Microbial metagenomics in effluent treatment plant
Other Authors: | Shah, Maulin P., ScienceDirect (Online Service) |
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Format: | Electronic |
Language: | English |
Published: |
[S.l.] :
Elsevier,
2024.
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Physical Description: |
1 online resource. |
Subjects: |
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.