Keywords

5.1 Introduction

DNA sequencing is the key step in genomic studies and molecular characterizations. Sequencing techniques are widely applied, but not limited to fields such as molecular biology, biotechnology, genetics, genome sequencing, forensic sciences, archaeology, anthropology, and metagenomics. Two decades ago, the sequenced genome of the first bacterial genome Haemophilus influenzae Rd. was reported (Fleischmann et al. 1995). The extensive technological advancements in sequencing chemistry, significant growth of genomes, expressed sequence tags (ESTs), and metagenomes were observed (Sayers et al. 2018), because of tremendous throughput and drastic reduction in sequencing cost. The genome of Eschericha Coli were repprted to harbor nearly 5000 proteins oer genome. (Cook and Ussery 2013).

In order to analyze the sequenced genomes, bioinformatic-driven analysis facilitated the harvesting of functional signatures, comparison, and visualization. For such task fulfillment, various tools have been developed among that majority for second-generation sequencer. As traditional assembler and annotation pipelines are unable to handle such enormous data, the new method is continuously developing (Pop 2009; Ekblom and Wolf 2014). Also development of efficient computational algorithms coupled with high-performance computers (HPC) facilitated the robust genome, metatranscriptome, and metagenome analysis and raw read archival system with significantly reduced time (Leinonen et al. 2011; Keegan et al. 2016; Mitchell et al. 2018; Mukherjee et al. 2018).

5.1.1 Sequencing Projects

The extensive data generation and efficient computational resource development facilitated the finishing of various complete genomes and draft genomes. As shown in Fig. 5.1a, there was a remarkable growth of complete genomes from year 2010 to 2018, which increased from 506 to 2058 and permanent drafts from 718 to 15,098. The majority of bacterial genomes were obtained from medical sector (59%), followed by environment (7%) and agriculture (7%) projects (Fig. 5.1b). It is obvious that pathogens are greatly spreading with gain of resistance against antibiotics; medical sector-associated pathogen genome analysis could provide more insights of drug resistance and management (Dethlefsen et al. 2008). Table 5.1 shows domain-specific genome projects in which more than one lakh bacterial whole genome sequencing (WGS) projects and more than 60 K metagenome projects and nearly 1.5 K archaeal WGS were contributed/deposited in Genomes OnLine Database (GOLD (Mukherjee et al. 2018)). Further looking to archaea phyla level, majority of projects were associated with Euryarchaeota (58.46%) and Crenarchaeota (23.64%) (Table 5.2a), whereas among bacteria, majority of projects were associated with Proteobacteria (51.19%), Firmicutes (29.66), Actinobacteria (12.11), Bacteroidetes (2.67), and Cyanobacteria (0.97) (Table 5.2b).

Fig. 5.1
figure 1

The number of complete and permanent draft genomes (a) and projects’ relevance to bacterial genome (b) in GOLD. Presented data accessed on December 26, 2018, from https://gold.jgi.doe.gov/

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Table 5.1 Phylogenetic distribution of genome projects in GOLD
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Table 5.2a Phylogenetic distribution of archaea at phyla level associated projects in GOLD
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Table 5.2b Phylogenetic distribution of bacteria at phyla level associated projects in GOLD
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It is also important to emphasize on the contribution of different ecological types in biosample and sequencing projects. It is observed that majority of projects were host-associated, followed by environment. Among the host-associated, majority were human, mammals, plants, arthropods, birds, and fungi. Among the environmental ecosystem, aquatic and terrestrial were in majority, and among the engineered ecosystem built environment, wastewater, food production modeled, and lab enrichment were in majority (Table 5.3). Looking in details, 111 different ecosystem types contributed to enormous biosamples. Among these, the digestive system, marine, freshwater, soil, and thermal springs were in majority, while tooth, solar panel, microbial solubilization of coal, and hair were the least (Table 5.4).

Table 5.3 The number of sequencing projects associated biosample from different ecosystem hosts submitted to GOLD
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Table 5.4 The number of sequencing projects associated biosample from different ecosystem types submitted to GOLD
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The Genomes Online Database (GOLD) contains 340,849 total organisms; among those 300,052 were bacteria and 3093 were archaea. The MG-RAST v4.03 system listed 362,238 metagenomes with 1329 billion sequences constituted 183.08 Tbp (Tera base pair). This shows the high demand of next-generation sequencing (NGS) in various ecosystem biosamples for their whole genome sequencing (WGS) and metagenomics. Microbial genomes available in Ensembl genome browser consist of 61 phyla, 1600 genera, and 9800 species. Interestingly, among the available sequenced genomes, Proteobacteria accounted the major fraction (Mukherjee et al. 2018). Additionally, the advancements in sequencing of uncultivable microbial genomes and reconstruction of genomes from metagenomes through second and third generation contribute in the enlargement of database repositories.

5.2 Genome Characteristics

The sequenced genomes deposited in public databases, such as NCBI, GOLD, ENA, DDBJ, and Ensembl, offer to study the functional features and contribution to the ecosystem (Leinonen et al. 2011). Also, there is a significant variation in gene content and genome size in species to species. Moreover, a species and strain display very streamlined and homogenous in terms of genetic variations observed in transposable elements and resistance genes (e.g., Mycobacterium tuberculosis) (Land et al. 2014). Comparisons made within genes and between genes of different organisms provide a distinct type of closeness, leading to the development of genes common to most genomes (core genes) and total genes (pan genes) set. This provides a reasonable knowledge of species closeness and molecular evolution. The wide range of E. coli genome analysis revealed that pan-genomes are increasing than core gene sets, and letter various pan and core genomes have been determined (Land et al. 2014).

Looking to inside of sequenced genomes showed that 2671 complete/finished genomes consist of 88% of average protein coding region in bacteria, available in GenBank, and it ranges between 40% and 97% (Land et al. 2014). Meanwhile bacteria generally consist of 5 Mb genome size which encodes near about 5000 proteins. Among the sequenced genomes available in GenBank, the largest genome is Sorangium cellulosum strain So0157–2 with a size of 14,782,125 bp and contains 11,021 genes (Han et al. 2013), and the smallest bacterial genome is Candidatus Nasuia deltocephalinicola strain NAS-ALF; the genome consists of 112,091 bp in length and encodes137 proteins (Bennett and Moran 2013). The microorganism such as Kineococcus radiotolerans SRS30216, Sorangium cellulosum So0157–2, and Rhodococcus aetherivorans strain IcdP1 consists of (%GC) 74.4, 72.1, and 70.6, respectively, whereas Candidatus Sulcia muelleri PSPU and Candidatus Nasuia deltocephalinicola strain NAS-ALF consist of (%GC) 20.9 and 17.1, respectively (Table 5.5). Further, biochemical processes are the primary mechanism for driving biological processes that occur in different species of a living organism. Using genome sequencing various key metabolic pathways could be efficiently identified (Francke et al. 2005). Using such technique, the species-specific association between phenotypes and genotypes by network reconstruction of metabolic pathway can be performed, as it is applied widely for genome-scale metabolic model (Thiele and Palsson 2010).

Table 5.5 List of microorganism with genome size, %GC, gene content, and accession number
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The bacterial genome average protein coding density (PCD) is 87% with a usual range of 85–80% (McCutcheon and Moran 2011), but in some bacterial genomes, the protein coding density is less than 40%. Among these several are obligate pathogens and symbionts or consist of pseudogenes. As an example in an insect cosymbiont Serratia symbiotica str. Cinara cedri, the PCD is 38% and it comprises at least 58 pseudogenes (Lamelas et al. 2011). Similarly, the symbiotic cyanobacteria Nostoc azollae 0708 residing with fresh water fern consist of 52% PCD, which is the lowest of any other cyanobacteria (Ran et al. 2010). Although cyanobacteria Trichodesmium erythraeum IMS101 with 63% PCD harbor 12% of pseudogenes without the influence of environment, these cyanobacteria are free-living, nitrogen-fixing, bloom-causing, filamentous, and colony-forming and thrive in tropical and subtropical oceans with suitability to known reasons for undergoing a genome reduction (Pfreundt et al. 2014).

5.3 First-Generation DNA Sequencing

The DNA sequencing technology in the market was automated capillary sequencer also called chain termination sequencing or Sanger sequencing. In this sequencing chemistry, DNA is randomly fragmented, cloned into plasmid, and transformed to generally E. coli. The cloned fragment is amplified using flanking PCR primer. Each PCR round is terminated using incorporation of fluorescently labelled dideoxyribonucleotide (ddNTP). The resultant terminated fragments are then separated in electrophoretic capillary containing polymer gel, following exposing capillary to excite the fluorescently labelled dye by argon laser, and then emitted spectrum is recorded in a form of chromatogram using charge-coupled device camera. This gives read length of 800 to 1000bp with base call accuracy of 99.99%. However, its technology with very low output and high production cost limits the application (Swerdlow and Gesteland 1990).

5.3.1 Next-Generation Sequencing

In year 2005, massive parallel high-throughput sequencing technologies arrived among the scientific community also referred as next-generation sequencing, which delivers the tremendous output with high coverage and eventually becomes one of the essential tools for microbial genomics (Cao et al. 2017). The revolution of NGS over Sanger sequencing can be presented as (1) construction of multiplexed sequencing library, (2) clonal amplification of libraries, (3) immobilization of amplified libraries on solid substrate, and (4) chip-based sequencing. Depending on the variation in methodology used to immobilize DNA on a solid substrate and detection, the following technologies were mostly utilized in scientific community: (1) pyrosequencing, (2) sequencing by reversible termination, and (3) semiconductor sequencing.

5.3.1.1 Pyrosequencing

The first commercially launched next-generation sequencer was 454 GS20 pyrosequencing machine (Margulies et al. 2005). This technology is based on sequencing by synthesis and inorganic pyrophosphate-light emission detection chemistry. In this technology, initially DNA molecule is sheared using frequent site cutter restriction enzyme or fragmented through sonicator (nebulization). The sheared/fragmented DNA is end repaired and then subjected to oligonucleotide adapters and barcode ligation for multiplexing, a process called library preparation. The prepared library is then clonally amplified on beads (28 μm bead) with supplement of dNTPs, polymerase, and primer in an oil-water emulsion mixture, a process called emulsion PCR. The clonally amplified libraries were recovered, enriched, hybridized with sequencing primer, and loaded on picotiter plate for sequencing in the machine. The oil-water mixture acts as a microreactor for clonal amplification of sample on beads. During the sequencing, clonally amplified DNA fragments polymerized by the addition of nucleotides into daughter strands by sequencing polymerase result in the release of inorganic pyrophosphate (PPi). This released PPi combines with APS to form the ATP by sulfurylase, and then ATP combines with luciferin by luciferase resulting in the emission of oxyluciferin and light. This released light is captured by CCD camera in image format and then converted to nucleotides through image processing. The subsequent/iterative flow of sequencing cycles generates the average mean read length of 400–500 nucleotides (Margulies et al. 2005). More details are shown in Table 5.6. While producing the tremendous output, this technology is prone to sequencing of homopolymer repeats (Goodwin et al. 2016). Applying this technique, the first sequenced genome was bacterium Myxococcus xanthus, a soil inhabitant (Vos and Velicer 2006). Using such technology, a study of buffalo rumen microbial diversity associated with high roughage diet (Pitta et al. 2014b; Singh et al. 2015a) and fresh water (Dinsdale et al. 2008) has been performed.

Table 5.6 List of NGS machines with their chemistry, throughput, and runtime
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5.3.1.2 Sequencing by Reversible Termination

The sequencing by reversible termination technology was implemented in Illumina Genome Analyzer (SOLEXA) marketed in the year 2006 (Fedurco et al. 2006). In this method, the sample preparation involves the random fragmentation, followed by the ligation of oligonucleotide adaptors and indexes, called sequencing libraries. The prepared libraries were amplified through bridge amplification (Adessi et al. 2000; Fedurco et al. 2006). The PCR forward and reverse primers complementary with adapters are hybridized on glass surface, amplified using modified DNA polymerase, a process called cluster generation. It is then followed by annealing of sequencing primer with adapters and followed by sequencing. In this sequencing chemistry, a modified DNA polymerase and different fluorophore-labelled nucleotides at 3′ are used. In each cycle, incorporation of single nucleotide followed to cleavage of fluorescent reporter which is the corresponding to the incorporated base and recorded by camera (Ju et al. 2006). The advancements in this technology permitted the 300∗2 paired-end sequencing with a total average read length of 600 nucleotides (Table 5.6) (Goodwin et al. 2016). The limitation of this technology is high error rate of transition (Ts) to transversion (Tv) SNPs and Ts/Tv ratio.

5.3.1.3 Semiconductor Sequencing

This sequencing technology is based on the detection of proton (H+) released after the incorporation of nucleotide in a complementary strand. This released proton ion triggers an ion-sensitive field-effect transistor (ISFET) ion sensor as a signal, and generated signal is translated into the corresponding nucleotide through signal processing by Torrent Suite. The device on which sample is loaded consists of millions of microwells on a semiconductor chip in which sequencing occurs (Pennisi 2010). This technology library preparation is similar to pyrosequencing. The difference in library amplification through emulsion PCR, recovery and enrichment wherein pyrosequencing is time consuming, laborious while semiconductor (Ion Torrent) takes less time and labor.

5.4 Single-Molecule Real-Time (SMRT) Sequencing

The third-generation sequencer involves direct DNA sequencing without utilizing the PCR amplification step, as amplification introduces a bias in read content and presence of high GC content affects depth and coverage. The major advantage of this technique is the longer read length with an average of 5–10 Kb. With this technology, the first commercially launched chemistry was single-molecule real-time (SMRT®) by Pacific Biosciences (Eid et al. 2009). In this chemistry, sample library preparation involves the incorporation of DNA molecule to be circularized by ligating the adapter to both the ends of template. The prepared circular library is placed into SMRT® cell comprising 150,000 zeptoliter wells. Each well of SMRT cell contains single immobilized DNA polymerase (modified) at the bottom. The DNA polymerase binds with adapter sequence and then initiates the template replication. The incorporation of complementary four different fluorescently labelled nucleotides into reaction well. As the labelled base gets incorporated enzymatically, a light signal is generated and identified as the corresponding nucleotide (Eid et al. 2009). The general data output of PacBio RS II machine is 0.5–1 billion bases per SMRT® cell with very high error rate (typically 10–15%) (Goodwin et al. 2016). More details are presented in Table 5.6.

5.5 Oxford Nanopore

Another third-generation sequencer is MinIon commercialized by Oxford Nanopore Technology in 2014. In this technology, DNA/RNA is passes through a nanopore through electrophoresis, involves utilization of electrolytic solutions with constant electric field. As the DNA/RNA passes through nanopore, alteration in current occurs, and the resultant magnitude is recorded. MinIon library preparation consists of DNA fragmentation and end repaired, and then poly A tail is added to 3’OH end. In this two different adapter, a hair pin adapter and Y adapter (shape based). With the help of motor protein, sequencing templated dsDNA is unzipped at Y adapter and passes the ssDNA through nanopore. It is followed through base calling of ssDNA and hundred to thousand base pair read length is obtained, with an accuracy of 88% (Laszlo et al. 2014). More details are presented in Table 5.6. This technology delivers long reads, low cost, and small size with real-time nature of sequencing and invites attention in genomics and microbial community study (Judge et al. 2015).

5.5.1 Microbial Genome Sequencing and Bioinformatic Analysis

On the publication of first bacterial genome Haemophilus influenza (Fleischmann et al. 1995), the revolution in genomics data grew with tremendous improvements in sequencing mechanism such as application of paired-end sequencing and mate-pair sequencing (Pop 2009; Forde and O’Toole 2013; Cao et al. 2017). The publication of the first complete genome has led to the efforts to scientific community for the sequencing of larger genomes of E. coli (Blattner et al. 1997), Bacillus subtilis (Kunst et al. 1997), and eukaryotic genomes of Saccharomyces cerevisiae (Goffeau 1998), Arabidopsis thaliana (Arabidopsis Genome 2000), and ultimately the human genome (Venter et al. 2001). The advancement in genome sequencing has led to the development of various bioinformatic tools for de novo genome assembly and annotation. The most frequently used tools for genome assembly, majority of them, are command-line interface and available only for Ubuntu (free and open source) operating system. Among those, CLC-Bio, SOAP denovo2 (Luo et al. 2012), Velvet (Zerbino and Birney 2008), IDBA-UD (Peng et al. 2012), and SPAdes (Bankevich et al. 2012) are widely used. These tools detail algorithm and input data type, and dependencies are given in Table 5.7. With the development of computational tools for reference-based gene finder, the BLAST+ (Camacho et al. 2009), InterProScan (Quevillon et al. 2005), DIAMOND (Buchfink et al. 2015), and Blast2GO (Conesa et al. 2005) were highly used, while the ab initio gene prediction-based tools such as GeneMarkS (Besemer et al. 2001), GLIMMER (Delcher et al. 1999), AUGUSTUS (Stanke and Morgenstern 2005), and ORF Finder (Stothard 2000) were highly used. More details of each tool are presented in Table 5.8.

Table 5.7 List of widely used tools for the microbial genome assembly
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Table 5.8 List of tools used for gene identification and prediction in genomes and metagenomes
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5.6 Application of NGS in Microbiome Study

5.6.1 16S rRNA Gene-Based Community Analysis

Various bacteria are un-cultivable in laboratory conditions, either they are unknown or suitable media compositions are unknown. Therefore to comprehensively study microbial composition and diversity, metagenomics was extensively applied. Metagenomics is described as a culture-independent approach to investigate the genetic diversity, community composition, and their interaction in their habitat (Handelsman 2004). The initial metagenomic study involves the microbial diversity using 16S rRNA gene-targeted amplicon sequencing (Schloss and Handelsman 2005; Xu 2006) and later followed by whole metagenome shotgun sequencing (Reddy et al. 2014; Singh et al. 2014a) using NGS platforms.

The 16S rRNA gene consists of hypervariable regions of V1 to V9, with some conserveness between species to species, thus utilized as a molecular tool for bacterial characterization (Kolbert and Persing 1999). The high-throughput 16S rRNA amplicon sequencing analysis of habitats such as the gut (Claesson et al. 2012), oral cavity (Crielaard et al. 2011), and buffalo rumen (Pitta et al. 2014a) microbiota has been characterized. The taxonomic composition estimation using 16S rRNA depends on sampling site and varies organism to organism. As an instance, buffalo rumen (Patel et al. 2014; Singh et al. 2015a) and human digestive tract prevalent with Bacteroidetes and Fermicutes bacterial phyla with remarkable difference at phyla level (Human Microbiome Project Consortium 2012b). The 16S rRNA-based taxon abundance has been correlated with diet and health in human (Claesson et al. 2012; Conlon and Bird 2014). In summary, 16S rRNA-based study provides information for microbial abundance, diversity, and variation to diet alteration, effect of disease condition, and contribution in the ecosystem.

5.6.2 Whole Community Shotgun Metagenomics

The functional contribution of microorganism in various habitats is identifiable by performing the whole metagenome sequencing, and their annotation determines the functional genes (Singh et al. 2014b). The whole metagenome study revealed that the prevailing organism in the environment is correlated with genome size, GC content, horizontal gene transfer and optimum growth temperature (Popa et al. 2011; Wu et al. 2014), and antibiotic and metal ion resistance genes (Reddy and Dubey 2018). Metagenomic investigations also identified that microbes which thrive in soil generally have higher GC content with larger genome size compared to aquatic environment (Wu et al. 2014).

5.6.3 Metagenomics of Rumen

The animal rumen is anaerobic in nature and prevailing microbes are generally anaerobes, and thus these microbes are very difficult to culture in laboratory conditions and determination of molecular diversity. With the massive advancements of microbial community study using targeted 16S rRNA amplicon high-throughput sequencing, it becomes possible to explore the deeper insights of rumen microbiome diversity efficiently. Using such technique, various researchers applied the targeted 16S rRNA amplicon sequencing to characterize the adaptation of microbial community in response to experimental conditions. As an example, V3–V5 targeted amplicon in pre-ruminant calves results in the identification of 15 different phyla. Among these phyla, Bacteroidetes constituted 78% at the 42-day-old age and also in agreement that Bacteroidetes is one of the abundant phyla in ruminants (Li et al. 2012b). The wild ruminant Tragelaphus strepsiceros’s first metagenomic report showed that Firmicutes is dominant with 39% contribution of the total microbiota, followed by ~22% unassigned bacteria and then occurrence of Bacteroidetes (~18%) (Dube et al. 2015). The rumen microbiome adaptation to 50–100% forage diet investigation with respect to liquid and solid fraction, using V1 to V9 targeted amplicon study, indicated that Bacteroidetes were dominant in liquid fraction while Fermicutes were dominant in solid fractions (Pitta et al. 2014b). However, amplicon sequencing analysis provides insights of microbial community structure but is unable to explore the microbiota functional role in defined ecological niche. Therefore, application of whole metagenome sequencing removes such limitation and provides the functional role of microbes in the given niche. Using such technique, various studies had shown that various genes were involved in carbohydrate metabolism, protein metabolism, hydrolase activity, transferase and oxidoreductase activity, DNA and RNA metabolic process, butyrate and propionate metabolism (Patel et al. 2014), and methanogenesis and acetogenesis (Singh et al. 2015b). Functional annotation of whole metagenome data of Mehasani buffalo breed revealed that various environmental gene tags (EGTs) were involved in virulence disease and defense, stress response, and phages and prophages. The virulence disease and defense deeper study revealed that majority of EGTs were associated with resistance to antibiotic and toxic compounds (RATC). Similarly, stress response and phages and prophages extensive study revealed that heat shock, oxidative stress, and phages-prophages and pathogenicity islands were in majority (Reddy et al. 2014). Similarly, functional annotation of whole metagenome data of Jafarabadi buffalo revealed that various EGTs were significantly varied with a variation of feeding diet in liquid and solid fraction. In such study, EGTs such as carbohydrate, nitrogen, protein, DNA, sulfur, amino acid and derivative etc. EGTs exclusively associated with carbohydrate metabolism and protein metabolism such as monosaccharides, polysaccharides, di- and oligosaccharides, amino sugars and protein biosynthesis, protein degradation, and protein folding respectively, were also detected (Nathani et al. 2015). The most widely used tools for 16S rRNA amplicon classification are Quantitative Insights Into Microbial Ecology (QIIME (Kuczynski et al. 2011)), Mothur (Schloss et al. 2009), Ribosomal Database Project (Wang et al. 2007) etc., while for functional classification, Metagenomics Rapid Annotation using Subsystem Technology (MG-RAST (Keegan et al. 2016)), MEtaGenome ANalyzer (MEGAN (Huson et al. 2007)), and EBI-Metagenomics (Mitchell et al. 2018) have been frequently used. In overall, it gives the functional mechanism mediated by microbes in response to experimental conditions and invites the attention for developing catalogue of functional genes of aerobic and anaerobic microbes.

5.7 Metagenomics of Soil

Soil is the main site of food production and peculiar to support life functionality. Soil plays an essential role for plant growth, cycling of carbon, and other nutrients which are mainly mediated by soil microbiota. The first report on soil microbial community using DNA-based study revealed that soil microbiota composition is enormously diversified (Torsvik et al. 1996). The microbial community diversity of soil is mainly driven by soil properties and minimum by temperature and elevation (Xue et al. 2018). It is estimated that more than 10 K bacterial species are present in one gram of soil, with strongly correlated complex network (Nesme et al. 2016). The advancements in microbial genomics facilitated the soil microbiome study at various levels such as genus and species with abundance estimation (Nannipieri 2014), including the functional gene content and actively involved genes. Additionally, it is reported that microorganism displayed increased activity in soil hot spots such as mycosphere, rhizosphere, drilosphere, and detritusphere. The soil rhizosphere consists of surrounding complex microorganism and influenced by plant root, and these microbes play a vital role in plant growth and health promotion. For example, microorganisms beneficial to plants are symbiotic nitrogen-fixing rhizobia, the phosphate-solubilizing bacteria, and pathogen defeating such as Pseudomonads and Bacilli (Berendsen et al. 2012).

The one of highly studied genes of soil microbiota characteristic is nif various types. Among those, nifH was extensively targeted with different PCR primers for identification of N-fixing bacteria through molecular approach (Widmer et al. 1999; Zani et al. 2000), which is time-consuming with limited microorganism identification. The high-throughput sequencing analysis offers new horizons of diversity and composition estimation of soil microbiota across various soil niches without cultivation (Thompson et al. 2017). The deep metagenome study explored the microbial community functional capacity for carbon cycling (Howe et al. 2016) and correlation among community’s functional genes (Hartman et al. 2017). There are some examples of big soil microbiome projects such as Earth Microbiome Project (EM) (Gilbert et al. 2014)), Brazilian Microbiome Project (Pylro et al. 2014), TerraGenome (Vogel et al. 2009), China Soil Microbiome Initiative (http://english.issas.cas.cn/), MicroBlitz (http://www.microblitz.com.au/), and EcoFINDERS (http://ecofinders.dmu.dk/), which characterized the soil microbiota community structure and functional diversity.

5.8 Metagenomics of Human Gut

Initially, the NGS-based 16S rRNA targeted amplicon sequencing provided the fast and cost-effective information of bacteria present in human gut (Qin et al. 2010). The MetaHIT consortium firstly performed the metagenomic study of the human microbiome of 124 Spanish and Danish subject stool samples. They showed that 1150 bacterial species were common in gut and a total of 3.3 million genes. However, 294,000 genes from 75 organisms were common in more than half samples (Qin et al. 2010). Sequenced data functional annotation revealed that various genes and pathways are involved in complex sugar metabolism, cell adhesion, vitamin, xenobiotic, and halogenated aromatic compound metabolism. On the other hand, the human microbiome project (HMP) was the largest for human host-associated microbiota characterization and reported that 3500 and 35,000 species-level operational taxonomic units (OTUs) in humans (Human Microbiome Project, 2012b). The GIT, oral cavity, and stool were the highly diversified, covering over 1000 OTUs from near about 150 genera. HMP data also showed that oral and GIT are more diversified than the back side of the elbow and ear. The diversity index of vaginal microbiome was the lowest with dominance of Lactobacillus (Huse et al. 2012) and becomes less diverse during pregnancy (Aagaard et al. 2012). Looking to the involvement of microbes for a functional role, stool dominated with complex carbohydrate degradation genes, whereas gut dominated with low abundance of hydrogen sulfide production and methionine degradation. The oral microbiota harbored genes for simple sugar metabolism and mostly for dextran, whereas vaginal microbiota harbored genes for glycogen and peptidoglycan degradation (Morgan et al. 2013).

Interestingly, high gut microbial community diversity is an essential feature of health. Aging and Crohn’s disease are associated with bacterial diversity. The alteration of gut microbial community is well known to offer the progression of obesity, diabetes, and irritable bowel disease (Dicksved et al. 2008). The pathobionts are generally found in normal microbiota, while with certain alteration in homeostasis of the host, they increase the disparity by promoting the inflammation and production of bacteriocin and sometimes improving pathogenicity of other pathogens (Cho and Blaser 2012). It is established that the adult’s microbiota is steady; however, broad-spectrum antibiotics kill the majority of commensal gut microbiota (Yassour et al. 2016). An experiment of ciprofloxacin 5-day course causes the reduction of gut bacterial diversity and quantified 30% species abundance (Dethlefsen et al. 2008). As antibiotic usually equally targets commensal microbes which are involved in metabolism and immunity, its removal potentially triggers malfunctioned metabolism and immune system. This offers development of susceptible environment for intestinal pathogens and homeostasis disparities.

The some examples of big project are the HMP (http://www.hmpdacc.org), MetaHIT (http://www.metahit.eu), and Global Ocean Survey (http://www.jcvi.org/cms/research/projects/gos/) applied such technique to explore the microbial diversity and functional genes, allowed our understanding of microbe contribution to sampled ecosystems. The National Institute of Health (NIH) sponsored HMP (http://www.hmpdacc.org) developed the 16S rRNA and whole metagenome data of large populations with comprehensive details of microbial communities at different bodies (Human Microbiome Project Consortium 2012a). This project developed an extensive reference of normal individuals and comparable with diseased individual microbiota (Human Microbiome Project Consortium 2012b; Li et al. 2012a).

5.9 Conclusion

The advent of high-throughput sequencing technology robustly enhanced the data generation, which allowed the massive whole genome sequencing, metagenomics, and their characterization. The taxonomic and functional analysis coupled with bioinformatic tools facilitated the development of microbial community and function genes catalogue. Among the published whole genomes, phyla such as Proteobacteria, Firmicutes, Actinobacteria, Bacteroidetes, and Cyanobacteria constitute nearly 96% of total phyla. The medical sector has contributed in the majority of genome projects as pathogens are greatly spreading with gain of resistance against antibiotics and host-associated ecosystem as a majority for biosamples. The metagenomic sequencing is a widely used tool for taxonomy and functional annotation and provided the identification of various novel genes from different ecological niches. This study shed light on available whole genomes and metagenomes and further provides the base for advanced application of next-generation sequencing and functional annotation.