Several tools have been developed to integrate metadata and sequence data, allowing downstream comparative analyses of different datasets using a number of ecological indices. In 2007, Folker Meyer and Robert Edwards and a team at  Argonne National Laboratory  and the  University of Chicago  released the Metagenomics Rapid Annotation using Subsystem Technology server ( MG-RAST ) a community resource for metagenome data set analysis. [ 52 ]  As of June 2012 over 14.8 terabases (14x10 12  bases) of DNA have been analyzed, with more than 10,000 public data sets freely available for comparison within MG-RAST. Over 8,000 users now have submitted a total of 50,000 metagenomes to MG-RAST. The  Integrated Microbial Genomes/Metagenomes  (IMG/M) system also provides a collection of tools for functional analysis of microbial communities based on their metagenome sequence, based upon reference isolate genomes included from the  Integrated Microbial Genomes  (IMG) system and the  Genomic Encyclopedia of Bacteria and Archaea (GEBA)  project. [ 53 ]


One of the first standalone tools for analysing high-throughput metagenome shotgun data was  MEGAN  (MEta Genome ANalyzer). [ 49 ] [ 42 ]  A first version of the program was used in 2005 to analyse the metagenomic context of DNA sequences obtained from a mammoth bone. [ 17 ]  Based on a BLAST comparison against a reference database, this tool performs both taxonomic and functional binning, by placing the reads onto the nodes of the NCBI taxonomy using a simple lowest common ancestor (LCA) algorithm or onto the nodes of the  SEED  or  KEGG  classifications, respectively. [ 54 ]


With the advent of fast and inexpensive sequencing instruments, the growth of databases of DNA sequences is now exponential (e.g., the NCBI GenBank database  [ 55 ] ). Faster and efficient tools are needed to keep pace with the high-throughput sequencing, because the BLAST-based approaches such as MG-RAST or MEGAN run slowly to annotate large samples (e.g., several hours to process a small/medium size dataset/sample  [ 56 ] ). Thus, ultra-fast classifiers have recently emerged, thanks to more affordable powerful servers. These tools can perform the taxonomic annotation at extremely high speed, for example CLARK  [ 57 ]  (according to CLARK's authors, it can classify accurately "32 million metagenomic short reads per minute"). At such a speed, a very large dataset/sample of a billion short reads can be processed in about 30 minutes.


With the increasing availability of samples containing ancient DNA and due to the uncertainty associated with the nature of those samples (ancient DNA damage), [ 58 ]  a fast tool capable of producing conservative similarity estimates has been made available. According to FALCON's authors, it can use relaxed thresholds and edit distances without affecting the memory and speed performance.


Comparative analyses between metagenomes can provide additional insight into the function of complex microbial communities and their role in host health. [ 59 ]  Pairwise or multiple comparisons between metagenomes can be made at the level of sequence composition (comparing  GC-content  or genome size), taxonomic diversity, or functional complement. Comparisons of population structure and phylogenetic diversity can be made on the basis of  16S rRNA  and other phylogenetic marker genes, or—in the case of low-diversity communities—by genome reconstruction from the metagenomic dataset. [ 60 ]  Functional comparisons between metagenomes may be made by comparing sequences against reference databases such as  COG  or  KEGG , and tabulating the abundance by category and evaluating any differences for statistical significance. [ 54 ]  This gene-centric approach emphasizes the functional complement of the  community  as a whole rather than taxonomic groups, and shows that the functional complements are analogous under similar environmental conditions. [ 60 ]  Consequently, metadata on the environmental context of the metagenomic sample is especially important in comparative analyses, as it provides researchers with the ability to study the effect of habitat upon community structure and function. [ 1 ]


Additionally, several studies have also utilized oligonucleotide usage patterns to identify the differences across diverse microbial communities. Examples of such methodologies include the dinucleotide relative abundance approach by Willner et al. [ 61 ]  and the HabiSign approach of Ghosh et al. [ 62 ]  This latter study also indicated that differences in tetranucleotide usage patterns can be used to identify genes (or metagenomic reads) originating from specific habitats. Additionally some methods as TriageTools [ 63 ]  or Compareads [ 64 ]  detect similar reads between two read sets. The  similarity measure  they apply on reads is based on a number of identical words of length  k  shared by pairs of reads.


A key goal in comparative metagenomics is to identify microbial group(s) which are responsible for conferring specific characteristics to a given environment. However, due to issues in the sequencing technologies artifacts need to be accounted for like in metagenomeSeq. [ 30 ]  Others have characterized inter-microbial interactions between the resident microbial groups. A  GUI -based comparative metagenomic analysis application called Community-Analyzer has been developed by Kuntal et al.
 [ 65 ]  which implements a correlation-based graph layout algorithm that not only facilitates a quick visualization of the differences in the analyzed microbial communities (in terms of their taxonomic composition), but also provides insights into the inherent inter-microbial interactions occurring therein. Notably, this layout algorithm also enables grouping of the metagenomes based on the probable inter-microbial interaction patterns rather than simply comparing abundance values of various taxonomic groups. In addition, the tool implements several interactive GUI-based functionalities that enable users to perform standard comparative analyses across microbiomes.


In many bacterial communities, natural or engineered (such as  bioreactors ), there is significant division of labor in metabolism ( syntrophy ), during which the waste products of some organisms are metabolites for others. [ 66 ]  In one such system, the  methanogenic  bioreactor, functional stability requires the presence of several  syntrophic  species ( Syntrophobacterales  and  Synergistia ) working together in order to turn raw resources into fully metabolized waste ( methane ). [ 67 ]  Using comparative gene studies and expression experiments with  microarrays  or  proteomics  researchers can piece together a metabolic network that goes beyond species boundaries. Such studies require detailed knowledge about which versions of which proteins are coded by which species and even by which strains of which species. Therefore, community genomic information is another fundamental tool (with  metabolomics  and proteomics) in the quest to determine how metabolites are transferred and transformed by a community. [ 68 ]


Metagenomics allows researchers to access the functional and metabolic diversity of microbial communities, but it cannot show which of these processes are active. [ 60 ]  The extraction and analysis of metagenomic  mRNA  (the  metatranscriptome ) provides information on the  regulation  and  expression  profiles of complex communities. Because of the technical difficulties (the  short half-life  of mRNA, for example) in the collection of  environmental RNA  there have been relatively few  in situ  metatranscriptomic studies of microbial communities to date. [ 60 ]  While originally limited to  microarray  technology, metatranscriptomics studies have made use of  transcriptomics technologies  to measure whole-genome expression and quantification of a microbial community, [ 60 ]  first employed in analysis of ammonia oxidation in soils. [ 69 ]


Metagenomic sequencing is particularly useful in the study of viral communities. As viruses lack a shared universal phylogenetic marker (as  16S RNA  for bacteria and archaea, and  18S RNA  for eukarya), the only way to access the genetic diversity of the viral community from an environmental sample is through metagenomics. Viral metagenomes (also called viromes) should thus provide more and more information about viral diversity and evolution. [ 70 ] [ 71 ] [ 72 ] [ 73 ] [ 74 ]  For example, a metagenomic pipeline called  Giant Virus Finder  showed the first evidence of existence of  giant viruses  in a saline desert [ 75 ]  and in Antarctic dry valleys. [ 76 ]


Metagenomics has the potential to advance knowledge in a wide variety of fields. It can also be applied to solve practical challenges in  medicine ,  engineering ,  agriculture ,  sustainability  and  ecology . [ 31 ] [ 77 ]


The  soils  in which plants grow are inhabited by microbial communities, with one gram of soil containing around 10 9 -10 10  microbial cells which comprise about one gigabase of sequence information. [ 78 ] [ 79 ]  The microbial communities which inhabit soils are some of the most complex known to science, and remain poorly understood despite their economic importance. [ 80 ]   Microbial consortia  perform a wide variety of  ecosystem services  necessary for plant growth, including  fixing atmospheric nitrogen ,  nutrient cycling , disease suppression, and  sequester   iron  and other  metals . [ 81 ]  Functional metagenomics strategies are being used to explore the interactions between plants and microbes through cultivation-independent study of these microbial communities. [ 82 ] [ 83 ]  By allowing insights into the role of previously uncultivated or rare community members in nutrient cycling and the promotion of plant growth, metagenomic approaches can contribute to improved disease detection in  crops  and  livestock  and the adaptation of enhanced  farming  practices which improve crop health by harnessing the relationship between microbes and plants. [ 31 ]


Biofuels  are  fuels  derived from  biomass  conversion, as in the conversion of  cellulose  contained in  corn  stalks,  switchgrass , and other biomass into  cellulosic ethanol . [ 31 ]  This process is dependent upon microbial consortia (association) that transform the cellulose into  sugars , followed by the  fermentation  of the sugars into  ethanol . Microbes also produce a variety of sources of  bioenergy  including  methane  and  hydrogen . [ 31 ]


The  efficient industrial-scale deconstruction  of biomass requires novel  enzymes  with higher productivity and lower cost. [ 28 ]  Metagenomic approaches to the analysis of complex microbial communities allow the targeted  screening  of  enzymes  with industrial applications in biofuel production, such as  glycoside hydrolases . [ 84 ]  Furthermore, knowledge of how these microbial communities function is required to control them, and metagenomics is a key tool in their understanding. Metagenomic approaches allow comparative analyses between  convergent  microbial systems like  biogas  fermenters [ 85 ]  or  insect   herbivores  such as the  fungus garden  of the  leafcutter ants . [ 86 ]


Microbial communities produce a vast array of biologically active chemicals that are used in competition and communication. [ 81 ]  Many of the drugs in use today were originally uncovered in microbes; recent progress in mining the rich genetic resource of non-culturable microbes has led to the discovery of new genes, enzymes, and natural products. [ 60 ] [ 87 ]  The application of metagenomics has allowed the development of  commodity  and  fine chemicals ,  agrochemicals  and  pharmaceuticals  where the benefit of  enzyme-catalyzed   chiral synthesis  is increasingly recognized. [ 88 ]


Two types of analysis are used in the  bioprospecting  of metagenomic data: function-driven screening for an expressed trait, and sequence-driven screening for DNA sequences of interest. [ 89 ]  Function-driven analysis seeks to identify clones expressing a desired trait or useful activity, followed by biochemical characterization and sequence analysis. This approach is limited by availability of a suitable screen and the requirement that the desired trait be expressed in the host cell. Moreover, the low rate of discovery (less than one per 1,000 clones screened) and its labor-intensive nature further limit this approach. [ 90 ]  In contrast, sequence-driven analysis uses  conserved DNA sequences  to  design PCR primers  to screen clones for the sequence of interest. [ 89 ]  In comparison to cloning-based approaches, using a sequence-only approach further reduces the amount of bench work required. The application of massively parallel sequencing also greatly increases the amount of sequence data generated, which require high-throughput bioinformatic analysis pipelines. [ 90 ]  The sequence-driven approach to screening is limited by the breadth and accuracy of gene functions present in public sequence databases. In practice, experiments make use of a combination of both functional and sequence-based approaches based upon the function of interest, the complexity of the sample to be screened, and other factors. [ 90 ] [ 91 ]  An example of success using metagenomics as a biotechnology for drug discovery is illustrated with the  malacidin  antibiotics. [ 92 ]


Metagenomics can provide valuable insights into the functional ecology of environmental communities. [ 93 ]  Metagenomic analysis of the bacterial consortia found in the defecations of Australian sea lions suggests that nutrient-rich sea lion faeces may be an important nutrient source for coastal ecosystems. This is because the bacteria that are expelled simultaneously with the defecations are adept at breaking down the nutrients in the faeces into a bioavailable form that can be taken up into the food chain. [ 94 ]


DNA sequencing can also be used more broadly to identify species present in a body of water, [ 95 ]  debris filtered from the air, sample of dirt, or animal's faeces, [ 96 ]  and even detect diet items from blood meals. [ 97 ]  This can establish the range of  invasive species  and  endangered species , and track seasonal populations.


Metagenomics can improve strategies for monitoring the impact of  pollutants  on  ecosystems  and for cleaning up contaminated environments. Increased understanding of how microbial communities cope with pollutants improves assessments of the potential of contaminated sites to recover from pollution and increases the chances of  bioaugmentation  or  biostimulation  trials to succeed. [ 98 ]


Microbial communities  play a key role in preserving human  health , but their composition and the mechanism by which they do so remains mysterious. [ 99 ]  Metagenomic sequencing is being used to characterize the microbial communities from 15–18 body sites from at least 250 individuals. This is part of the  Human Microbiome initiative  with primary goals to determine if there is a core  human microbiome , to understand the changes in the human microbiome that can be correlated with human health, and to develop new technological and  bioinformatics  tools to support these goals. [ 100 ]


Another medical study as part of the MetaHit (Metagenomics of the Human Intestinal Tract) project consisted of 124 individuals from Denmark and Spain consisting of healthy, overweight, and irritable bowel disease patients. [ 101 ]  The study attempted to categorize the depth and phylogenetic diversity of gastrointestinal bacteria. Using Illumina GA sequence data and SOAPdenovo, a de Bruijn graph-based tool specifically designed for assembly short reads, they were able to generate 6.58 million contigs greater than 500 bp for a total contig length of 10.3 Gb and a N50 length of 2.2 kb.


The study demonstrated that two bacterial divisions, Bacteroidetes and Firmicutes, constitute over 90% of the known phylogenetic categories that dominate distal gut bacteria. Using the relative gene frequencies found within the gut these researchers identified 1,244 metagenomic clusters that are critically important for the health of the intestinal tract. There are two types of functions in these range clusters: housekeeping and those specific to the intestine. The housekeeping gene clusters are required in all bacteria and are often major players in the main metabolic pathways including central carbon metabolism and amino acid synthesis. The gut-specific functions include adhesion to host proteins and the harvesting of sugars from globoseries glycolipids. Patients with irritable bowel syndrome were shown to exhibit 25% fewer genes and lower bacterial diversity than individuals not suffering from irritable bowel syndrome indicating that changes in patients' gut biome diversity may be associated with this condition. [ 101 ]


While these studies highlight some potentially valuable medical applications, only 31–48.8% of the reads could be aligned to 194 public human gut bacterial genomes and 7.6–21.2% to bacterial genomes available in GenBank which indicates that there is still far more research necessary to capture novel bacterial genomes. [ 102 ]


In the  Human Microbiome Project  (HMP), gut microbial communities were assayed using high-throughput DNA sequencing. HMP showed that, unlike individual microbial species, many metabolic processes were present among all body habitats with varying frequencies. Microbial communities of 649 metagenomes drawn from seven primary body sites on 102 individuals were studied as part of the  human microbiome  project. The metagenomic analysis revealed variations in niche specific abundance among 168 functional modules and 196 metabolic pathways within the microbiome. These included  glycosaminoglycan  degradation in the gut, as well as phosphate and amino acid transport linked to host phenotype (vaginal pH) in the posterior fornix. The HMP has brought to light the utility of metagenomics in diagnostics and  evidence-based medicine . Thus metagenomics is a powerful tool to address many of the pressing issues in the field of  personalized medicine . [ 103 ]


In animals, metagenomics can be used to profile their gut microbiomes and enable detection of antibiotic-resistant bacteria. [ 104 ]  This can have implications in monitoring the spread of diseases from wildlife to farmed animals and humans.


Differentiating between infectious and non-infectious illness, and identifying the underlying etiology of infection, can be challenging. For example, more than half of cases of  encephalitis  remain undiagnosed, despite extensive testing using state-of-the-art clinical laboratory methods.  Clinical metagenomic sequencing  shows promise as a sensitive and rapid method to diagnose infection by comparing genetic material found in a patient's sample to databases of all known microscopic human pathogens and thousands of other bacterial, viral, fungal, and parasitic organisms and databases on antimicrobial resistances gene sequences with associated clinical phenotypes. [ 105 ]


Metagenomics has been an invaluable tool to help characterise the diversity and ecology of pathogens that are vectored by  hematophagous  (blood-feeding) insects such as mosquitoes and ticks. [ 106 ] [ 107 ] [ 108 ]  Metagenomics is [ when? ]  routinely used by public health officials and organisations [ where? ]  for the surveillance of  arboviruses . [ 109 ] [ 110 ]


Metatranscriptomics  is the set of techniques used to study  gene  expression of  microbes  within natural environments, i.e., the metatranscriptome. [ 1 ]


While  metagenomics  focuses on studying the genomic content and on identifying which microbes are present within a community, metatranscriptomics can be used to study the diversity of the active genes within such community, to quantify their expression levels and to monitor how these levels change in different conditions (e.g., physiological vs. pathological conditions in an organism). The advantage of metatranscriptomics is that it can provide information about differences in the active functions of  microbial communities  that would otherwise appear to have similar make-up. [ 2 ]


The  microbiome  has been defined as a microbial community occupying a well-defined habitat. [ 3 ]  These communities are ubiquitous and can play a key role in maintenance of the characteristics of their environment, and an imbalance in these communities can negatively affect the activities of the setting in which they reside. To study these communities, and to then determine their impact and correlation with their niche, different  omics  approaches have been used. While metagenomics can help researchers generate a  taxonomic  profile of the sample, metatranscriptomics provides a  functional  profile by analysing which genes are expressed by the community. It is possible to infer what genes are expressed under specific conditions, and this can be done using functional annotations of expressed genes.


Since metatranscriptomics focuses on what genes are expressed, it enables the characterization of the active functional profile of the entire microbial community. [ 4 ]  The overview of the gene expression in a given sample is obtained by capturing the total  mRNA  of the microbiome and performing whole-metatranscriptomics  shotgun sequencing .


Although  microarrays  can be exploited to determine the gene expression profiles of some model organisms,  next-generation sequencing  and  third-generation sequencing  are the preferred techniques in metatranscriptomics. The protocol that is used to perform a metatranscriptome analysis may vary depending on the type of sample that needs to be analysed. Indeed, many different protocols have been developed for studying the metatranscriptome of microbial samples. Generally, the steps include sample harvesting,  RNA extraction  (different extraction methods for different kinds of samples have been reported in the literature), mRNA enrichment, cDNA synthesis and preparation of metatranscriptomic libraries, sequencing and data processing and analysis. mRNA enrichment is one of the most technically challenging steps, for which different strategies have been proposed:


The last two strategies are not recommended as they have been reported to be highly biased. [ 6 ]


A typical metatranscriptome analysis pipeline:


The first strategy maps reads to reference genomes in databases, to collect information that is useful to deduce the relative expression of the single genes. Metatranscriptomic reads are mapped against databases using alignment tools, such as  Bowtie2 , BWA, and  BLAST . Then, the results are annotated using resources, such as  GO ,  KEGG , COG, and  Swiss-Prot . The final analysis of the results is carried out depending on the aim of the study. One of the latest metatranscriptomics techniques is  stable isotope probing  (SIP), which has been used to retrieve specific targeted transcriptomes of  aerobic  microbes in lake sediment. [ 7 ]  The limitation of this strategy is its reliance on the information of reference genomes in databases.


The second strategy retrieves the abundance in the expression of the different genes by assembling metatranscriptomic reads into longer fragments called  contigs  using different software. The  Trinity software  for  RNA-seq , in comparison with other de novo transcriptome assemblers, was reported to recover more full-length transcripts over a broad range of expression levels, with a sensitivity similar to methods that rely on genome alignments. This is particularly important in the absence of a reference genome. [ 8 ]


A quantitative pipeline for transcriptomic analysis was developed by Li and Dewey  [ 9 ]  and called RSEM (RNA-Seq by Expectation Maximization). It can work as stand-alone software or as a plug-in for Trinity. RSEM starts with a reference transcriptome or assembly along with RNA-Seq reads generated from the sample and calculates normalized transcript abundance (meaning the number of RNA-Seq reads cor-responding to each reference transcriptome or assembly). [ 10 ] [ 11 ]


Although both Trinity and RSEM were designed for transcriptomic datasets (i.e., obtained from a single organism), it may be possible to apply them to metatranscriptomic data (i.e., obtained from a whole microbial community). [ 12 ] [ 13 ] [ 14 ] [ 15 ] [ 16 ] [ 17 ]


The use of computational analysis tools has become more important as DNA sequencing capabilities have grown, particularly in metagenomic and metatranscriptomic analysis, which can generate a huge volume of data. Many different bioinformatic pipelines have been developed for these purposes, often as open source platforms such as HUMAnN and the more recent HUMAnN2, MetaTrans, SAMSA, Leimena-2013 and mOTUs2. [ 18 ]


HUMAnN2 is a bioinformatic pipeline designed from the previous HUMAnN software, which was developed during the  Human Microbiome Project  (HMP), implementing a “tiered search” approach. In the first tier, HUMAnN2 screens DNA or RNA reads with MetaPhlAn2 in order to identify already-known microbes and constructing a sample-specific database by merging pangenomes of annotated species; in the second tier, the algorithm performs a mapping of the reads against the assembled pangenome database; in the third tier, non-aligned reads are used for a translated search against a protein database. [ 19 ]


MetaTrans is a pipeline that exploits  multithreading  to improve efficiency. Data is obtained from paired-end RNA-Seq, mainly from  16S RNA  for taxonomy and mRNA for gene expression levels. The pipeline is divided in 4 major steps. Firstly, paired-end reads are filtered for quality control purposes, then sorted and filtered for taxonomic analysis (by removal of tRNA sequences) or functional analysis (by removal of both tRNA and rRNA reads). For the taxonomic analysis, sequences are mapped against 16S rRNA Greengenes v13.5 database using SOAP2, while for functional analysis sequences are mapped against a functional database such as MetaHIT-2014 always by using SOAP2 tool. This pipeline is highly flexible, since it offers the possibility to use third-party tools and improve single modules as long as the general structure is preserved. [ 20 ]


This pipeline is designed specifically for metatranscriptomics data analysis, by working in conjunction with the  MG-RAST  server for metagenomics. This pipeline is simple to use, requires low technical preparation and computational power and can be applied to a wide range of microbes. First, sequences from raw sequencing data are filtered for quality and then submitted to MG-RAST (which performs further steps such as quality control, gene calling, clustering of  amino acid  sequences and use of sBLAT on each cluster to detect the best matches). Matches are then aggregated for taxonomic and functional analysis purposes. [ 21 ]


This pipeline does not have an official name and is usually referred to using the first author of the article in which it is described. This algorithm foresees the implementation of alignment tools such as BLAST and MegaBLAST. Reads are clustered in groups of identical sequences and then processed for in-silico removal of  tRNA  and  rRNA  sequences. Remaining reads are then mapped to NCBI databases using BLAST and MegaBLAST, then classified by their bitscore. Sequences with higher bitscores are used to predict phylogenetic origin and function, and lower-score reads are aligned with the more sensitive BLASTX and eventually can be aligned in protein databases so that their function can be characterized. [ 12 ]


The  mOTUs2  profiler, [ 22 ]  which is based on essential  housekeeping genes , is demonstrably well-suited for quantification of basal transcriptional activity of microbial community members. [ citation needed ]  Depending on environmental conditions, the number of transcripts per cell varies for most genes. An exception to this are housekeeping genes that are expressed constitutively and with low variability under different conditions. [ citation needed ]  Thus, the abundance of transcripts from such genes strongly correlate with the abundance of active cells in a community.


Another method that can be exploited for metatranscriptomic purposes is  tiling microarrays . In particular, microarrays have been used to measure microbial transcription levels, to detect new transcripts and to obtain information about the structure of mRNAs (for instance, the UTR boundaries). Recently, it has also been used to find new regulatory ncRNA. However, microarrays are affected by some pitfalls:


RNA-Seq can overcome these limitations: it does not require any previous knowledge about the genomes that have to be analysed and it provides high throughput validation of genes prediction, structure, expression. Thus, by combining the two approaches it is possible to have a more complete representation of bacterial transcriptome. [ 1 ]


The  gut microbiome  has emerged in recent years as an important player in human health. Its prevalent functions are related to the fermentation of indigestible food components, competitions with pathogen, strengthening of the intestinal barrier, stimulation and regulation of the immune system. [ 23 ] [ 24 ] [ 25 ] [ 26 ] [ 27 ] [ 28 ] [ 29 ]
Although much has been learnt about the microbiome community in the last years, the wide diversity of microorganisms and molecules in the gut requires new tools to enable new discoveries. By focusing on changes in the expression of the genes, metatrascriptomics can generate a more dynamic picture of the state and activity of the microbiome than metagenomics. It has been observed that metatranscriptomic functional profiles are more variable than what might have been reckoned only by metagenomic information. This suggests that non-housekeeping genes are not stably expressed  in situ [ 30 ] [ 31 ]


One example of metatranscriptomic application is in the study of the gut microbiome in inflammatory bowel disease.  Inflammatory bowel disease  (IBD) is a group of chronic diseases of the digestive tract that affects millions of people worldwide. [ 32 ]
Several human genetic mutations have been linked to an increased susceptibility to IBD, but additional factors are needed for the full development of the disease.


Regarding the relationship between IBD and gut microbiome, it is known that there is a  dysbiosis  in patients with IBD but microbial taxonomic profiles can be highly different among patients, making it difficult to implicate specific microbial species or strains in disease onset and progression. In addition, the gut microbiome composition presents a high variability over time among people, with more pronounced variations in patient with IBD. [ 33 ] [ 34 ]
The functional potential of an organism, meaning the genes and pathways encoded in its genome, provides only indirect information about the level or extent of activation of such functions. So, the measurement of functional activity (gene expression) is critical to understand the mechanism of the gut microbiome dysbiosis.


Alterations in transcriptional activity in IBD, established on the rRNA expression, indicate that some bacterial populations are active in patients with IBD, while other groups are inactive or latent. [ 35 ]


A metatranscriptomics analysis measuring the functional activity of the gut microbiome reveals insights only partially observable in metagenomic functional potential, including disease-linked observations for IBD. It has been reported that many IBD-specific signals are either more pronounced or only detectable on the RNA level. [ 33 ]
These altered expression profiles are potentially the result of changes in the gut environment in patients with IBD, which include increased levels of inflammation, higher concentrations of oxygen and a diminished mucous layer. [ 36 ]
Metatranscriptomics has the advantage of allowing researchers to skip the assaying of biochemical products in situ (like mucus or oxygen) and enables evaluation of effects of environmental changes on microbial expression patterns in vivo for large human populations. In addition, it can be coupled with  longitudinal sampling  to associate modulation of activity with the disease progression. Indeed, it has been shown that while a particular path may remain stable over time at the genomic level, the corresponding expression varies with the disease severity. [ 33 ]  This suggests that microbial dysbiosis affect the gut health through changing in the transcriptional programmes in a stable community. In this way, metatranscriptomic profiling emerges as an important tool for understanding the mechanisms of that relationship.


Some technical limitations of the RNA measurements in stool are related to the fact that the extracted RNA can be degraded and, if not, it still represents only the organisms presents in the stool sample.


Examples of techniques applied:
Microarrays: allow the monitoring of changes in the expression levels of many genes in parallel for both host and pathogen. First microarray approaches have shown the first global analysis of gene expression changes in pathogens such as  Vibrio cholerae ,  Borrelia burgdorferi ,  Chlamydia trachomatis ,  Chlamydia pneumoniae  and  Salmonella enterica , revealing the strategies that are used by these microorganisms to adapt to the host.
In addition, microarrays only provide the first global insights about the host  innate immune response  to  PAMPs , as the effects of bacterial infection on the expression of various host factor.
Anyway, the detection through microarrays of both organisms at the same time could be problematic.
Problems:


Dual RNA-Seq: this technique allows the simultaneous study of both host and pathogen transcriptomes as well. It is possible to monitor the expression of genes at different time points of the infection process; in this way could it be possible to study the changes in cellular networks in both organisms starting from the initial contact until the manipulation of the host (interplay host-patogen).


Moreover, RNA-Seq is an important approach for identifying coregulated genes, enabling the organization of pathogen genomes into  operons . Indeed, genome annotation has been done for some eukaryotic pathogens, such as  Candida albicans ,  Trypanosoma brucei  and  Plasmodium falciparum .


Despite the increasing sensitivity and depth of sequencing now available, there are still few published RNA-Seq studies concerning the response of the mammalian host cell to the infection. [ 37 ] [ 38 ]


Methylated DNA immunoprecipitation  (MeDIP or mDIP) is a large-scale ( chromosome - or  genome -wide) purification technique in molecular biology that is used to enrich for  methylated DNA sequences .  It consists of isolating methylated DNA fragments via an  antibody  raised against  5-methylcytosine  (5mC).  This technique was first described by Weber M.  et al. [ 1 ]  in 2005 and has helped pave the way for viable  methylome -level assessment efforts, as the purified fraction of methylated DNA can be input to high-throughput DNA detection methods such as high-resolution  DNA microarrays  ( MeDIP-chip ) or  next-generation sequencing  (MeDIP-seq).  Nonetheless, understanding of the methylome remains rudimentary; its study is complicated by the fact that, like other  epigenetic  properties, patterns vary from cell-type to cell-type.


DNA methylation , referring to the reversible methylation of the 5 position of cytosine by  methyltransferases , is a major  epigenetic  modification in multicellular organisms. [ 2 ]   In mammals, this modification primarily occurs at  CpG sites , which in turn tend to cluster in regions called  CpG islands . [ 3 ]   There is a small fraction of CpG islands that can overlap or be in close proximity to promoter regions of transcription start sites.  The modification may also occur at other sites, [ 4 ]  but methylation at either of these sites can repress gene expression by either interfering with the binding of  transcription factors  or modifying  chromatin  structure to a repressive state. [ 5 ]


Disease condition studies have largely fueled the effort in understanding the role of DNA methylation.  Currently, the major research interest lies in investigating disease conditions such as  cancer  to identify regions of the DNA that has undergone extensive methylation changes.  The genes contained in these regions are of functional interest as they may offer a mechanistic explanation to the underlying genetic causes of a disease.  For instance, the abnormal methylation pattern of cancer cells [ 6 ] [ 7 ] [ 8 ]  was initially shown to be a mechanism through which  tumor suppressor -like genes are silenced, [ 9 ]  although it was later observed that a much broader range of gene types are affected. [ 10 ] [ 11 ] [ 12 ]


There are two approaches to methylation analysis: typing and profiling technologies.  Typing technologies are targeted towards a small number of loci across many samples, and involve the use of techniques such as  PCR ,  restriction enzymes , and  mass spectrometry .  Profiling technologies such as MeDIP are targeted towards a  genome - or  methylome -wide level assessment of methylation; this includes  restriction landmark genomic scanning  (RLGS), [ 13 ]  and  bisulfite conversion -based methods, which rely on the treatment of DNA with  bisulfite  to convert unmethylated  cytosine  residues to  uracil . [ 14 ] [ 15 ] [ 16 ] [ 17 ]


Other methods mapping and profiling the methylome have been effective but are not without their limitations that can affect resolution, level of throughput, or experimental variations.  For instance, RLGS is limited by the number of restriction sites in genome that can be targets for the restriction enzyme; typically, a maximum of ~4100 landmarks can be assessed. [ 18 ]   Bisulfite sequencing -based methods, despite possible single-nucleotide resolution, have a drawback: the conversion of unmethylated cytosine to uracil can be unstable. [ 19 ]   In addition, when bisulfite conversion is coupled with  DNA microarrays  to detect bisulfite converted sites, the reduced sequence complexity of DNA is a problem.  Microarrays capable of comprehensively profiling the whole-genome become difficult to design as fewer unique probes are available. [ 20 ]


The following sections outline the method of MeDIP coupled with either high-resolution array hybridization or high-throughput sequencing.  Each DNA detection method will also briefly describe post-laboratory processing and analysis.  Different post-processing of the raw data is required depending on the technology used to identify the methylated sequences.  This is analogous to data generated using  ChIP-chip  and  ChIP-seq .


Genomic DNA is extracted ( DNA extraction ) from the cells and purified.  The purified DNA is then subjected to sonication to shear it into random fragments.  This sonication process is quick, simple, and avoids  restriction enzyme  biases.  The resulting fragments range from 300 to 1000 base pairs (bp) in length, although they are typically between 400 and 600 bp. [ 21 ]   The short length of these fragments is important in obtaining adequate resolution, improving the efficiency of the downstream step in immunoprecipitation, and reducing fragment-length effects or biases.  Also, the size of the fragment affects the binding of 5-methyl-cytidine (5mC) antibody because the antibody needs more than just a single 5mC for efficient binding. [ 22 ]   To further improve binding affinity of the antibodies, the DNA fragments are denatured to produce single-stranded DNA.  Following denaturation, the DNA is incubated with  monoclonal  5mC antibodies.  The classical  immunoprecipitation  technique is then applied: magnetic beads conjugated to anti-mouse- IgG  are used to bind the anti-5mC antibodies, and unbound DNA is removed in the supernatant.  To purify the DNA,  proteinase K  is added to digest the antibodies and release the DNA, which can be collected and prepared for DNA detection.


For more details regarding the experimental steps see. [ 1 ] [ 19 ] [ 23 ] [ 24 ]


A fraction of the input DNA obtained after the sonication step above is labeled with  cyanine -5 (Cy5; red) deoxy-cytosine-triphosphate while the methylated DNA, enriched after the immunoprecipitation step, is labeled with  cyanine -3 (Cy3; green).  The labeled DNA samples are cohybridized on a 2-channel, high-density genomic microarray to probe for presence and relative quantities.  The purpose of this comparison is to identify sequences that show significant differences in hybridization levels, thereby confirming the sequence of interest is enriched.
Array-based identification of MeDIP sequences are limited to the array design.  As a result, the resolution is restricted to the probes in the array design.  There are additional standard steps required in signal processing to correct for hybridization issues such as noise, as is the case with most array technologies.


See  [ 23 ] [ 24 ] [ 25 ]  for more details.


The MeDIP-seq approach, i.e. the coupling of MeDIP with next generation, short-read sequencing technologies such as  454 pyrosequencing  or  Illumina  (Solexa), was first described by Down  et al.  in 2008. [ 20 ]   The high-throughput sequencing of the methylated DNA fragments produces a large number of short reads (36-50bp [ 26 ]  or 400 bp, [ 27 ]  depending on the technology).  The short reads are aligned to a reference genome using alignment software such as Mapping and Assembly with Quality ( Maq ), which uses a  Bayesian  approach, along with base and mapping qualities to model error probabilities for the alignments. [ 28 ]   The reads can then be extended to represent the ~400 to 700 bp fragments from the sonication step.  The coverage of these extended reads can be used to estimate the methylation level of the region.  A  genome browser  such as  Ensembl  can also be used to visualize the data.


Validation of the approach to assess quality and accuracy of the data can be done with  quantitative PCR .  This is done by comparing a sequence from the MeDIP sample against an unmethylated control sequence.  The samples are then run on a  gel  and the band intensities are compared. [ 19 ]   The relative intensity serves as the guide for finding enrichment.  The results can also be compared with MeDIP-chip results to help determine coverage needed.


The DNA methylation level estimations can be confounded by varying densities of methylated CpG sites across the genome when observing data generated by MeDIP.  This can be problematic for analyzing CpG-poor (lower density) regions.  One reason for this density issue is its effect on the efficiency of immunoprecipitation. In their study, Down  et al. [ 20 ]  developed a tool to estimate absolute methylation levels from data generated by MeDIP by modeling the density of methylated CpG sites.  This tool is called  Bayesian tool for methylation analysis (Batman) .  The study reports the coverage of ~90% of all CpG sites in promoters, gene-coding regions, islands, and regulatory elements where methylation levels can be estimated; this is almost 20 times better coverage than any previous methods.


Studies using MeDIP-seq or MeDIP-chip are both genome-wide approaches that have the common aim of obtaining the functional mapping of the methylome. Once regions of DNA methylation are identified, a number of bioinformatics analyses can be applied to answer certain biological questions.  One obvious step is to investigate genes contained in these regions and investigate the functional significance of their repression. For example, silencing of tumour-suppressor genes in cancer can be attributed to DNA methylation. [ 29 ]   By identifying mutational events leading to hypermethylation and subsequent repression of known tumour-suppressor genes, one can more specifically characterize the contributing factors to the cause of the disease.  Alternatively, one can identify genes that are known to be normally methylated but, as a result of some mutation event, is no longer silenced.


Also, one can try and investigate and identify whether some epigenetic regulator has been affected such as DNA methyltransferase (DNMT); [ 21 ]  in these cases, enrichment may be more limited.


Gene-set analysis (for example using tools like DAVID and GoSeq) has been shown to be severely biased when applied to high-throughput methylation data (e.g. MeDIP-seq and MeDIP-ChIP); it has been suggested that this can be corrected using sample label permutations or using a statistical model to control for differences in the numberes of CpG probes / CpG sites that target each gene. [ 30 ]


Limitations to take note when using MeDIP are typical experimental factors.  This includes the quality and cross-reactivity of 5mC antibodies used in the procedure.  Furthermore, DNA detection methods (i.e. array hybridization and high-throughput sequencing) typically involve well established limitations.  Particularly for array-based procedures, as mentioned above, sequences being analyzed are limited to the specific array design used.


Most typical limitations to high-throughput, next generation sequencing apply.  The problem of alignment accuracy to repetitive regions in the genome will result in less accurate analysis of methylation in those regions.  Also, as was mentioned above, short reads (e.g. 36-50bp from an  Illumina Genome Analyzer ) represent a part of a sheared fragment when aligned to the genome; therefore, the exact methylation site can fall anywhere within a window that is a function of the fragment size. [ 19 ]   In this respect, bisulfite sequencing has much higher resolution (down to a single CpG site; single nucleotide level).  However, this level of resolution may not be required for most applications, as the methylation status of CpG sites within < 1000 bp has been shown to be significantly correlated. [ 20 ]


The  Min System  is a mechanism composed of three proteins  MinC ,  MinD , and  MinE  used by  E. coli  as a means of properly localizing the  septum  prior to  cell division . Each component participates in generating a dynamic oscillation of  FtsZ  protein inhibition between the two bacterial poles to precisely specify the mid-zone of the cell, allowing the cell to accurately divide in two. This system is known to function in conjunction with a second negative regulatory system, the nucleoid occlusion system (NO), to ensure proper spatial and temporal regulation of chromosomal segregation and division.


The initial discovery of this family of proteins is attributed to Adler et al. (1967). First identified as  E. coli  mutants that could not produce a properly localized septum, resulting in the generation of  minicells [ 1 ] [ 2 ]
due to mislocalized cell division occurring near the bacterial poles. This caused miniature vesicles to pinch off, void of essential molecular constituents permitting it to exist as a viable bacterial cell. Minicells are achromosomal cells that are products of aberrant cell division, and contain  RNA  and protein, but little or no chromosomal  DNA . This finding led to the identification of three interacting proteins involved in a dynamic system of localizing the mid-zone of the cell for properly controlled cell division. [ citation needed ]


The Min proteins prevent the FtsZ ring from being placed anywhere but near the mid cell and are hypothesized to be involved in a spatial regulatory mechanism that links size increases prior to cell division to FtsZ polymerization in the middle of the cell. [ citation needed ]


One model of Z-ring formation permits its formation only after a certain spatial signal that tells the cell that it is big enough to divide. [ 3 ]
The MinCDE system prevents FtsZ polymerization near certain parts of the plasma membrane.  MinD  localizes to the membrane only at cell poles and contains an ATPase and an ATP-binding domain. MinD is only able to bind to the membrane when in its ATP-bound conformation. Once anchored, the protein polymerizes, resulting in clusters of MinD. These clusters bind and then activate another protein called  MinC , which has activity only when bound by MinD. [ 4 ]
MinC serves as a FtsZ inhibitor that prevents FtsZ polymerization. The high concentration of a FtsZ polymerization inhibitor at the poles prevents FtsZ from initiating division at anywhere but the mid-cell. [ 5 ]


MinE is involved in preventing the formation of MinCD complexes in the middle of the cell. MinE forms a ring near each cell pole. This ring is not like the Z-ring. Instead, it catalyzes the release of MinD from the membrane by activating MinD's ATPase. This hydrolyzes the MinD's bound ATP, preventing it from anchoring itself to the membrane.


MinE prevents the MinD/C complex from forming in the center but allows it to stay at the poles. Once the MinD/C complex is released, MinC becomes inactivated. This prevents MinC from deactivating FtsZ. As a consequence, this activity imparts regional specificity to Min localization. [ 6 ]
Thus, FtsZ can form only in the center, where the concentration of the inhibitor MinC is minimal. Mutations that prevent the formation of MinE rings result in the MinCD zone extending well beyond the polar zones, preventing FtsZ to polymerize and to perform cell division. [ 7 ]
MinD requires a nucleotide exchange step to re-bind to ATP so that it can re-associate with the membrane after MinE release. The time lapse results in a periodicity of Min association that may yield clues to a temporal signal linked to a spatial signal.
In vivo observations show that the oscillation of Min proteins between cell poles occurs approximately every 50 seconds. [ 8 ]
Oscillation of Min proteins, however, is not necessary for all bacterial cell division systems.  Bacillus subtilis  has been shown to have static concentrations of MinC and MinD at the cell poles. [ 9 ]
This system still links cell size to the ability to form a septum via FtsZ and divide.


The dynamic behavior of Min proteins has been reconstituted in vitro using artificial lipid bilayers, [ 10 ]  with varying lipid composition [ 11 ]  and different confinement geometry [ 12 ]  as mimics for the cell membrane.  The first pattern to be reconstituted were spiraling waves of MinD chased by MinE, [ 13 ]  followed by the reconstitution of waves of all three proteins, MinD, MinE and MinC. [ 14 ]  Importantly, MinD and MinE can self-organize into a wide variety of patterns depending on the reaction conditions. [ 15 ] [ 16 ]


Additional study is required to elucidate the extent of temporal and spatial signaling permissible by this biological function. These  in vitro  systems offered unprecedented access to features such as residence times and molecular motility.


Miniature Inverted-repeat Transposable Elements  (MITEs) are a group of non-autonomous Class II  transposable elements  ( DNA sequences ). Being non-autonomous, MITEs cannot code for their own transposase. They exist within the  genomes  of animals, plants, fungi, bacteria and even viruses. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ] [ 6 ]  MITEs are generally short (50 to 500 bp) elements with terminal inverted repeats (TIRs; 10–15 bp) and two flanking target site duplications (TSDs). Like other  transposons , MITEs are inserted predominantly in  gene -rich regions and this can be a reason that they affect gene expression and play important roles in accelerating eukaryotic evolution. [ 7 ] [ 8 ]  Their high copy number in spite of small sizes has been a topic of interest.


A detailed study of MITEs reveals that MITE subfamilies have arisen from related autonomous elements from a single genome and these subfamilies constitute the MITE families. One type of autonomous element can give rise to one or more MITE families. [ 9 ]


Based on their relations in sequences of TIRs with known TE superfamilies, MITEs have been classified into certain families. For example,  wTourist ,  Acrobat ,  Hearthealer  are MITE families in some plant species are under the TE superfamily  PIF/Harbinger .  Stowaway  is a MITE family in  Pisum sativum L.  with TSD TA in relation to  Tc1/mariner   TE superfamily. A group of MITEs known as CMITES related to  Piggybac  superfamily were found in certain coral species. [ 10 ]


While most of the MITEs are grouped, some of them are yet to be allotted their TE superfamilies. Such families include  AtATE  in  Arabidopsis thaliana  and  ATon  family found in  Aedes aegypti . Besides this, many more MITE families are likely to be discovered.


MITEs were first discovered in plants. Elements belonging to the  CACTA ,  hAT ,  Mutator ,  PIF , and  Tc1 / Mariner  superfamilies have been described. [ 11 ]  Depending upon the similarity of their terminal inverted repeats and target site duplications, most of the MITEs in plant genomes are divided into two major groups:  Tourist -like MITEs (derived from  PIF ) [ 12 ]  and  Stowaway -like MITEs (derived from  Tc1 / mariner ). [ 13 ] Stowaway  and  Tourist  elements  differ remarkably in their sequences. Still, they have been found to have significant structural similarities.


Stowaway  elements possess target site specificity, have small size and conserved terminal inverted repeat. So is the case determined in  Tourist  like MITEs. They can form stable DNA secondary structures which can be very useful in identifying them. A few  Stowaway  elements also contain cis-acting regulatory domains.


Other MITE superfamilies have also been described in plants, such as  hAT -type MITEs in banana [ 14 ]  and the nightshades. [ 15 ]


Based on the presence or absence of MITE family  Heartbreaker  ( Hbr ) in maize genome, a  molecular marker  was developed. These  Hbr  markers have been proved to be stable, uniformly distributed in maize genome. A study by Casa  et al . showed that HBr markers could be used along with other molecular markers to study genotype of related maize inbred lines. [ 16 ]


Software like FINDMITE use sequence entries of some average sized bp to identify MITE families. A MATLAB-based program called  detectMITE  can detect MITEs on a genome wide scale and was tested on the rice genome. [ 17 ]  Other tools like MUST and MITE-Hunter are also used for similar purposes. To characterize MITE families a toolkit has been developed called MITE Analysis Kit MAK by Yang and Hall. [ 18 ]


The  minimal genome  is a concept which can be defined as the set of genes sufficient for life to exist and propagate under nutrient-rich and stress-free conditions. Alternatively, it may be defined as the gene set supporting life on an  axenic  cell culture in rich media, and it is thought what makes up the minimal genome will depend on the environmental conditions that the organism inhabits. [ 1 ]


This minimal genome concept assumes that  genomes  can be reduced to a bare minimum, given that they contain many non-essential genes of limited or situational importance to the organism. Therefore, if a collection of all the  essential genes  were put together, a minimum genome could be created artificially in a stable environment. By adding more genes, the creation of an organism of desired characteristics is possible. The concept of minimal genome arose from the observations that many genes do not appear to be necessary for survival. [ 2 ] [ 3 ]


In order to create a new organism a scientist must determine the minimal set of genes required for  metabolism  and  replication . This can be achieved by experimental and computational analysis of the biochemical pathways needed to carry out basic metabolism and reproduction. [ 4 ]  A good model for a minimal genome is  Mycoplasma genitalium  due to its very small genome size. Most genes that are used by this organism are usually considered essential for survival; based on this concept a minimal set of 256 genes has been proposed. [ 5 ]


Scientifically, minimal genome projects allow the identification of the most essential genes, and the reduction of genetic complexity, making engineered strains more predictable. [ 6 ]  Industrially and agriculturally, they could be used to engineer plants to resist herbicides or harsh environments; bacteria to synthetically produce chemicals; or microbes to produce beneficial bio-products. [ 6 ]  Environmentally, they could be a source of clean energy or renewable chemicals, or help in carbon sequestration from the atmosphere. [ 6 ]


By one early investigation, the minimal genome of a  bacterium  should include a virtually complete set of proteins for replication and translation, a transcription apparatus including four subunits of  RNA polymerase  including the  sigma factor  rudimentary proteins sufficient for recombination and repair, several chaperone proteins, the capacity for anaerobic metabolism through  glycolysis  and  substrate-level phosphorylation , transamination of glutamyl-tRNA to glutaminyl-tRNA, lipid (but no fatty acid) biosynthesis, eight cofactor enzymes, protein export machinery, and a limited metabolite transport network including membrane ATPases. [ 7 ]  Proteins involved in the minimum bacterial genome tend to be substantially more related to proteins found in  archaea  and  eukaryotes  compared to the average gene in the bacterial genome more generally indicating a substantial number of universally (or near universally) conserved proteins. The minimal genomes reconstructed on the basis of existing genes does not preclude simpler systems in more primitive cells, such as an  RNA world  genome which does not have the need for DNA replication machinery, which is otherwise part of the minimal genome of current cells. [ 7 ]


The genes which most frequently survive gene loss include those involved in DNA replication, transcription, and translation, although a number of exceptions are known. For example, loss can be frequently seen in subunits of the DNA polymerase holoenzyme and some  DNA repair  genes. The majority of ribosomal proteins are retained (though some like RpmC are sometimes missing). In some cases, some tRNA synthetases are lost. Gene loss is also seen in genes for components in the cellular envelope, biosynthesis of biomolecules like purine, energy metabolism, and more. [ 8 ]


The minimal genome corresponds to small genome sizes, as bacterial genome size correlates with the number of protein-coding genes, typically one gene per kilobase. [ 1 ]   Mycoplasma genitalium , with a 580 kb genome and 482 protein-coding genes, is a key model for minimal genomes. [ 9 ]


The smallest known genome of a free-living bacterium is 1.3 Mb with ~1100 genes. [ 10 ]  However, significantly more reduced genomes are commonly observed in naturally occurring symbiotic and parasitic organisms. Genome reduction driven by  mutation  and  genetic drift  in small and asexual populations with biases for gene deletion can be seen in symbionts and parasites, which commonly experience rapid evolution, codon reassignments, biases for AT nucleotide compositions, and elevated levels of  protein misfolding  which results in a heavy dependence on  molecular chaperones  to ensure protein functionality. [ 1 ]  These effects, which coincide with the proliferation of mobile genetic elements, pseudogenes, genome rearrangements, and chromosomal deletion are best studied and observed in more recently evolved symbionts. [ 11 ] [ 12 ] [ 13 ]


The cause for this is that the symbiont or parasite can outsource a usual cellular function to another cell and so, in the absence of needing to carry out this function for itself, subsequently lose its own genes meant to perform this function. The most extreme examples of genome reduction have been found in maternally transmitted endosymbionts which have experienced lengthy coevolution with their hosts and, in the process, lost a substantial amount of their cellular autonomy. Beneficial symbionts have a greater capacity for genome reduction than do parasites, as host co-adaptation allows them to lose additional crucial genes. [ 14 ]  Another important distinction between genome reduction in parasites and genome reduction in endosymbionts is that parasites lose both the gene and its associated function, whereas endosymbionts often retain the function of the lost gene since that function is taken over by the host. [ 15 ]


For endosymbionts in some lineages, it is possible for the entire genome to be lost. For example, some  mitosomes  and  hydrogenosomes  (degenerate versions of the mitochondria known in some organisms) have experienced a total gene loss and have no remaining genes, whereas the human  mitochondria  still retains some of its genome. The extant genome in the human mitochondrial organelle is 16.6kb in length and contains 37 genes. [ 16 ]  Between organisms, the mitochondrial genome can code for between 3 and 67 proteins, with suggestions that the last eukaryotic common ancestor encoded a minimum of 70 genes in its genome. [ 17 ]  The smallest known mitochondrial genome is that of  Plasmodium falciparum , with a genome size of 6kb containing three protein-coding genes and a few rRNA genes. (On the other hand, the largest known mitochondrial genome is 490kb. [ 18 ] ) Genomes nearly as small can be found in related apicomplexans as well. [ 19 ]  On the other hands, the mitochondrial genomes of land plants have expanded to over 200kb with the largest one (at over 11Mb) exceeding the size of the genome of bacteria and even the simplest eukaryotes. [ 20 ]  Organelles known as  plastids  in plants (including  chloroplasts ,  chromoplasts , and  leucoplasts ), once free-living  cyanobacteria , typically retain longer genomes on the order of 100-200kb with 80-250 genes. [ 21 ]  In one analysis of 15 chloroplast genomes, the analyzed chloroplasts had between 60 and 200 genes. Across these chloroplasts, a total of 274 distinct protein-coding genes were identified, and only 44 of them were universally found in all sequenced chloroplast genomes. [ 22 ]  Examples of organisms which have experienced genome reduction include species of  Buchnera ,  Chlamydia ,  Treponema ,  Mycoplasma , and many others. Comparisons of multiple sequenced genomes of endosymbionts in multiple isolates of the same species and lineages have confirmed that even long-time symbionts are still experiencing ongoing gene loss and transfer to the nucleus. [ 15 ] [ 8 ]  Nuclear integrants of mitochondrial or plastid DNA have sometimes been termed "numts" and "nupts" respectively. [ 15 ]


A number of symbionts have now been discovered with genomes under 500 kb in length, the majority of them being bacterial symbionts of insects typically from the taxa  Pseudomonadota  and  Bacteroidota . [ 8 ]  The parasitic archaea  Nanoarchaeum equitans  has a genome 491 kb in length. [ 23 ]  In 2002, it was found that some species of the genus  Buchnera  have a reduced genome of only 450 kb in size. [ 24 ]  In 2021, the endosymbiont " Candidatus  Azoamicus ciliaticola" was found to have a genome 290 kb in length. [ 25 ]  The symbiont  Zinderia insecticola  was found to have a genome of 208 kb in 2010. [ 26 ]  In 2006, another endosymbiont  Carsonella ruddii  was found with a reduced genome 160 kb in length encompassing 182 protein-coding genes. [ 27 ]  Surprisingly, it was found that gene loss in  Carsonella  symbionts is an ongoing process. [ 28 ]  Other intermediate stages in gene loss have been observed in other reduced genomes, including the transition of some genes into pseudogenes as a result of accumulating mutations that are not selected against since the host carries out the needed purpose of that gene. [ 8 ]  The genome of  Candidatus  Hodgkinia cicadicola, a symbiont of cicadas, was found to be 144 kb. [ 29 ]  In 2011,  Tremblaya princeps  was found to contain an intracellular endosymbiont with a genome of 139 kb, reduced to the point that even some translation genes had been lost. [ 30 ]  In the smallest to date, a 2013 study found some bacterial symbionts of insects with even smaller genomes. Specifically, two leafhopper symbionts contained highly reduced genomes: while  Sulcia muelleri  had a genome of 190 kb,  Nasuia deltocephalinicola  had a genome of only 112 kb and contains 137 protein-coding genes. Combined, the genomes of these two symbionts can only synthesize ten amino acids, in addition to some of the machinery involved in DNA replication, transcription, and translation. The genes for ATP synthesis through oxidative phosphorylation have been lost, however. [ 31 ]


Viruses and virus-like particles have the smallest genomes in nature. For instance,  bacteriophage MS2  consists of only 3569 nucleotides (single-stranded RNA) and encodes just four proteins which overlap to make efficient use of the genome space. [ 32 ]   Similarly, among eukaryotic viruses,  porcine circoviruses  are among the smallest. [ 33 ]   They encode only 2–3  open reading frames .  Viroids  are circular molecules RNA which do not have any protein-coding genes at all, although the RNA molecule itself acts as a ribozyme to help enable its replication. The genome of a viroid is between 200 and 400 nucleotides in length. [ 34 ]


This concept arose as a result of a collaborative effort between  National Aeronautics and Space Administration  (NASA) and two scientists:  Harold Morowitz  and Mark Tourtellotte. In the 1960s, NASA was searching for extraterrestrial life forms, assuming that if they existed they may be simple creatures. To attract people's attention, Morowitz published about mycoplasmas as being the smallest and simplest self-replicating creatures. NASA and the two scientists grouped together and came up with the idea to assemble a living cell from the components of mycoplasmas. Mycoplasmas were selected as the best candidate for cell reassembly, since they are composed of a minimum set of organelles, such as a plasma membrane,  ribosomes  and a circular double stranded DNA. Morowitz' major idea was to define the entire machinery of mycoplasmas cell in molecular level. He announced that an international effort would help him accomplish this main objective.


By the 1980s, Richard Herrmann's laboratory had fully sequenced and genetically characterized the 800kb genome of  M. pneumoniae . Despite the small size of the genome, the process took three years. In 1995, another laboratory from Maryland the Institute for Genomic Research (TIGR) collaborated with the teams of Johns Hopkins and the University of North Carolina. This group chose to sequence the genome of  Mycoplasma genitalium , consisting of only 580 kb genome. This was completed in 6 months.


Sequencing M. genitalium revealed conserved genes crucial for defining essential life functions in a minimal self-replicating cell, making it a key candidate for the minimal genome project.


Finding a minimal set of essential genes is usually done by selective inactivation or deletions of genes and then testing the effect of each under a given set of conditions. The J. Craig Venter institute conducted these types of experiment on  M. genitalium  and found 382 essential genes.


The J.Craig Venter institute later started a project to create a synthetic organism named Mycoplasma laboratorium, using the minimal set genes identified from  M. genitalium . [ 9 ]


Reconstruction of a minimal genome is possible by using the knowledge of existing
genomes via which the sets of genes, essential for living can also be determined. Once
the set of essential genetic elements are known, one can proceed to define the key
pathways and core-players by modeling simulations and wet lab genome engineering. [ 3 ]


As of 1999, the two organisms upon which the ‘minimal gene set for cellular life' have been applied are:  Haemophilus influenzae , and  M. genitalium . A list of  orthologous  proteins were compiled in hope that it would contain protein necessary for cell survival, as orthologous analysis determines how two organisms evolved and shed away any non-essential genes. Since  H. influenza  and  M. genitalium  are  Gram negative  and  Gram positive bacteria  and due to their vast evolution it was expected that these organisms would be enriched genes that were of universal importance. However, 244
detected orthologs discovered contained no parasitism-specific proteins. The conclusion of this analysis was that similar biochemical functions might be performed by non-orthologous proteins. Even when biochemical pathways of these two organisms were
mapped, several pathways were present but many were incomplete. Proteins determined
to be common between the two organisms were non-orthologous to each other. [ 3 ]


Much of the research mainly focuses on the ancestral genome and less on the minimal
genome. Studies of these existing genomes have helped determine that orthologous gene
found in these two species are not necessarily essential for survival, in fact non-orthologous genes were found to be more important. Also, it was determined that in order for proteins to share same functions they do not need to have same sequence or common three dimensional folds. Distinguishing between orthologs and  paralogs  and detecting displacements of orthologs have been quiet beneficial in reconstructing evolution and determining the minimal gene set required for a cellular life. Instead, of conducting a strict orthology study, comparing groups of orthologs and occurrence in most  clades  instead of every species helped encounter genes lost or displaced. Only genomes that have been completely sequenced have enabled in studying orthologs among the group of organisms. Without a fully sequenced genome it would not be possible to determine the essential minimal gene set required for survival. [ 3 ]


J. Craig Venter Institute  (JCVI) conducted a study to find all the  essential genes  of  M. genitalium  through global  transposon  mutagenesis. As a result, they found that 382 out of 482 protein coding genes were essential. Genes encoding proteins of unknown function constitute 28% of the essential protein coding genes set. Before conducting this study the JCVI had performed another study on the non-essential genes, genes not required for growth, of  M.genitalium , where they reported the use of transposon  mutagenesis . Despite figuring out the non-essential genes, it is not confirmed that the products that these genes make have any important biological functions. It was only through gene essentiality studies of bacteria that JCVI has been able to compose a hypothetical minimal gene sets.


In JCVI's 1999 study among the two organisms,  M. genitalium  and  Mycoplasma pneumoniae  they mapped around 2,200 transposon insertion sites and identified 130 putative non-essentials genes in  M. genitalium  protein coding genes or  M. pneumoniae  orthologs of  M. genitalium  genes. In their experiment they grew a set of Tn4001 transformed cells for many weeks and isolated the genomic DNA from these mixture of  mutants .  Amplicons  were sequenced to detect the transposon insertion sites in mycoplasma genomes. Genes that contained the transposon insertions were hypothetical proteins or proteins considered non-essential.


Meanwhile, during this process some of the disruptive genes once considered non-essential, after more analyses turned out essential. The reason for this error could have been due to genes being tolerant to the transposon insertions and thus not being disrupted; cells may have contained two copies of the same gene; or gene product was supplied by more than one cell in those mixed pools of mutants. Insertion of transposon in a gene meant it was disturbed, hence non-essential, but because they did not confirm the absence of gene products they mistook all disruptive genes as non-essential genes.


The same study of 1999 was later expanded and the updated results were then published in 2005.


Some of the disruptive genes thought to be essential were isoleucyl and tyrosyl-tRNA synthetases (MG345 and MG455), DNA replication gene  dnaA  (MG469), and  DNA polymerase  III subunit a (MG261). The way they improved this study was by isolating and characterizing  M. genitalium  Tn4001 insertions in each colony one by one. The individual analyses of each colony showed more results and estimates of essential genes necessary for life.  The key improvement they made in this study was isolating and characterizing individual transposon mutants. Previously, they isolated many colonies containing a mixture of mutants. The filter cloning approach helped in separating the mixtures of mutants.


Now, they claim completely different sets of non-essential genes. The 130 non-essential genes claimed at first have now reduced to 67. Of the remaining 63 genes 26 genes were only disrupted in  M. pneumoniae  which means that some  M. genitalium  orthologs of non-essential  M. pneumoniae  genes were actually essential.


They have now fully identified almost all of the non-essential genes in  M. genitalium , the number of gene disruptions based on colonies analyzed reached a plateau as function and they claim a total of 100 non-essential genes out of the 482 protein coding genes in  M. genitalium .


The ultimate result of this project has now come down to constructing a synthetic organism,  Mycoplasma laboratorium  based on the 387 protein coding region and 43 structural RNA genes found in  M. genitalium . [ 35 ]  This project is currently still going on. [ needs update ]


Researchers at the JCVI in 2010 successfully created a synthetic bacterial cell that was capable of replicating itself. The team synthesized a 1.08 million base pair chromosome of a modified  Mycoplasma mycoides . The synthetic cell is called:  Mycoplasma mycoides  JCVI-syn1.0. The DNA was designed in a computer, synthesized, and transplanted into a cell from which the original genome had been removed. The original molecules and on-going reaction networks of the recipient cell then used the artificial DNA to generate daughter cells.  These daughter cells are of synthetic origin and capable of further replication, solely controlled by the synthetic genome. [ 36 ]


The first half of the project took 15 years to complete. The team designed an accurate, digitized genome of  M. mycoides . A total of 1,078 cassettes were built, each 1,080 base pairs long. These cassettes were designed in a way that the end of each DNA cassette overlapped by 80 base pairs. The whole assembled genome was transplanted in yeast cells and grown as yeast artificial chromosome. [ 36 ]


Based on JCVI's progress in the field of synthetic biology, it is possible that in near future scientists will be able to propagate  M. genitalium's  genome in the form of naked DNA, into recipient mycoplasmas cells and replace their original genome with a synthetic genome. Since, mycoplasmas have no cell wall, the transfer of a naked DNA into their cell is possible. The only requirement now is the technique to include the synthetic genome of  M. genitalium  into mycoplasma cells. To some extent this has become possible, the first replicating synthetic cell has already been developed by the JCVI and they are now on to creating their first synthetic life, consisting of minimal number of essential genes. This new breakthrough in synthetic biology will certainly bring in a new approach to understand biology; and this redesigning and prototyping genomes will later become beneficial to biotechnology companies, enabling them to produce synthetic microbes that produce new, cheaper and better bio-products. [ 9 ]


A number of projects have attempted to identify the  essential genes  of a species. This number should approximate the "minimal genome". For instance, the genome of  E. coli  has been reduced by about 30%, demonstrating that this species can live with much fewer genes than the wild-type genome contains. [ 37 ]


The following table contains a list of such minimal genome projects (including the various techniques used). [ 38 ]


The number of  essential genes  is different for each organism. In fact, each organism has a different number of essential genes, depending on which strain (or individual) is tested. In addition, the number depends on the conditions under which an organism is tested. In several bacteria (or other microbes such as yeast) all or most genes have been deleted individually to determine which genes are "essential" for survival. Such tests are usually carried out on rich media which contain all nutrients. However, if all nutrients are provided, genes required for the synthesis of nutrients are not "essential". When cells are grown on minimal media, many more genes are essential as they may be needed to synthesize such nutrients (e.g. vitamins). The numbers provided in the following table typically have been collected using rich media (but consult original references for details).


The number of essential genes were collected from the Database of Essential Genes (DEG), [ 50 ]  except for  B. subtilis , where the data comes from Genome News Network [ 51 ] [ 52 ]  The organisms listed in this table have been systematically tested for essential genes. For more information about minimal genome Please refer also to section  'Other Genera' at 'Mycoplasma laboratorium' .


Genome mining  describes the exploitation of genomic information for the discovery of biosynthetic pathways of natural products and their possible interactions. [ 1 ]  It depends on computational technology and  bioinformatics  tools. The mining process relies on a huge amount of data (represented by  DNA sequences  and annotations) accessible in genomic  databases . By applying data mining  algorithms ,  the data can be used to generate new knowledge in several areas of  medicinal chemistry , [ 2 ] [ 3 ]  such as discovering novel  natural products . [ 4 ]


In the mid- to late 1980s, researchers have increasingly focused on genetic studies with the advancing  sequencing technologies . [ 5 ]  The  GenBank database  was established in 1982 for the collection, management, storage, and distribution of DNA sequence data due to the increasing availability of DNA sequences. With the increasing number of genetic data, biotechnological companies have been able to use human DNA sequence to develop protein and antibody drugs through genome mining since 1992. [ 6 ]  In the late 1990s, many companies, such as Amgen, Immunec, Genentech were able to develop drugs that progressed to the clinical stage by adopting genome mining. [ 7 ]  Since the  Human Genome Project  was completed in the early 2000, researchers have been sequencing the genomes of many  microorganisms . [ 8 ]  Subsequently, many of these genomes have been carefully studied to identify new genes and biosynthetic pathways. [ 9 ]


As large quantities of genomic sequence data began to accumulate in public databases,  genetic algorithms  became important to decipher the enormous collection of genomic data. They are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection. [ 10 ]  The followings are commonly used genetic algorithms:


Genome mining applies on the discovery of natural product by facilitating the characterization of novel molecules and biosynthetic pathways. [ 4 ] [ 17 ]


The production of  natural products  is regulated by the biosynthetic  gene clusters  (BGCs) encoded in the microorganism. [ 18 ]  By adopting genome mining, the BGCs that produce the target natural product can be predicted. [ 19 ]  Some important enzymes responsible for the formation of natural products are  polyketide  synthases (PKS),  non-ribosomal peptide  synthases (NRPS),  ribosomally and post-translationally modified peptides  (RiPPs), and  terpenoids , and many more. [ 20 ]  Mining for enzymes, researchers can figure out the classes that BGCs encode and compare target gene clusters to known gene clusters. [ 21 ]  To verify the relation between the BGCs and natural products, the target BGCs can be expressed by suitable host through the use of  molecular cloning . [ 22 ]


Genetic data has been accumulated in databases. Researchers are able to utilize algorithms to decipher the data accessible from databases for the discovery of new processes, targets, and products. [ 10 ]  The following are databases and tools:


Molecular models of DNA structures  are representations of the  molecular geometry  and  topology  of deoxyribonucleic acid ( DNA ) molecules using one of several means, with the aim of simplifying and presenting the essential, physical and chemical, properties of DNA molecular structures either  in vivo  or  in vitro . These representations include closely packed spheres ( CPK  models) made of plastic, metal wires for  skeletal models , graphic computations and animations by computers, artistic rendering. Computer molecular models also allow animations and molecular dynamics simulations that are very important for understanding how DNA functions  in vivo .


The more advanced, computer-based  molecular models  of DNA involve  molecular dynamics  simulations and  quantum mechanics  computations of vibro-rotations, delocalized  molecular orbitals  (MOs),  electric dipole moments ,  hydrogen-bonding , and so on.  DNA molecular dynamics modeling  involves simulating deoxyribonucleic acid ( DNA )  molecular geometry  and  topology  changes with time as a result of both intra- and inter- molecular interactions of DNA. Whereas molecular models of DNA molecules such as closely packed spheres (CPK models) made of plastic or metal wires for  skeletal models  are useful representations of static DNA structures, their usefulness is very limited for representing complex DNA dynamics. Computer molecular modeling allows both animations and molecular dynamics simulations that are very important to understand how DNA functions  in vivo .


From the very early stages of structural studies of DNA by  X-ray diffraction  and biochemical means, molecular models such as the Watson-Crick  nucleic acid double helix  model were successfully employed to solve the 'puzzle' of DNA structure, and also find how the latter relates to its key functions in living cells. The first high quality X-ray diffraction patterns
of A-DNA were reported by  Rosalind Franklin  and  Raymond Gosling  in 1953. [ 1 ]  Rosalind Franklin made the critical observation that DNA exists in two distinct forms, A and B, and produced the sharpest pictures of both through X-ray diffraction technique. [ 2 ]  The first calculations of the Fourier transform of an atomic helix were reported one year earlier by Cochran, Crick and Vand, [ 3 ]  and were followed in 1953 by the computation of the Fourier transform of a coiled-coil by Crick. [ 4 ]


Structural information is generated from X-ray diffraction studies of oriented DNA fibers with the help of molecular models of DNA that are combined with crystallographic and mathematical analysis of the X-ray patterns.


The first reports of a double helix molecular model of B-DNA structure were made by James Watson and Francis Crick in 1953. [ 5 ] [ 6 ]  That same year, Maurice F. Wilkins,
A. Stokes and H.R. Wilson, reported the first X-ray patterns
of  in vivo  B-DNA in partially oriented salmon sperm heads. [ 7 ]


The development of the first correct double helix molecular model of DNA by Crick and Watson may not have been possible without the biochemical evidence for the nucleotide base-pairing ([A---T]; [C---G]), or  Chargaff's rules . [ 8 ] [ 9 ] [ 10 ] [ 11 ] [ 12 ] [ 13 ]  Although such initial studies of DNA structures with the help of molecular models were essentially static, their consequences for explaining the  in vivo  functions of DNA were significant in the areas of protein biosynthesis and the quasi-universality of the genetic code. Epigenetic transformation studies of DNA  in vivo  were however much slower to develop despite their importance for embryology, morphogenesis and cancer research. Such chemical dynamics and biochemical reactions of DNA are much more complex than the molecular dynamics of DNA physical interactions with water, ions and proteins/enzymes in living cells.


An old standing dynamic problem is how DNA "self-replication" takes place in living cells that should involve transient uncoiling of supercoiled DNA fibers. Although DNA consists of relatively rigid, very large elongated biopolymer molecules called  fibers  or chains (that are made of repeating  nucleotide  units of four basic types, attached to deoxyribose and phosphate groups), its molecular structure  in vivo  undergoes dynamic configuration changes that involve dynamically attached water molecules and ions. Supercoiling, packing with histones in chromosome structures, and other such supramolecular aspects also involve  in vivo   DNA topology  which is even more complex than DNA molecular geometry, thus turning molecular modeling of DNA into an especially challenging problem for both molecular biologists and biotechnologists. Like other large molecules and biopolymers, DNA often exists in multiple stable geometries (that is, it exhibits  conformational isomerism ) and configurational, quantum states which are close to each other in energy on the potential energy surface of the DNA molecule.


Such varying molecular geometries can also be computed, at least in principle, by employing  ab initio   quantum chemistry  methods that can attain high accuracy for small molecules, although claims that acceptable accuracy can be also achieved for polynuclelotides, and DNA conformations, were recently made on the basis of  vibrational circular dichroism  (VCD) spectral data. Such quantum geometries define an important class of  ab initio  molecular models of DNA which exploration has barely started, especially related to results obtained by VCD in solutions. More detailed comparisons with such  ab initio  quantum computations are in principle obtainable through 2D-FT NMR spectroscopy and relaxation studies of polynucleotide solutions or specifically labeled DNA, as for example with deuterium labels.


In an interesting twist of roles, the DNA molecule was proposed to be used for  quantum computing  via DNA. Both DNA nanostructures and  DNA computing  biochips have been built.


The chemical structure of DNA is insufficient to understand the complexity of the 3D structures of DNA. In contrast, animated molecular models allow one to visually explore the three-dimensional (3D) structure of DNA. The DNA model shown (far right) is a space-filling, or  CPK , model of the DNA double helix. Animated molecular models, such as the wire, or skeletal, type shown at the top of this article, allow one to visually explore the three-dimensional (3D) structure of DNA. Another type of DNA model is the space-filling, or CPK, model.


The hydrogen bonding dynamics and proton exchange is very different by many orders of magnitude between the two systems of fully hydrated DNA and water molecules in ice. Thus, the DNA dynamics is complex, involving nanosecond and several tens of picosecond time scales, whereas that of liquid ice is on the picosecond time scale, and that of proton exchange in ice is on the millisecond time scale. The proton exchange rates in DNA and attached proteins may vary from picosecond to nanosecond, minutes or years, depending on the exact locations of the exchanged protons in the large biopolymers.


A simple  harmonic oscillator  'vibration' is only an oversimplified dynamic representation of the longitudinal vibrations of the DNA intertwined helices which were found to be  anharmonic  rather than harmonic as often assumed in quantum  dynamic simulations  of DNA.


The structure of  DNA  shows a variety of forms, both double-stranded and single-stranded. The mechanical properties of DNA, which are directly related to its structure, are a significant problem for  cells . Every process which binds or reads  DNA  is able to use or modify the mechanical properties of DNA for purposes of recognition, packaging and modification. The extreme length (a  chromosome  may contain a 10 cm long DNA strand), relative rigidity and helical structure of DNA has led to the  evolution  of  histones  and of  enzymes  such as  topoisomerases  and  helicases  to manage a cell's DNA. The properties of DNA are closely related to its molecular structure and sequence, particularly the weakness of the hydrogen bonds and electronic interactions that hold strands of DNA together compared to the strength of the bonds within each strand.


Experimental methods which can directly measure the mechanical properties of DNA are relatively new, and high-resolution visualization in solution is often difficult. Nevertheless, scientists have uncovered large amount of data on the mechanical properties of this polymer, and the implications of DNA's mechanical properties on cellular processes is a topic of active current research.


The DNA found in many cells can be macroscopic in length: a few centimetres long for each human chromosome. Consequently, cells must compact or  package  DNA to carry it within them. In  eukaryotes  this is carried by spool-like  proteins  named  histones , around which DNA winds. It is the further compaction of this DNA-protein complex which produces the well known mitotic eukaryotic  chromosomes .


In the late 1970s, alternate  non-helical models of DNA structure  were briefly considered as a potential solution to problems in  DNA replication  in  plasmids  and  chromatin . However, the models were set aside in favor of the double-helical model due to subsequent experimental advances such as  X-ray crystallography  of DNA duplexes, and later the  nucleosome core particle , and the discovery of  topoisomerases . Such non-double-helical models are not currently accepted by the mainstream scientific community. [ 14 ] [ 15 ]


After DNA has been separated and purified by standard biochemical methods, one has a sample in a jar much like in the figure at the top of this article. Below are the main steps involved in generating structural information from X-ray diffraction studies of oriented DNA fibers that are drawn from the hydrated DNA sample with the help of molecular models of DNA that are combined with crystallographic and mathematical analysis of the X-ray patterns.


A  paracrystalline  lattice, or paracrystal, is a molecular or atomic lattice with significant amounts (e.g., larger than a few percent) of partial disordering of molecular arrangements. Limiting cases of the paracrystal model are  nanostructures , such as  glasses ,  liquids , etc., that may possess only local ordering and no global order. A simple example of a paracrystalline lattice is shown in the following figure for a silica glass:


Liquid crystals  also have paracrystalline rather than  crystalline  structures.


Highly hydrated B-DNA occurs naturally in living cells in such a paracrystalline state, which is a dynamic one despite the relatively rigid DNA double helix stabilized by parallel hydrogen bonds between the nucleotide base-pairs in the two complementary, helical DNA chains (see figures). For simplicity most DNA molecular models omit both water and ions dynamically bound to B-DNA, and are thus less useful for understanding the dynamic behaviors of B-DNA  in vivo . The physical and mathematical analysis of X-ray [ 16 ] [ 17 ]  and spectroscopic data for paracrystalline B-DNA is thus far more complex than that of crystalline, A-DNA X-ray diffraction patterns. The paracrystal model is also important for DNA technological applications such as  DNA nanotechnology . Novel methods that combine X-ray diffraction of DNA with X-ray microscopy in hydrated living cells are now also being developed. [ 18 ]


There are various uses of DNA molecular modeling in Genomics and Biotechnology research applications, from DNA repair to PCR and  DNA nanostructures . Two-dimensional DNA junction arrays have been visualized by  Atomic force microscopy . [ 19 ]


DNA molecular modeling has various uses in  genomics  and  biotechnology , with research applications ranging from DNA repair to PCR and DNA nanostructures. These include computer molecular models of molecules as varied as RNA polymerase, an E. coli, bacterial DNA primase template suggesting very complex dynamics at the interfaces between the enzymes and the DNA template, and molecular models of the mutagenic, chemical interaction of potent carcinogen molecules with DNA. These are all represented in the gallery below.


Technological application include a DNA biochip and DNA nanostructures designed for  DNA computing  and other dynamic applications of  DNA nanotechnology . [ 20 ] [ 21 ] [ 22 ] [ 23 ] [ 24 ] [ 25 ]
The image at right is of self-assembled DNA nanostructures. The DNA "tile" structure in this image consists of four branched junctions oriented at 90° angles. Each tile consists of nine DNA oligonucleotides as shown; such tiles serve as the primary "building block" for the assembly of the DNA nanogrids shown in the AFM micrograph.


Quadruplex DNA may be involved in certain cancers. [ 26 ] [ 27 ]  Images of quadruplex DNA are in the gallery below.


Monopartite  refers to the class of genome that is presented in the genome of the  virus . As opposed to  multipartite , viruses composed of monopartite genomes have a single molecule of nucleic acid. [ 1 ]  Most  dsDNA  viruses are monopartite.


This  virus -related article is a  stub . You can help Wikipedia by  expanding it .


Multipartite  is a class of  virus  that have segmented nucleic acid genomes, with each segment of the genome enclosed in a separate viral particle. Only a few  ssDNA  viruses have multipartite genomes, but a many more RNA viruses have multipartite genomes. [ 1 ]  An advantage of multipartite genome is its ability to synthesize multiple  mRNA  strands to avoid the cellular constraint of  monocistronicity . Until recently, it was not known how multipartite viruses could efficiently infect a single cell with all the segments that comprise their genome simultaneously, which was thought to be necessary for replication. It has since been shown that the segments typically do not infect the same cell. Rather, segments accumulate in different cells and the viral system functions through exchange of material between cells. [ 2 ]  Thus, multipartite viruses are not localized in space but rather more like a distributed network of chemical reactions. In this sense, they are an even further departure from other organisms than monopartite viruses. Multipartite viruses represent 35-40% of the viral genera and families that have been described in plants and fungi, but otherwise appear to be rare. [ 2 ]


The  mutanome  is the entirety of somatic  cancer   mutations  in an individual tumor. [ 1 ]


Carcinogenesis  is largely driven by changes in the  DNA  sequence of the  genomes  of cancer cells. [ 1 ] [ 2 ]  This process leads to a unique repertoire of mutations (′the mutanome′) in every patient's tumor. [ 3 ] [ 4 ]  The mutanome encodes peptides that can be targets for  T cells , which play a central role in the immune response. [ 5 ]


A description of individual mutanomes of human tumors has been made feasible by the introduction of  Next Generation Sequencing  Technology (NGS). [ 6 ]  Cancer mutanomes can be defined by comparing  exome  sequencing data obtained by NGS of individual healthy tissue with sequences from tumor-derived  nucleic acids . [ 7 ]  As the vast majority of cancer-associated mutations are patient specific, shared mutations are rare, even within the same type of cancer. [ 7 ] [ 8 ] [ 9 ]


The mutanome encodes a pattern of tumor-specific mutated peptides referred to as  neoantigens  or m-peptides. Neoantigens are products of mutations that first occur in the course of cancer development. Each and every tumor has its own unique neoantigenic pattern, and even within the same type of cancer, only a small percentage of neoantigens are shared. [ 5 ] [ 10 ]


Neoantigens are presented on  major histocompatibility complex  (MHC) molecules of tumor cells. [ 8 ] [ 10 ] [ 11 ]  The antigenic determinants of neoantigens –  neoepitopes  – are recognized by the immune system as a target for T cells, thus triggering immune responses against cancer. [ 1 ] [ 12 ]


A neoepitope is an epitope the immune system has not encountered before. Therefore, it is not subject to tolerance mechanisms of the immune system. [ 13 ]  Since the mutated gene product is only expressed in tumors, but not on healthy cells, neoepitopes may evoke vigorous T cell response. [ 6 ]


The vast majority of cancer mutations are unique to the individual patient, [ 9 ]  and a significant portion of mutations (21-45%) are immunogenic. [ 1 ]  Therefore, the patients' individual mutanome and the neoepitopes are used as the basis for a novel strategy against cancer, which is referred to as  individualized cancer immunotherapy . [ 14 ]   Cancer immunotherapy  uses the body's own immune defense system and is based on the recognition by cytotoxic and helper T cells with antitumor activity. [ 10 ]


Strategies to immunotherapeutically address the individual mutanome are currently under investigation. [ 8 ] [ 11 ] [ 14 ] [ 15 ]  Ongoing mutanome  cancer vaccine  trials use synthetic peptides and antigen-encoding  DNA  or  RNA  as formats. [ 7 ]  For an individualized treatment, neoepitopes that are thought to elicit a strong immune response are selected out of the patient's mutanome. [ 14 ] [ 16 ]


An increasingly explored concept for individualized cancer immunotherapy is the treatment of a patient with immunogenic  mRNA vaccines  for a given patient's individual cancer mutanome (IVAC - Individualized Vaccine Against Cancer). [ 14 ] [ 16 ]  The IVAC concept is based on decoding the individual mutanome by NGS and on-demand mRNA manufacturing for use in single patients to produce therapeutic vaccines against cancer. [ 8 ] [ 14 ] [ 15 ]


Since 2016, the IVAC concept has been developed as  Individualized Neoantigen Specific Therapy (iNeST) .


The process of NSG based cancer mutanome mapping, target selection, prioritization approaches, synthetic mRNA vaccine manufacturing and delivery is also referred to as  MERIT  (mutanome engineered RNA immunotherapy). [ 8 ] [ 17 ]  By vaccination with the synthetic mRNA, which contains the blueprint of a mutant tumor peptide,  T lymphocytes  are activated against the tumor. [ 7 ] [ 17 ]


In  computational biology ,  N50  and  L50  are statistics of a set of  contig  or  scaffold  lengths.  The  N50  is similar to a  mean  or  median  of lengths, but has greater weight given to the longer contigs.  It is used widely in  genome assembly , especially in reference to contig lengths within a draft assembly.  There are also the related  U50 ,  UL50 ,  UG50 ,  UG50% ,  N90 ,  NG50 , and  D50  statistics.


To provide a better assessment of assembly output for viral and microbial datasets, a new metric called  U50  should be used. The  U50  identifies unique, target-specific contigs by using a reference genome as baseline, aiming at circumventing some limitations that are inherent to the  N50  metric. The use of the  U50  metric allows for a more accurate measure of assembly performance by analyzing only the unique, non-overlapping contigs. Most viral and microbial sequencing have high background noise (i.e., host and other non-targets), which contributes to having a skewed, misrepresented  N50  value - this is corrected by  U50 . [ 1 ]