF file. By leveraging a parallel "chromosome sweeping" algorithm, we demonstrate substantial performance gains by annotating ~85,000 variants per second with 50 attributes from 17 commonly used genome annotation resources. Vcfanno is available at https://github.com/brentp/vcfanno under the MIT license. PMID:27250555

  11. Metabolomics Data Normalization with EigenMS

    PubMed Central

    Karpievitch, Yuliya V.; Nikolic, Sonja B.; Wilson, Richard; Sharman, James E.; Edwards, Lindsay M.

    2014-01-01

    Liquid chromatography mass spectrometry has become one of the analytical platforms of choice for metabolomics studies. However, LC-MS metabolomics data can suffer from the effects of various systematic biases. These include batch effects, day-to-day variations in instrument performance, signal intensity loss due to time-dependent effects of the LC column performance, accumulation of contaminants in the MS ion source and MS sensitivity among others. In this study we aimed to test a singular value decomposition-based method, called EigenMS, for normalization of metabolomics data. We analyzed a clinical human dataset where LC-MS serum metabolomics data and physiological measurements were collected from thirty nine healthy subjects and forty with type 2 diabetes and applied EigenMS to detect and correct for any systematic bias. EigenMS works in several stages. First, EigenMS preserves the treatment group differences in the metabolomics data by estimating treatment effects with an ANOVA model (multiple fixed effects can be estimated). Singular value decomposition of the residuals matrix is then used to determine bias trends in the data. The number of bias trends is then estimated via a permutation test and the effects of the bias trends are eliminated. EigenMS removed bias of unknown complexity from the LC-MS metabolomics data, allowing for increased sensitivity in differential analysis. Moreover, normalized samples better correlated with both other normalized samples and corresponding physiological data, such as blood glucose level, glycated haemoglobin, exercise central augmentation pressure normalized to heart rate of 75, and total cholesterol. We were able to report 2578 discriminatory metabolite peaks in the normalized data (p<0.05) as compared to only 1840 metabolite signals in the raw data. Our results support the use of singular value decomposition-based normalization for metabolomics data. PMID:25549083

  12. THE METABOLOMIC WINDOW INTO HEPATOBILIARY DISEASE

    PubMed Central

    Beyoğlu, Diren; Idle, Jeffrey R.

    2014-01-01

    Summary The emergent discipline of metabolomics has attracted considerable research effort in hepatology. Here we review the metabolomic data for nonalcoholic fatty liver disease (NAFLD), nonalcoholic steatohepatitis (NASH), cirrhosis, hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), alcoholic liver disease (ALD), hepatitis B and C, cholecystitis, cholestasis, liver transplantation and acute hepatotoxicity in animal models. A metabolomic window has permitted a view into the changing biochemistry occurring in the transitional phases between a healthy liver and hepatocellular carcinoma or cholangiocarcinoma. Whether provoked by obesity and diabetes, alcohol use or oncogenic viruses, the liver develops a core metabolomic phenotype (CMP) that involves dysregulation of bile acid and phospholipid homeostasis. The CMP commences at the transition between the healthy liver (Phase 0) and NAFLD/NASH, ALD or viral hepatitis (Phase 1). This CMP is maintained in the presence or absence of cirrhosis (Phase 2) and whether or not either HCC or CCA (Phase 3) develop. Inflammatory signalling in the liver triggers the appearance of the CMP. Many other metabolomic markers distinguish between Phases 0, 1, 2 and 3. A metabolic remodelling in HCC has been described but metabolomic data from all four Phases demonstrate that the Warburg shift from mitochondrial respiration to cytosolic glycolysis foreshadows HCC and may occur as early as Phase 1. The metabolic remodelling also involves an upregulation of fatty acid β-oxidation, also beginning in Phase 1. The storage of triglycerides in fatty liver provides high energy-yielding substrates for Phases 2 and 3 of liver pathology. The metabolomic window into hepatobiliary disease sheds new light on the systems pathology of the liver. PMID:23714158

  13. Metabolomics data normalization with EigenMS.

    PubMed

    Karpievitch, Yuliya V; Nikolic, Sonja B; Wilson, Richard; Sharman, James E; Edwards, Lindsay M

    2014-01-01

    Liquid chromatography mass spectrometry has become one of the analytical platforms of choice for metabolomics studies. However, LC-MS metabolomics data can suffer from the effects of various systematic biases. These include batch effects, day-to-day variations in instrument performance, signal intensity loss due to time-dependent effects of the LC column performance, accumulation of contaminants in the MS ion source and MS sensitivity among others. In this study we aimed to test a singular value decomposition-based method, called EigenMS, for normalization of metabolomics data. We analyzed a clinical human dataset where LC-MS serum metabolomics data and physiological measurements were collected from thirty nine healthy subjects and forty with type 2 diabetes and applied EigenMS to detect and correct for any systematic bias. EigenMS works in several stages. First, EigenMS preserves the treatment group differences in the metabolomics data by estimating treatment effects with an ANOVA model (multiple fixed effects can be estimated). Singular value decomposition of the residuals matrix is then used to determine bias trends in the data. The number of bias trends is then estimated via a permutation test and the effects of the bias trends are eliminated. EigenMS removed bias of unknown complexity from the LC-MS metabolomics data, allowing for increased sensitivity in differential analysis. Moreover, normalized samples better correlated with both other normalized samples and corresponding physiological data, such as blood glucose level, glycated haemoglobin, exercise central augmentation pressure normalized to heart rate of 75, and total cholesterol. We were able to report 2578 discriminatory metabolite peaks in the normalized data (p<0.05) as compared to only 1840 metabolite signals in the raw data. Our results support the use of singular value decomposition-based normalization for metabolomics data. PMID:25549083

  14. Alignment-Annotator web server: rendering and annotating sequence alignments

    PubMed Central

    Gille, Christoph; Fähling, Michael; Weyand, Birgit; Wieland, Thomas; Gille, Andreas

    2014-01-01

    Alignment-Annotator is a novel web service designed to generate interactive views of annotated nucleotide and amino acid sequence alignments (i) de novo and (ii) embedded in other software. All computations are performed at server side. Interactivity is implemented in HTML5, a language native to web browsers. The alignment is initially displayed using default settings and can be modified with the graphical user interfaces. For example, individual sequences can be reordered or deleted using drag and drop, amino acid color code schemes can be applied and annotations can be added. Annotations can be made manually or imported (BioDAS servers, the UniProt, the Catalytic Site Atlas and the PDB). Some edits take immediate effect while others require server interaction and may take a few seconds to execute. The final alignment document can be downloaded as a zip-archive containing the HTML files. Because of the use of HTML the resulting interactive alignment can be viewed on any platform including Windows, Mac OS X, Linux, Android and iOS in any standard web browser. Importantly, no plugins nor Java are required and therefore Alignment-Anotator represents the first interactive browser-based alignment visualization. Availability: http://www.bioinformatics.org/strap/aa/ and http://strap.charite.de/aa/. PMID:24813445

  15. A Manual Curation Strategy to Improve Genome Annotation: Application to a Set of Haloarchael Genomes

    PubMed Central

    Pfeiffer, Friedhelm; Oesterhelt, Dieter

    2015-01-01

    Genome annotation errors are a persistent problem that impede research in the biosciences. A manual curation effort is described that attempts to produce high-quality genome annotations for a set of haloarchaeal genomes (Halobacterium salinarum and Hbt. hubeiense, Haloferax volcanii and Hfx. mediterranei, Natronomonas pharaonis and Nmn. moolapensis, Haloquadratum walsbyi strains HBSQ001 and C23, Natrialba magadii, Haloarcula marismortui and Har. hispanica, and Halohasta litchfieldiae). Genomes are checked for missing genes, start codon misassignments, and disrupted genes. Assignments of a specific function are preferably based on experimentally characterized homologs (Gold Standard Proteins). To avoid overannotation, which is a major source of database errors, we restrict annotation to only general function assignments when support for a specific substrate assignment is insufficient. This strategy results in annotations that are resistant to the plethora of errors that compromise public databases. Annotation consistency is rigorously validated for ortholog pairs from the genomes surveyed. The annotation is regularly crosschecked against the UniProt database to further improve annotations and increase the level of standardization. Enhanced genome annotations are submitted to public databases (EMBL/GenBank, UniProt), to the benefit of the scientific community. The enhanced annotations are also publically available via HaloLex. PMID:26042526

  16. IMG ER: A System for Microbial Genome Annotation Expert Review and Curation

    SciTech Connect

    Markowitz, Victor M.; Mavromatis, Konstantinos; Ivanova, Natalia N.; Chen, I-Min A.; Chu, Ken; Kyrpides, Nikos C.

    2009-05-25

    A rapidly increasing number of microbial genomes are sequenced by organizations worldwide and are eventually included into various public genome data resources. The quality of the annotations depends largely on the original dataset providers, with erroneous or incomplete annotations often carried over into the public resources and difficult to correct. We have developed an Expert Review (ER) version of the Integrated Microbial Genomes (IMG) system, with the goal of supporting systematic and efficient revision of microbial genome annotations. IMG ER provides tools for the review and curation of annotations of both new and publicly available microbial genomes within IMG's rich integrated genome framework. New genome datasets are included into IMG ER prior to their public release either with their native annotations or with annotations generated by IMG ER's annotation pipeline. IMG ER tools allow addressing annotation problems detected with IMG's comparative analysis tools, such as genes missed by gene prediction pipelines or genes without an associated function. Over the past year, IMG ER was used for improving the annotations of about 150 microbial genomes.

  17. Plant metabolomics: from holistic data to relevant biomarkers.

    PubMed

    Wolfender, Jean-Luc; Rudaz, Serge; Choi, Young Hae; Kim, Hye Kyong

    2013-01-01

    Metabolomics is playing an increasingly important role in plant science. It aims at the comprehensive analysis of the plant metabolome which consists both of primary and secondary metabolites. The goal of metabolomics is ultimately to identify and quantify this wide array of small molecules in biological samples. This new science is included in several systems biology approaches and is based primarily on the unbiased acquisition of mass spectrometric (MS) or nuclear magnetic resonance (NMR) data from carefully selected samples. This approach provides the most ''functional'' information of the 'omics' technologies of a given organism since metabolites are the end products of the cellular regulatory processes. The application of state-of-the-art data mining, that includes various untargeted and targeted multivariate data analysis methods, to the vast amount of data generated by this data-driven approach leads to sample classification and the identification of relevant biomarkers. The biological areas that have been successfully studied by this holistic approach include global metabolite composition assessment, mutant and phenotype characterisation, taxonomy, developmental processes, stress response, interaction with the environment, quality control assessment, lead finding and mode of action of botanicals. This review summarises the main MS- and NMR-based approaches that are used to perform these studies and discusses the potential and current limitations of the various methods. The intent is not to provide an exhaustive overview of the field, which has grown considerably over the past decade, but to summarise the main strategies that are used and to discuss the potential and limitations of the different approaches as well as future trends. PMID:23210790

  18. Loss-of-function variants influence the human serum metabolome.

    PubMed

    Yu, Bing; Li, Alexander H; Metcalf, Ginger A; Muzny, Donna M; Morrison, Alanna C; White, Simon; Mosley, Thomas H; Gibbs, Richard A; Boerwinkle, Eric

    2016-08-01

    The metabolome is a collection of small molecules resulting from multiple cellular and biological processes that can act as biomarkers of disease, and African-Americans exhibit high levels of genetic diversity. Exome sequencing of a sample of deeply phenotyped African-Americans allowed us to analyze the effects of annotated loss-of-function (LoF) mutations on 308 serum metabolites measured by untargeted liquid and gas chromatography coupled with mass spectrometry. In an independent sample, we identified and replicated four genes harboring six LoF mutations that significantly affected five metabolites. These sites were related to a 19 to 45% difference in geometric mean metabolite levels, with an average effect size of 25%. We show that some of the affected metabolites are risk predictors or diagnostic biomarkers of disease and, using the principle of Mendelian randomization, are in the causal pathway of disease. For example, LoF mutations in SLCO1B1 elevate the levels of hexadecanedioate, a fatty acid significantly associated with increased blood pressure levels and risk of incident heart failure in both African-Americans and an independent sample of European-Americans. We show that SLCO1B1 LoF mutations significantly increase the risk of incident heart failure, thus implicating the metabolite in the causal pathway of disease. These results reveal new avenues into gene function and the understanding of disease etiology by integrating -omic technologies into a deeply phenotyped population study. PMID:27602404

  19. Loss-of-function variants influence the human serum metabolome

    PubMed Central

    Yu, Bing; Li, Alexander H.; Metcalf, Ginger A.; Muzny, Donna M.; Morrison, Alanna C.; White, Simon; Mosley, Thomas H.; Gibbs, Richard A.; Boerwinkle, Eric

    2016-01-01

    The metabolome is a collection of small molecules resulting from multiple cellular and biological processes that can act as biomarkers of disease, and African-Americans exhibit high levels of genetic diversity. Exome sequencing of a sample of deeply phenotyped African-Americans allowed us to analyze the effects of annotated loss-of-function (LoF) mutations on 308 serum metabolites measured by untargeted liquid and gas chromatography coupled with mass spectrometry. In an independent sample, we identified and replicated four genes harboring six LoF mutations that significantly affected five metabolites. These sites were related to a 19 to 45% difference in geometric mean metabolite levels, with an average effect size of 25%. We show that some of the affected metabolites are risk predictors or diagnostic biomarkers of disease and, using the principle of Mendelian randomization, are in the causal pathway of disease. For example, LoF mutations in SLCO1B1 elevate the levels of hexadecanedioate, a fatty acid significantly associated with increased blood pressure levels and risk of incident heart failure in both African-Americans and an independent sample of European-Americans. We show that SLCO1B1 LoF mutations significantly increase the risk of incident heart failure, thus implicating the metabolite in the causal pathway of disease. These results reveal new avenues into gene function and the understanding of disease etiology by integrating -omic technologies into a deeply phenotyped population study. PMID:27602404

  20. Next generation models for storage and representation of microbial biological annotation

    PubMed Central

    2010-01-01

    Background Traditional genome annotation systems were developed in a very different computing era, one where the World Wide Web was just emerging. Consequently, these systems are built as centralized black boxes focused on generating high quality annotation submissions to GenBank/EMBL supported by expert manual curation. The exponential growth of sequence data drives a growing need for increasingly higher quality and automatically generated annotation. Typical annotation pipelines utilize traditional database technologies, clustered computing resources, Perl, C, and UNIX file systems to process raw sequence data, identify genes, and predict and categorize gene function. These technologies tightly couple the annotation software system to hardware and third party software (e.g. relational database systems and schemas). This makes annotation systems hard to reproduce, inflexible to modification over time, difficult to assess, difficult to partition across multiple geographic sites, and difficult to understand for those who are not domain experts. These systems are not readily open to scrutiny and therefore not scientifically tractable. The advent of Semantic Web standards such as Resource Description Framework (RDF) and OWL Web Ontology Language (OWL) enables us to construct systems that address these challenges in a new comprehensive way. Results Here, we develop a framework for linking traditional data to OWL-based ontologies in genome annotation. We show how data standards can decouple hardware and third party software tools from annotation pipelines, thereby making annotation pipelines easier to reproduce and assess. An illustrative example shows how TURTLE (Terse RDF Triple Language) can be used as a human readable, but also semantically-aware, equivalent to GenBank/EMBL files. Conclusions The power of this approach lies in its ability to assemble annotation data from multiple databases across multiple locations into a representation that is understandable to

  1. Next Generation Models for Storage and Representation of Microbial Biological Annotation

    SciTech Connect

    Quest, Daniel J; Land, Miriam L; Brettin, Thomas S; Cottingham, Robert W

    2010-01-01

    Background Traditional genome annotation systems were developed in a very different computing era, one where the World Wide Web was just emerging. Consequently, these systems are built as centralized black boxes focused on generating high quality annotation submissions to GenBank/EMBL supported by expert manual curation. The exponential growth of sequence data drives a growing need for increasingly higher quality and automatically generated annotation. Typical annotation pipelines utilize traditional database technologies, clustered computing resources, Perl, C, and UNIX file systems to process raw sequence data, identify genes, and predict and categorize gene function. These technologies tightly couple the annotation software system to hardware and third party software (e.g. relational database systems and schemas). This makes annotation systems hard to reproduce, inflexible to modification over time, difficult to assess, difficult to partition across multiple geographic sites, and difficult to understand for those who are not domain experts. These systems are not readily open to scrutiny and therefore not scientifically tractable. The advent of Semantic Web standards such as Resource Description Framework (RDF) and OWL Web Ontology Language (OWL) enables us to construct systems that address these challenges in a new comprehensive way. Results Here, we develop a framework for linking traditional data to OWL-based ontologies in genome annotation. We show how data standards can decouple hardware and third party software tools from annotation pipelines, thereby making annotation pipelines easier to reproduce and assess. An illustrative example shows how TURTLE (Terse RDF Triple Language) can be used as a human readable, but also semantically-aware, equivalent to GenBank/EMBL files. Conclusions The power of this approach lies in its ability to assemble annotation data from multiple databases across multiple locations into a representation that is understandable to

  2. Systematic Applications of Metabolomics in Metabolic Engineering

    PubMed Central

    Dromms, Robert A.; Styczynski, Mark P.

    2012-01-01

    The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a target compound. Using the analysis of extracellular or intracellular levels of the target compound (or a few closely related molecules) to drive metabolic engineering is quite common. However, there is surprisingly little systematic use of metabolomics datasets, which simultaneously measure hundreds of metabolites rather than just a few, for that same purpose. Here, we review the most common systematic approaches to integrating metabolite data with metabolic engineering, with emphasis on existing efforts to use whole-metabolome datasets. We then review some of the most common approaches for computational modeling of cell-wide metabolism, including constraint-based models, and discuss current computational approaches that explicitly use metabolomics data. We conclude with discussion of the broader potential of computational approaches that systematically use metabolomics data to drive metabolic engineering. PMID:24957776

  3. Basics of mass spectrometry based metabolomics.

    PubMed

    Courant, Frédérique; Antignac, Jean-Philippe; Dervilly-Pinel, Gaud; Le Bizec, Bruno

    2014-11-01

    The emerging field of metabolomics, aiming to characterize small molecule metabolites present in biological systems, promises immense potential for different areas such as medicine, environmental sciences, agronomy, etc. The purpose of this article is to guide the reader through the history of the field, then through the main steps of the metabolomics workflow, from study design to structure elucidation, and help the reader to understand the key phases of a metabolomics investigation and the rationale underlying the protocols and techniques used. This article is not intended to give standard operating procedures as several papers related to this topic were already provided, but is designed as a tutorial aiming to help beginners understand the concept and challenges of MS-based metabolomics. A real case example is taken from the literature to illustrate the application of the metabolomics approach in the field of doping analysis. Challenges and limitations of the approach are then discussed along with future directions in research to cope with these limitations. This tutorial is part of the International Proteomics Tutorial Programme (IPTP18). PMID:25168716

  4. Application of Metabolomics in Thyroid Cancer Research

    PubMed Central

    Wojakowska, Anna; Chekan, Mykola; Widlak, Piotr; Pietrowska, Monika

    2015-01-01

    Thyroid cancer is the most common endocrine malignancy with four major types distinguished on the basis of histopathological features: papillary, follicular, medullary, and anaplastic. Classification of thyroid cancer is the primary step in the assessment of prognosis and selection of the treatment. However, in some cases, cytological and histological patterns are inconclusive; hence, classification based on histopathology could be supported by molecular biomarkers, including markers identified with the use of high-throughput “omics” techniques. Beside genomics, transcriptomics, and proteomics, metabolomic approach emerges as the most downstream attitude reflecting phenotypic changes and alterations in pathophysiological states of biological systems. Metabolomics using mass spectrometry and magnetic resonance spectroscopy techniques allows qualitative and quantitative profiling of small molecules present in biological systems. This approach can be applied to reveal metabolic differences between different types of thyroid cancer and to identify new potential candidates for molecular biomarkers. In this review, we consider current results concerning application of metabolomics in the field of thyroid cancer research. Recent studies show that metabolomics can provide significant information about the discrimination between different types of thyroid lesions. In the near future, one could expect a further progress in thyroid cancer metabolomics leading to development of molecular markers and improvement of the tumor types classification and diagnosis. PMID:25972898

  5. Recent Applications of Metabolomics Toward Cyanobacteria

    PubMed Central

    Schwarz, Doreen; Orf, Isabel; Kopka, Joachim; Hagemann, Martin

    2013-01-01

    Our knowledge on cyanobacterial molecular biology increased tremendously by the application of the “omics” techniques. Only recently, metabolomics was applied systematically to model cyanobacteria. Metabolomics, the quantitative estimation of ideally the complete set of cellular metabolites, is particularly well suited to mirror cellular metabolism and its flexibility under diverse conditions. Traditionally, small sets of metabolites are quantified in targeted metabolome approaches. The development of separation technologies coupled to mass-spectroscopy- or nuclear-magnetic-resonance-based identification of low molecular mass molecules presently allows the profiling of hundreds of metabolites of diverse chemical nature. Metabolome analysis was applied to characterize changes in the cyanobacterial primary metabolism under diverse environmental conditions or in defined mutants. The resulting lists of metabolites and their steady state concentrations in combination with transcriptomics can be used in system biology approaches. The application of stable isotopes in fluxomics, i.e. the quantitative estimation of carbon and nitrogen fluxes through the biochemical network, has only rarely been applied to cyanobacteria, but particularly this technique will allow the making of kinetic models of cyanobacterial systems. The further application of metabolomics in the concert of other “omics” technologies will not only broaden our knowledge, but will also certainly strengthen the base for the biotechnological application of cyanobacteria. PMID:24957891

  6. Metabolomics: Applications and Promise in Mycobacterial Disease.

    PubMed

    Mirsaeidi, Mehdi; Banoei, Mohammad Mehdi; Winston, Brent W; Schraufnagel, Dean E

    2015-09-01

    Until recently, the study of mycobacterial diseases was trapped in culture-based technology that is more than a century old. The use of nucleic acid amplification is changing this, and powerful new technologies are on the horizon. Metabolomics, which is the study of sets of metabolites of both the bacteria and host, is being used to clarify mechanisms of disease, and can identify changes leading to better diagnosis, treatment, and prognostication of mycobacterial diseases. Metabolomic profiles are arrays of biochemical products of genes in their environment. These complex patterns are biomarkers that can allow a more complete understanding of cell function, dysfunction, and perturbation than genomics or proteomics. Metabolomics could herald sweeping advances in personalized medicine and clinical trial design, but the challenges in metabolomics are also great. Measured metabolite concentrations vary with the timing within a condition, the intrinsic biology, the instruments, and the sample preparation. Metabolism profoundly changes with age, sex, variations in gut microbial flora, and lifestyle. Validation of biomarkers is complicated by measurement accuracy, selectivity, linearity, reproducibility, robustness, and limits of detection. The statistical challenges include analysis, interpretation, and description of the vast amount of data generated. Despite these drawbacks, metabolomics provides great opportunity and the potential to understand and manage mycobacterial diseases. PMID:26196272

  7. Systems Theory and Communication. Annotated Bibliography.

    ERIC Educational Resources Information Center

    Covington, William G., Jr.

    This annotated bibliography presents annotations of 31 books and journal articles dealing with systems theory and its relation to organizational communication, marketing, information theory, and cybernetics. Materials were published between 1963 and 1992 and are listed alphabetically by author. (RS)

  8. Annotated Bibliography, Grades K-6.

    ERIC Educational Resources Information Center

    Massachusetts Dept. of Education, Boston. Bureau of Nutrition Education and School Food Services.

    This annotated bibliography on nutrition is for the use of teachers at the elementary grade level. It contains a list of books suitable for reading about nutrition and foods for pupils from kindergarten through the sixth grade. Films and audiovisual presentations for classroom use are also listed. The names and addresses from which these materials…

  9. Vietnamese Amerasians: An Annotated Bibliography.

    ERIC Educational Resources Information Center

    Johnson, Mark C.; And Others

    This annotated bibliography on Vietnamese Amerasians includes primary and secondary sources as well as reviews of three documentary films. Sources were selected in order to provide an overview of the historical and political context of Amerasian resettlement and a review of the scant available research on coping and adaptation with this…

  10. Radiocarbon Dating: An Annotated Bibliography.

    ERIC Educational Resources Information Center

    Fortine, Suellen

    This selective annotated bibliography covers various sources of information on the radiocarbon dating method, including journal articles, conference proceedings, and reports, reflecting the most important and useful sources of the last 25 years. The bibliography is divided into five parts--general background on radiocarbon, radiocarbon dating,…