- Open Access
Analysis and Interpretation of metagenomics data: an approach
Biological Procedures Online volume 24, Article number: 18 (2022)
Advances in next-generation sequencing technologies have accelerated the momentum of metagenomic studies, which is increasing yearly. The metagenomics field is one of the versatile applications in microbiology, where any interaction in the environment involving microorganisms can be the topic of study. Due to this versatility, the number of applications of this omics technology reached its horizons. Agriculture is a crucial sector involving crop plants and microorganisms interacting together. Hence, studying these interactions through the lenses of metagenomics would completely disclose a new meaning to crop health and development. The rhizosphere is an essential reservoir of the microbial community for agricultural soil. Hence, we focus on the R&D of metagenomic studies on the rhizosphere of crops such as rice, wheat, legumes, chickpea, and sorghum. These recent developments are impossible without the continuous advancement seen in the next-generation sequencing platforms; thus, a brief introduction and analysis of the available sequencing platforms are presented here to have a clear picture of the workflow. Concluding the topic is the discussion about different pipelines applied to analyze data produced by sequencing techniques and have a significant role in interpreting the outcome of a particular experiment. A plethora of different software and tools are incorporated in the automated pipelines or individually available to perform manual metagenomic analysis. Here we describe 8–10 advanced, efficient pipelines used for analysis that explain their respective workflows to simplify the whole analysis process.
Microorganisms are omnipresent and hence have an immense effect on the biosphere of the Earth. All organisms, from humans to plants, impact the microorganisms in their vicinity . For instance, they are the catalyst in maintaining a healthy relationship between man and the food (i.e., the crops) he eats to flourish and evolve. This thought raises many questions about microbial populations hidden in the soil, which indirectly contribute to human health but are unknown due to our inability to culture them. There was no approach to this thought until, in 1998, Jo Handelsman coined the term 'Metagenomics' – which has the potential to reveal the secrets of the microbial world. She described it as the cloning and functional analysis of collective genomes of soil microflora to be the metagenome of the soil. In her article, she also stated that: not all soil microflora is culturable; hence, soil alone has a huge number of untapped microbial communities yet to be explored . Since then, it has developed as a platform with the broadest applications in molecular biology . It is as if Handelsman has given us the key to unlock the mysteries of the microbial world. This key is currently being applied to study the microbiome of various environments, from the human gut to the sea floor . The science of metagenomics can extensively help us to understand the relationship of the crop with the microbes present in the soil. The soil is a rich source of microorganisms, but when it comes to crops, the rhizosphere is like the coral reef of the soil where all the significant microbial species reside. We discuss further in the review metagenomic studies on rhizosphere and its respective crops  (Table 1).
As mentioned earlier, metagenomics is a versatile branch of science, having two basic approaches: (A) taxonomic application (sequence-based analysis) and (B) Functional application (Function driven analysis) or a combination of both, depending on the requirement of the objective.
This approach is used to find out the phylogenetic relationships of the sequenced gene with taxonomic groups of microorganisms known in the database. In this case, the phylogenetic clusters like 16S rRNA gene sequence are targeted where operational taxonomic units (OTUs)  are compared against their amplitude to estimate the microbial species abundance in that particular environment .
One such application of metagenomic analysis involves taxonomic profiling and identification of plant pathogens  using next-generation sequencing along with disease diagnostics, microbiome analyses, and outbreak tracing . Taxonomic profiling is also used in metabarcoding (similar to metagenomic analysis), which has the potential to identify all the microbes, including rare and abundant taxa [8, 10].
This approach is used to find a sequence with a functional gene having a particular activity or if the gene is novel having a specific function in a functional pathway [11, 12]. This is achieved by shotgun metagenomics, which includes whole-genome sequencing, which further involves functional annotation of a gene [13, 14]. Functional annotation is divided into two steps: gene prediction and gene annotation, where gene prediction helps to find the potential protein sequences. After identification, these sequences encoding the protein are compared with protein families in databases and annotated functionally by matching the family's function .
Functional metagenomics broadly identifies novel proteins/genes that contribute to the microbial population's function and affect the environment . For example, soil collected from different parts would identify novel antimicrobials like Terbomycine A and B, novel anti-infectives like lactonases, and bacterial NHLase .
Road map to metagenomics study
Following are the fundamental steps involved in a metagenomics experiment where each step has significance.
Step 1: sample collection
This is the very first and essential step to begin a metagenomics project where the particular sample to be examined is selected and used for DNA isolation. The sample collection time points may vary depending on the study and are expected to be used freshly to isolate DNA [14, 18, 19]. For instance, the microbial community in the peanut plant rhizosphere was analyzed by collecting the sample around the roots during the nodulation period . In another example, infants' fecal samples from the Oukasie clinic were collected to analyze the enteric RNA virome in the northwest province of South Africa . The participants were immunized with the Rotarix vaccine, after which samples were collected at three different time points . Another study analyzed freshwater lakes for their metagenomics content . This conveyed the importance of time-period, geographical conditions, and pre-treatments present during sample collection.
Step 2: isolation of DNA
The method for isolating DNA from the sample collected has to be chosen appropriately because it would result in erroneous results, and highly reliable knowledge would be lost. The selection of the method depends on the type of sample collected [14, 19, 23]. Environmental and human-sourced samples have many microbial cells belonging to different phyla/classes . Because of this, the sample contains heterogeneous cells as far as the genomic contents, architecture, and morphology of their cell wall are concerned. It becomes mandatory to process the samples first and then add lysis reagents to extract a sufficient quantity of good-quality community DNA. In the experiment, three enzymes, lysozyme, lysostaphin, and mutanolysin, can brake either 1,4-beta glycoside linkages or transpeptidase bonds present in Gram-positive and Gram-negative bacteria cell walls and assist in spheroplast formation. The Spheroplast formed is extremely liable and breaks easily in the presence of lysis reagents, physical pressure, or mechanical forces .
Step 3: NGS library preparation and sequencing
One of the critical steps in the NGS workflow is preparing the DNA for sequencing, i.e., creating an NGS DNA library which is a collection of similarly sized DNA fragments with known adapter sequences added to the 5' and 3' end of the sequence.
The isolated DNA is subjected to library preparation which consists of 4 basic steps as follows:
DNA fragmentation/Target selection
Final library quantification and QC
The isolated DNA is fragmented using physical or enzymatic methods (whole genome), or if the sequence of the specific target sequence in the fragment is known, PCR amplification of these known fragments is done to produce DNA amplicons (16S rRNA target sequence is extensively used) within the desired size range. Next, the specific DNA adapter sequences are annealed (ligated) to these fragments at the 3' and 5' ends. These double-stranded adapters are around 20-40 bp fragments with known sequences. One adapter contains the primer annealing site, and the other adapter is used for anchoring the DNA fragment to a surface for sequencing. For example, beads or a solid surface containing a complementary DNA sequence. The size selection of the ligated DNA fragments is made by gel electrophoresis (PACBIO SMRT bell Express Template Preparation Kit), columns (Qiagen), or magnetic beads . If the size of the targeted DNA fragment is known, then the magnetic beads become a better option as compared to gel electrophoresis. Quantifying the library is the last crucial step which is checked on a Bioanalyser system, giving information on the concentration of the library and the different fragment size lengths present. Quantitative real-time PCR (qPCR) is another method for quantification and is known to give the precise quantity of the library but is unable to estimate the library size . Based on the requirement of the experiment, the DNA library is prepared by either amplicon fragmentation or whole genome fragmentation and forwarded for sequencing.
Targeted sequencing (amplicon sequencing)
The targeted sequencing approach is the most extensively applied strategy to characterize microbial populations. The basic technique used in this method is DNA isolation. PCR amplification is performed with polymerase chain reaction primer sets that individually target a taxonomically informative gene common to eukaryotes and prokaryotes.
16S rRNA gene amplicon sequencing
16S rRNA sequence is considered the most conserved taxonomic marker (bacterial) as it is sequenced in considerably less time. Hence, it is a gold standard for extensive phylogenetic analysis . The same technique is used for metagenomic studies to identify the sample's taxonomic profile of microbial communities. There are approximately 1500 base pairs (bp) in size, with nine highly conserved regions and nine variable regions (V1–V9) in the complete 16S rRNA. The conserved regions of the genes are used for primer binding during PCR amplification, whereas the hypervariable regions are used for identifying sequence diversity in prokaryotes . Platforms like Illumina sequencing use V3 and V4 regions to obtain the taxonomic classification by comparing these regions with those already known and available on large public databases like NCBI , SILVA , GreenGene , RDP , and 16S rRNA gene. Gene sequencing is the most widely used approach to disclose the identity of the pathogen as they are signature-specific sequences in bacterial species with higher accuracy. Bacterial wilt disease in Cucurbita maxima in China caused by Ralstonia solanacearum was identified using 16S rRNA gene sequencing of the isolates collected from the plants infected by wilt disease . A recent study with 16S rRNA gene amplicon metagenomic analysis resulted in the identification of a rhizospheric microbial community of plants like Eichhornia crassipes  and mangrove species (Sonneratia alba, Rhizophora mucronata, Ceriops tagal, and Avicennia marina) .
Internal transcribed spacer (ITS) of the nuclear ribosomal DNA is used to identify the eukaryotes in the particularly fungal community in the metagenomic samples. The isolated DNA sample is subjected to PCR with primers specific to regions of 5.8S and LSU rRNA, flanked by the ITS2 region . The Library of the amplified DNA is then used for sequencing, for instance, the Illumina MiSeq platform. The generated sequenced data is analyzed using PIPITS. The first pipeline with complete bioinformatics automation is wholly devoted to sequencing ITS regions belonging to fungal origin. The PIPITS_PROCESS part of the pipeline uses the VSEARCH tool for clustering sequences into OTUs . These OTUs are further processed to build OTU tables, which are the final interpretable results of the analysis. Recent studies used high-throughput Internal Transcribed Spacer Amplicon Sequencing to analyze field-grown maize and soybean microbiomes from southeastern and central Brazil . They identified degrader bacteria and fungi of rhizosphere soil from a toluene phytoremediation site .
Whole genome sequencing (shotgun metagenomics)
Shotgun sequencing is another method used in characterizing the abundance of microorganisms available in a particular environment. This method not only identifies the microbial species but also can generate information about the genes (including 16S rRNA) present in the metagenomic sample. This approach offers information about the functional characterization of the genes belonging to the microbial communities in the sample. This method is PCR independent, where there is no chance of biasing due to primer binding. This factor is helpful for finding unknown microbes in the sample, which may otherwise not be detected by targeted sequencing . This method also helps identify and discover novel viruses in the given environment. Indeed, broad-range genetic markers are unavailable for viruses; shotgun sequencing has developed the technique to identify viruses. Recent studies with shotgun sequencing include assessing the functional genes of maize rhizosphere microbiota, which were found to be diverse. Genes involved in nitrogen fixation, phosphate solubilization, quorum sensing, trehalose and siderophore production, phenazine biosynthesis, daunorubicin resistance, acetoin, and 1aminocyclopropane-1-carboxylate deaminase were reported . Both the sequencing mentioned above are performed by anyone method of next-generation sequencing technologies: nanopore technology, sequencing by synthesis, pyrosequencing, sequencing by ligation, and single-molecule real-time sequencing, ion torrent sequencing .
Step 4: metagenomic sequence data analysis
After the sequencing is done, this is the critical part of the entire experiment where the sequenced data generated will include multiple samples with billions of sequence data reads. Hence, the data needs to be trimmed down to a meaningful nucleotide sequence supporting the stated hypothesis to pull out sensible and reasonable information. To analyze big data, different software is developed and devoted to a particular function.
A. Taxonomic analysis
The metagenomes are analyzed by comparing them with sequences already present in the databases or by a particular activity. For example, the software DOTUR is developed to study the operational taxonomic units, thus predicting the richness of the microbial population present in the given environment . There are automated pipelines developed for the complete analyses of the metagenome where a series of software are applied together step by step to achieve interpretable results. For example, Metagenomic Rapid Annotation using subsystem Technology (MGRAST), a platform available on the web, is programmed for processing, analyzing, and sharing metagenomic data . Details about the pipelines and software/tools will be discussed further in the review. A Recently developed pipeline CAMAMED a composition-aware mapping-based metagenomic pipeline, is used for both taxonomic and functional profiling levels. The pipeline was used to check the taxonomic profile of gut microbiota from colorectal adenoma and colorectal carcinoma individuals. The result predicted a significantly changed gut species ratio to 2.67% of the total 374 species . ezTree, a computational pipeline, is developed to automatically identify single-copy marker genes for a group of genomes and build phylogenetic trees from the marker genes. ezTree was tested on a group of proteobacteria species which revealed that ezTree was highly influential in pinpointing marker genes and constructing reliable trees for different groups of bacterial genomes .
B. Functional analysis
When the metagenomic data are studied for identifying genes and enzymes of a particular function, it is a function-driven analysis. Such studies are paramount as they may reveal any possible novel enzymes or pathways. For such analysis, pipelines are designed like the DMAP (Dragon Metagenomic Analysis Platform). The platform annotates and comparisons of genomic or metagenomic sequence data via its Annotation and Compare Modules . There is a repertoire of different pipelines for such annotations and comparisons available currently, which are highly efficient with such an enormous quantity of sequenced data. Recently developed FMAP, a functional mapping and analysis pipeline, which is not used for functional profiling but is also used for pathway analysis (for example, Crohn's disease) which listed ten pathways significant to the phenotype of Crohn’s disease . A couple of pipelines used in functional profiling are MOCAT2 for metagenomic assembly and annotation  and MetaStorm for customizable metagenomic annotation of target genes .
Milestones in metagenomics
To understand the journey of metagenomics through the years, we have summarized the important milestones that constitute the metagenomics era. The diagram represents the time of metagenomics from when Leeuwenhoek reported oral microbiota in 1676 to the milestones achieved in 2019 in the human genome project. We describe here the objectives of the latest significant milestones achieved in the past five years (since 2015) around the world.
Ocean sampling day (2015)
The ocean sampling day was an initiation taken and organized under the funding provided by European Micro B3 (Marine Microbial Biodiversity and Bioinformatics) to get a picture of the marine microbial biodiversity along with the role played by oceans of the world concerning the microbial communities. It was considered the worldwide mega-scale sequencing drive to generate the most extensive standardized microbial data set acquired in one day. The study aims to analyze marine microbial community composition and functional traits. Researchers all over the globe were successful in obtaining a generous amount of environmental metadata that included precisely 150 metagenomes along with 18S/16S rRNA amplicon sequence data sets .
Host-targeted drugs affect microbiota populations (2015)
The study states that the use of commonly consumed medications affects the gastrointestinal microbial richness and their respective gene expression, which would affect human health positively or negatively concerning drug treatment. The proton pump inhibitors (xenobiotics) were studied and checked for their effect on the microbiota of the lumen of the GIT. Xenobiotics are reported to change the functions and gene expression of the dynamic microbiome of the human gut .
Human skin microbiome (2016)
The work studied the coherent analysis of bacterial, fungal, and viral species that interpreted the site-specificity of the microbiota and individual signatures .
Human microbiota affects cancer therapy (2018)
The gut microbiota is involved in altering the body's response to a cancer patient.
(melanoma, advanced kidney, or Lung cancer) against the drug treatment. For instance, the Gut microbiome regulates the efficiency of PD-1-dependent immunotherapy targeting epithelial tumors .
Genomes assembled using metagenomics anticipate unusual characterization of microbiota associated with humans (2019)
Metagenomic analysis showed the presence of an unknown uncultured bacterial candidate present in the human body where the individuals belonged to different ages, geography, and lifestyle . It can be said that the gut has a novel set of microbiomes that expands the phylogenetic divergence of the human metagenomic database . The above examples give a glimpse of the diversity of research done. Each year, numerous research articles are added to the prior art on metagenomic studies on various topics. Every year, the study parameters of the metagenomic analysis are evolving, unfolding the various branches of applications metagenomics can serve us. One such application is the interplay of the microbial community in the rhizosphere and its effect on the health and development of the crop.
Metagenomics and crops
The plant Microbiome is an active community of microorganisms associated with a particular plant. A plant's microbiome is divided into two parts: (A) Microbes inhabiting the atmospheric section of the plant are known as the Phyllosphere, and (B) Microbial communities inhabiting the below-ground portion of plants are called the rhizosphere. It is the fraction of soil beneath the root secretions we, as science students, have studied since our school days. It is estimated that rhizosphere soil can nurture approximately 1011 microbial populations for every gram of soil collected , along with precisely 30,000 prokaryotic species . Due to the discharge and intake of a diverse array of chemicals/compounds from the soil, various groups of microorganisms can be metabolically active , thus making the rhizosphere the most active niche of the soil .
The diversity of Rhizosphere soil can be classified into six classes, namely (I) bacteria, (II) viruses, (III) archaea, (IV) fungi, (V) algae, and (VI) protozoa and their abundance in the rhizosphere are in the decreasing order with Bacteria is the most abundant of them all (108 - 109 g-1). This group of unicellular organisms, together with the plant roots, forms the most complex habitat on Earth . Thus, we focus on the hotspot of the soil, i.e., the rhizosphere. The involvement of rhizospheric microbial species with the plants marks a significant area for conducting metagenomic research. Understanding the metagenomic analysis of such interactions can be valuable in various agriculture applications like crop rotation and soil tillage, levels of nutritionally essential elements present in the soil, etc. The below table states different objectives studied with metagenomics in recent years. It gives us an overview of the depth and direction of the research currently being pursued.
The diagram below illustrates the overview of the study performed on the Pea plant rhizosphere . This study aimed to check how the Pea plant affects the soil's microbial community and can shape its rhizosphere microbiome. The study was done with two types of soils (I) pea plant rhizospheric soil and (II) bulk soils with nutrients in the form of fertilizer. Metagenomic analysis was done by amplifying the V4 region of the bacterial 16S rRNA gene using universal bacterial primers. This particular research article was chosen to get insights into the latest studies on the legume rhizosphere. In the figure, the dotted arrows represent the elevation/enhancement offered by one element towards another, and the inhibition arrows depict the inhibition provided by an element for the other. The green arrows explain that the microbial species/phyla are observed in abundance in the presence of the pea plant rhizosphere. The red arrows explain that the presence of the particular microbial species has decreased in the presence of the pea plant rhizosphere.
The following part of the article will explain the details and technicalities of metagenomic studies, including next-generation sequencing, software, and metagenomic analysis workflows (Table 1). The microbial species Chloroflexi and Nitrospirae are seen to be decreasing in the presence of pea plant rhizosphere because they are slow growers and are unable to cope in front of other fast-growing microbes present (star marked) .
Within the last decade, the cost of sequencing the exome of a human has decreased approximately 15,000 times, going from 15 million USD to 1000 USD (https://www.genome.gov/27565109/the-cost-ofsequencing-a-human-genome/). Michael L. Metzker explains that we should not focus on DNA sequencing technology. Still, we should expand the limitations of the research we do to collect sufficient complex data which can apply to interpret the answers to numerous questions simultaneously . One of the examples of such thoughts is metagenomics, where the sequencing technologies are similar, but the ability to create high throughput data and analyze it is different. It can answer several complex questions at the same time. This brings our review to study important sequencing platforms that have evolved through time and experience.
The utilization of DNA sequencing has changed over time. Earlier it was answering simple questions like a sequence of nucleotides and predicting the gene function by homology studies. Later, it became the ultimate tool for understanding taxonomic and phylogenetic relationships between different organisms. This was possible due to the decreased sequencing costs that supported sequencing and comparing thousands of genomic sequences for microbial taxonomic studies . One such field that uses NGS to identify microbes is microbial diagnostics, where even epidemics can be traced on a real-time basis .
NGS has many applications in the field of molecular biology and biomedical sciences. Currently, it is massively used in metagenomic studies where complex analysis of microbial communities is performed to answer critical scientific problems. Complex investigations of metabolic characteristics of bacterial communities or even symbiotic development of bacteria and the host have been possible in recent years . The discovery of new bacteria by metagenomics can eventually lead to the discovery of novel antibiotics. Further ahead, the availability of whole-genome sequences can be used for analyzing the metatranscriptome of the microbiota and its interactions in the gut  or, in our case, the rhizosphere, and the crop plants. The below table explains the technicalities of each sequencing platform available currently in the market with their respective manufacturers.
Software’s/tools used in the metagenomics analysis
The above tasks require a large amount of processing power and storage capacity and a thorough understanding of using computational methods from many areas (information theory, signal processing, and systems science) in conjunction with one year of experience to provide trustworthy findings. Therefore, metagenomic analysis systems with automated workflows for various processing purposes, combining tools in the form of services operative inside processing pipelines, are in high demand (Table 2). Several analytic pipelines have been created for metagenomic research (Table 2). Several pipelines have been developed to analyze single-organism genomic data [66,67,68]. When using NGS for metagenomic analysis, however, the limits of comparable methods created for single organism data have been exposed for purposes of metagenomic research.
Pipelines for metagenomics analysis
To analyse metagenomic sequencing data, bioinformatics programs like CloVR-metagenomics,  (ii) Galaxy platform (metagenomics pipeline) [69, 70] (iii) IMG/M [71, 72] (iv) MetAMOS , (v) MG-RAST [74, 75] (vi) RAMMCAP , and (vii) Smash Community  are available (Table 3). They are very much efficient in effectively analyzing the metagenome data.
Two distinct inputs are required to run CloVR-metagenomics (CloVR: Cloud Virtual Resource), desktop software for automating sequence analysis. The raw sequencing data (in fasta format) and the metadata file (tab-delimited) with sample-specific information for comparative analysis are required. Booting from their website needs a Virtual Machine (VM) player, which is free. Visitors to Amazon Cloud can establish a cloud-based instance and utilize the Request Instances Wizard to discover an accessible Amazon Machine Image (AMI). As a first step, the process employs UCLUST  to cluster duplicate sequence reads and then conducts BLAST  homology searches against the COG  and RefSeq  databases for functional and taxonomic identification, respectively.
To discover differentially abundant characteristics, the results of the two studies are fed into the integrated Meta stats software . Integrated custom scripts in R language (The R Project for Statistical Computing 2) are used to normalize taxonomic or functional counts for grouping and visualization. Most importantly, CloVR's design allows users to choose between local resources and cloud computing capability. Quality control and gene identification (available exclusively in the single-genome and 16S rRNA software) make the platform largely reliant on third-party software, making it more vulnerable to the read length of sequencing datasets as a possible drawback.
Galaxy platform (metagenomics pipeline)
A general open-source framework for integrating computational tools and databases into a coherent, concerted workspace, Galaxy is being advanced for medicinal research that requires a large amount of data. Another option is for users to download and install Galaxy on their computers to fully use local resources, tools, and databases for bespoke workflows. To install locally, simply run the appropriate BASH run. sh (added to the original downloaded directory) script. The new Galaxy method for metagenomic data conducts automated analyses utilizing integrated specialist tools  when combined with raw sequencing results (raw reads). When used with raw sequencing reads, it executes a series of computerized studies using specialized integrated tools, according to a new Galaxy process for metagenomic datasets .
Those studies include:
Checking the readings for quality and filtration (custom tool),
Editing text and converting data formats (custom tools),
Searching the NCBI-NT database for homology,
taxonomic research (custom tools), and
Results visualization with the help of custom tools.
Most significantly, this platform enables any user to develop workflow processes by integrating any custom tools of their choice (third party or proprietary) capable of handling various analytical activities, all while offering a highly intuitive user interface. For a complete local installation, however, advanced programming skills are required, making the solution unsuitable for anyone who is not an expert.
Experimental metagenome data management and analysis tool IMG/M includes a database of bacteria and other archaeal species and tools for data exploration and comparison analysis. Assembled sequence data may be searched for genes, contigs, and scaffolding, as well as their related functional characterizations, using data exploration tools. The comparative data analysis suite includes methods for (i) determining the gene content and phylogenetic profile of any metagenomic sample, including I profile-based selection tools, (ii) gene neighborhood analysis tools, and (iii) multiple sequence alignment tools. Through its web server's GUI, this platform may publish and manage a user's (Meta) genome while using its cloud infrastructure. Despite this, the user is still responsible for quality control of raw readings and assembly. IMG/M is developed only for metagenome assembly, unlike other metagenome tools. All users must have an IMG account that may seek on the IMG website.
This pipeline receives raw sequence reads or completed contigs as input and assembles them into a metagenomic dataset. The modules of this pipeline make up an entire analytical workflow that includes: (i) quality control using two different tools (FASTX-Toolkit 5, Babraham Bioinformatics - FastQC 6), (ii) sequence assembly to contigs with eight different assembly methods exploiting four different assembly tools. Several tools can be incorporated into MetAMOS processes to comprehensively analyze metagenomic datasets, including raw sequencing reads, contig, and scaffold data, which may be automated. Moreover, the lack of a user-friendly interface makes accessing its extensive collection of tools complex since all actions should be done from the Linux command-line shell. At the same time, its customization requires the use of scripts. A Python script, namely INSTALL.py, automates the complete installation procedure by obtaining the newest version and running it.
Both raw sequences read datasets, and previously assembled contigs can be used as inputs in this process. The user must register for the online service to upload metagenome datasets and create tasks.
It consists of four major tasks, which are divided into modules:
Finding putative protein-coding genes and coding elements by screening the sequences against public databases using predefined default search criteria.
Input data, computation of functional annotations and taxonomy designations, and (iv) Result in visualization with the SEED Viewer .
All job-relevant resultant data is saved in flat file and SQLite (SQLite 7) formats throughout pipeline installation to achieve the best data management based on relational database technology. Comparative metagenomic analysis of the original dataset may be performed using the results obtained from the preceding stages compared to additional metagenomes or whole genomes obtained from the SEED environment . This platform, like IMG/M, provides a user-friendly GUI behind an HTTP server, making data management and analysis as straightforward as feasible. Other than that, it offers a wide range of functional and comparative genomics tools and the ability to handle assembled and unassembled data. Regardless of the absence of components for basic read quality assurance and assembly operations, the pipeline offers a practical and well-established taxonomic annotation system that fully exploits the potential of public sequence databases .
Rapid Analysis of Multiple Metagenomes with a Clustering and Annotation Pipeline, RAMMCAP is a metagenomic platform that focuses on programmatic optimization to reduce the computing cost of the different processing activities. However, since CAMERA has been discontinued, the RAMMCAP pipeline is only accessible as a standalone utility for installation on your computer or laptop . To install, you'll need to download the most recent package, which contains all of the necessary applications and databases . After then, each pipeline's required applications must be built and installed independently before the automated pipeline can use them. CD-HIT method is used to cluster sequences from one or more metagenomic samples. This is followed by a second clustering of the protein sequences, which is done in parallel with the ORF discovery task on the raw reads, using a local algorithm (ORF finder)  (Li 2009).
Smash Community may be viewed as the metagenomic version of its predecessor SmashCell; a program developed to study single-cell amplified microbial genomes in high-throughput . Users must download the current version of SmashCommunity and compile/install it using the standard BASH instructions to install the package on their system (configure, make, make install). Installation of necessary applications and databases is required before installing the pipeline. To do this, run the BASH scripts offered in the release (such as install dependencies.ubuntu.sh). Raw read files from 454 or Sanger sequencing methods are required for this process (i.e., long-read sequence data) . In addition to the command-line-only package, the user must manually install the various necessary programs that make up the entire analytical pipeline. Assembler limitations are also carried down the pipeline, limiting its performance to only long-read sequencing data. This is why only long-read sequencing data can benefit from this technique (an issue that will soon be obsolete as even Illumina machines are increasing their read length output with each new sequencer release) . Despite that, experts will find it an excellent choice for doing extensive and completely automated metagenomic investigations on a dedicated local server.
In "Yet Another Metagenomics Pipeline", AMP, an already-containerized workflow, handles shotgun metagenomics sequencing data up to the taxonomic and functional annotation stage utilizing state-of-the-art tools and software. YAMP is implemented in NextFlow  and is accompanied by a Docker  and a Singularity  container. The YAMP script, parameters, and documentation are available at https://github.com/alessia/YAMP.
Each block in the YAMP process is broken down into three parts . YAMP removes all duplicates before cutting to eliminate bias introduced by reading changes. There are numerous phases of assessment and display of data quality, as shown in Fig. 1. De-duplication, an optional step in the QC process, is used to eliminate identical readings that PCR may have produced. Using PCR-free library preparation techniques (e.g., TruSeq) enables biological duplicates to be kept. It is next necessary to remove adapters, artifacts, and phiX from the reads. The reads are subsequently quality trimmed. Trimmed reads are deleted if they've gotten too short. Because they may map to several genomes or genomic areas, their presence may compromise subsequent studies . If you're working with paired-end reads, you should use singleton reads (i.e., paired-end reads that have been stripped of their mates). Lastly, readings were filtered for the presence of pollutants, such as reads that do not belong to the investigated environment, before they were included in the study. Many low-complexity sequences and some characteristics (such as ribosomes) are highly conserved throughout species. They should be deleted from the custom database of contaminant reads to avoid false-positive matches. Numerous phases are included in the QC process to determine the quality of the readings and evaluate the trimming and purification steps . Many procedures are then taken to estimate -diversity and characterize the microbial community's taxonomic and functional profiles, including identifying and quantifying the microorganisms present (taxonomic binning and profiling) and their functional capabilities (functional characterization) .
A workflow management system, NextFlow, was utilized to create YAMP, which has been used in numerous life-science initiatives [92, 93]. Because of NextFlow's user-transparent high-level parallelization, big applications are assured of scalability. A UNIX-based system's executor allows workflows seamlessly port to any UNIX-based system (e.g., a local machine or HPC facility). In addition to YAMP, a Docker container and a Singularity container [89, 90] are installed. Platform independent virtualized operating system Docker provides all the applications required by YAMP and tracks their versions. As a result of singularity, these characteristics may be transferred to HPC systems, with which Docker is incompatible. YAMP supports both a single container and a multi-container scenario. To analyze the metagenomic data, YAMP incorporates state-of-the-art technologies  (Figs. 2, 3 and 4).
Several well-established programs in the BBmap suite  are used to perform quality control (QC) on single-end. Paired-end reads from all major sequencing platforms, including clumpify, Bduk, BBwrap, and BBduk (i.e., Illumina, Roche 454 pyrosequencing, Sanger, Ion Torrent, Pacific Biosciences, and Oxford Nanopore). Additionally, they are very scalable to big metagenomics projects and samples due to their computational efficiency. FastQC, which offers comprehensive information on reads’ quality, is employed to do QC evaluation and visualization .
YAMP supports both single-end and paired-end FASTQ files as inputs for processing. Outputs provided by the program include the taxonomic composition, a relatively significant quantity of genes and pathways for microorganisms, and pathways coverage for multiple -diversities. Users may tailor workflow execution by utilizing command-line arguments or editing a simple plain-text configuration file. Users can retain temporary files, such as those created by the QC stages or during the HUMAnN2 execution if they so want .
NextFlow and metaflow|mics pipelines for microbiome marker data analysis
MetaFlow|mic is a new framework for microbiome data analysis that summarizes its functioning from DADA2, Mothur, VSEARCH, and other tools into an easy-to-use set of pipelines. Beyond a simple set of commands, our pipeline is a complete system based on standards that allow for cross-platform portability, flexibility, and repeatability . In addition, it contains three high-level tasks, a proprietary demultiplexing pipeline, and two end-to-end analysis pipelines, one for investigating bacterial data (16S marker) and the other for analyzing fungal data (ITS marker). To optimize the deployment of processes across different platforms, NextFlow was developed. Because of this, all the necessary software is included in each analytic pipeline to QA/QC the reads and estimate diversity at the operational taxonomic unit and the exact sequence variation levels. In addition to Nextflow, R, Python, Docker, and Singularly containers are used to spread the analytic pipelines . The pipelines used in MetaFlow|mics were developed in conjunction with the Center for MICROBIOME analysis via Island Knowledge and Investigations (C-MAIKI), the Hawaii EPSCoR Ike Wai project, and the Hawaii Data Science Institute .
MetaFlow|mics are composed of three distinct microbiological analysis pipelines
As a result, we developed a probabilistic process for demultiplexing sequencing reads and 16S barcode pipelines for bacteria and fungus data analysis. Figure 5 summarizes the pipelines, which are further explained in detail . It is now possible to generate terabytes of data with modern sequencing devices, far beyond the minimal throughput required for sequencing a single biological sample. Several biopsies are regularly mixed and sequenced in the same run. The samples were first identified by a DNA barcode comprising a few DNA nucleotides (A, C, G, and T) . To demultiplex, or unpool, the DNA sequences created by the sequencing apparatus, the index found at the beginning of each DNA sequence must be read and placed into the proper sample file. Using a unique probabilistic approach, the MetaFlow|mics demultiplexing barcode parts that do not match any of the known barcodes can be recovered using the script. Using NextFlow's domain-specific language, the demultiplexing parallelization of pipelines is possible regardless of infrastructure.
A large amount of computing power is required to analyze modern metagenomic studies containing millions of reads. In microbiome experiments, the supply of such materials varies from project to study, which is unfortunate. As part of our pipeline, we're working to make it easier to deploy and parallelize it on high-performance settings, such as HPC clusters or cloud computing services .
Through containerized computing, a major focus of MetaFlow|mics is durability. In the case of an analytic pipeline and a dataset, the description of the program and the active Operating System (OS) are the major causes of variance in the findings. Each pipeline's computing environment may be set up using Dockerfiles, which includes all of the necessary deployment information, such as operating system type and version and application versions. As a result, Dockerfiles are used to faithfully replicate any environment as a standalone container for each analysis. They provide consumers with a transcript that can be shared and reused. Prior pipeline versions may be accessible on GitHub for backward compatibility concerns, giving the user a means of switching back to previous run parameters if necessary .
Parallelization is effective and resource-efficient since many pipeline modules handle each sample individually. By seamlessly moving data across computers and gathering outcomes from several actions performed simultaneously (or linearly if resources are limited), NextFlow simplifies parallelization and makes it easier to use . Deployment on high-performance computing clusters (SLURM and SGE, for example) and cloud settings, the pipeline includes pre-configured configuration files (Google Cloud). Instance: in a supercomputer system, the user can specify queue names for each task under the pipeline, or machine types can be assigned automatically in specific scenarios based on established default values. Cloud computing's fine-grained resource allocation can speed up the runtime and save expenses because of its fine-grained resource allocation. MetaFlow|mics can automatically scale up when unexpected resource consumption arises in a process . An unfinished or overly memory-intensive procedure will be resubmitted .
Because implementation environments are independent of the source code used for analytical logic, as was explained earlier in this article. They may thus be used safely in a multi-user system where user privileges are limited. Users need just install Docker (or Singularity) on their computer. Most shared systems, such as HPC clusters and cloud services, come pre-installed with these frameworks, which is not uncommon .
Especially when identifying abnormalities in the execution of the pipeline or monitoring the run progress, pipelines might provide outputs that are difficult to read and comprehend. As a result of this, MetaFlow|mics offer two different forms of execution results . It is also possible to produce a series of data visualizations (such as heat maps, scatter plots, and box plots), which graphically represent the results .
SqueezeMeta, a highly portable, fully automatic metagenomic analysis pipeline
When it comes to studying huge numbers of metagenomes or metatranscriptomes, SqueezeMeta is a very flexible pipeline . Everything from assembly through taxonomic/functional assignment of the resultant genes to abundance estimate is provided. Because it uses a sequential metagenomic assembly and later contig merging, SqueezeMeta can run on moderately-sized computing infrastructures, alleviating the stress of co-assembling tens of metagenomes. For processing MinION sequences, the program comprises specialized software and modifications . Some of SqueezeMeta's sophisticated features set it apart from previous pipelines, such as the following.
Use of co-assembling and read mapping for estimating gene abundances in each metagenome.
It’s possible to process an endless number of metagenomes by combining separate metagenomes using a different co-assembling method.
The ability to do nanopore long readings.
To get individual genome, binning and bin checking must be used.
An internal check on the contigs and bins taxonomy annotations.
Metatranscriptomic support is provided by mapping cDNA readings to reference metagenomes or co-assembling the two.
Results may be stored in a MySQL database and then exported, shared, or viewed from anywhere using a web interface 
SqueezeMeta is designed to analyze several metagenomes in one go. This program has three different operating modes to choose from the figure.
Sequential mode: There is a sequential analysis of all metagenomes. Binning is not used in this mode, as each metagenome is processed individually .
Co-assembly mode: After that, the data from all samples are blended and assembled into one single data set. Once the co-assembling is completed, reads from individual samples are mapped back to the co-assembling. Contigs can be classified into genomic bins based on their abundance [101, 102].
Merged mode: because it is a computationally-intensive process, co-assembly takes an enormous amount of random access memory (RAM). If the number of samples is large, the computer infrastructure may not be able to meet the demands. While in the merged mode, SqueezeMeta allows for the assembly of a large number of samples, utilizing a technique similar to that of TARA Oceans and binning to extract the maximum number of genomes from the samples .
Conclusion and future prospective
Next-generation Sequencing and Metagenomic analysis/Interpretation are the two most dynamic technologies that constitute Metagenomics. These two technologies are the backbone and the soul of this field of study. Metagenomics applications in crop sciences are humungous and can solve the mysteries that can improve crop development and health. The in situ DNA sequencing and sample preparation with real-time data analysis and Interpretation is highly advantageous and time-efficient. The Oxford nanopore portable system is the latest sequencing technology used for current metagenomic analysis. This development in sequencing technologies is a token of their rate of evolution. Such action is currently being used in some of the most challenging parts of the Earth, where the heavy machinery of NGS is impossible to transport; studying its metagenome would be impossible without the Oxford Nanopore technology. One study involved DNA sequencing in the Antarctic dry valley region, where environmental samples were used to perform metagenomic analyses using a completely portable sequencer and allied tools .
The study of the rhizosphere is only the tip of the ice burg; so many other parts of the plant can reveal unimaginable concepts and strategies we humans can use for the betterment of agriculture. The example of the pea plant rhizosphere gives the amplitude of information that can be predicted using a metagenomic approach. Here we attempted to simplify the metagenomic elements and their catalytic role in understanding the relationship between the rhizosphere and the crops. Analysis and Interpretation of the metagenomic data is a whole new world because of the heavy quantity and quality of bioinformatics used in them. The fact is that these tools and software are program-driven; there will always be new creativity in these tools to improve the understanding of data and minimize the manual inputs required in the process.
Furthermore, this topic does not end here but is only the start. In our case, the rhizosphere can be further studied using an integrated multi-OMICS approach called the new branch of System Biology . It is an interdisciplinary field involving complex interactions in the biological world and can extract information starting from the genome (metagenomic), mRNA (metatranscriptomics), protein (metaproteome), and metabolites (metametabolome) .
Availability of data and materials
All the data and literature associated with this study were taken from publicly available resources. All are cited in the manuscript text.
Next Generation Sequencing
Operational taxonomic units
Internal transcribed spacer
Quantitative Polymerase Chain Reaction
Council NR. The New Science of Metagenomics: Revealing the secrets of our Microbial planet. ISBN 978-0-309-10676-4, Washington, DC: The National Academies Press; 2007, 12-31. Available from: https://nap.nationalacademies.org/catalog/11902/the-new-science-of-metagenomics-revealing-the-secrets-of-our.
Handelsman J, Rondon MR, Brady SF, Clardy J, Goodman RM. Molecular biological access to the chemistry of unknown soil microbes: a new frontier for natural products. Chem Biol. 1998;5:R245-9 https://www.sciencedirect.com/science/article/pii/S1074552198901089.
Laudadio I, Fulci V, Stronati L, Carissimi C. Next-generation metagenomics methodological challenges and opportunities. Omi A J Integr Biol. 2019;23:327–33. https://doi.org/10.1089/omi.2019.0073 Mary Ann Liebert Inc, publishers.
Acinas SG, Sánchez P, Salazar G, Cornejo-Castillo FM, Sebastián M, Logares R, et al. Deep ocean metagenomes provide insight into the metabolic architecture of bathypelagic microbial communities. Commun Biol. 2021;4:604. https://doi.org/10.1038/s42003-021-02112-2.
Molefe RR, Amoo AE, Babalola OO. Metagenomic insights into the bacterial community structure and functional potentials in the rhizosphere soil of maize plants. J Plant Interact 2021;16:258–69. https://doi.org/10.1080/17429145.2021.1936228 Taylor Francis.
Franzén O, Hu J, Bao X, Itzkowitz SH, Peter I, Bashir A. Improved OTU-picking using long-read 16S rRNA gene amplicon sequencing and generic hierarchical clustering. Microbiome. 2015;3:43. https://doi.org/10.1186/s40168-015-0105-6.
Edgar RC. Accuracy of taxonomy prediction for 16S rRNA and fungal ITS sequences. PeerJ. 2018;6:e4652.
Piombo E, Abdelfattah A, Droby S, Wisniewski M, Spadaro D, Schena L. Metagenomics approaches for the detection and surveillance of emerging and recurrent plant pathogens. Microorganisms. 2021;9:188 https://www.mdpi.com/2076-2607/9/1/188.
Kumar Awasthi M, Ravindran B, Sarsaiya S, Chen H, Wainaina S, Singh E, et al. Metagenomics for taxonomy profiling: tools and approaches. Bioengineered. 2020;11:356–74. https://doi.org/10.1080/21655979.2020.1736238 Taylor & Francis.
Bush A, Compson ZG, Monk WA, Porter TM, Steeves R, Emilson E, et al. Studying ecosystems with DNA metabarcoding: lessons from biomonitoring of aquatic macroinvertebrates. Front Ecol Evol. 2019;7:434. https://doi.org/10.3389/fevo.2019.00434https://www.frontiersin.org/articles/.
Sharon I, Bercovici S, Pinter RY, Shlomi T. Pathway-based functional analysis of metagenomes. J Comput Biol. 2011;18:495–505. https://doi.org/10.1089/cmb.2010.0260 Mary Ann Liebert Inc, publishers.
Bercovici S, Sharon I, Pinter RY, Shlomi T. Pathway-based functional analysis of Metagenomes BT - research in computational molecular biology. In: Berger B, editor. Berlin. Heidelberg: Springer, Berlin Heidelberg; 2010. p. 50–64.
Brown SM, Chen H, Hao Y, Laungani BP, Ali TA, Dong C, et al. MGS-Fast: Metagenomic shotgun data fast annotation using microbial gene catalogs. Gigascience. 2019;8:giz020. https://doi.org/10.1093/gigascience/giz020.
Nayfach S, Bradley PH, Wyman SK, Laurent TJ, Williams A, Eisen JA, et al. Automated and accurate estimation of gene family abundance from shotgun metagenomes PLOS Comput Biol Public Libr Sci. 2015;11:e1004573. https://doi.org/10.1371/journal.pcbi.1004573.
Gill SR, Pop M, DeBoy RT, Eckburg PB, Turnbaugh PJ, Samuel BS, et al. Metagenomic analysis of the human Distal Gut Microbiome Sci. 2006;312:1355–9. https://doi.org/10.1126/science.1124234 Am Association Advancement Sci.
Lam KN, Cheng J, Engel K, Neufeld JD, Charles TC. Current and future resources for functional metagenomics. Front Microbiol. 2015;6:1196. https://doi.org/10.3389/fmicb.2015.01196, https://www.frontiersin.org/articles/.
Coughlan LM, Cotter PD, Hill C, Alvarez-Ordóñez A. Biotechnological applications of functional metagenomics in the food and pharmaceutical industries. Front Microbiol. 2015;6:1–22.
Soni R, Goel R. Triphasic approach to assessment of bacterial population in different soil systems. Ekologija. 2010;6(3-4):99-104.
Vavourakis CD, Andrei A-S, Mehrshad M, Ghai R, Sorokin DY, Muyzer G. A metagenomics roadmap to the uncultured genome diversity in hypersaline soda lake sediments. Microbiome. 2018;6:168. https://doi.org/10.1186/s40168-018-0548-7.
Hinsu A, Dumadiya A, Joshi A, Kotadiya R, Andharia K, Koringa P, et al. To culture or not to culture: a snapshot of culture-dependent and culture-independent bacterial diversity from peanut rhizosphere. PeerJ. 2021;9:e12035.
Mogotsi MT, Mwangi PN, Bester PA, Mphahlele MJ, Seheri ML, O’Neill HG, et al. Metagenomic analysis of the enteric RNA virome of infants from the Oukasie clinic, North West Province, South Africa, reveals diverse Eukaryotic viruses. Viruses . 2020.
Saleem F, Azim MK, Mustafa A, Kori JA, Hussain MS. Metagenomic profiling of fresh water lakes at different altitudes in Pakistan. Ecol Inform. 2019;51:73–81 https://www.sciencedirect.com/science/article/pii/S1574954118302061.
Felczykowska A, Krajewska A, Zielińska S, Łos JM. Sampling, metadata and DNA extraction - important steps in metagenomic studies. Acta Biochim Pol. 2015;62:151–60.
Bag S, Saha B, Mehta O, Anbumani D, Kumar N, Dayal M, et al. An improved method for high quality metagenomics DNA extraction from human and environmental samples. Sci Rep. 2016;6:26775. https://doi.org/10.1038/srep.
Xu Y, Vaidya B, Patel AB, Ford SM, McCarley RL, Soper SA. Solid-phase reversible immobilization in microfluidic chips for the purification of dye-labeled DNA sequencing fragments. Anal Chem 2003;75:2975–84. https://doi.org/10.1021/ac030031n Ame Chem Soc.
Hess JF, Kohl TA, Kotrová M, Rönsch K, Paprotka T, Mohr V, et al. Library preparation for next generation sequencing: A review of automation strategies. Biotechnol Adv. 2020;41:107537 https://www.sciencedirect.com/science/article/pii/S0734975020300343.
Boers SA, Jansen R, Hays JP. Understanding and overcoming the pitfalls and biases of next-generation sequencing (NGS) methods for use in the routine clinical microbiological diagnostic laboratory. Eur J Clin Microbiol Infect Dis. 2019;38:1059–70. https://doi.org/10.1007/s10096-019-03520-3.
Chakravorty S, Helb D, Burday M, Connell N, Alland D. A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria. J Microbiol Methods. 2007;69:330–9 https://www.sciencedirect.com/science/article/pii/S0167701207000565.
Federhen S. The NCBI Taxonomy database. Nucleic Acids Res. 2012;40:D136–43. https://doi.org/10.1093/nar/gkr1178.
Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 2007;35:7188–96. https://doi.org/10.1093/nar/gkm864.
DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a Chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–72 (LP).
Cole JR, Chai B, Farris RJ, Wang Q, Kulam SA, McGarrell DM, et al. The Ribosomal Database Project (RDP-II): sequences and tools for high-throughput rRNA analysis. Nucleic Acids Res. 2005;33:D294–6. https://doi.org/10.1093/nar/gki038.
She X, Yu L, Lan G, Tang Y, He Z. Identification and genetic characterization of ralstonia solanacearum species complex isolates from cucurbita maxima in China. Front Plant Sci. 2017;8:1794.
Sharma R, Kumar A, Singh N, Sharma K. 16S rRNA gene profiling of rhizospheric microbial community of Eichhornia crassipes. Mol Biol Rep. 2021;48:4055–64. https://doi.org/10.1007/s11033-021-06413-x.
Muwawa EM, Obieze CC, Makonde HM, Jefwa JM, Kahindi JHP, Khasa DP. 16S rRNA gene amplicon-based metagenomic analysis of bacterial communities in the rhizospheres of selected mangrove species from Mida Creek and Gazi Bay Kenya. PLoS One. 2021;16:1–22. https://doi.org/10.1371/journal.pone.0248485.
Gweon HS, Oliver A, Taylor J, Booth T, Gibbs M, Read DS, et al. PIPITS an automated pipeline for analyses of fungal internal transcribed spacer sequences from the Illumina sequencing platform. Methods Ecol Evol John Wiley Sons Ltd; 2015;6:973–80. https://doi.org/10.1111/2041-210X.12399.
Maike R, Maltez TA, Sato GS, Farage ML, Patrik I, Victoria K-C, et al. Microbiomes of field-grown maize and soybean in Southeastern and Central Brazil inferred by high-throughput 16S and internal transcribed spacer amplicon sequencing. Microbiol Resour Announc. 2021;10:e00528-21. https://doi.org/10.1128/MRA.00528-21 Ame Soc Microbiol.
BenIsrael M, Habtewold JZ, Khosla K, Wanner P, Aravena R, Parker BL, et al. Identification of degrader bacteria and fungi enriched in rhizosphere soil from a toluene phytoremediation site using DNA stable isotope probing. Int J Phytoremediation 2021;23:846–56. https://doi.org/10.1080/15226514.2020.1860901 Taylor & Francis.
Venter JC, Remington K, Heidelberg JF, Halpern AL, Rusch D, Eisen JA, et al. Environmental genome shotgun sequencing of the Sargasso Sea. Sci. 2004;304:66–74. https://doi.org/10.1126/science.1093857 Ame Association Adv Sci.
Akinola SA, Ayangbenro AS, Babalola OO. The diverse functional genes of maize rhizosphere microbiota assessed using shotgun metagenomics. J Sci Food Agric 2021;101:3193–201. https://doi.org/10.1002/jsfa.10948 John Wiley Sons Ltd.
Kozińska A, Seweryn P, Sitkiewicz I. A crash course in sequencing for a microbiologist. J Appl Genet. 2019;60:103–11. https://doi.org/10.1007/s13353-019-00482-2.
Schloss PD, Handelsman J. Introducing DOTUR, a computer program for defining operational taxonomic units and estimating species richness. Appl Environ Microbiol. 2005;71:1501–6. https://doi.org/10.1128/AEM.71.3.1501-1506.2005 American Society for Microbiology.
Meyer F, Paarmann D, D’Souza M, Olson R, Glass EM, Kubal M, et al. The metagenomics RAST server – a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinform. 2008;9:386. https://doi.org/10.1186/1471-2105-9-386.
Norouzi-Beirami MH, Marashi S-A, Banaei-Moghaddam AM, Kavousi K. CAMAMED: a pipeline for composition-aware mapping-based analysis of metagenomic data. NAR Genomics Bioinforma. 2021;3:lqaa107. https://doi.org/10.1093/nargab/lqaa107.
Wu Y-W. ezTree: an automated pipeline for identifying phylogenetic marker genes and inferring evolutionary relationships among uncultivated prokaryotic draft genomes. BMC Genomics. 2018;19:921. https://doi.org/10.1186/s12864-017-4327-9.
Alam I, Antunes A, Kamau AA, Ba alawi W, Kalkatawi M, Stingl U, et al. INDIGO – integrated data warehouse of microbial genomes with examples from the Red Sea extremophiles. PLoS One. 2013;8:e82210. https://doi.org/10.1371/journal.pone.0082210 Public Library of Science.
Kim J, Kim MS, Koh AY, Xie Y, Zhan X. FMAP: Functional Mapping and Analysis Pipeline for metagenomics and metatranscriptomics studies. BMC Bioinformatics. 2016;17:420. https://doi.org/10.1186/s12859-016-1278-0.
Kultima JR, Coelho LP, Forslund K, Huerta-Cepas J, Li SS, Driessen M, et al. MOCAT2: a metagenomic assembly, annotation and profiling framework. Bioinformatics. 2016;32:2520–3. https://doi.org/10.1093/bioinformatics/btw183.
Arango-Argoty G, Singh G, Heath LS, Pruden A, Xiao W, Zhang L. MetaStorm: A public resource for customizable metagenomics annotation. PLoS ONE. 2016;11:1–13.
Kopf A, Bicak M, Kottmann R, Schnetzer J, Kostadinov I, Lehmann K, et al. The ocean sampling day consortium. Gigascience. 2015;4:27. https://doi.org/10.1186/s13742-015-0066-5.
Tsuda A, Suda W, Morita H, Takanashi K, Takagi A, Koga Y, et al. Influence of proton-pump inhibitors on the luminal microbiota in the gastrointestinal tract. Clin Transl Gastroenterol. 2015;6:e89 https://journals.lww.com/ctg/Fulltext/2015/06000/Influence_of_Proton_Pump_Inhibitors_on_the_Luminal.2.aspx.
Oh J, Byrd AL, Deming C, Conlan S, Barnabas B, Blakesley R, et al. Biogeography and individuality shape function in the human skin metagenome. Nature. 2014;514:59–64. https://doi.org/10.1038/nature13786.
Routy B, LeChatelier E, Derosa L, Duong CPM, Alou MT, Daillère R, et al. Gut microbiome influences efficacy of PD 1 based immunotherapy against epithelial tumors Sci. 2018;359:91–7. https://doi.org/10.1126/science.aan3706 American Association for the Advancement of Science.
Pasolli E, Asnicar F, Manara S, Zolfo M, Karcher N, Armanini F, et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography, and lifestyle. Cell. 2019;176:649-662.e20 https://www.sciencedirect.com/science/article/pii/S0092867419300017.
Almeida A, Mitchell AL, Boland M, Forster SC, Gloor GB, Tarkowska A, et al. A new genomic blueprint of the human gut microbiota. Nature. 2019;568:499–504. https://doi.org/10.1038/s41586-019-0965-1.
Egamberdieva D, Kamilova F, Validov S, Gafurova L, Kucharova Z, Lugtenberg B. High incidence of plant growth-stimulating bacteria associated with the rhizosphere of wheat grown on salinated soil in Uzbekistan. Environ Microbiol. 2008;10:1–9. https://doi.org/10.1111/j.1462-2920.2007.01424.x John Wiley & Sons, Ltd.
Mendes R, Kruijt M, de Bruijn I, Dekkers E, van der Voort M, Schneider JHM, et al. Deciphering the rhizosphere microbiome for disease-suppressive bacteria Sci. 2011;332:1097–100. https://doi.org/10.1126/science.1203980 American Association for the Advancement of Science.
Lugtenberg B, Kamilova F. Plant-growth-promoting rhizobacteria. Annu Rev Microbiol 2009;63:541–56. https://doi.org/10.1146/annurev.micro.62.081307.162918 Annual Reviews.
Walker TS, Bais HP, Grotewold E, Vivanco JM. Update on root exudation and rhizosphere biology root exudation and rhizosphere biology. Plant Physiol. 2003;132:44–51.
Raaijmakers JM, Paulitz TC, Steinberg C, Alabouvette C, Moënne-Loccoz Y. The rhizosphere: a playground and battlefield for soilborne pathogens and beneficial microorganisms. Plant Soil. 2009;321:341–61. https://doi.org/10.1007/s11104-008-9568-6.
Chaudhari D, Rangappa K, Das A, Layek J, Basavaraj S, Kandpal BK, et al. Pea (Pisum sativum l.) Plant shapes its rhizosphere microbiome for nutrient uptake and stress amelioration in acidic soils of the North-East Region of India. Front Microbiol. 2020;11:1–15.
Metzker ML. Sequencing technologies the next generation. Nat Rev Genet. 2010;11:31–46. https://doi.org/10.1038/nrg2626.
Long SW, Beres SB, Olsen RJ, Musser JM. Absence of patient-to-patient intrahospital transmission of Staphylococcus aureus as determined by whole-genome sequencing. MBio. 2014;5:01692–14.
Peisl BYL, Schymanski EL, Wilmes P. Dark matter in host-microbiome metabolomics: tackling the unknowns–a review. Anal Chim Acta. 2018;1037:13–27 https://www.sciencedirect.com/science/article/pii/S0003267017314575.
Lavelle A, Sokol H. Beyond metagenomics, metatranscriptomics illuminates microbiome functionality in IBD. Nat Rev Gastroenterol Hepatol. 2018;15:193–4. https://doi.org/10.1038/nrgastro.2018.15.
Maida Y, Masutomi K. RNA-dependent RNA polymerases in RNA silencing. Biol Chem. 2011;392:299–304. https://doi.org/10.1515/bc.2011.035.
Harrington ED, Arumugam M, Raes J, Bork P, Relman DA. SmashCell: a software framework for the analysis of single-cell amplified genome sequences. Bioinformatics. 2010;26:2979–80. https://doi.org/10.1093/bioinformatics/btq564 (Available from:).
Amarasinghe KC, Li J, Halgamuge SK. CoNVEX: copy number variation estimation in exome sequencing data using HMM. BMC Bioinformatics. 2013;14:S2. https://doi.org/10.1186/1471-2105-14-S2-S2.
Giardine B, Riemer C, Hardison RC, Burhans R, Elnitski L, Shah P, et al. Galaxy: a platform for interactive large-scale genome analysis. Genome Res. 2005;15:1451–5 http://genome.cshlp.org/content/15/10/1451.abstract.
Kosakovsky Pond S, Wadhawan S, Chiaromonte F, Ananda G, Chung W-Y, Taylor J, et al. Windshield splatter analysis with the galaxy metagenomic pipeline. Genome Res. 2009;19:2144–53 http://genome.cshlp.org/content/19/11/2144.abstract.
Markowitz VM, Chen I-MA, Chu K, Szeto E, Palaniappan K, Pillay M, et al. IMG/M 4 version of the integrated metagenome comparative analysis system. Nucleic Acids Res. 2014;42:568–73. https://doi.org/10.1093/nar/gkt919.
Markowitz VM, Ivanova NN, Szeto E, Palaniappan K, Chu K, Dalevi D, et al. IMG/M: a data management and analysis system for metagenomes. Nucleic Acids Res. 2008;36:D534-8. https://doi.org/10.1093/nar/gkm869.
Treangen TJ, Darling AE, Achaz G, Ragan MA, Messeguer X, Rocha EPC. A novel Heuristic for local multiple alignment of interspersed DNA repeats. IEEE/ACM Trans Comput Biol Bioinforma. 2009;6:180–9.
Michaud J, Simpson KM, Escher R, Buchet-Poyau K, Beissbarth T, Carmichael C, et al. Integrative analysis of RUNX1 downstream pathways and target genes. BMC Genomics. 2008;9:363. https://doi.org/10.1186/1471-2164-9-363.
Yun EH, Kang YH, Lim MK, Oh J-K, Son JM. The role of social support and social networks in smoking behavior among middle and older aged people in rural areas of South Korea: a cross-sectional study. BMC Public Health. 2010;10:78. https://doi.org/10.1186/1471-2458-10-78.
Li W. Analysis and comparison of very large metagenomes with fast clustering and functional annotation. BMC Bioinformatics. 2009;10:359. https://doi.org/10.1186/1471-2105-10-359.
Lee KC, Archer SDJ, Boyle RH, Lacap-Bugler DC, Belnap J, Pointing SB. Niche filtering of bacteria in soil and rock habitats of the Colorado Plateau Desert, Utah, USA. Front Microbiol. 2016;7:1–7.
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10 http://www.sciencedirect.com/science/article/pii/S0022283605803602.
Xia X. Bioinformatics and Drug Discovery. Curr. Top. Med. Chem. 2017;17(15):1709–26. Available from: http://www.eurekaselect.com/article/79752.
Pruitt KD, Tatusova T, Maglott DR. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2007;35:D61-5. https://doi.org/10.1093/nar/gkl842.
White JR, Nagarajan N, Pop M. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLOS Comput Biol. 2009;5:e1000352. https://doi.org/10.1371/journal.pcbi.1000352 Public Library of Science.
Pond S, Wadhawan S, Chiaromonte F, Ananda G, Chung W-Y, Taylor J, et al. Windshield splatter analysis with the Galaxy metagenomic pipeline. Genome Res. 2009;19:2144–53.
Keegan KP, Glass EM, Meyer F. MG-RAST, a Metagenomics service for analysis of microbial community structure and function. In: Martin F, Uroz S, editors. Microb Environ Genomics. New York, NY: Springer New York; 2016. p. 207–33. Available from: https://doi.org/10.1007/978-1-4939-3369-3_13
Meyer F, Bagchi S, Chaterji S, Gerlach W, Grama A, Harrison T, et al. MG-RAST version 4—lessons learned from a decade of low-budget ultra-high-throughput metagenome analysis. Brief Bioinform. 2019;20:1151–9. https://doi.org/10.1093/bib/bbx105.
Arumugam M, Harrington ED, Foerstner KU, Raes J, Bork P. Smashcommunity: a metagenomic annotation and analysis tool. Bioinformatics. 2010;26:2977–8. https://doi.org/10.1093/bioinformatics/btq536.
Liu Y-X, Qin Y, Chen T, Lu M, Qian X, Guo X, et al. A practical guide to amplicon and metagenomic analysis of microbiome data. Protein Cell. 2021;12:315–30. https://doi.org/10.1007/s13238-020-00724-8.
Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017;35:316–9. https://doi.org/10.1038/nbt.3820.
Forde JZ, Bussonnier M, Fortin F-A, Granger BE, Head TD, Holdgraf C, et al. Reproducing machine learning research on binder. NIPS Workshop 2018, Dec 3-8, Montreal, Canada.
Kurtzer GM, Sochat V, Bauer MW. Singularity: scientific containers for mobility of compute. PLoS One. 2017;12:e0177459. https://doi.org/10.1371/journal.pone Public Library of Science.
Visconti A, Martin TC, Falchi M. YAMP: a containerized workflow enabling reproducibility in metagenomics research. Gigascience. 2018;7:giy072.
Guzman C, D’Orso I. CIPHER: a flexible and extensive workflow platform for integrative next-generation sequencing data analysis and genomic regulatory element prediction. BMC Bioinform. 2017;18:363. https://doi.org/10.1186/s12859-017-1770-1.
Cario CL, Witte JS. Orchid: a novel management, annotation and machine learning framework for analyzing cancer mutations. Bioinformatics. 2018;34:936–42. https://doi.org/10.1093/bioinformatics/btx709.
Bushnell B, Rood J, Singer E. BBMerge Accurate paired shotgun read merging via overlap. PLoS One. 2017;12:e0185056. https://doi.org/10.1371/journal.pone.0185056 Public Library of Science.
Schulfer AF, Battaglia T, Alvarez Y, Bijnens L, Ruiz VE, Ho M, et al. Intergenerational transfer of antibiotic-perturbed microbiota enhances colitis in susceptible mice. Nat Microbiol. 2018;3:234–42. https://doi.org/10.1038/s41564-017-0075-5.
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3. https://doi.org/10.1038/nmeth.38695.
Arisdakessian C, Cleveland SB, Belcaid M. MetaFlow|mics: Scalable and Reproducible Nextflow Pipelines for the Analysis of Microbiome Marker Data. Pract Exp Adv Res Comput. New York, NY, USA: Association for Computing Machinery; 2020. p. 120–124. Available from: https://doi.org/10.1145/3311790.3396664
Medlin L, Elwood HJ, Stickel S, Sogin ML. The characterization of enzymatically amplified eukaryotic 16S-like rRNA-coding regions. Gene. 1988;71:491–9 https://www.sciencedirect.com/science/article/pii/0378111988900662.
Woese CR. Bacterial evolution. Microbiol Rev. 1987;51:221–71. https://doi.org/10.1128/mr.51.2.221-271.1987 American Society for Microbiology.
Tamames J, Puente-Sánchez F. A highly portable, fully automatic metagenomic analysis pipeline. Front Microbiol. 2019;9:3349. https://doi.org/10.3389/fmicb.2018.03349.
Pan X, Wu W, Gu Y. Study and optimization based on MySQL storage engine. Jin D, Lin S, editors. Adv. Multimedia, Softw. Eng. Comput. Vol.2. Berlin, Heidelberg: Springer Berlin Heidelberg; 2012.
Johnson SS, Zaikova E, Goerlitz DS, Bai Y, Tighe SW. Real-time DNA sequencing in the antarctic dry valleys using the Oxford nanopore sequencer. J Biomol Tech. 2017;28:2–7.
Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6. https://doi.org/10.1038/nmeth.3103.
Shinano T. Research on ways to improve crop productivity through the regulation of rhizosphere environments. Soil Sci Plant Nutr. 2020;66:10–4. https://doi.org/10.1080/00380768.2019.1666301 Taylor & Francis.
Pinu FR, Beale DJ, Paten AM, Kouremenos K, Swarup S, Schirra HJ, et al. Systems biology, and multi-omics integration: Viewpoints from the metabolomics research community. Metabolites. 2019;9(4):76.
Ijaz M, Iqbal M, Rasool B, Zubair M, Umirbekovna IA, Bukhari SA, et al. Rhizosphere dynamics: An OMICS perspective. In: Pudake RN, Sahu BB, Kumari M, Sharma AK, editors. Omi Sci Rhizosph Biol. Singapore: Springer Singapore; 2021. p. 73–88.
Wang S, Guo Z, Zhang L, Jiang F, Wang X, et al. Wheat rhizosphere metagenome reveals newfound potential soil zn-mobilizing bacteria contributing to cultivars’ variation in grain zn concentration. Front Microbiol. 2021;12:689855.
Pramanik K, Das A, Banerjee J, Das A, Chatterjee S, Sharma R, et al. Metagenomic insights into rhizospheric microbiome profiling in lentil cultivars unveils differential microbial nitrogen and phosphorus metabolism under rice-fallow ecology. Int J Mol Sci. 2020;21:1–22.
Liu F, Rice JH, Lopes V, Grewal P, Lebeis SL, Hewezi T, et al. Overexpression of strigolactone-associated genes exerts fine-tuning selection on soybean rhizosphere bacterial and fungal microbiome. Phytobiomes J. 2020;4:239–51. https://doi.org/10.1094/PBIOMES-01-20-0003-R.
Zhou Y, Coventry DR, Gupta VVSR, Fuentes D, Merchant A, Kaiser BN, et al. The preceding root system drives the composition and function of the rhizosphere microbiome. Genome Biol 2020;21:1–19 Genome biology.
Reid TE, Kavamura VN, Abadie M, Torres-Ballesteros A, Pawlett M, Clark IM, et al. Inorganic chemical fertilizer application to wheat reduces the abundance of putative plant growth-promoting rhizobacteria. Front Microbiol. 2021;12:642587. https://doi.org/10.3389/fmicb.2021.642587.
Usyskin-Tonne A, Hadar Y, Yermiyahu U, Minz D. Elevated CO2 and nitrate levels increase wheat root-associated bacterial abundance and impact rhizosphere microbial community composition and function. ISME J. 2021;15:1073–84. https://doi.org/10.1038/s41396-020-00831-8.
Brígido C, Singh S, Menéndez E, Tavares MJ, Glick BR, Félix MD, et al. Diversity and functionality of culturable endophytic bacterial communities in chickpea plants. Plants. 2019;(2):42.
Chiniquy D, Barnes EM, Zhou J, Hartman K, Li X, Sheflin A, et al. Microbial community field surveys reveal abundant pseudomonas population in sorghum rhizosphere composed of many closely related phylotypes. Front Microbiol. 2021;12:598180. https://doi.org/10.3389/fmicb.2021.598180.
Sun Y, Tian L, Chang J, Shi S, Zhang J, Xie H, et al. Rice domestication influences the composition and function of the rhizosphere bacterial chemotaxis systems. Plant Soil. 2021;466:81–99. https://doi.org/10.1007/s11104-021-05036-2.
Annapurna K, Govindasamy V, Sharma M, Ghosh A, Chikara SK. Whole genome shotgun sequence of Bacillus paralicheniformis strain KMS 80, a rhizobacterial endophyte isolated from rice (Oryza sativa L.). 3 Biotech. 2018;8:223.
Preepremmot P, Amkha S, Chungopast S, Mala T. Effect of nitrogen fertilizer and Azospirillum product on growth of rice variety Pathum Thani 1 and bacterial diversity in the rhizosphere. Int J Agric Technol. 2020;16:1199–216.
Liu J, Ma Q, Hui X, Ran J, Ma Q, Wang X, et al. Long-term high-P fertilizer input decreased the total bacterial diversity but not phoD-harboring bacteria in wheat rhizosphere soil with available-P deficiency. Soil Biol Biochem. 2020;149:107918 https://www.sciencedirect.com/science/article/pii/S0038071720302157.
Zhu L, Li W, Deng Z, Li H, Zhang B. The composition and antioxidant activity of bound phenolics in three legumes, and their metabolism and bioaccessibility of gastrointestinal tract. Foods. 2020;9(12):1816.
Jaiswal SK, Maredi MP, Dakora FD. Rhizosphere P-Enzyme activity, mineral nutrient concentrations, and microbial community structure are altered by intra-hole cropping of cowpea with cereals. Front Agron. 2021;3. https://www.frontiersin.org/articles/10.3389/fagro.2021.666351
Ethics approval and consent to participate
Consent for publication
There is no competing of interest to declare.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Navgire, G.S., Goel, N., Sawhney, G. et al. Analysis and Interpretation of metagenomics data: an approach. Biol Proced Online 24, 18 (2022). https://doi.org/10.1186/s12575-022-00179-7
- Next-generation sequencing