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Biblioteca(s): |
Epagri-Sede. |
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Data corrente: |
09/11/2012 |
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Data da última atualização: |
09/11/2012 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
MARSCHIN, M.; KUHNEN, S.; LEMOS, P. M. M.; OLIVEIRA, S. K.; SILVA, D. A.; TOMAZZOLI, M. M.; SOUZA, A. C. V.; PINTO, M. R. R.; UARROTA, V. G.; CELLA, I.; FERREIRA, A. G.; ZEGGIO, A. R. S.; VELEIRINHO, M. B. R.; DELGADILLO, I.; VIEIRA, F. A. |
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Afiliação: |
Epagri |
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Título: |
Metabolomics and Chemometrics as Tools for Chemo(bio)diversity Analysis - Maize Landraces and Propolis. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
In: VAMURZA, K. Chemometrics in practical applications. Croácia: InTech, 2012. p. 253-270. |
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ISBN: |
9789535104384 |
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Idioma: |
Inglês |
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Conteúdo: |
Developments in analytical techniques (GC-MS, LC-MS, 1H-, 13C-NMR, FT-MS, e.g.) are progressing rapidly and have been driven mostly by the requirements in the healthcare and food sectors. Simultaneous high-throughput measurements of several analytes at the level of the transcript (transcriptomics), proteins, (proteomics), and metabolites (metabolomics) are currently performed, producing a prodigious amount of data. Thus, the advent of omic studies has created an information explosion, resulting in a paradigm shift in the emphasis of analytical research of biological systems. The traditional approaches of biochemistry and molecular cell biology, where the cellular processes have been investigated individually and often independent of each other, are giving way to a wider approach of analyzing the cellular composition in its entirety, allowing achieving a quasi-complete metabolic picture. The exponential growth of data, largely from genomics and genomic technologies, has changed the way biologists think about and handle data. In order to derive meaning from these large data sets, tools are required to analyze and identify patterns in the data, and allow data to be placed into a biological context. In this scenario, biologists have a continuous need for tools to manage and analyze the ever-increasing data supply. Optimal use of the data set, primarily of chemical nature, requires effective methods to analyze and manage them. It is obvious that all omic approaches will rely heavily upon bioinformatics for the storage, retrieval, and analysis of large data sets. Thus, and taking into account the multivariate nature of analysis in omic technologies, there is an increase emphasis in research on the application of chemometric techniques for extracting relevant information. Metabolomics and chemometrics have been used in a number of areas to provide biological information beyond the simple identification of cell constituents. These areas include: MenosDevelopments in analytical techniques (GC-MS, LC-MS, 1H-, 13C-NMR, FT-MS, e.g.) are progressing rapidly and have been driven mostly by the requirements in the healthcare and food sectors. Simultaneous high-throughput measurements of several analytes at the level of the transcript (transcriptomics), proteins, (proteomics), and metabolites (metabolomics) are currently performed, producing a prodigious amount of data. Thus, the advent of omic studies has created an information explosion, resulting in a paradigm shift in the emphasis of analytical research of biological systems. The traditional approaches of biochemistry and molecular cell biology, where the cellular processes have been investigated individually and often independent of each other, are giving way to a wider approach of analyzing the cellular composition in its entirety, allowing achieving a quasi-complete metabolic picture. The exponential growth of data, largely from genomics and genomic technologies, has changed the way biologists think about and handle data. In order to derive meaning from these large data sets, tools are required to analyze and identify patterns in the data, and allow data to be placed into a biological context. In this scenario, biologists have a continuous need for tools to manage and analyze the ever-increasing data supply. Optimal use of the data set, primarily of chemical nature, requires effective methods to analyze and manage them. It is obvious that all omic approaches will rely heav... Mostrar Tudo |
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Palavras-Chave: |
Chemobiodiversity analysis; Maize landraces; Metabolomic; Propolis. |
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Categoria do assunto: |
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Marc: |
LEADER 02983naa a2200349 a 4500
001 1087496
005 2012-11-09
008 2012 bl uuuu u00u1 u #d
020 $a9789535104384
100 1 $aMARSCHIN, M.
245 $aMetabolomics and Chemometrics as Tools for Chemo(bio)diversity Analysis - Maize Landraces and Propolis.
260 $c2012
520 $aDevelopments in analytical techniques (GC-MS, LC-MS, 1H-, 13C-NMR, FT-MS, e.g.) are progressing rapidly and have been driven mostly by the requirements in the healthcare and food sectors. Simultaneous high-throughput measurements of several analytes at the level of the transcript (transcriptomics), proteins, (proteomics), and metabolites (metabolomics) are currently performed, producing a prodigious amount of data. Thus, the advent of omic studies has created an information explosion, resulting in a paradigm shift in the emphasis of analytical research of biological systems. The traditional approaches of biochemistry and molecular cell biology, where the cellular processes have been investigated individually and often independent of each other, are giving way to a wider approach of analyzing the cellular composition in its entirety, allowing achieving a quasi-complete metabolic picture. The exponential growth of data, largely from genomics and genomic technologies, has changed the way biologists think about and handle data. In order to derive meaning from these large data sets, tools are required to analyze and identify patterns in the data, and allow data to be placed into a biological context. In this scenario, biologists have a continuous need for tools to manage and analyze the ever-increasing data supply. Optimal use of the data set, primarily of chemical nature, requires effective methods to analyze and manage them. It is obvious that all omic approaches will rely heavily upon bioinformatics for the storage, retrieval, and analysis of large data sets. Thus, and taking into account the multivariate nature of analysis in omic technologies, there is an increase emphasis in research on the application of chemometric techniques for extracting relevant information. Metabolomics and chemometrics have been used in a number of areas to provide biological information beyond the simple identification of cell constituents. These areas include:
653 $aChemobiodiversity analysis
653 $aMaize landraces
653 $aMetabolomic
653 $aPropolis
700 1 $aKUHNEN, S.
700 1 $aLEMOS, P. M. M.
700 1 $aOLIVEIRA, S. K.
700 1 $aSILVA, D. A.
700 1 $aTOMAZZOLI, M. M.
700 1 $aSOUZA, A. C. V.
700 1 $aPINTO, M. R. R.
700 1 $aUARROTA, V. G.
700 1 $aCELLA, I.
700 1 $aFERREIRA, A. G.
700 1 $aZEGGIO, A. R. S.
700 1 $aVELEIRINHO, M. B. R.
700 1 $aDELGADILLO, I.
700 1 $aVIEIRA, F. A.
773 $tIn: VAMURZA, K. Chemometrics in practical applications. Croácia: InTech, 2012. p. 253-270.
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