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The global mandate for safer, cleaner and renewable energy has accelerated research on microbes that convert carbon sources to end-products serving as biofuels of the so-called first, second or third generation – e.g., bioethanol or biodiesel derived from starchy, sugar-rich or oily crops; bioethanol derived from composite lignocellulosic biomass; and biodiesels extracted from oil-producing algae and cyanobacteria, respectively. Recent advances in ‘omics’ applications are beginning to cast light on the biological mechanisms underlying biofuel production. They also unravel mechanisms important for organic solvent or high-added-value chemical production, which, along with those for fuel chemicals, are significant to the broader field of Bioenergy. The Frontiers in Microbial Physiology Research Topic that led to the current e-book publication, operated from 2013 to 2014 and welcomed articles aiming to better understand the genetic basis behind Bioenergy production. It invited genetic studies of microbes already used or carrying the potential to be used for bioethanol, biobutanol, biodiesel, and fuel gas production, as also of microbes posing as promising new catalysts for alternative bioproducts. Any research focusing on the systems biology of such microbes, gene function and regulation, genetic and/or genomic tool development, metabolic engineering, and synthetic biology leading to strain optimization, was considered highly relevant to the topic. Likewise, bioinformatic analyses and modeling pertaining to gene network prediction and function were also desirable and therefore invited in the thematic forum. Upon e-book development today, we, at the editorial, strongly believe that all articles presented herein – original research papers, reviews, perspectives and a technology report – significantly contribute to the emerging insights regarding microbial-derived energy production. Katherine M. Pappas, 2016

Metabolic Engineering --- metabolic modeling --- bioenergy --- Comparative genomics --- Biofuels --- lignocellulolysis --- tran

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The global mandate for safer, cleaner and renewable energy has accelerated research on microbes that convert carbon sources to end-products serving as biofuels of the so-called first, second or third generation – e.g., bioethanol or biodiesel derived from starchy, sugar-rich or oily crops; bioethanol derived from composite lignocellulosic biomass; and biodiesels extracted from oil-producing algae and cyanobacteria, respectively. Recent advances in ‘omics’ applications are beginning to cast light on the biological mechanisms underlying biofuel production. They also unravel mechanisms important for organic solvent or high-added-value chemical production, which, along with those for fuel chemicals, are significant to the broader field of Bioenergy. The Frontiers in Microbial Physiology Research Topic that led to the current e-book publication, operated from 2013 to 2014 and welcomed articles aiming to better understand the genetic basis behind Bioenergy production. It invited genetic studies of microbes already used or carrying the potential to be used for bioethanol, biobutanol, biodiesel, and fuel gas production, as also of microbes posing as promising new catalysts for alternative bioproducts. Any research focusing on the systems biology of such microbes, gene function and regulation, genetic and/or genomic tool development, metabolic engineering, and synthetic biology leading to strain optimization, was considered highly relevant to the topic. Likewise, bioinformatic analyses and modeling pertaining to gene network prediction and function were also desirable and therefore invited in the thematic forum. Upon e-book development today, we, at the editorial, strongly believe that all articles presented herein – original research papers, reviews, perspectives and a technology report – significantly contribute to the emerging insights regarding microbial-derived energy production. Katherine M. Pappas, 2016

Metabolic Engineering --- metabolic modeling --- bioenergy --- Comparative genomics --- Biofuels --- lignocellulolysis --- tran

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Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme “Best Practices in Metabolomics Data Analysis”. Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows.

Research & information: general --- metabolic networks --- mass spectral libraries --- metabolite annotation --- metabolomics data mapping --- nontarget analysis --- liquid chromatography mass spectrometry --- compound identification --- tandem mass spectral library --- forensics --- wastewater --- gut microbiome --- meta-omics --- metagenomics --- metabolomics --- metabolic reconstructions --- genome-scale metabolic modeling --- constraint-based modeling --- flux balance --- host–microbiome --- metabolism --- global metabolomics --- LC-MS --- spectra processing --- pathway analysis --- enrichment analysis --- mass spectrometry --- liquid chromatography --- MS spectral prediction --- metabolite identification --- structure-based chemical classification --- rule-based fragmentation --- combinatorial fragmentation --- time series --- PLS --- NPLS --- variable selection --- bootstrapped-VIP --- data repository --- computational metabolomics --- reanalysis --- lipidomics --- data processing --- triplot --- multivariate risk modeling --- environmental factors --- disease risk --- chemical classification --- in silico workflows --- metabolome mining --- molecular families --- networking --- substructures --- mass spectrometry imaging --- metabolomics imaging --- biostatistics --- ion selection algorithms --- liquid chromatography high-resolution mass spectrometry --- data-independent acquisition --- all ion fragmentation --- targeted analysis --- untargeted analysis --- R programming --- full-scan MS/MS processing --- R-MetaboList 2 --- liquid chromatography–mass spectrometry (LC/MS) --- fragmentation (MS/MS) --- data-dependent acquisition (DDA) --- simulator --- in silico --- untargeted metabolomics --- liquid chromatography–mass spectrometry (LC-MS) --- experimental design --- sample preparation --- univariate and multivariate statistics --- metabolic pathway and network analysis --- LC–MS --- metabolic profiling --- computational statistical --- unsupervised learning --- supervised learning

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Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme “Best Practices in Metabolomics Data Analysis”. Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows.

Research & information: general --- metabolic networks --- mass spectral libraries --- metabolite annotation --- metabolomics data mapping --- nontarget analysis --- liquid chromatography mass spectrometry --- compound identification --- tandem mass spectral library --- forensics --- wastewater --- gut microbiome --- meta-omics --- metagenomics --- metabolomics --- metabolic reconstructions --- genome-scale metabolic modeling --- constraint-based modeling --- flux balance --- host–microbiome --- metabolism --- global metabolomics --- LC-MS --- spectra processing --- pathway analysis --- enrichment analysis --- mass spectrometry --- liquid chromatography --- MS spectral prediction --- metabolite identification --- structure-based chemical classification --- rule-based fragmentation --- combinatorial fragmentation --- time series --- PLS --- NPLS --- variable selection --- bootstrapped-VIP --- data repository --- computational metabolomics --- reanalysis --- lipidomics --- data processing --- triplot --- multivariate risk modeling --- environmental factors --- disease risk --- chemical classification --- in silico workflows --- metabolome mining --- molecular families --- networking --- substructures --- mass spectrometry imaging --- metabolomics imaging --- biostatistics --- ion selection algorithms --- liquid chromatography high-resolution mass spectrometry --- data-independent acquisition --- all ion fragmentation --- targeted analysis --- untargeted analysis --- R programming --- full-scan MS/MS processing --- R-MetaboList 2 --- liquid chromatography–mass spectrometry (LC/MS) --- fragmentation (MS/MS) --- data-dependent acquisition (DDA) --- simulator --- in silico --- untargeted metabolomics --- liquid chromatography–mass spectrometry (LC-MS) --- experimental design --- sample preparation --- univariate and multivariate statistics --- metabolic pathway and network analysis --- LC–MS --- metabolic profiling --- computational statistical --- unsupervised learning --- supervised learning