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© The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
R. K. Dubey et al., Unravelling the Soil Microbiome, SpringerBriefs in
Environmental Science, https://doi.org/10.1007/978-3-030-15516-2_4
Chapter 4
Single-Cell Genomics and Metagenomics
for Microbial Diversity Analysis
Abstract Soil metagenomic analysis was previously limited by technological
restrictions and the few reference genomes. The advent of next-generation ‘omics’
technologies has provided high-throughput methods for analysing community
structure and reconstructing soil metagenomes. High-throughput sequencing technology and single-cell genomics have revolutionized metagenomic analysis by
enabling large-scale sequencing at reduced sequencing costs with less time required.
In the present chapter we discuss various technological advances in metagenomics,
their processes and the methods of data analysis, and metagenomic success stories
under various environments that can be applied for studying the functional and
structural diversity of soil microorganisms.
Keywords Functional annotation · Microbial community structure · Nextgeneration sequencing (NGS) technology · Single-cell genomics · Metagenome
Polymerase chain reaction (PCR)-based microbial diversity analysis has its own
limitations. The isolation process of DNA and RNA bases in PCR results and harsh
isolation methods cause shearing of the DNA, which creates problems in PCR
detection and primer annealing. Environmental samples have a large amount of
humic acid that usually is coprecipitated with DNA and induces bias during molecular microbial diversity analysis. Sequences 16S and 18S rRNA, and internal transcribed spacers (ITS), are highly conserved regions among all organisms. Differential
amplification of these genes biases the results of PCR-based prediction of the
microbial community. Several microbial genomes have high guanine-cytosine (GC)
content, a different copy number of template gene primer specificity, and a high rate
of hybridization in the soils (von Wintzingerode et al. 1997), and these differences
cause potential bias in the PCR-based community structure prediction. Advances in
the identification of microbial diversity are still ongoing, and chip-based methods
have recently been under investigation (Stanley and van der Heijden 2017).
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
R. K. Dubey et al., Unravelling the Soil Microbiome, SpringerBriefs in
Environmental Science, https://doi.org/10.1007/978-3-030-15516-2_4
Chapter 4
Single-Cell Genomics and Metagenomics
for Microbial Diversity Analysis
Abstract Soil metagenomic analysis was previously limited by technological
restrictions and the few reference genomes. The advent of next-generation ‘omics’
technologies has provided high-throughput methods for analysing community
structure and reconstructing soil metagenomes. High-throughput sequencing technology and single-cell genomics have revolutionized metagenomic analysis by
enabling large-scale sequencing at reduced sequencing costs with less time required.
In the present chapter we discuss various technological advances in metagenomics,
their processes and the methods of data analysis, and metagenomic success stories
under various environments that can be applied for studying the functional and
structural diversity of soil microorganisms.
Keywords Functional annotation · Microbial community structure · Nextgeneration sequencing (NGS) technology · Single-cell genomics · Metagenome
Polymerase chain reaction (PCR)-based microbial diversity analysis has its own
limitations. The isolation process of DNA and RNA bases in PCR results and harsh
isolation methods cause shearing of the DNA, which creates problems in PCR
detection and primer annealing. Environmental samples have a large amount of
humic acid that usually is coprecipitated with DNA and induces bias during molecular microbial diversity analysis. Sequences 16S and 18S rRNA, and internal transcribed spacers (ITS), are highly conserved regions among all organisms. Differential
amplification of these genes biases the results of PCR-based prediction of the
microbial community. Several microbial genomes have high guanine-cytosine (GC)
content, a different copy number of template gene primer specificity, and a high rate
of hybridization in the soils (von Wintzingerode et al. 1997), and these differences
cause potential bias in the PCR-based community structure prediction. Advances in
the identification of microbial diversity are still ongoing, and chip-based methods
have recently been under investigation (Stanley and van der Heijden 2017).