Innovations in next-generation sequencing (NGS) technology and the decline in the sequencing cost have triggered a revolution to gain an understanding into the diversity, structure, and function of complex microbial processes (Vlahou and Fountoulakis, 2005). NGS has enhanced our concept of various influences of microbes in maintaining equilibrium in the environment and accentuating the function of diverse hosts including humans (Li et al., 2008). NGS has expedited the interpretation of microbiome using techniques, such as metagenomics, metatranscriptomics, metabolomics, proteomics, and single cell genomics. Apart from sequencing, bioinformatics and statistical tools also represent a significant role in sequence assembly, alignment, binning, and annotation. Software of bioinformatic assists in decoding the identity, abundance profile, genetic composition and functional channels of an organism or for a microbial community (Meyer et al., 2008; Glass et al., 2010). Genomics deals with sequencing of the whole genome of a distinct organism whilst metagenomics study a pool of genomes of a community of different populations (Handelsman, 2004). According to the central dogma, the flow of genetic information in a cell is from DNA (genome) to RNA (transcriptome) and then it is translated into proteins (proteome) (Crick, 1970). Genomics elucidates presence of the gene in an organism, while transcriptomics provides the information about the genes that are actively expressed in an organism and proteomics study the structure and function of every protein in an organism. A novel technique called single cell genomics takes in record information of the sequence from an individual cell which procures a better degree of accuracy in cellular differences and a finer understanding into the function of a particular cell in an ecosystem (Eberwine et al., 2014). However, metabolomics includes the analysis of metabolites of an organism and these results may vary from genomics and transcriptomics data as they are influenced by surrounding environments (Apel and Hirt, 2004). Apart from sequencing, bioinformatics and statistical tools assist in sequence assembly, alignment, binning and annotation.

Whilst, the information about bacteria present on earth is better understood, data regarding viruses particularly bacteriophages (henceforth called phages) is still in its infancy. Phages are the most abundant and diverse group of viruses found on Earth (Short and Suttle, 2005). Interactions between the bacterial host and phage have significanty played an role in biogeochemical cycles, regulation of the microbial community structures and governing the microbial populations (Figure 1). Recent surveys have documented the capacity of phages in maintaining the stability of microflora in the human gut (Minot et al., 2011) and regulation of pathogen and multidrug resistance (MDR) in the environment (Parmar et al., 2017). In bacteria, 16S rRNA genes and several house-keeping genes serve as a biomarker which facilitate their identification, whereas, there is an absence of biomarker gene among phages, which poses as a hindrance for identification of phages and hence the phage database is quite insufficient (Edwards and Rohwer, 2005). Addressing the challenge to design a biomarker for phages may uncover new avenues in better understanding the virosphere. Plaque assay being a culture-dependent technique isolates a specific phage against a bacterial host. Subsequently, their identification can be worked out using phenotypic characteristics and sequencing (Sanchez et al., 2015). In an approach to improve a comprehensive insight into uncultivable phage and their interaction with the bacterial host, this review summarizes different NGS techniques and the bioinformatics tools that are applied to evaluate such data.

Phages are in 10:1 ratio with bacteria on Earth, though viral DNA corresponds to only 0.1% of total DNA among microbial communities (Qin et al., 2010). Phages are ubiquitous in the environment and are found abundantly where bacterial hosts thrive. Mostly phages flourish in oceans, soil, wastewater treatment plants, hot-water springs and animal gut (Wommack and Colwell, 2000; Prigent et al., 2005; Prestel et al., 2008; Srinivasiah et al., 2008). Phages are classified on the basis of their size, structural composition, genome organization and on the host it infects (Ackermann, 2009). Electron microscopy assists in studying phenotypic characteristics of phage on the basis of the size and shape of head, tail and tail fibers. The most abundant phages in the environment are dsDNA belonging to order Caudovirales (Weinbauer and Rassoulzadegan, 2004). Caudovirales are furthermore classified into Podoviridae having a short tail, Siphoviridae with long non-contractile tail and Myoviridae possessing a long contractile tail. Every phage is specific toward a particular bacterial host and may have a narrow or broad host range depending on its infection capability. Hosts provide the enzymatic machinery for the phages to replicate and multiply by infecting the most active (exponentially growing) bacteria as implied by “kill the winner” hypothesis (Rodrigue et al., 2009). Phages undergo two types of life cycles, (1) in the lytic lifecycle, phage injects own DNA into a host cell and multiply by manipulating host replicating machinery. After replication, phage cleaves the host bacterial cells releasing progeny virus particles into the environment. Whereas, in (2) lysogeny lifecycle, phage DNA integrates into the bacterial genome, replicates their DNA along the bacterial genome and transfer on to the progeny of host cells (Bertani, 1951).

The interplay between host and phage particle initiates as soon as the phage recognizes specific receptors on the bacterial cell wall. The tail proteins of phage particle recognize the receptor protein(s) of bacteria and inject own DNA into host cytoplasm choosing either lytic or lysogenic lifecycle. Once the phage DNA is inserted into the bacterial cell, the cell is termed as a “virocell” carrying virus auxiliary metabolic genes (vAMGs), which are believed to augment the metabolic potential of the host during infection process as shown in Figure 1 (Rosenwasser et al., 2016). The phages acquire new genes into their genomes by interactions with the host genome in order to replicates in the host cells. The bacterial genes that attach near to the prophage attachment site, suggests the genes were acquired by inaccurate prophage excision. Some novel genes can similarly be transmitted into the interior part of the genome by some unexplained mechanism (Juhala et al., 2000). However, these genes may be autonomous transcripts or repressed prophages that provide benefit to hosts (Brüssow et al., 2004). Horizontal gene transfer (HGT) by these phage particles from one host to another host genomes, results in an increased microbial diversity (Dutta and Pan, 2002; Weinbauer and Rassoulzadegan, 2004). Thus, the interaction between phage and host chiefly emphasizes the structure of microbial communities (Rohwer and Thurber, 2009). Some genes derived by phage also aid in nutrient cycling and gear up the biogeochemical cycles on Earth. Furthermore, phages have a crucial aspect in host mortality, carbon cycling (Breitbart et al., 2004) and nutrient cycling (Suttle, 2007). Also, microbial lysis by phage infection has significance in bacterial population control and the debris of these dead microbes act as a food source in the food web of the environment (Sime-Ngando and Colombet, 2009), thus involved the cycling nutrients (Figure 1). Phages are thus accounted as an application to limit bacterial pathogens and multi-drug resistant organisms in the environment by the mechanism of specifically lysing the bacterial hosts (Parmar et al., 2017). Despite an immense abundance and diversity of phages and their reimbursement in the global webs, molecular knowledge of phage-host interactions is missing. In the era of NGS, employing genomics, single cell genomics, transcriptomics, proteomics, and metabolomics can be a smart attempt to understand the interaction among the phages and their bacterial hosts (Figure 2). A review of the literature has been solicited to confer the claims of omics approach in phage research (Table 1).

Owing to the insufficiency of viral database, there is more than 90% viral dark matter (Hurwitz et al., 2013, 2015). Additionally, the absence of a biomarker gene among phages leads to sequence the whole phage genome for its understanding (Thurber et al., 2009). The genomics of phages would elucidate the genetic composition and putative functional role in the environment. Phage metagenomics would furthermore assist in determining the diversity of phages in a community and reveal novel genes demonstrating phages to be the most diverse beings on the globe (Edwards and Rohwer, 2005). Subsequently, interpretation of functional channels of bacterial viruses would illuminate host-phage interactions (Brum et al., 2015).

A typical genomic experiment begins with isolation of genomic DNA of virus particles (Figure 3). The primary step is filtration of the sample through 0.22 μm filters for elimination of bacterial constituents and other contaminations. Samples are then concentrated by ultracentrifugation or polyethylene glycol precipitation (Helms et al., 1985) and subjected to DNase and RNase treatments to exclude residual genomic material from any contaminant bacteria that may pass through 0.22 μm filter. This treated sample would include only virus particles which can be cleaved and their genome can be extracted using kits or standard methods (Adhikary et al., 2014). In order to examine with NGS platforms, DNA is fragmented, ends are repaired and are ligated with adaptors (Holmfeldt et al., 2013). Finally, fragmented DNA library is cleaned and amplified through PCR as well as is quantified and sequenced. Several sequencing platforms are available such as Ion Torrent, Illumina, PacBio which are preferred as per the requirement of their read length, coverage, paired reads, insert size, accuracy, error rates, sequencing yield, run time and sequencing cost (Quail et al., 2012). To reduce chances of bacterial contamination in the library, a section of DNA is PCR amplified for 16S rRNA genes, and if bands are detected, it conveys the presence of host contamination. In the instance of lesser viral DNA yields, amplification using multiple displacement amplification (MDA) can be performed, but it may generate chimeras (Yilmaz et al., 2010). Apparently, amplifications using linkers may depict impartial viromes (Duhaime et al., 2012; Hurwitz et al., 2015). The sequences acquired by sequencing are developed for data filtering and the sequence reads that passes quality check, is mapped to reference genomes or assembled de novo. If the sequence of contaminating host is furthermore present in reads even after purifying the sample, it can be distinguished by comparing reads to reference bacterial genome or 16S rRNA database (Hurwitz et al., 2013). For annotation of viral genomes, a database such as NCBI non-redundant nucleotides can be used. ORFs can be determined and annotated using CyVerse (Goff et al., 2011) in the PCPipe application through the iVirus project (Hurwitz et al., 2014).

Bioinformatic tools mine enormous volume of sequence data to determine common patterns that govern microbes in an ecosystem. Viral diversity can be estimated using PHACCS toolkit (Angly et al., 2005). To decode the correlation between virus community and environmental factors, an application called Fizkin by CyVerse cyber infrastructure iVirus project selects 300K reads arbitrarily from viromes and examines it using Jellyfish that generates a matrix of shared sequence counts between each virome pair. This matrix uses an input file for Bayesian network analysis resulting in a table of the relevance of environmental factor that determines the diversity of virus and a social network graph (Hurwitz et al., 2014, 2016). This will assist in ecological profiling of viral communities without requiring assembly and annotation. To elucidate sequence matches with a reference database, BLAST is regularly employed along with MG-RAST (Glass et al., 2010), MetaPhyler (Liu et al., 2010) or CARMA (Gerlach et al., 2009). For a taxonomy of viruses, MEGAN (Huson et al., 2007) software can be used whereas Hidden Markov Models, e.g., HMMER (Finn et al., 2011) are applied to match Pfam or KEGG domains. To find specific viral species present in metagenome, k-mer based algorithms such as CLARK (CLAssifier based on Reduced K-mers) (Ounit et al., 2015), USEARCH (Edgar, 2010), KRAKEN (Wood and Salzberg, 2014), and NBC (Naïve Bayes Classifier) (Rosen et al., 2011) have been applied. Sometimes, whole host genome can be observed in viromes when gene transfer agents (GTAs cluster) have filtered along with virus-like particles (Roux et al., 2013b). GTAs and sporadic contaminations can likewise be recognized using software CLARK (Ounit et al., 2015). Alignment of sequences with reference bacteria genome may reveal a prophage viral element using “recruitment plot” in the bacterial genome. Some of the bioinformatics tools adapted for prophage detection include ACLAME, Prophinder, PHAST and PhiSpy which can serve in confirming phage annotation (Akhter et al., 2012).

Along with elucidating diversity and taxonomy of phages, establishing the origin of genes (bacterial or viral) is vague. This ambiguity occur because of vAMGs which incorporates (enhancing cell metabolism in the host) into host cells or some viruses may also uptake some bacterial genes near the prophage excision site. Nevertheless, during integrating into host tRNAs, phages carry an attachment site (attP) which denote a definite match of a host tRNA gene. Example, integrase gene and an attP site (53 bp) of the Prochlorococcus phage P-SS2 is a precise analogue of the host tRNA (attB, 36 bp) of Prochlorococcus MIT9313 (Sullivan et al., 2009). Such phages that display a putative attP site and an integrase identical to a host tRNA gene fragment are suggestive of a host-phage association (Mizuno et al., 2013). Metagenomics serves to find diversity among phages but knowledge about interaction among phage and host is relatively scanty. By analyzing the spacers in CRISPR to phage metagenomes, the bacterial host of phage (Dutilh et al., 2014) and phage-host interactions can be deduced (Anderson et al., 2011; Berg Miller et al., 2012; Edwards et al., 2016). Characterizing these constraints is requisite to develop our insight about bacterial-phage co-evolution.

Transcriptomics provides a measure to investigate the active microorganisms within a community at a specific time and under a definite array of conditions. Study of the transcriptome is critical to analyze molecular constituent of phages and to understand genome function during a distinct period or situation such as development or infection state. The principle objectives of transcriptomics include recording transcripts of all species including mRNAs, non-coding RNAs and small RNAs, to estimate transcriptional organization of genes in terms of 5′ end and 3′ end, gene splicing and post-transcriptional modification as well as to demonstrate varying activity of each gene under different conditions (Bikel et al., 2015). Several technologies have been developed to inspect and determine transcriptomes, such as DNA hybridization technique, DNA microarray, cDNA-amplified fragment length polymorphism (cDNA-AFLP), expressed sequence tag (EST) sequencing, serial analysis of gene expression (SAGE), massive parallel signature sequencing (MPSS), and RNA-seq (Mutz et al., 2013). DNA hybridization employs fluorescently labeled cDNA to hybridize with DNA templates on microarray chips. However, this tool possesses some reserves as it relies on the hitherto studied genome sequences, high background levels for cross-hybridization and a smaller detection range. Phage meta-transcriptomics may present few difficulties as phages are incredibly diverse and their database is considerably less. Also, the availability of RNA, especially mRNA may be in a rather less volume because of inactive phase when phage is not in association with bacterial hosts. Thus, this may lead to challenges in isolating and enriching mRNA for sequencing.

A typical transcriptomics analysis using NGS includes isolation of the total RNA from the virus particles, depending on the RNA to be sequenced (mRNA, lincRNA or microRNA). Initially, the bacterial fractions are separated and purified from the phage particles. DNase and RNase treatments are administered to filter phage particles from any free DNA or RNA of bacteria. The RNA is thus extracted from virus particle using RNA extraction kits such as RNeasy mini kit. Selective elimination of rRNAs can be achieved using rRNA removing kits or using probes complementary to the rRNA region that is attached to magnetic beads. The mRNA can be enriched by magnetic bead capture method of rRNA, preferential polyadenylation of mRNA or preferential digestion of rRNA through enzymes. The cDNA are synthesized using random hexamers or oligo (dT) primers or priming with poly dT primers after polyadenylation. For amplification, RNA polymerase (Ozsolak and Milos, 2011) or MDA (Gonzalez et al., 2005) or emulsion PCR is/are performed (Tang et al., 2009). The 5′ and/or 3′ ends of the cDNA are then repaired along with adapter ligation, following library cleanup, amplification, quantification and sequencing of the library. Single-end or paired-end libraries can be prepared using kits like ScriptSeq RNA-Seq library preparation kit (Illumina, San Diego, CA) and can be sequenced on platforms such as Illumina HiSeq2500. Sometimes, conversion of RNA into cDNA introduces bias into the quantification of transcripts, thus a semi-direct sequencing of RNA by-passing the synthesis of cDNA has been established (Hickman et al., 2013).

The bioinformatics analysis of raw data retrieved by transcriptome sequencing uses reference genes and genomes to map against the raw reads or performing a de novo assembly for unreported transcriptomes. Mapping of transcripts against reference genome would confer taxonomy and function of active phages. Mapping the active functional pathways would expound the up-regulated, down-regulated or unaffected genes of phage during development or infection cycle. The same can be advised for the bacterial host during infection by a phage. The transcriptome reads which are short can be assembled de novo using several softwares such as Trinity. Efficiency and sensitivity of the software are exceptionally promising in procuring full-length transcripts (Ghaffari et al., 2014). The assembled contigs that are obtained by de novo or reference-based assembly can be equated with the NCBI viral reference amino acid sequence database using USEARCH (Edgar, 2010). Moreover, the virus annotated hits can be compared with NCBI non-redundant database using BLASTX. Bowtei software can be used to calculate sequence read number and coverage depth (Langmead et al., 2009). Alignment of reads can be prepared using software called MUSCLE (Edgar, 2004) and for constructing neighbor-joining tree, MEGA software can be employed as uses for the bacterial analyses (Tamura et al., 2013).

Several phage meta-transcriptomics studies have been conducted in an attempt to analyze active phage communities. Studies have revealed the effectiveness of phage metagenome for constructing templates in the microarray (Virochip) to annotate and identify the sample (Santos et al., 2011). The total RNA extracted from the sample can be converted to cDNA, labeled and allowed to hybridize with the virochip (Santos et al., 2012). A combination of metagenomics and meta-transcriptomics study would specifically determine active phages in an environment in which phage transcripts may vary as compared to their genomic abundance (Lim et al., 2013). There can be a case when a particular set of family of genes remain less abundant in metagenomic analyses whilst those genes may be remarkably active in meta-transcriptomics dataset and/or vice versa (Franzosa et al., 2014). This insinuates that performing only a metagenomic study may not be a perfect snapshot of functional active genes in a metagenome. To overcome the tedious isolation of viral mRNA from total mRNA, SCG can be conducted along with microarrays to designate the phage-host systems without cultivating them (Santos I. M. et al., 2015). In another study, phage-host pairs have been investigated in which a fosmid viral metagenomic library was constructed and immobilized on microarray “virochip,” along with them. The genomes of uncultured bacterial host cells can be sorted by fluorescence-activated cell sorting (FACS) followed by amplification via MDA. Single host cells were hybridized on virochip, and the host cells and immobilized phages with positive results were sequenced (Martínez-Garcia et al., 2014). With this new technique, advancements toward the discovery of phage-host interactions arise in current decade (Santos F. et al., 2015). Moreover, meta-transcriptomics based enzyme discovery from phages can assist in utilizing novel enzymes with specific enzymatic characteristics for the industries and scientific communities (Schoenfeld et al., 2010).

Transcriptomics can be employed to analyze the influencing of a phage on the bacterial host after the phage infection. One such response includes induction of Shiga toxin production and acid resistance in E. coli by Shiga toxigenic phages (Veses-Garcia et al., 2015). Studies confirm the fact that host genes get differentially expressed after the phage infection such as a phage “PaP3” had a down regulatory impact on host transcriptional regulators and it proved early genes of phage affected strongly by regulation of hosts (Zhao et al., 2016). This feature of phages can be promoted for formulating a phage therapy. Transcriptomics studies of phage during infection of the host can serve an insight of sequence of transcriptional events, such as initial phase consisting of gene metabolism, DNA synthesis, and regulation genes, is accompanied by a prolonged phase of structural and lysis genes (Halleran et al., 2015). These views can yield information about vAMGs which alters metabolic functions of bacterial host after phage infection. During late phase of phage infection, several up-regulating mechanisms have been observed in bacterial gene expression including stress response and stability of membranes (Leskinen et al., 2016). Additionally, enrichment of ATP synthase and ribosomal protein genes have been revealed during phage infection of phosphorous starved Cyanobacterium host (Lin et al., 2016). With further advancement and a few drawbacks, transcriptomics gives an insight of the phage-host interactions and evaluates the regulatory mechanism in bacterial hosts by phage and/or vice versa (effect of host interactions on phage regulation) which are noteworthy for developing phage therapy and comprehend novel phage antimicrobial compounds.

A proteome can be represented as a set of all expressed proteins in a cell, tissue or an organism (Theodorescu and Mischak, 2007). Proteomics is a methodology for the characterization of genetic data in a cell or an organism via protein pathways and networks (Petricoin et al., 2002) and for distinguishing the functional implication of proteins (Vlahou and Fountoulakis, 2005). It focuses at cataloging protein expression profiles at a specific period, in a definite location of the cell and as a response to foreign stimulations. It is applied to design a plot of protein networks which can be used to demonstrate interaction among protein in an organism (Corpillo et al., 2004). It provides an estimate of occurrence, quantity and modified state of proteins in an environment in a significant-throughput method.

Genome and transcriptome analysis evaluate the indirect functional profile of a cell or a community whereas proteomic reveal a direct estimate of functional activity of a cell (Schwanhäusser et al., 2011). Abundance profiles of proteins can be plotted using comparative metaproteomics, while the reduction or increase in the quantity of some proteins may signify a distinct purpose in an organism or during particular situations of phage infection on the bacterial host (Sangha et al., 2014). The post-infection protein expression changes can be classified as (1) function which alters rapidly on phage-infection, but can get reverted back (2) variations that develop gradually and persist consistent or cannot revert back, and (3) alterations that appear abruptly and are maintained for a longer term.

Developments in next generation tools have drastically enhanced quantification and identification of proteins (Schleicher and Wieland, 1978). The proteomic analysis commences with phage concentration accompanied by lysing phage using physical and chemical agents, consequently releasing phage proteins (Figure 2). The concentration of proteins can be measured using Bradford's method (Bradford, 1976) or can be denatured using urea (Lavigne et al., 2006) or can be digested by trypsin (Borriss et al., 2007). Several approaches and facilities have been in practice for proteomic studies (Chandramouli and Qian, 2009), however, employing some tools such as a mass spectrometer (MS) and protein-chips (microarray) have significantly contributed in the field (Horgan and Kenny, 2011). Proteins have earlier been detected and quantified using enzyme-linked immunosorbent assay (ELISA) and Western blot where proteins were initially separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) (Lavigne et al., 2006). Studies have been performed to understand phage proteins using MS after separating by 1D and 2D PAGE (Clement et al., 2013). Additionally, mass-spectrometry-based techniques such as matrix-assisted laser desorption/ionization (MALDI-MS) (Borriss et al., 2007) and electron spray ionization (ESI) (Carvalho et al., 2012) have been established for analyzing various proteins of phages. Recently, fluorescence 2D differential gel electrophoresis has been employed to distinguish between amounts of human lymph and plasma proteins (Clement et al., 2013). Structural proteomics can interpret the structure of proteins thereby determining the functions of novel genes. Lavigne et al. (2006) described structural proteome of phiKMV, a lytic bacteriophage of Pseudomonas aeruginosa using SDS-PAGE, LC-ESI-MS/MS, and GC-MS. Nuclear magnetic resonance (NMR) (Horgan and Kenny, 2011) and X-ray crystallography (Drulis-Kawa et al., 2012) can be employed to investigate the interaction between phage-binding protein and receptor site on the bacterial host (Sundell and Ivarsson, 2014).

Protein analysis using MS requires a prior separation of the sample either by 2D-gel electrophoresis (Renesto et al., 2006) or isotope-coded affinity tag (ICAT) labeling (Weston and Hood, 2004), accompanied by digestion into peptides and separating peptides using LC. Microarrays can be applied for assorting protein interaction with DNA, protein or ligands. Protein microarray technique can be exploited in the analytical study to check for presence/absence of a distinct protein in a sample (biomarker detection during phage infections) or for defining function (Uzoma and Zhu, 2013). When phage proteins are immobilized on a microarray chip, it can be applied to probe for complementary bacterial host receptors that bind with phage recognition proteins (Santos F. et al., 2015). Reverse-phase protein microarray can serve as a comparative protein profile in case of phage-infected and uninfected bacterial host (Haider and Pal, 2013). Thus, correlative examination of proteome and genome provide an interpretation of the post-translational modifications.

Functional identification of hypothetical phage proteins is performed using MS analysis after affinity purification of host protein mixtures (Van den Bossche et al., 2014). MS/MS spectra can be interpreted using SEQUEST (http://fields.scripps.edu/sequest/) or Mascot (Matrix Sciences) and classifying using DTASelect and Contrast softwares (Tabb et al., 2002). Proteomic phage display techniques are similarly employed to identify target proteins and consensus motifs (Sundell and Ivarsson, 2014). Whole phage shotgun analysis (WSA) is a recently developed technique for protein analysis using NGS platform. It is a culture-independent technique which offers annotation of proteins associated with phages. WSA combines all structural proteins separated on the basis of mass and charge before identification (Lavigne et al., 2006). After separation, the data can be annotated to open reading frames (ORFs) by aligning with reference protein sequences using BLASTP. HHpred is another tool for assigning the protein structure (Hildebrand et al., 2009). The function and evolution of identified proteins can be determined by program COGnitor (www.ncbi.nlm.nih.gov/COG) and InterProScan to find conserved domains (Eyer et al., 2007). When a predicted protein does not match along known proteins from the database, protein clustering can be developed for the comparative analyses to assess the protein diversity (Hurwitz et al., 2013; Brum et al., 2016). Some software can extract the data from MS and microarray and decipher protein identification using databases such as UniProt (http://www.uniprot.org/), PROSITE (http://prosite.expasy.org/), Pfam, Conserved Domain and PDB databases. Thus, with an advent in high-throughput proteomic technology, analytical tools, bioinformatics software and database, research on proteins have emerged as an easy task to elucidate protein matter in an environment.

The breakdown products of metabolism or intermediates involved in the process of metabolism are termed as metabolites. Metabolites can be (1) primary- which are precisely involved in process of metabolism or (2) secondary- which may not directly take part in the growth of an organism. The metabolome of an organism corresponds to a set of metabolites including hormones, intermediates, signaling and secondary molecules in a particular cell, tissue, organ or an organism (Griffin and Vidal-Puig, 2008; Jordan et al., 2009). To explain the physiology of a particular cell, the study of metabolites is very substantial as every cell possesses a specific metabolic catalog which can influence the accurate implication of function of a cell or an organism (Nicholson and Wilson, 2003; Zhang et al., 2016). These are results of gene transcriptional and translational mechanisms which remain exceptionally complex, hence variations in metabolites intensify as compared to variations among transcriptome and proteome.

Various approaches have been established for separation and detection of metabolites, chiefly when metabolites are of higher molecular mass. The segregations of metabolites can be carried out using gas chromatography (GC) and high performance liquid chromatography (HPLC), capillary electrophoresis, electron spray ionization (ESI) accompanied by GC, atmospheric-pressure chemical ionization (APCI) on the ground of characteristics of metabolite to be processed (Alonso et al., 2015). Detection of separated metabolites have furthermore been attainable by using nanostructure-initiator MS (NIMS), MALDI-MS, secondary ion mass spectrometry (SIMS), desorption electron spray ionization (DESI), and NMR (Drexler et al., 2007; Cornett et al., 2008; Wiseman et al., 2008; Greer et al., 2011). Statistical tools are additionally applicable for the evaluation of elicited data such as XCMS (Patti et al., 2013), MZmine (Katajamaa et al., 2006), MetAlign (Lommen, 2009), MathDAMP (Baran et al., 2006), and LCMStats¹ (Gahlaut et al., 2013). The metabolic database is available in form of METLIN (Smith et al., 2005).

Metabolomics would serve in interpreting the significance of active phage community on the environment in real time. Based on distinct phases of the phage infection or metabolic profile of the host infected with a phage, the gene markers can be inferred. The modification in host-cell metabolism by phage-encoded genes (vAMGs) into the host genome, is described as a virocell amendment (Rosenwasser et al., 2016). Studying highly specific metabolic profiles of a virocell can improve in interpreting metabolic profile of vAMGs. Such comparisons were conducted to recognize host-viral interactions (Vardi et al., 2009, 2012; Fulton et al., 2014). Metabolomic analysis of phage interprets the influence of the vAMGs which is responsible for enhancing nucleotide biosynthesis (De Smet et al., 2016) via degrading host macromolecules such as DNA through catabolic pathways. The vAMGs encoded nucleases can generally degrade host DNA and encoded triglyceride lipase can degrade host triacylglycerols which yield energy and ultimately engages in the the formation of virus membrane (Malitsky et al., 2016). Example, ceramidase in Mimivirus helps in the catabolism of sphingolipids (Arslan et al., 2011). Thus, the vAMGs develop the metabolic potential of virocell through triggering novel enzymes which were not present in host machinery prior to phage infection (DeAngelis et al., 1997; Graves et al., 1999). The vAMGs can serve as a shunt between phage and their host by imparting several functional genes from one another especially assisting during stress conditions (Rosenwasser et al., 2016). These mechanisms illustrate unique attributes of gene products of phage that can mediate dynamics phage-host interaction, as an effect, shaping the microbial communities in an environment. Thus, biochemical composition and metabolic profile of bacterial hosts are greatly governed by phages and released metabolites in the environment influence the microbial food web (Miki et al., 2008). Cataloging the metabolome of phages can elucidate special phage-derived metabolites which usually act as decision making between lytic or lysogenic lifecycle in virocell. Study of metagenomic and metabolomic profiles can simultaneously determine whether the metabolites are encoded by the phage or the host. Furthermore, metabolic profiles of phage can aid in tracing a novel biomarker to recognize the nutrient source in biogeochemical cycles. Thus, the advents in the omics approaches utilizing NGS techniques bear a tremendous potential in exploring virosphere and thus the microbial world.

Advancements in the field of NGS have facilitated the discoveries on the verge of a revolution in the course of microbial research. There has been a tremendous microbial data generated about the microbes present on Earth and their diversity and functional roles in regulating the ecosystem. Progress in interpreting the phage diversity and functions as well as the interactions among phages and their hosts are promising using the “omics” concepts. This would illuminate the function of phages in regulating microbial diversity by HGTs, governing the biogeochemical cycles, host population controls and determining the novel biomarkers. NGS will also nurture the upcoming phage therapy research for limiting MDR pathogens. With strong prospects in the field and developments in phage database, “omics” approach is witnessing a remarkable motive for a transformation in the yet uncultivable microbial research.

KP, PD, and RS conceived and designed the work. KP, SG, and RS wrote the manuscript. PD, RK, and RS carefully checked the manuscript and corrected. All of the authors contributed to the discussion and approved the final manuscript.