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Metabolomics Resources: An Introduction of Databases and Their Future Prospective

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Recent Trends and Techniques in Plant Metabolic Engineering

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Abstract

Metabolomics, an extended branch deals with targeted metabolite analysis, takes transcriptome and proteome analysis in consideration to solve complex biological puzzles. Improved insight in the metabolomics has generated huge complex of data that makes room for improved in silico methodologies to reveal the basic biological mechanism from the generated datasets. Despite, the recently developed tools, various software and metabolomics resources available and other information in the form of databases are currently lacking in providing precise and required information. Therefore, this chapter will provide the readers an overview of available open-source tools, algorithms, and workflow strategy to familiarize, promote, and facilitate metabolomics research and data processing frameworks. Though most of the tools and resources that have been described in this chapter include data processing, data annotation, and data visualization in mass spectrometry (MS) and NMR-based metabolomics and specific tools for untargeted metabolomics workflows, few advanced tools will also be discussed. The tools and resources discussed here have well-known collaborations of analytical data with reliance in computational platform. In the end, we have discussed about the future prospective for metabolomics resources.

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Abbreviations

API:

Application programming interfaces

CFM:

Competitive fragmentation modeling

GC-MS:

Gas chromatography-mass spectroscopy

KEGG:

Kyoto Encyclopedia of Genes and Genomes

LC-MS:

Liquid chromatography-mass chromatography

MALDI:

Matrix-free laser desorption/ionization

NMR:

Nuclear magnetic resonance

References

  • Alcaraz N, Pauling J, Batra R, Barbosa E, Junge A, Christensen AG, Azevedo V, Ditzel HJ, Baumbach J (2014) KeyPathwayMiner 4.0: condition-specific pathway analysis by combining multiple omics studies and networks with Cytoscape. BMC Syst Biol 8:99

    Article  Google Scholar 

  • Allen F, Greiner R, Wishart D (2015) Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification. Metabolomics 11:98–110

    Article  CAS  Google Scholar 

  • Ara T, Enomoto M, Arita M, Ikeda C, Kera K, Yamada M, Nishioka T, Ikeda T, Nihei Y, Shibata D, Kanaya S, Sakurai N (2015) Metabolonote: a wiki-based database for managing hierarchical metadata of metabolome analyses. Front Bioeng Biotechnol. https://doi.org/10.3389/fbioe.2015.00038

  • Berdy J (2005) Bioactive microbial metabolites. J Antibiot 58:1–26

    Article  CAS  Google Scholar 

  • Broeckling CD, Afsar FA, Neumann S, Ben-Hur A, Prenni JE (2015) RAMClust: a novel feature clustering method enables spectral-matching-based annotation for metabolomics data. Anal Chem 86:6812–6817

    Article  Google Scholar 

  • Cicek AE, Qi X, Cakmak A, Johnson SR, Han X, Alshalwi S, Ozsoyoglu ZM, Ozsoyoglu G (2014) An online system for metabolic network analysis. Database (Oxford) pii: bau091. https://doi.org/10.1093/database/bau091

    Article  Google Scholar 

  • Coble JB, Fraga CG (2014) Comparative evaluation of preprocessing freeware on chromatography/mass spectroscopy data for signature discovery. J Chromatogr A 1358:155–164

    Article  CAS  Google Scholar 

  • Daly R, Rogers S, Wandy J, Jankevics A, Burgess KE, Breitling R (2014) MetAssign: probabilistic annotation of metabolites from LC-MS data using a Bayesian clustering approach. Bioinformatics 30:2764–2771

    Article  CAS  Google Scholar 

  • Dhanasekaran AR, Pearson JL, Ganesan B, Weimer BC (2015) Metabolome searcher: a high throughput tool for metabolite identification and metabolic pathway mapping directly from mass spectroscopy and using genome restriction. BMC Bioinform 16:62

    Article  Google Scholar 

  • Doerfler H, Sun X, Wang L, Engelmeier D, Lyon D, Weckwerth W (2014) mzGroupAnalyzer-predicting pathways and novel chemical structures from untargeted high-throughput metabolomics data. PloS One 9:e96188

    Article  Google Scholar 

  • Edmands WM, Barupal DK, Scalbert A (2014) MetMSLine: an automated and fully integrated pipeline for rapid processing of high-resolution LC-MS metabolomics datasets. Bioinformatics 31:788–790

    Article  Google Scholar 

  • Fernández-Albert F, Llorach R, Andrés-Lacueva C, Perera A (2014) An R package to analyse LC/MS metabolomics data: MAIT (metabolite automatic identification toolkit). Bioinformatics 30:1937–1939

    Article  Google Scholar 

  • French WR, Zimmerman LJ, Schilling B, Gibson BW, Miller CA, Townsend RR, Sherrod SD, Goodwin CR, McLean JA, Tabb DL (2014) Wavelet-based peak detection and a new charge inference procedure for MS/MS implemented in proteoWizard’s msConvert. J Proteome Res 14:1299–1307

    Article  Google Scholar 

  • Garg N, Conrad D, Dorrestein P (2015) Metabolomics by mass spectrometry based molecular networking and spatial mapping. FASEB J 29:369–371

    Google Scholar 

  • Grapov D, Fahrmann J, Hwang J, Poudel A, Jo J, Periwal V, Fiehn O, Hara M (2015) Diabetes associated metabolomics perturbations in NOD mice. Metabolomics 11:425–437

    Article  CAS  Google Scholar 

  • Griss J, Jones AR, Sachsenberg T, Walzer M, Gatto L, Hartler J, Thallinger GG, Salek RM, Steinbeck C, Neuhauser N, Cox J, Neumann S, Fan J, Reisinger F, Xu QW, Del Toro N, Pérez-Riverol Y, Ghali F, Bandeira N, Xenarios I, Kohlbacher O, Vizcaíno JA, Hermjakob H (2014) The mzTab data exchange format: communicating mass-spectrometry-based proteomics and metabolomics experimental results to a wider audience. Mol. Cell Proteomics 13:2765–2775

    Article  CAS  Google Scholar 

  • Hamdalla MA, Rajasekaran S, Grant DF, Măndoiu II (2015) Metabolic pathway predictions for metabolomics: a molecular structure matching approach. J Chem Inf Model 55:709–718

    Article  CAS  Google Scholar 

  • Haug K, Salek RM, Conesa P, Hastings J, de Matos P, Rijnbeek M, Mahendraker T, Williams M, Neumann S, Rocca-Serra P, Maguire E, González-Beltràn A, Sansone SA, Griffin JL, Steinbeck C (2012) MetaboLights-an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res 41:D781–D786

    Article  Google Scholar 

  • Jeffryes JG, Colastani RL, Elbadawi-Sidhu M, Kind T, Niehaus TD, Broadbelt LJ, Hanson AD, Fiehn O, Tyo KE, Henry CS (2015) MINE: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics. J Cheminform 7:44

    Article  Google Scholar 

  • Johnson SR, Lange BM (2015) Open-access metabolomics databases for natural product research: present capabilities and future potential. Front Bioeng Biotechnol 3:1–10

    Article  Google Scholar 

  • Kaever A, Landesfeind M, Feussner K, Mosblech A, Heilmann I, Morgenstern B, Feussner I, Meinicke P (2015) MarVis-pathway: integrative and exploratory pathway analysis of non-targeted metabolomics data. Metabolomics 11:764–777

    Article  CAS  Google Scholar 

  • Kessner D, Chambers M, Burke R, Agus D, Mallick P (2008) ProteoWizard: open source software for rapid protemomics tools development. Bioinformatics 24:2534–2536

    Article  CAS  Google Scholar 

  • Kim S, Fang A, Wang B, Jeong J, Zhang X (2011) An optimal peak alignment for comprehensive two-dimensional gas chromatography mass spectrometry using mixture similarity measure. Bioinformatics 27:1660–1666

    Article  CAS  Google Scholar 

  • Kirwan JA, Weber RJ, Broadhurst DI, Viant MR (2014) Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control. Sci Data 1:140012

    Article  CAS  Google Scholar 

  • Kotera M, Tabei Y, Yamanishi Y, Muto A, Moriya Y, Tokimatsu T, Goto S (2014) Metabolome-scale prediction of intermediate compounds in multistep metabolic pathways with a recursive supervised approach. Bioinformatics 30:i165–i174

    Article  CAS  Google Scholar 

  • Lee HS, Jo S, Mukherjee S, Park SJ, Skolnick J, Lee J, Im W (2015) GS-align for glycan strcutre alignment and similarity measurement. Bioinformatics 31:2653–2659

    Article  CAS  Google Scholar 

  • Li JW, Vederas JC (2009) Drug discovery and natural products: end of an era or an endless frontier? Science 325:161–165

    Article  Google Scholar 

  • Liu Y, Liang Y, Wishart D (2015) PolySearch2: a significantly improved text-mining system for discovering associations between human diseases, genes, drugs, metabolites, toxins and more. Nucleic Acids Res 43:W535–W542

    Article  CAS  Google Scholar 

  • Newman DJ, Cragg GM (2012) Natural products as source of new drugs over the 30 years from 1981 to 2010. J Nat Prod 75:311–335

    Article  CAS  Google Scholar 

  • Nikolskiy I, Siuzdak G, Patti GJ (2015) Discriminating precursors of common fragments for large-scale metabolite profiling by triple quadrupole mass spectrometry. Bioinformatics 31:2017–2023

    Article  CAS  Google Scholar 

  • Ogura T, Bamba T, Tai A, Fukusaki E (2015) Method for the compound annotation of conjugates in nontargeted metabolomics using accurate mass spectroscopy, multistage product ion spectra and compound database searching. Mass Spectrom 4:A0036

    Article  Google Scholar 

  • Ohtana Y, Abdullah AA, Altaf-Ul-Amin M, Huang M, Ono N, Sato T, Sugiura T, Horai H, Nakamura Y, Morita HA, Lange KW, Kibinge NK, Katsuragi T, Shirai T, Kanaya S (2014) Clustering of 3D-strcuture similarity based network of secondary metabolites reveals their relationship with biological activities. Mol Inform 33:790–801

    CAS  PubMed  Google Scholar 

  • Over B, Wetzel S, Grutter C, Nakai Y, Renner S, Rauh D, Waldmann H (2013) Natural-product-derived fragments for fragment-based ligand discovery. Nat Chem 5:21–28

    Article  CAS  Google Scholar 

  • Pluskal T, Castillo S, Villar-Briones A, Oresic M (2010) MZmine 2: modular framework for processing, visualizing, and analyzing mass spectroscopy. BMC Bioinform 11:395

    Article  Google Scholar 

  • Pon A, Jewison T, Su Y, Liang Y, Knox C, Maciejewski A, Wilson M, Wishart DS (2015) Pathways with PathWhiz. Nucleic Acids Res 43:W552–W559

    Article  CAS  Google Scholar 

  • Rolda’n C, de la Torre A, Mota S, Mprales-Soto A, Menendez J, Segura-Carretero A (2013) Idetification of active compounds in vegetal extracts based on correlation between activity and HPLC-MS data. Food Chem 136:392–399

    Article  Google Scholar 

  • Sakurai N, Ara T, Enomoto M, Motegi T, Morishita Y, Kurabayashi A, Iijima Y, Ogata Y, Nakajima D, Suzuki H, Shibata D (2014) Tools and databases of the KOMICS web portal for preprocessing, mining, and dissemination of metabolomics data. Biomed Res Int 2014:194812

    Article  Google Scholar 

  • Smith CA, Want EJ, O’Maille G, Abagyan R, Siuzdak G (2006) XCMS: Processing Mass Spectroscopy data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78:779–787

    Article  CAS  Google Scholar 

  • Sun H, Wang H, Zhu R, Tang K, Gong Q, Cui J, Cao Z, Liu Q (2014) iPEAP: integrating multiple omics and genetic data for pathway enrichment analysis. Bioinformatics 30:737–739

    Article  CAS  Google Scholar 

  • Tengstrand E, Lindberg J, Aberg KM (2014) TracMass 2-a modular suite of tools for processing chromatography-full scan mass spectroscopy data. Anal Chem 86:3435–3442

    Article  CAS  Google Scholar 

  • Winnikoff JR, Glukhov E, Watrous J, Dorrestein PC, Gerwick WH (2014) Quantitative molecular networking to profile marine cyanobacterial metabolomes. J Antibiot 67:105–112

    Article  CAS  Google Scholar 

  • Wishart DS (2008) Quantitative metabolomics using NMR. TrAC Trends Anal Chem 27:228–237

    Article  CAS  Google Scholar 

  • Xu QW, Griss J, Wang R, Jones AR, Hermjakob H, VizcaÍno J (2014) A (2014) jmzTab: a java interface to the mzTab data standard. Proteomics 14:1328–1332

    Article  CAS  Google Scholar 

  • Yu T, Jones DP (2014) Improving peak detection in high-resolution LC/MS metabolomics data using preexisting knowledge and machine learning approach. Bioinformatics 30:2941–2948

    Article  CAS  Google Scholar 

  • Zhu ZJ, Schultz AW, Wang J, Johnson CH, Yannone SM, Patti GJ, Siuzdak G (2013) Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database. Nat Prot 8:451–460

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Functional Genomics and Complex System Laboratory, Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India

    Neeraj Kumar & Vishal Acharya

  2. Academy of Scientific and Innovative Research (AcSIR), CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT) Campus, Palampur, Himachal Pradesh, India

    Neeraj Kumar & Vishal Acharya

Authors
  1. Neeraj Kumar
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  2. Vishal Acharya
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Correspondence to Vishal Acharya .

Editor information

Editors and Affiliations

  1. Center of Innovative and Applied Bioprocessing (CIAB), Mohali, Punjab, India

    Sudesh Kumar Yadav

  2. Department of Plant Sciences, School for Basic and Applied Sciences, Central University of Punjab (CUPB), Bathinda, Punjab, India

    Vinay Kumar

  3. Center of Innovative and Applied Bioprocessing (CIAB), Mohali, Punjab, India

    Sudhir P Singh

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Kumar, N., Acharya, V. (2018). Metabolomics Resources: An Introduction of Databases and Their Future Prospective. In: Yadav, S., Kumar, V., Singh, S. (eds) Recent Trends and Techniques in Plant Metabolic Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-2251-8_7

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