Phytomicrobiome Interactions and Sustainable Agriculture. Группа авторов

databases for the generation of new information in a short time interval. Majority of the DNA sequences generated in metagenomic studies come from Sanger sequencing and different next‐generation sequencing platforms. The pyrosequencing and illumina sequencing are such platforms which produce long and short reads, respectively. Several software packages have been developed for the analysis of data generated by these sequencing techniques.

      3.6.1 Mothur

      Mothur (https://mothur.org) is one such in‐silico tool that is now being widely used for the analysis of sequence generated by Sanger and/or 454 ribosomal pyro‐tag sequencing (Patrick et al. 2009). Dr. Patrick Schloss and his research group in the Department of Microbiology and Immunology at University of Michigan initiated Mothur in order to develop a single piece of open‐source, expandable software that could be able to fill the bioinformatics needs of the microbial ecology community. Since its development, this tool has helped several metagenome researchers and has become among the most cited in‐silico tools to generate a meaningful information from 16s rRNA sequences.

      Similarly, other bioinformatics tools, such as QIIME and MEGEN are also being widely utilized by research community to analyze metagenomic data.

      3.6.2 Quantitative Insights into Microbial Ecology (QIIME)

      QIIME (http://qiime.org) is also an open‐source bioinformatics tool that is designed to generate quality graphics and statistics from raw data generated from Illumina or other platforms (Caporaso et al. 2010). The OTUs (operational taxonomic units) picking is the main output from QIIME pipeline, which illustrates the abundance of sequence variant in quarry samples. Other features such taxonomic assignment, phylogenetic reconstruction, and diversity analyses and visualizations provide additional strength to QIIME. Recently, an advance variant of QIIME, QIIME2 (https://qiime2.org) has been launched (Bolyen et al. 2019). Its q‐2‐micom plug‐in allows users to build and simulate metagenome‐scale metabolic community models and they can also predict metabolic fluxes, which are undergoing in microbial consortium.

      3.6.3 MEta Genome Analyzer (MEGAN)

      The Metagenome Analysis Software (MEGAN) (https://bio.tools/megan) is an in‐silico tool that allows analysis of a large set of metagenomic data on desktop/laptop. It provides a platform to use several interactive tools under a single application, such as NCBI taxonomy or a more customized taxonomy (e.g. SILVA) for the taxonomic analysis, InterPro2GO, SEED, eggNOG or KEGGforfunctional analysis. In addition to this, one can use bar charts, word count, vronoi tree maps and many other charts to analyze metagenomic dataset.

3.7 Recent Progress in Metagenomic Studies of Plant Microbiome

      A lot of metagenomics studies have been conducted in the recent past to characterize the rhizosphere bacterial diversity based on 16S rRNA gene amplification (Soni et al. 2010). Several other advanced molecular techniques such as PCR‐RFLP (Singh et al. 2010), DGGE and TTGE (Soni et al. 2010) and real‐time PCR (Premalatha et al. 2009; Soni and Goel 2010; Suyal et al. 2015a, 2015b) have also been coupled together with metagenomics to explore the plant microbiome. Researchers have also been able to beautifully paint the dominance of novel genes, such as csp (Premalatha et al. 2009) and nifH genes (Soni and Goel 2010; Soni et al. 2016) from the Himalayan region in Uttarakhand state of north India. Like nifH genes, nif might evolve from their nearest genes or adjacent regions and become specific in their functions in due course of time (Soni and Goel 2010). Using metagenomics, the existence of diverse diazotrophic microbial assemblages in the rhizosphere of indigenous red kidney bean (RKB) was also discovered (Suyal et al. 2015a, 2015b). Besides this, several studies related to plant microbiome have been reviewed and compiled to provide comprehensive knowledge about applications of metagenomics in plant microbiome studies (Soni et al. 2012, 2017; Soni 2013; Suyal et al. 2016; Goel et al. 2017a, 2017b, 2017c; Goel et al. 2018; Kumar et al. 2018a).

3.8 Conclusion and Future Prospects

      The recent advancement in molecular biology and biotechnology opens several new areas of research especially in plant microbial interactions. Still several unsaid biomes are waiting for their turn. The techniques, such as metagenomics, may provide some new insights to explore these microbiomes. Scientific groups need to focus on these unexplored resources and reservoir of immense potential by using such advanced methodologies.

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