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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_6
Chapter 6
Bioinformatics Tools for Soil Microbiome
Analysis
Abstract Metagenomic approaches aid in exploring the structural and functional
diversity of soil microorganisms. Sequence analysis of the large amount of data
generated from soil microbial communities sequencing is a challenging issue. It is
made feasible through bioinformatics tools, which provide sequence pipelines for
the high-throughput screening of the soil metagenomic libraries. Such sequence
analysis of metagenomic datasets reveals the genetic structure, gene prediction, proposed functions, and metabolic pathways of the analyzed microbial communities.
Bioinformatic tools provide statistical procedures not only for comparison of
metagenomic libraries but also to report the sampling and library creation artifacts.
Here we discuss the bioinformatics tools for accessing the metagenomic information and platforms for data storage within databases (GenBank env), access, synthesis, and analysis.
Keywords Bioinformatics · Functional diversity · Gene prediction · Metagenomic
· Structural diversity · Microbial taxonomy · Metabolic pathways
Metagenomics and metatranscriptomics data analysis and interpretation includes
different steps: assembly and annotation, taxonomic and functional attributes, intraand inter-environmental interaction networks, single-cell sequencing, simulation
studies, and statistical analysis of downstream processes (Segata et al. 2013).
Different tools and software employed at different steps of meta-omics data analysis
and interpretations are provided in Table 6.1, and a brief explanation is also provided here.
6.1 Tools for Assembly and Annotation
(i) Genovo: Genovo is utilized for the de novo assembly of the genetic sequence
that finds similar sequence chemistry under the platform. It tells us about the
probable idea of read generation from the environmental sample. On
© 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_6
Chapter 6
Bioinformatics Tools for Soil Microbiome
Analysis
Abstract Metagenomic approaches aid in exploring the structural and functional
diversity of soil microorganisms. Sequence analysis of the large amount of data
generated from soil microbial communities sequencing is a challenging issue. It is
made feasible through bioinformatics tools, which provide sequence pipelines for
the high-throughput screening of the soil metagenomic libraries. Such sequence
analysis of metagenomic datasets reveals the genetic structure, gene prediction, proposed functions, and metabolic pathways of the analyzed microbial communities.
Bioinformatic tools provide statistical procedures not only for comparison of
metagenomic libraries but also to report the sampling and library creation artifacts.
Here we discuss the bioinformatics tools for accessing the metagenomic information and platforms for data storage within databases (GenBank env), access, synthesis, and analysis.
Keywords Bioinformatics · Functional diversity · Gene prediction · Metagenomic
· Structural diversity · Microbial taxonomy · Metabolic pathways
Metagenomics and metatranscriptomics data analysis and interpretation includes
different steps: assembly and annotation, taxonomic and functional attributes, intraand inter-environmental interaction networks, single-cell sequencing, simulation
studies, and statistical analysis of downstream processes (Segata et al. 2013).
Different tools and software employed at different steps of meta-omics data analysis
and interpretations are provided in Table 6.1, and a brief explanation is also provided here.
6.1 Tools for Assembly and Annotation
(i) Genovo: Genovo is utilized for the de novo assembly of the genetic sequence
that finds similar sequence chemistry under the platform. It tells us about the
probable idea of read generation from the environmental sample. On