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Microbial metagenomics in effluent treatment plant /

This book, 'Metagenomics in Effluent Treatment Plant', edited by Maulin P. Shah, provides an in-depth exploration of the application of metagenomics in the analysis and improvement of wastewater treatment processes. It covers topics such as the degradation of polycyclic aromatic hydrocarbo...

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Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Shah, Maulin P.
Format: eBook
Language:English
Published: [S.l.] : Elsevier, 2024.
Subjects:
Sewage > Purification.
Eaux usées > Épuration.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Front Cover
  • Microbial Metagenomics in Effluent Treatment Plant
  • Copyright Page
  • Contents
  • List of contributors
  • 1 Polycyclic aromatic hydrocarbon degradation by bacterial communities: a sustainable approach
  • 1.1 Introduction
  • 1.2 Genetics of polycyclic aromatic hydrocarbon-degrading bacteria
  • 1.3 Conclusion and future perspectives
  • References
  • 2 Analysis of complex microbial communities in soil and wastewater treatment processes
  • 2.1 Introduction
  • 2.1.1 Anaerobic digestion and composting
  • 2.2 Value of researching microbial communities in waste-transformation procedures
  • 2.3 Cooccurrence network analysis for the characterization of microbial communities
  • 2.3.1 Antibiotic resistance gene and microbial genotoxin detection by metagenomics in a natural setting
  • 2.3.2 Antibiotics are being filtered out of wastewater
  • 2.3.3 Toxic byproduct
  • 2.4 Research aimed toward Phylogenetic Fingerprinting of the Whole Communities
  • 2.4.1 Wastewater treatment plant microbiological diversity
  • 2.4.2 The microbial mechanism for metal tolerance
  • 2.5 Conclusion
  • List of abbreviations
  • 3.10.1 Carbon cycle and soil microbes
  • 3.10.2 Effect of biotic factors on soil rhizosphere
  • 3.11 Recent developments in molecular methods for analyzing the soil microbiome
  • 3.12 Changes in plant-microbe interaction caused by global warming
  • 3.13 Case study: drought impacts on microbial communities in both minimally and heavily managed grassland
  • 3.14 Case study microorganism
  • 3.14.1 Heavy rainfall
  • 3.15 Conclusion
  • Abbreviations
  • References
  • 4 Gene prediction through metagenomics
  • 4.1 Introduction
  • 4.2 Genomics versus metagenomics
  • 4.3 Gene prediction in Eukaryotes versus prokaryotes
  • 4.4 Significance of metagenomics
  • 4.5 Methods of gene prediction
  • 4.6 Models and algorithms
  • 4.7 MetaGUN for metagenomic fragments based on a machine learning approach of support vector machine
  • 4.7.1 Architecture of MetaGUN algorithm
  • 4.8 Glimmer
  • 4.9 Algorithm structure
  • 4.10 Ab initio gene identification in metagenomic sequences
  • 4.11 Heuristic system of model parameters derivation
  • 4.12 Orphelia
  • 4.12.1 Metaprodigal
  • 4.12.2 MGC
  • 4.13 Metageneannotator
  • 4.14 Predictions on short genomic sequences