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      Development and Application of Multidimensional Lipid Libraries to Investigate Lipidomic Dysregulation Related to Smoke Inhalation Injury Severity

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          A SIMPLE METHOD FOR THE ISOLATION AND PURIFICATION OF TOTAL LIPIDES FROM ANIMAL TISSUES

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            MetaboAnalyst: a web server for metabolomic data analysis and interpretation

            Metabolomics is a newly emerging field of ‘omics’ research that is concerned with characterizing large numbers of metabolites using NMR, chromatography and mass spectrometry. It is frequently used in biomarker identification and the metabolic profiling of cells, tissues or organisms. The data processing challenges in metabolomics are quite unique and often require specialized (or expensive) data analysis software and a detailed knowledge of cheminformatics, bioinformatics and statistics. In an effort to simplify metabolomic data analysis while at the same time improving user accessibility, we have developed a freely accessible, easy-to-use web server for metabolomic data analysis called MetaboAnalyst. Fundamentally, MetaboAnalyst is a web-based metabolomic data processing tool not unlike many of today's web-based microarray analysis packages. It accepts a variety of input data (NMR peak lists, binned spectra, MS peak lists, compound/concentration data) in a wide variety of formats. It also offers a number of options for metabolomic data processing, data normalization, multivariate statistical analysis, graphing, metabolite identification and pathway mapping. In particular, MetaboAnalyst supports such techniques as: fold change analysis, t-tests, PCA, PLS-DA, hierarchical clustering and a number of more sophisticated statistical or machine learning methods. It also employs a large library of reference spectra to facilitate compound identification from most kinds of input spectra. MetaboAnalyst guides users through a step-by-step analysis pipeline using a variety of menus, information hyperlinks and check boxes. Upon completion, the server generates a detailed report describing each method used, embedded with graphical and tabular outputs. MetaboAnalyst is capable of handling most kinds of metabolomic data and was designed to perform most of the common kinds of metabolomic data analyses. MetaboAnalyst is accessible at http://www.metaboanalyst.ca
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              Update of the LIPID MAPS comprehensive classification system for lipids.

              In 2005, the International Lipid Classification and Nomenclature Committee under the sponsorship of the LIPID MAPS Consortium developed and established a "Comprehensive Classification System for Lipids" based on well-defined chemical and biochemical principles and using an ontology that is extensible, flexible, and scalable. This classification system, which is compatible with contemporary databasing and informatics needs, has now been accepted internationally and widely adopted. In response to considerable attention and requests from lipid researchers from around the globe and in a variety of fields, the comprehensive classification system has undergone significant revisions over the last few years to more fully represent lipid structures from a wider variety of sources and to provide additional levels of detail as necessary. The details of this classification system are reviewed and updated and are presented here, along with revisions to its suggested nomenclature and structure-drawing recommendations for lipids.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Journal of Proteome Research
                J. Proteome Res.
                American Chemical Society (ACS)
                1535-3893
                1535-3907
                January 07 2022
                December 07 2021
                January 07 2022
                : 21
                : 1
                : 232-242
                Affiliations
                [1 ]Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
                [2 ]Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona 85721, United States
                [3 ]Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
                [4 ]Arizona Burn Center, Valleywise Health, Phoenix, Arizona 85008, United States
                [5 ]Comparative Medicine Institute, North Carolina State University, Raleigh, North Carolina 27695, United States
                Article
                10.1021/acs.jproteome.1c00820
                34874736
                0aadd1d6-db2e-413a-a059-d74646b3824e
                © 2022

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-037

                https://doi.org/10.15223/policy-045

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