Molecular Techniques to Assess Microbial Community Structure, Function, and Dynamics in the Environment

  • Gurdeep Rastogi
  • Rajesh K. Sani


Culture-based methods are important in investigating the microbial ecology of natural and anthropogenically impacted environments, but they are extremely biased in their evaluation of microbial genetic diversity by selecting a particular population of microorganisms. With recent advances in genomics and sequencing technologies, microbial community analyses using culture-independent molecular techniques have initiated a new era of microbial ecology. Molecular analyses of environmental communities have revealed that the cultivable fraction represents <1% of the total number of prokaryotic species present in any given sample. A variety of molecular methods based on direct isolation and analysis of nucleic acids, proteins, and lipids from environmental samples have been discovered and revealed structural and functional information about microbial communities. Molecular approaches such as genetic fingerprinting, metagenomics, metaproteomics, metatranscriptomics, and proteogenomics are vital for discovering and characterizing the vast microbial diversity and understanding their interactions with biotic and abiotic environmental factors. This chapter summarizes recent progress in the area of molecular microbial ecology with an emphasis on novel techniques and approaches that offer new insights into the phylogenetic and functional diversity of microbial assemblages. The advantages and pitfalls of commonly used molecular methods to investigate microbial communities are discussed. The potential applications of each molecular technique and how they can be combined for a greater comprehensive assessment of microbial diversity has been illustrated with example studies.


Microbial Community Clone Library Soil Microbial Community Terminal Restriction Fragment Length Polymorphism Metagenomic Library 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to sincerely thank Dr. David E. Cummings, Point Loma Nazarene University, San Diego, CA for providing critical comments and suggestions on this chapter.


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© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Chemical and Biological EngineeringSouth Dakota School of Mines and TechnologyRapid CityUSA
  2. 2.Department of Plant PathologyUniversity of CaliforniaDavisUSA

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