Most aspects of human biology involve both genetic (inherited) and non-genetic (environmental) factors. Some inherited variation influences aspects of our biology that are not medical in nature (height, eye color, ability to taste or smell certain compounds, etc.). Moreover, some genetic disorders only cause disease in combination with the appropriate environmental factors (such as diet). With these caveats, genetic disorders may be described as clinically defined diseases caused by genomic DNA sequence variation. In the most straightforward cases, the disorder can be associated with variation in a single gene. For example,  cystic fibrosis  is caused by mutations in the CFTR gene and is the most common recessive disorder in caucasian populations with over 1,300 different mutations known. [ 100 ]


Disease-causing mutations in specific genes are usually severe in terms of gene function and are rare, thus genetic disorders are similarly individually rare. However, since there are many genes that can vary to cause genetic disorders, in aggregate they constitute a significant component of known medical conditions, especially in pediatric medicine. Molecularly characterized genetic disorders are those for which the underlying causal gene has been identified. Currently there are approximately 2,200 such disorders annotated in the  OMIM  database. [ 100 ]


Studies of genetic disorders are often performed by means of family-based studies. In some instances, population based approaches are employed, particularly in the case of so-called founder populations such as those in Finland, French-Canada, Utah, Sardinia, etc. Diagnosis and treatment of genetic disorders are usually performed by a  geneticist -physician trained in clinical/medical genetics. The results of the  Human Genome Project  are likely to provide increased availability of  genetic testing  for gene-related disorders, and eventually improved treatment. Parents can be screened for hereditary conditions and  counselled  on the consequences, the probability of inheritance, and how to avoid or ameliorate it in their offspring.


There are many different kinds of DNA sequence variation, ranging from complete extra or missing chromosomes down to single nucleotide changes. It is generally presumed that much naturally occurring genetic variation in human populations is phenotypically neutral, i.e., has little or no detectable effect on the physiology of the individual (although there may be fractional differences in fitness defined over evolutionary time frames). Genetic disorders can be caused by any or all known types of sequence variation. To molecularly characterize a new genetic disorder, it is necessary to establish a causal link between a particular genomic sequence variant and the clinical disease under investigation. Such studies constitute the realm of human molecular genetics.


With the advent of the Human Genome and  International HapMap Project , it has become feasible to explore subtle genetic influences on many common disease conditions such as diabetes, asthma, migraine, schizophrenia, etc. Although some causal links have been made between genomic sequence variants in particular genes and some of these diseases, often with much publicity in the general media, these are usually not considered to be genetic disorders  per se  as their causes are complex, involving many different genetic and environmental factors. Thus there may be disagreement in particular cases whether a specific medical condition should be termed a genetic disorder.


Additional genetic disorders of mention are  Kallman syndrome  and  Pfeiffer syndrome  (gene FGFR1),  Fuchs corneal dystrophy  (gene TCF4),  Hirschsprung's disease  (genes RET and FECH), Bardet-Biedl syndrome 1 (genes CCDC28B and BBS1), Bardet-Biedl syndrome 10 (gene BBS10), and facioscapulohumeral muscular dystrophy type 2 (genes D4Z4 and SMCHD1). [ 101 ]


Genome sequencing is now able to narrow the genome down to specific locations to more accurately find mutations that will result in a genetic disorder.  Copy number variants  (CNVs) and  single nucleotide variants  (SNVs) are also able to be detected at the same time as genome sequencing with newer sequencing procedures available, called Next Generation Sequencing (NGS). [ 102 ]  This only analyzes a small portion of the genome, around 1–2%. The results of this sequencing can be used for clinical diagnosis of a genetic condition, including  Usher syndrome , retinal disease, hearing impairments, diabetes, epilepsy,  Leigh disease , hereditary cancers, neuromuscular diseases, primary immunodeficiencies,  severe combined immunodeficiency  (SCID), and diseases of the mitochondria. [ 103 ]  NGS can also be used to identify carriers of diseases before conception. The diseases that can be detected in this sequencing include  Tay-Sachs disease ,  Bloom syndrome ,  Gaucher disease ,  Canavan disease ,  familial dysautonomia , cystic fibrosis,  spinal muscular atrophy , and  fragile-X syndrome . The Next Genome Sequencing can be narrowed down to specifically look for diseases more prevalent in certain ethnic populations. [ 104 ]


1:15000 in American Caucasians


1:176 in Mennonite/Amish communities


1:250000 in Austria


1:28000 in Norway


1:12500 in Germany


Comparative genomics  studies of mammalian genomes suggest that approximately 5% of the human genome has been conserved by evolution since the divergence of extant lineages approximately 200 million years ago, containing the vast majority of genes. [ 105 ] [ 106 ]  The published  chimpanzee  genome differs from that of the human genome by 1.23% in direct sequence comparisons. [ 107 ]  Around 20% of this figure is accounted for by variation within each species, leaving only ~1.06% consistent sequence divergence between humans and chimps at shared genes. [ 108 ]  This nucleotide by nucleotide difference is dwarfed, however, by the portion of each genome that is not shared, including around 6% of functional genes that are unique to either humans or chimps. [ 109 ]


In other words, the considerable observable differences between humans and chimps may be due as much or more to genome level variation in the number, function and expression of genes rather than DNA sequence changes in shared genes. Indeed, even within humans, there has been found to be a previously unappreciated amount of copy number variation (CNV) which can make up as much as 5–15% of the human genome. In other words, between humans, there could be +/- 500,000,000 base pairs of DNA, some being active genes, others inactivated, or active at different levels. The full significance of this finding remains to be seen. On average, a typical human protein-coding gene differs from its chimpanzee  ortholog  by only two  amino acid  substitutions; nearly one third of human genes have exactly the same protein translation as their chimpanzee orthologs. A major difference between the two genomes is human  chromosome 2 , which is equivalent to a fusion product of chimpanzee chromosomes 12 and 13. [ 110 ]  (later renamed to chromosomes 2A and 2B, respectively).


Humans have undergone an extraordinary loss of  olfactory receptor  genes during our recent evolution, which explains our relatively crude sense of  smell  compared to most other mammals. Evolutionary evidence suggests that the emergence of  color vision  in humans and several other  primate  species has diminished the need for the sense of smell. [ 111 ]


In September 2016, scientists reported that, based on human DNA genetic studies, all  non-Africans in the world today  can be traced to a  single population  that  exited Africa  between 50,000 and 80,000 years ago. [ 112 ]


The human  mitochondrial DNA  is of tremendous interest to geneticists, since it undoubtedly plays a role in  mitochondrial disease . It also sheds light on human evolution; for example, analysis of variation in the human mitochondrial genome has led to the postulation of a recent common ancestor for all humans on the maternal line of descent (see  Mitochondrial Eve ).


Due to the damage induced by the exposure to Reactive Oxygen Species mitochondrial DNA (mtDNA) has a more rapid rate of variation than nuclear DNA. This 20-fold higher mutation rate allows mtDNA to be used for more accurate tracing of maternal ancestry. [ citation needed ]  Studies of mtDNA in populations have allowed ancient migration paths to be traced, such as the migration of  Native Americans  from  Siberia [ 113 ]  or  Polynesians  from southeastern  Asia . [ citation needed ]  It has also been used to show that there is no trace of  Neanderthal  DNA in the European gene mixture inherited through purely maternal lineage. [ 114 ]  Due to the restrictive all or none manner of mtDNA inheritance, this result (no trace of Neanderthal mtDNA) would be likely unless there were a large percentage of Neanderthal ancestry, or there was strong positive selection for that mtDNA. For example, going back 5 generations, only 1 of a person's 32 ancestors contributed to that person's mtDNA, so if one of these 32 was pure Neanderthal an expected ~3% of that person's autosomal DNA would be of Neanderthal origin, yet they would have a ~97% chance of having no trace of Neanderthal mtDNA. [ citation needed ]


Epigenetics describes a variety of features of the human genome that transcend its primary DNA sequence, such as  chromatin  packaging,  histone  modifications and  DNA methylation , and which are important in regulating gene expression, genome replication and other cellular processes. Epigenetic markers strengthen and weaken transcription of certain genes but do not affect the actual sequence of DNA nucleotides. DNA methylation is a major form of epigenetic control over gene expression and one of the most highly studied topics in epigenetics. During development, the human DNA methylation profile experiences dramatic changes. In early germ line cells, the genome has very low methylation levels. These low levels generally describe active genes. As development progresses, parental imprinting tags lead to increased methylation activity. [ 115 ] [ 116 ]


Epigenetic patterns can be identified between tissues within an individual as well as between individuals themselves. Identical genes that have differences only in their epigenetic state are called  epialleles . Epialleles can be placed into three categories: those directly determined by an individual's genotype, those influenced by genotype, and those entirely independent of genotype. The epigenome is also influenced significantly by environmental factors. Diet, toxins, and hormones impact the epigenetic state. Studies in dietary manipulation have demonstrated that methyl-deficient diets are associated with hypomethylation of the epigenome. Such studies establish epigenetics as an important interface between the environment and the genome. [ 117 ]


Human Heredity and Health in Africa , or  H3Africa , is an initiative to study the  genomics  and  medical genetics  of  African people . Its goals are to build the continent's research infrastructure, train researchers and clinicians, and to study questions of scientific and medical interest to Africans. [ 1 ]  The H3Africa Consortium was formally launched in 2012 in  Addis Ababa  and has grown to include research projects across 32 countries, a pan-contintental bioinformatics network, and the first  whole genome sequencing  of many African  ethnolinguistic groups . [ 2 ] [ 3 ]


The H3Africa initiative was conceived to address inequalities in global health and genetic research. Though significant progress had been made in genomics, African scientists were typically not involved in collaborations beyond sample collection, and very few medical genetics studies were carried out on African populations despite their considerable genetic variation. One of the goals of the consortium became to train and retain African scientists and to develop genomic infrastructure of the continent in support of such studies. [ 1 ]  The policy framework for the initiative was centred around fairness in genomics and avoiding exploitation while building the continent's research capacity. This led to a strong emphasis on African leadership and giving African researchers preferential access to resources like funding, samples, and data. [ 4 ]


During a meeting in Cairo in 2007, the  African Society of Human Genetics  (AfSHG) membership agreed to spearhead an African Genome Project (AGP). [ 5 ]  The AGP would have four components:  population genetics ,  medical genetics , training, and infrastructure. Among its goals, it would sample at least 100 ethnic groups from the continent, develop large-scale resource to study  gene-environment interplay  of diseases in Africa, train African scientists, and establish laboratories and local research capacity. [ 5 ]  At the 2009 AfSHG meeting in  Yaoundé ,  Cameroon , the concept was renamed Human Heredity and Health in Africa" to reflect its goals and scope. [ 5 ]  The AfSHG, along with the United States'  National Institutes of Health  (NIH), and the United Kingdom's  Wellcome Trust  convened to create a research agenda, [ 6 ]  and announced initial funding for H3Africa in  London ,  England , in 2010. [ 5 ]  In 2011, researchers released the H3Africa  white paper , [ 7 ]  and the first H3Africa investigators met in 2012 in  Addis Ababa ,  Ethiopia . [ 5 ]


Since its inception, members of the H3Africa Consortium have started several major projects and developed  H3ABioNet , a pan-continental bioinformatics network. [ 2 ]  Studies have focused on topics including  cardiovascular disease ,  schizophrenia , and communicable diseases such as  trypanosomiasis  and  HIV . Several  genetic loci  related to  stroke  have been discovered, and the H3Africa  array  was developed to better suit  single nucleotide polymorphisms  (SNPs) that were common in African populations and act as a base for  genome-wide association studies . [ 8 ]  Variation not previously captured by public databases has also been observed. [ 9 ]


To study cardiovascular disease, the group developed the Cardiovascular H3Africa Innovation Resource (CHAIR), which combined six different projects across 15 countries with the goal of eventually recruiting 55,000 study participants. [ 10 ]


The Consortium published a major study in 2020 reporting on the  whole genome sequencing  of 426 individuals from 50  ethnolinguistic  groups across Africa, discovering numerous areas of the human genome under strong  selection  involved in immune response and DNA repair and metabolism. [ 11 ]  The study was a major milestone in genomics, identifying over three million new genetic variants. [ 12 ]


The  Human Pangenome Reference  is a collection of genomes from a diverse cohort of individuals compiled by the Human Pangenome Reference Consortium (HPRC).
This first draft  pangenome  comprises 47 phased, diploid assemblies from a diverse cohort of individuals and was intended to capture the genetic diversity of the human population. The development of this pangenome seeks to address perceived shortcomings in the current human reference genome by offering a more comprehensive and inclusive resource for genomic research and analysis. [ 1 ]


The  pangenome  concept, originating from the study of  prokaryotes , has been extended to  multicellular   eukaryotic   organisms , including humans. The human pangenome has significant implications for  population genetics ,  phylogenetics , and public health policy, as it can inform the genetic basis of diseases and personalized treatments by providing insights into the genetic diversity of human populations. [ 2 ]


The new human pangenome reference integrates the missing 8% of the  human genome  sequence, adding over 100 million new bases. It aims to capture more population diversity than the previous reference sequence and is based on 94 high-quality haploid assemblies from individuals with broad genetic diversity. The generation of this reference genome focuses on eliminating gaps, incorporating complex genomic sequence features, and encompassing a broader spectrum of human genome diversity. [ 3 ]


The human reference genome, initially drafted over 20 years ago, is a composite of merged  haplotypes  from more than 20 individuals, with a single individual contributing to approximately 70% of the sequence. However, it has limitations, including biases and errors, and, as would be the case for any linear human genome reference sequence, can not fully represent the global human genomic variation. The majority of genomic research  has focused on individuals of European descent which leads to a bias in available datasets for analysis. Consequently, precision medicine primarily relies on genomic variations found within populations of European ancestry. This limited scope overlooks a significant portion of global genetic diversity crucial for understanding clinical phenotypes. [ 4 ]  To overcome this, the  Human Pangenome Reference Consortium (HPRC) has been working on creating a more complete human reference genome with a graph-based, telomere-to-telomere representation of global genomic diversity integrating genome sequences from a diverse array of individuals. Its primary objectives include enhancing gene-disease association studies across populations and serving as an extensive genetic resource for future biomedical research and precision medicine endeavors. [ 1 ] [ 4 ]


The  Pangenome Reference Consortium  has developed a draft human pangenome reference, which includes 47 phased, diploid assemblies from a genetically diverse cohort of individuals. The HPRC samples were sequenced using Pacific Biosciences (PacBio) high-fidelity (HiFi) and Oxford Nanopore Technologies (ONT) long-read sequencing, Bionano optical maps and high-coverage Hi-C Illumina short-read sequencing. [ 1 ]


These assemblies are reported to cover more than 99% of the expected sequence in each genome and exhibit an accuracy of over 99% at both the structural and base pair levels. The pangenome captures known variants and  haplotypes , reveals new  alleles  at structurally complex loci, and adds 119 million base pairs of  euchromatic  polymorphic sequence and 1,115 gene duplications relative to the existing reference  GRCh38, with roughly 90 million of the additional base pairs derived from structural variation. Using this draft pangenome for analyzing short-read data has shown a 34% reduction in small variant discovery errors and a 104% increase in the detection of  structural variants  per  haplotype  compared to GRCh38-based workflows. [ 1 ]


The PRC's efforts are part of a broader initiative to sequence and assemble  genomes  from individuals across diverse populations, with the goal of better representing the genomic landscape of human diversity. The consortium aims to increase the number of genome sequences to 350 by mid-2024, providing a more complete and inclusive resource for genomic research and analysis. [ 1 ] The development of the human pangenome reference marks a notable advancement in genomics, as it offers a more accurate and diverse depiction of global genomic variation. This development is expected to enhance gene-disease association studies across populations, broaden the scope of genomics research to encompass the most repetitive and polymorphic regions of the genome, and serve as a valuable genetic resource for future studies. [ 1 ]


HPRC sample subpopulations includes ACB, African Caribbean in  Barbados ; ASW, African Ancestry in Southwest US; CHS, Han Chinese South; CLM, Colombian in Medellin,  Colombia ; ESN, Esan in  Nigeria ; GWD, Gambian in Western Division; KHV, Kinh in Ho Chi Minh City,  Vietnam ; MKK, Maasai in Kinyawa,  Kenya ; MSL, Mende in  Sierra Leone ; PEL, Peruvian in Lima,  Peru ; PJL, Punjabi in Lahore,  Pakistan ; PUR, Puerto Rican in  Puerto Rico ; YRI, Yoruba in Ibadan, Nigeria. [ 1 ]  The human pangenome reference is more comprehensive than previous reference sequences. It incorporates over 100 million new bases from 47 people with diverse ancestries, capturing more population diversity than previous references. [ 5 ] [ 3 ]


The pangenome reference includes 47 fully phased  diploid  genomes. Among these, 29 genomes were entirely generated by HPRC, while the remaining 18 were produced by other efforts. [ 1 ]


These sequencing technologies were used to collect information:  Pacific Biosciences  (PacBio) high-fidelity (HiFi) with 39.7× HiFi sequence depth of coverage,  Oxford Nanopore Technologies  (ONT)  long-read sequencing , and Bionano optical maps and high-coverage Hi-C  Illumina  short-read sequencing. To analyze the 18 additional samples, they employed the nanopore unsheared long-read sequencing protocol, resulting in approximately 60× coverage of unsheared sequencing data. [ 1 ]


The Trio-Hifiasm. [ 6 ] [ 7 ]  tool was selected as the primary  assembler  following thorough benchmarking of multiple alternatives. Trio-Hifiasm leverages PacBio HiFi long-read sequences and parental Illumina short-read sequences to generate highly phased contig assemblies. [ 1 ]


Three different tools were used to construct the pangenome graph:


An application of note is pangenome-based short variant discovery, involving the alignment of short reads to a pangenome graph to enhance the accuracy of calling small variants like  SNPs  and  indels . This method should exhibit improved performance compared to traditional approaches, particularly in regions of complexity and genes of medical relevance. Furthermore, the pangenome purportedly aids in variant calling in parent-child trios, potentially enhancing accuracy in this context. [ 1 ]


Another key application lies in  SV   genotyping , where the sequence-resolved  structural variants  (SVs) within the pangenome enable the identification and  genotyping  of diverse  SV  alleles. [ 1 ]


Improvements in  VNTR (Variable Number Tandem Repeat)  regions mapping,  RNA sequencing  mapping,  chromatin immunoprecipitation  and  sequencing  analysis were also reported.  [ citation needed ] In summary, the pangenome is regarded as a resource with potential for enhancing variant discovery,  population genetics  analyses, and the detection of complex genetic events that may not be identified by conventional reference  genomes . [ 1 ]


Most of the current tools developed are compatible with  GRCh38 , the human reference genome. It is known that variant discovery using the human reference genome fails to capture all the variations because it lacks diversity and is not complete and accurate. Using graph-based references for alignment can increase the accuracy of the analysis as it is more diverse and complete. [ 12 ]


The estimates show that by 2025, the number of genomes that are sequenced will be 100 million to 2 billion which according to price trends, the storage for storing these data would be expensive and problematic. [ 12 ]  With the increasing availability of personal genome data, the initial dataset size -currently in the thousands of gigabase-scale genomes- is poised to expand exponentially. This growth will necessitate the development of more efficient analysis algorithms and data representation formats that can handle the escalating demands on time, memory, and storage space. [ 12 ]


Expanding the human pangenome reference to proposed 700 haplotypes (350 individuals) poses challenges in ensuring inclusivity due to linguistic, literacy, socioeconomic barriers, and distrust among racial-ethnic minorities and aborigines. Obtaining informed consent becomes complex as participants need to understand project implications. Balancing the release of post-analysis genomic data with ethical considerations presents dilemmas concerning complete information disclosure. [ 12 ]


The Centre of Genomics and Policy  (previously the HumGen team) is affiliated with  McGill University  and the Genome Innovation Centre  Canada . The Centre was launched to respond to the urgent need for informed public policy and analyses on socio-ethical issues related to  human genetics  research at the international, national, and provincial levels. Its website provides policy makers and the public  access  to policy statements concerning  genetic research .


Located within the McGill Genome Centre, the Centre of Genomics and Policy (CGP) works at the crossroads of law, medicine, and public policy. Applying a multidisciplinary perspective and collaborating with national and international partners, the CGP analyzes the socio-ethical and legal norms influencing the promotion, prevention and protection of human health.


Currently, the CGP's research covers several areas of genomics and policy that include: stem cell research and therapies, personalized medicine, prevention and treatment of cancer, data sharing in research, pediatrics, genetic counselling, digital health and AI, intellectual property and open science, epigenetics, intersex and diversity in health, gene editing, genetic discrimination and biobanking (population genetics).


These domains are approached using three guiding foundations: internationalization, policy development and knowledge transfer. First, the CGP promotes internationalization by undertaking comparative analyses of policies and guidelines around the world. Secondly, the CGP actively participates in the creation of international consortia with a view to promoting multidisciplinary policymaking. Finally, via its numerous workshops and lecture series, the CGP encourages knowledge transfer.


The CGP team was formerly affiliated with the Centre de recherche en droit public (CRDP) of the  Université de Montréal  under the name Genetics and Society Project. [ 1 ]


Faced with rapid advances in human genetics research, policy makers continue to struggle with a host of complex ethical, legal, and social questions. The HumGen website gives policy makers and the public access to legislation, policy, guidelines, and recommendations from government and nongovernmental organizations worldwide. [ 1 ] [ 2 ]


The website is a unique source of international genetic policy information and has been cited as a critical resource for geneticists who are interested in examining the wider implications of their work. [ 3 ]


The Genome-based Research and Population Health International Network (GraPH-INT) is an international community of population health experts. GRaPH-INT promotes the use of genome-based research from the sciences, humanities, and social sciences to improve population health. [ 4 ]


The website was launched in June 2006. In 2007, development began on the BioPortal search engine, aimed at optimizing searches on public health genome-based subjects (ethical, legal and social issues, research, literature, epidemiological data, and news and events).


Impute.me  was an open-source non-profit  web application  that allowed members of the public to use their data from  direct-to-consumer (DTC) genetic tests  (including tests from  23andMe  and  Ancestry.com ) to calculate  polygenic risk scores (PRS)  for complex diseases and cognitive and personality traits. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]  In July 2022, Lasse Folkerson, initiator and operator of impute.me, took the website offline. [ 6 ] [ 7 ]


Impute.me calculates PRSs, which are used to estimate the risk of developing complex diseases from the combined effects of numerous common  single nucleotide polymorphisms  in the  human genome . [ 8 ] [ 9 ]


It is intended for use by  people who have obtained genetics data from a  direct to consumer genetic testing company . [ 10 ] [ 11 ]  If they upload the files, the uploaded   data is expanded into ungenotyped SNPs and the overlap with public GWAS  summary statistics  used to estimate risk. [ 1 ]  The data is then subjected to analysis scripts including PRS calculations for approximately 2,000 traits and complex diseases. [ 1 ]  PRSs are calculated based on the combined effect of all SNPs reported in the summary statistics of the underlying GWAS or of the top, genome-wide significant SNPs in the underlying GWAS. [ 1 ]  The scores based on all SNPs are only available for about 20 complex diseases and traits. [ 1 ]  Users can then make use of  the web tool GenoPred [ 12 ]  to translate their PRSs onto an  absolute risk  scale using summary statistics from the GWAS studies. [ 13 ]


Numerous criticisms have been raised against consumers accessing their own genetic information, including findings that more than 30% of direct-to-consumer related contacts to clinical genetics departments involve the use of imputed risk estimates and that third party genetics analysis site generally invoke science's power without accepting its limits, while failing to make clear the limitations and potential dangers. [ 14 ] [ 15 ]  In addition there are concerns that many people will react negatively to accessing their own polygenic risk scores, with findings that over 5% of users score over the threshold for potential  post-traumatic stress disorder . [ 16 ]
Notably, this criticism match the FDA-regulation imposed on the major direct-to-consumer genetics company  23andme . [ 17 ]


Individualized cancer immunotherapy , also referred to as  individualized immuno-oncology , is a novel concept for therapeutic  cancer vaccines  that are truly personalized to a single individual.


The human  immune system  is generally able to recognize and fight  cancer cells . However, this ability is usually insufficient and the cancer continues to spread. [ 1 ]  Cancer immunotherapy is based on harnessing and potentiating the ability of the immune system to fight cancer.


Each tumor has its own individual genetic fingerprint, the  mutanome ,  that includes numerous genetic alterations. As opposed to a preformed drug, individualized cancer  vaccination  is a therapy that targets specific cancer  mutations  of the individual patient's tumor. [ 2 ]  The production of vaccines tailored to match a person's individual constellation of cancer mutations has become a new field of research. [ 3 ]


The concept of individualized  cancer immunotherapy  aims to identify individual mutations in the  tumor  of a patient, that are crucial for the proliferation, survival or  metastasis  of tumor cells. [ 2 ]  For this purpose, the individual genetic blueprint of the tumor is decrypted by sequencing and, using this blueprint as a template, a synthetic  vaccine  tailored to the tumor of the individual patient is prepared. This vaccine is designed to control and train the body's immune system to fight the cancer. [ 4 ]


Cancer is characterized by an accumulation of genetic alterations. A tumor may acquire up to thousands of different  somatic  mutations during the process of initiation and progression.  A smaller number of cancer mutations interfere with normal cell regulation and help to drive cancer growth. [ 5 ]


Somatic mutations in the tumor  genome  can cause tumors to express mutant  proteins  ( neoantigens ) that are recognized by autologous  T cells  as foreign and constitute cancer vaccine targets. [ 2 ] [ 6 ]   Tumor Mutational Burden  (TMB, the number of mutations within a targeted genetic region in the cancerous cell's DNA) have been thus suggested to correlate with patient survival post immunotherapy, although the findings are disputed. [ 7 ] [ 8 ] [ 9 ]


Such neoantigens are specifically expressed by tumor tissue and are not found on the surface of normal cells. They can upregulate tumor-specific  T cells  in patients without killing normal cells. [ 10 ]


T cells are key effectors of anticancer immunity. They are capable of distinguishing tumor cells from normal ones by recognizing  HLA -bound cancer-specific peptides. [ 10 ]  A requirement for the recognition of neoantigens by the immune system is that the neoantigens and their  antigenic determinants , the  neoepitopes , are processed and presented by  human leukocyte antigen   (HLA)   molecules . [ 5 ]  These molecules may be recognized by  CD8+   cytotoxic   T lymphocytes  as foreign neoepitopes and, with the help of  CD4+  T lymphocytes, trigger an  immune response  leading to tumor-specific killing. [ 4 ]  CD8+ T cells are specialized for direct tumor cell killing. CD4+ T cells can interact with antigen-presenting cells such as  dendritic cells  to recruit other immune cells or stimulate  effector cells . [ 10 ]


Most cancer neoantigens in humans arise from unique mutations. A patient's cancer is intra- as well as interlesionally heterogeneous and changes its composition over time. [ 11 ]  Each patient has an individual  mutational signature  (mutanome), and only a very small portion of the mutations are shared between patients. [ 10 ] [ 12 ]  A concept is therefore that an immunotherapy directed at neoantigens needs to be individualized. [ citation needed ]


The development of  sequencing technology  has improved the accuracy of identification and localization of neoantigens. With the advent of  next-generation sequencing  (NGS), it has become possible to systematically predict cancer neoantigens for individual patients. [ 5 ] [ 13 ]


In animal models, several independent studies have shown that vaccines consisting of computationally predicted neoepitopes mediated anti-tumor activity in mice. [ 13 ] [ 14 ] [ 15 ] [ 16 ] [ 17 ]


The translation of individualized neoepitope vaccines into clinical oncology is under investigation. Formats under consideration for individualized vaccines are synthetic  peptides ,  messenger RNA ,  DNA   plasmids ,  viral vectors , engineered  bacteria , and  antigen -loaded  dendritic cells . [ 2 ]


In 2015, a first step towards individualized neoantigen vaccination was achieved by treating three  melanoma  patients with  autologous   dendritic cells  loaded with a personalized mixture of seven peptides (neoantigens) that were predicted to bind to human leukocyte antigens (HLA). The neoantigen-loaded dendritic cells were cultured  in vitro  for autologous  transfusion . Results showed that the vaccine enhanced the existing immune response and elicited a neoantigen-specific T cell response that was not detected prior to injection. [ 18 ]


Sahin et al. were the first to identify suitable neoantigens using next generation sequencing (NGS) and used them to produce customized  RNA vaccines  capable of encoding these neoantigens. [ 19 ]  A total of 13 patients with melanoma received the RNA vaccine, eight of which had no tumor development during the follow-up. Immune surveillance analysis of  peripheral blood mononuclear cells  (PBMCs) in patients demonstrated that the RNA vaccines expanded preexisting T cells and induced de novo T cell responses against neoepitopes not recognized prior to vaccination. [ 19 ]


Another study group (Ott et al.) identified neoantigens in six melanoma patients and used them to create a customized vaccine for each patient with long  peptides  representing up to 20 mutations per patient. After surgical resection of the tumor, the vaccine was injected. The results showed that the tumor did not reappear in four patients during an observation period of 32 months after vaccination. [ 20 ]


Hilf et al. administered individualized neoantigen vaccines to 15 patients with  glioblastoma . The vaccine triggered T cell immune responses to the predicted neoantigens. [ 21 ]


Keskin et al. investigated individualized neoantigen vaccines in eight glioblastoma patients after surgical resection and conventional  radiotherapy . The study group observed that the vaccine increased the number of tumor-infiltrating T cells that migrated from the peripheral blood into the brain. [ 22 ]


Individualized cancer vaccines typically consist of multiple predicted neoepitopes. The manufacturing process involves several steps. [ citation needed ]


Tumor biopsies and healthy tissue (e.g., peripheral blood cells) of a patient diagnosed with cancer are examined by  NGS . Tumor-specific mutations in protein-coding  genes  are then identified by comparison of sequences from tumor and normal DNA. Computational tools classify these mutations for the highest likelihood of  immunogenicity , that is, for the predicted expression and binding affinity of neoepitopes on HLA molecules. The top rankers are then used for the production of the vaccine. [ 4 ]


The intended output is an on-demand vaccine with a unique composition tailored to the patient's individual cancer mutanome. [ 10 ]


The research approach to mobilize an immune response tailored to the individual tumor of a patient is also referred to as individualized neoantigen-specific immunotherapy (iNeST).


iNeST is based on the specific tumor mutations (neoantigens) of a single patient, with the aim of triggering high-affinity immune responses of T cells to the individual patient-specific cancer. [ 19 ]  The development of iNeST is driven by  biotech  companies [ 23 ] [ 24 ]


The  INO80 subfamily  of  chromatin remodeling complexes  are  ATPases , and includes the INO80 and SWR1  complexes . [ 1 ] [ 2 ]


One of the main roles of the INO80 subfamily is the incorporation and removal of alternate  histones  in the  nucleosome . [ 1 ]  In the presence of the  H2A.Z histone , the INO80 subfamily of remodelers  catalyze  the repositioning and eventual removal of this alternate  histone . [ 3 ]  The  H2A.Z histone  is found on the first  nucleosome  at the beginning of  genes . [ 3 ]  The INO80 subfamily of  remodelers  will also be recruited to the H2A.X  histone  in the  homologous recombination  repair pathway. [ 3 ]


In addition to this function, the INO80 subfamily plays a role in  transcriptional regulation  and  genomic recombination . [ 4 ]  In the  DNA damage pathways , the INO80 subfamily aids in  repair ,  recombination , and  cell cycle regulation . [ 4 ]  The INO80 subfamily can activate the recruitment of  replication checkpoint  factors and can aid in the recovery from  replicative  stress on the  DNA  strand. [ 4 ]  The subfamily’s ability to incorporate alternate  histones  is important for  genome  stability, disease pathogenesis, and  stem cell  identity. [ 4 ]  INO80  complexes  commonly bind to  nucleosome  free regions at  transcription  start sites and termination sites. [ 2 ]  INO80 is the only  remodeler  that is able to use the energy from  ATP hydrolysis  to create  nucleosome  free regions and cooperate with other  remodelers  to equally space  nucleosomes . [ 3 ]


Chromatin remodelers  in the INO80 subfamily are made of multiple  subunit   complexes  with split  ATPase   domains . [ 3 ]  The INO80 subfamily's  protein domains  are an  N-terminus , two RuvB-like proteins ( Rvb1  and  Rvb2 ), and a  C-terminus . [ 1 ]  The  ATPase   domain  of the  N-terminus  functions in the identification of  DNA damage  and aids in the stability of  telomeres . [ 4 ]  A long  insertion  in the  ATPase   domain  is what recruits the  Rvb1  and  Rvb2   helicases . [ 2 ]  These  helicases  contribute to genome maintenance and are unique to the INO80 subfamily of  chromatin remodeling complexes . [ 4 ]  This subfamily also contains an Arp4- actin   complex  that aids in stability of  genes . [ 2 ]  The Arp5  subunit  is required for  ATPase  function, binding to the  DNA , and relocation of  nucleosomes . [ 3 ]


INSACOG  ( Indian SARS-CoV-2 Consortium on Genomics [ 1 ]  or   Indian SARS-CoV-2 Genetics Consortium [ 2 ] ) is the forum set up under the  Ministry of Health and Family Welfare  by the  Government of India  on 30 December 2020, to study and monitor genome sequencing and virus variation of circulating strains of  COVID-19 in India . [ 1 ] [ 2 ] [ 3 ]  Initially it was tasked to study the virus variant  Lineage B.1.1.7  earlier found in  United Kingdom  in December 2020.


INSACOG works as a consortium of 38 [ 4 ]  national laboratories for genome sequencing located in different parts of  India . [ 5 ]  All these 10 laboratories are required to share 5% of positive samples to INSACOG for further research and studies. A  National Centre for Disease Control  (NCDC) nodal unit maintain a database of all samples of the new variants. Two national genomic sequencing database centers are appointed 1)  National Institute of Biomedical Genomics  and 2)  CSIR Institute of Genomics and Integrative Biology . [ 6 ]  In its early research INSACOG identified virus variant  Lineage B.1.617  referred to as a double mutation variant. [ 7 ] [ 8 ]


CSIR Institute of Genomics and Integrative Biology  ( CSIR-IGIB ) is a scientific  research institute  devoted primarily to biological research. It is a part of  Council of Scientific and Industrial Research  (CSIR), India. [ 1 ]


The institute was founded in 1977 as the  Center for Biochemical Technology  with a primary focus on biochemical research, but has since shifted its research focus to integrative biology.


The Institute has two campuses in Delhi. The North Campus is the older of the two campuses and is in the campus of  Delhi University , on Mall Road opposite to Jubilee Hall.
The new campus is in  South Delhi , on Mathura Road at Sukhdev Vihar.


IGIB was established in 1977 as the  Center for Biochemical Technology  (CBT). The Functional Genomics Unit was established in 1998 with the focus shifting from chemical to genomics research. The institute was renamed "Institute of Genomics and Integrative Biology" in 2002.


In 2009, a team at the institute sequenced the  genome  of the wild-type  zebrafish , with about 1.7 billion base pairs. This made the fish, which is native to the  Himalayan  region, the first  vertebrate  to have its whole genome sequenced in India, as previously Indian scientists had only sequenced bacteria and plant genomes. [ 2 ] [ 3 ]


In December 2009, scientists at IGIB performed the first re-sequencing of a human genome in India. [ 4 ] [ 5 ] [ 6 ]  The Institute also collaborated on decoding the first Sri Lankan  [ 7 ] [ 8 ]  genome and Malaysian genome. [ 9 ]  The Institute is also a member of the Open Personal Genomics Consortium. [ 10 ]


The institute plays a leading role in the understanding of new variants of SARS-CoV-2 in India [ 11 ] [ 12 ]  and across the world. [ 13 ]  The institute also maintains a number of informatics resources which are key to identification and characterization of emerging variants of SARS-CoV-2. [ 14 ] [ 15 ]


Researchers at the institute reported the first high-throughput [ 16 ]  next-generation sequencing based approach for detection and genetic epidemiology of SARS-CoV-2. [ 17 ]  This approach has been extensively used to understand the genetic epidemiology of SARS-CoV-2 in the state of  Kerala , [ 18 ]  which has significantly influenced policy and preparedness [ 19 ]  in the state to curb the spread of the epidemic as well as implementation of evidence based policies. [ 20 ]


The institute has been instrumental in creating the concept of MegaLabs for COVID-19 testing and genomic surveillance [ 21 ]  which also provided the genetic epidemiology of states including Kerala, [ 22 ]  Andhra Pradesh [ 23 ]  and Maharashtra. [ 24 ]


Researchers at the institute also reported the first cases of COVID-19 reinfection in the country [ 25 ]  apart from identifying a novel clade of SARS-CoV-2 in India named I/A3i. [ 26 ]  Researchers at the institute also discovered an emerging lineage with N440K mutation in spike protein associated with immune escape. [ 27 ] [ 28 ]  A comprehensive suite for computational resources to understand the genomes and genetic epidemiology of SARS-CoV-2 has been maintained. [ 15 ]  One of the first genetically characterized cases of COVID-19 vaccine breakthrough infections was reported by the institute. [ 29 ]


Researchers at the institute have been instrumental in the development of COVIDSure [ 30 ]  an  Reverse transcription polymerase chain reaction  based kit for diagnosis of COVID-19 and now widely used in India and marketed by  Trivitron  Healthcare. [ 31 ]


Researchers at CSIR-IGIB also developed the test called FELUDA based on  CRISPR gene editing  which is highly efficient and fast to combat the testing capacity of the country and has been approved by  Drugs Controller General of India . The test has been licensed to  Tata Group  for commercial production. [ 32 ]


The institute has also been playing a leading role in the national COVID-19 genomics consortium [ 33 ]  and maintains the informatics resources and dashboards. [ 14 ]


The institute has initiated a unique programme to use cutting-edge genomic technologies to understand Rare  genetic diseases  in India and pioneering the application of genomics for  Precision Medicine  in clinics. One of the programmes which has been initiated on this front is Genomics for Understanding Rare Disease, India Alliance Network (GUaRDIAN). [ 34 ]  GUaRDIAN is a large-scale collaborative network of clinicians from around India trying to use genomics in clinical practice, with the focused aim to understand genetic structure of rare genetic diseases in India. [ 35 ]


The consortium aims at using cutting-edge genomics technology to enable identification of genetic variations in diseases  and enable clinicians arrive at precise diagnosis for rare genetic disease. Apart from working closely with clinicians, the consortium aims to foster education, awareness and the widespread adoption of genomic technology in clinical settings, in addition to creating and disseminating the highest standards of genomic data generation and interpretation in India. [ 36 ]


In the few years, it has been able to work closely with clinicians in the network to be able to offer proof of principles for the application of Genomics for Precision Medicine. [ 37 ] [ 38 ] [ 39 ]  A comprehensive programme for patient referral is also functional. [ 40 ]


The IndiGen programme on Public Health Genomics [ 41 ]  aims to undertake whole genome sequencing  of 1000 Indian individuals representing diverse ethnic groups from India. The data generated as part of IndiGen would provide the baseline for allele frequencies of genetic variants for genetic epidemiology and aid policy decisions. The frequencies of clinically relevant genetic variants would form the template for enabling diagnostic approaches for prevalent genetic diseases and also for optimising therapies through pharmacogenomics. A comprehensive resource providing searchable access to the data is also made available. [ 42 ]


The main IGIB campus is located at Mall Road, New Delhi near Delhi University North Campus. IGIB's alliance with the Biotech/Pharmaceuticals has led to its growth and setting up of two extension centers - one at South Delhi (IGIB Annex at TCGA, Okhla) and the other at Western Delhi at Naraina.


IGIB was a co-host of the 13th meeting of the  Human Genome Organization  (HUGO) in 2008, held at  Hyderabad International Convention Centre . [ 43 ]


Klee diagrams , named for their resemblance to paintings by  Paul Klee , are  false-colour  maps that represent a way of assembling and viewing large  genomic   datasets . Contemporary research has produced genomic databases for an enormous range of life forms, inviting insights into the genetic basis of  biodiversity . [ 1 ]


Indicator vectors  are used to depict  nucleotide sequences . This technique produces correlation matrices or Klee diagrams. Researchers  Lawrence Sirovich , Mark Y. Stoeckle and Yu Zhang (2010) used their improved  algorithm  on a set of some 17000  DNA   barcode sequences  from 12 disparate animal  taxa , finding that indicator vectors were a viable taxonomic tool, and that discontinuities corresponded with taxonomic divisions.


Kryptonia  is a  bacterial phylum  with  candidate  status. It is a member of the  FCB  group. [ 1 ]


The phylum was first proposed in 2016 following the recovery of genomes from a large-scale effort to mine  metagenomic  and  single-cell genomic  datasets for novel bacterial diversity. [ 1 ]  Extensive analysis of 5.2 Tb of metagenomic data from around the world suggests members of Kryptonia are found exclusively in high-temperature pH-neutral geothermal springs, such as the Jinze pool (Yunnan Province, China), Dewar Creek Spring (British Columbia, Canada), and Great Boiling Spring (Nevada, USA). [ 1 ]  Due to primer mismatches, members of this phylum have been widely under-detected in  16S rRNA  sequencing-based surveys of community composition.


Analysis of the first genomes recovered from this group (from four different genera) suggests that members of Kryptonia are  heterotrophs  with a putative capacity for iron respiration. [ 1 ]  They are inferred to be incapable of some producing key metabolic compounds on their own (e.g.:  biotin  (also known as vitamin B7 or vitamin H) and certain  amino acids ), and thus may be metabolically dependent on other microbes in their environment, although the nature of such a relationship is unknown. [ 1 ]


The name "Kryptonia" is derived from the Greek work  "krupton" , which means "hidden" or "secret". This is a nod to the phylum having hitherto eluded detection due to SSU  rRNA primer  biases.


The currently accepted taxonomy is based on the  List of Prokaryotic names with Standing in Nomenclature  (LPSN) [ 2 ]  and  National Center for Biotechnology Information  (NCBI). [ 3 ]


" Ca.   K. mobilis "


" Ca.   K. kryptomonas "


" Ca.   K. tengchongensis "


" Ca.   K. thompsoni "


Locus Reference Genomic (LRG) [ 1 ] [ 2 ]  is a  DNA sequence  format that was developed to aid in curating locus specific databases (LSDBs) that record  DNA   sequence variation  which can result in inherited diseases. LRGs have fixed sequences that are independent of the genome so that they provide a stable framework for reporting variants. The LRG format uses extensible markup language ( XML ) to provide highly structured single records containing the genomic DNA sequence for individual  genes  along with the  mRNAs  and  proteins  encoded by these genes. LRG records are recommended in the  Human Genome Variation Society Nomenclature guidelines  as reference sequences to report sequence variants in LSDBs and the literature.


The LRG concept was developed by the  GEN2PHEN project  in conjunction with the  European Bioinformatics Institute  (EBI) and the  National Center for Biotechnology Information  (NCBI).


The  LRG homepage  provides access to existing LRG sequences and allows the submission of requests for the creation of new LRGs. This page also has a frequently asked questions ( FAQs ) section.


Multiple Annealing and Looping Based Amplification Cycles  ( MALBAC ) is a quasilinear whole  genome  amplification method. Unlike conventional  DNA  amplification methods that are non-linear or exponential (in each cycle, DNA copied can serve as template for subsequent cycles), MALBAC utilizes special primers that allow  amplicons  to have complementary ends and therefore to loop, preventing DNA from being copied exponentially. This results in amplification of only the original genomic DNA and therefore reduces amplification bias. MALBAC is “used to create overlapped shotgun amplicons covering most of the genome”. [ 1 ]  For next generation sequencing, MALBAC is followed by regular  PCR  which is used to further amplify amplicons.


Prior to MALBAC, a single cell is isolated by various methods including  laser capture microdissection ,  microfluidic  devices,  flow cytometry , or micro pipetting, then lysed. MALBAC single-cell whole-genome amplification involves 5 cycles of quenching, extending, melting, and looping.


The major advantage of MALBAC is that DNA is amplified almost linearly. The utilization of specialized  primers  enables looping of amplicons which then prevents them from being further amplified in subsequent cycles of MALBAC. These primers are 35 nucleotides long, with 8 variable nucleotides that hybridize to the templates and 27 common nucleotides. [ 1 ]  The common nucleotide sequence is GTG AGT GAT GGT TGA GGT AGT GTG GAG. The 8 variable nucleotides anneal randomly to the single stranded genomic DNA molecule. After one extension, semi-amplicon, an amplicon containing the common nucleotide sequence on only the 5’ end, is made. This semi-amplicon is used as a template for another round of extension, which then results in a full-amplicon, an amplicon where the 3’ end is complementary to the sequence on the 5’ end.


MALBAC primers have variable components which allow them to randomly bind to the template DNA. This means that on a single fragment at any cycle, there could be multiple primers annealed to the fragment.  A  DNA polymerase  such as one derived from  Bacillus stearothermophilus  ( Bst  polymerase) is able to displace the 5’ end of another upstream strand growing in the same direction. [ 2 ]


Bst  DNA polymerase has an error rate of 1/10000 bases. [ 3 ]


At the end of PCR, picograms of genetic material is amplified to microgram of DNA, yielding enough DNA to be sequenced.


MALBAC offers an unbiased approach to the amplification of DNA from a single cell. This method of single cell sequencing has a vast number of applications, many of which have yet to be exploited. MALBAC may aid in the analysis of forensic specimens, in pre-natal screening for genetic diseases, in understanding the development of reproductive cells, or in elucidating the complexity of a tumour. [ 1 ] [ 4 ]  At its foundation, this technology allows researchers to observe the frequency with which mutations accumulate in single cells. [ 1 ]  Moreover, it permits the detection of chromosomal abnormalities and gene  copy number variations  (CNVs) within and between cells, and further facilitates the detection of uncommon mutations that result in  single nucleotide polymorphisms  (SNPs). [ 1 ]


In the field of  cancer research , MALBAC has many applications. It may be used to examine intratumor heterogeneity, to identify genes which may confer an aggressive or metastatic phenotype, or to evaluate the potential for a tumour to develop  drug resistance . [ 4 ] [ 5 ]  A pioneering application of MALBAC was published in a December 2012 issue of Science and described the use of this technology to measure the mutation rate of the colon cancer cell line SW4802. [ 1 ]
By sequencing the amplified DNA of three kindred colon cancer cells in parallel with unrelated colon cancer cells from a different lineage, SNPs were identified with no false positives detected. [ 1 ]  It was also observed that purine-pyrimidine  transversions  occurred at a high frequency among the SNPs. [ 1 ]
The characterization of copy number and single nucleotide variations of single colon cancer cells highlighted the heterogeneity present within a tumour. [ 1 ]


MALBAC has been applied as a method to examine the genetic diversity amongst reproductive cells. By sequencing the genomes of 99 individual human sperm cells from an anonymous donor, MALBAC was used to examine  genetic recombination  events involving single gametes and ultimately provide insight into the dynamics of genetic recombination and its contribution to male infertility. [ 6 ]  Additionally, within an individual sperm, MALBAC identified duplicated or missing chromosomes, as well as SNPs or CNVs which could negatively affect fertility. [ 6 ]


MALBAC has resulted in many significant advances over other single cell sequencing techniques, foremost that it can report 93% of the genome of a single human cell. [ 1 ]  Some advantages of this technology include reduced amplification bias and increased genome  coverage , the requirement for very little template DNA, and low rates of false positive and false negative mutations. [ 4 ] [ 6 ]


MALBAC is a form of whole genome sequencing which reduces the bias associated with exponential PCR amplification by using a quasilinear phase of pre-amplification. [ 1 ]
MALBAC utilizes five cycles of pre-amplification and primers containing a 27 nucleotide common sequence and an 8 nucleotide variable sequence to produce fragments of amplified DNA (amplicons) which loop back on themselves to prevent additional copying and cross-hybridization. [ 1 ] [ 7 ]
These loops cannot be used as a template for amplification during MALBAC and therefore reduce the amplification bias commonly associated with the uneven exponential amplification of DNA fragments by polymerase chain reaction. [ 1 ]  MALBAC has been described to have better amplification uniformity than other methods of single sequencing, such as  multiple displacement amplification  (MDA). [ 1 ] [ 5 ]   MDA does not utilize DNA looping and amplifies DNA in an exponential fashion, resulting in bias. [ 1 ]   Accordingly, the amplification bias associated with other single cell sequencing methods results in low coverage of the genome. [ 1 ] [ 5 ]  The reduced bias associated with MALBAC has generated better genome sequence coverage than other single cell sequencing methods.


MALBAC can be used to amplify and subsequently sequence DNA when only one or a few cells are available, such as in the analysis of circulating tumour cells, pre-natal screens or forensic samples. [ 4 ] [ 7 ]  Only a small amount of starting template (picograms of DNA) is required to initiate the process, and therefore it is an ideal method for the sequencing of a single human cell. [ 1 ]


Single cell sequencing often has a high rate of false negative mutations. [ 1 ]  A false negative mutation rate is defined as the probability of not detecting a real mutation, and this may occur due to amplification bias resulting from the loss, or drop-out, of an allele. [ 8 ]  The sequence coverage uniformity of MALBAC in comparison to other single cell sequencing techniques has enhanced the detection of SNPs and reduced  allele  dropout rate. [ 1 ]  Allelic dropout rate increases when an allele of a  heterozygote  fails to amplify resulting in identification of a ‘false homozygote.’ This may occur due to low concentration of DNA template, or the uneven amplification of template resulting in one allele of a heterozygote being copied more than the other. [ 8 ]  The allele dropout rate of MALBAC has been shown to be much lower (approximately 1%) compared to MDA which is approximately 65%. In contrast to MDA which has been shown to have a 41% SNP detection efficiency in comparison with bulk sequencing, MALBAC has been reported to have SNP detection efficiency of 76%. [ 1 ]
MALBAC has also been reported to have a low  false positive rate . False positive mutations generated by MALBAC largely result from errors introduced by DNA polymerase during the first cycle of amplification that are further propagated during subsequent cycles. This false positive rate can be eliminated by sequencing 2-3 cells within a lineage derived from a single cell to verify the presence of a SNP, and by eliminating sequencing and amplification errors by sequencing unrelated cells from a separate lineage. [ 1 ]


Materiomics  is the holistic study of material systems. Materiomics examines links between physicochemical material properties and material characteristics and function. The focus of materiomics is system functionality and behavior, rather than a piecewise collection of properties, a paradigm similar to  systems biology . While typically applied to complex biological systems and biomaterials, materiomics is equally applicable to non-biological systems. Materiomics investigates the material properties of natural and synthetic materials by examining fundamental links between processes, structures and properties at multiple scales, from nano to macro, by using systematic experimental, theoretical or computational methods.


The term has been independently proposed with slightly different definitions in 2004 by T. Akita et al. (AIST/Japan [ 1 ] ), in 2008 by  Markus J. Buehler  (MIT/USA [ 2 ] [ 3 ] ), and  Clemens van Blitterswijk , Jan de Boer and Hemant Unadkat (University of Twente/The Netherlands [ 4 ] ) in analogy to  genomics , the study of an organism's entire  genome . Similarly, materiomics refers to the study of the processes, structures and properties of materials from a fundamental, systematic perspective by incorporating all relevant scales, from nano to macro, in the synthesis and function of materials and structures. The integrated view of these interactions at all scales is referred to as a material's materiome. [ 5 ]


[ 6 ]   [ 7 ]


New techniques for evaluating materials at the tissue level, such as  reference point indentation  (RPI) and  raman spectroscopy  are lending insight into the nature of these highly complex, functional relationships.


Materiomics is related to  proteomics , where the difference is the focus on material properties, stability, failure and mechanistic insight into multi-scale phenomena.


Medicago tribuloides   Desr.
 Medicago tribuloides  var.  breviaculeata   Moris
 Medicago truncatula  var.  breviaculeata   (Moris) Urb.
 Medicago truncatula  var.  longiaculeata   Urb.
 Medicago truncatula  var.  tribuloides   (Desr.) Burnat
 Medicago truncatula  f.  tricycla   Nègre
 Medicago truncatula  var.  tricycla   (Nègre) Heyn


Medicago truncatula , the  barrelclover , [ 2 ]   strong-spined medick , [ 3 ]   barrel medic , or  barrel medick , is a small annual  legume  native to the  Mediterranean  region that is used in  genomic  research. It is a low-growing,  clover -like plant 10–60 centimetres (3.9–23.6 in) tall with trifoliate  leaves . Each leaflet is rounded, 1–2 centimetres (0.39–0.79 in) long, often with a dark spot in the center. The  flowers  are yellow, produced singly or in a small  inflorescence  of two to five together; the  fruit  is a small, spiny pod.


This species is studied as a  model organism  for legume biology because it has a small  diploid   genome , is self-fertile, has a rapid generation time and prolific seed production, is amenable to genetic  transformation , and its genome has been sequenced. [ 4 ]


It forms  symbioses  with  nitrogen-fixing   rhizobia  ( Sinorhizobium meliloti  and  Sinorhizobium medicae ) and  arbuscular mycorrhizal  fungi including  Rhizophagus irregularis  (previously known as  Glomus intraradices ). The model plant  Arabidopsis thaliana  does not form either symbiosis, making  M. truncatula  an important tool for studying these processes.


It is also an important  forage  crop species in  Australia .


The draft sequence of the genome of  M. truncatula  cultivar A17 was published in the journal  Nature  in 2011. [ 4 ]


The  sequencing  was carried out by an international partnership of research laboratories involving researchers from the  University of Oklahoma  (US),  J. Craig Venter Institute   (US),  Genoscope  (France), and  Sanger Centre  (UK). Partner institutions included the  University of Minnesota  (US),  University of California-Davis  (US), the National Center for Genomic Resources (US),  John Innes Centre  (UK), Institut National de Recherche Agronomique (France),  Munich Information Center for Protein Sequences  (Germany),  Wageningen University  (the Netherlands), and  Ghent University  (Belgium). The  Medicago truncatula  Sequencing Consortium began in 2001 with a seed grant from the Samuel Roberts Noble Foundation. In 2003, the  National Science Foundation  and the European Union 6th Framework Programme began providing most of the funding. By 2009, 84% of the genome assembly had been completed. [ 5 ]


The assembly of the genome sequence in  M. truncatula  was based on  bacterial artificial chromosomes  (BACs). This is the same approach used to sequence the genomes of humans, the fruitfly,  Drosophila melanogaster , and the model plant,  Arabidopsis thaliana .  In July 2013, version 4.0 of the genome was released. [ 6 ]  This version combined sequences gained from  shotgun sequencing  with the BAC-based sequence assemblies, which has helped to fill in the gaps in the previously mapped sequences.


A parallel group known as the International  Medicago  Gene Annotation Group ( IMGAG ) is responsible for identifying and describing putative gene sequences within the genome sequence.


Researcher  Toby Kiers  of VU University Amsterdam and associates used  M. truncatula  to study symbioses between plants and fungi – and to see whether the partners in the relationship could distinguish between good and bad traders/suppliers. By using labeled carbon to track the source of nutrient flowing through the arbuscular mycorrhizal system, the researchers have proven that the plants had indeed given more carbon to the more generous fungus species. By restricting the amount of carbon the plants gave to the fungus, the researchers also demonstrated that the fungi did pass along more of their phosphorus to the more generous plants. [ 7 ]


Proteomic investigation by  mass spectrometry  has been performed by Wienkoop et al 2004 and Larrainzar et al 2007. [ 8 ]


Courty, Pierre Emmanuel; Smith, Penelope; Koegel, Sally; Redecker, Dirk; Wipf, Daniel (1 June 2015). "Inorganic Nitrogen Uptake and Transport in Beneficial Plant Root-Microbe Interactions".  Critical Reviews in Plant Sciences .  34  ( 1– 3):  4– 16.  doi : 10.1080/07352689.2014.897897 .  S2CID   85772198 .


Metabolic network modelling , also known as  metabolic network reconstruction  or  metabolic pathway analysis , allows for an in-depth insight into the molecular mechanisms of a particular organism. In particular, these models correlate the  genome  with molecular  physiology . [ 1 ]  A reconstruction breaks down  metabolic  pathways (such as  glycolysis  and the  citric acid cycle ) into their respective reactions and enzymes, and analyzes them within the perspective of the entire network. In simplified terms, a reconstruction collects all of the relevant metabolic information of an organism and compiles it in a mathematical model.  Validation and analysis of reconstructions can allow identification of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. This knowledge can then be applied to create novel  biotechnology .


In general, the process to build a reconstruction is as follows:


The related method of  flux balance analysis  seeks to mathematically simulate metabolism in genome-scale reconstructions of metabolic networks.


A metabolic reconstruction provides a highly mathematical, structured platform on which to understand the systems biology of metabolic pathways within an organism. [ 2 ]   The integration of biochemical metabolic pathways with rapidly available, annotated genome sequences has developed what are called genome-scale metabolic models. Simply put, these models correlate metabolic genes with metabolic pathways. In general, the more information about physiology, biochemistry and genetics is available for the target organism, the better the predictive capacity of the reconstructed models. Mechanically speaking, the process of reconstructing prokaryotic and eukaryotic  metabolic networks  is essentially the same. Having said this, eukaryote reconstructions are typically more challenging because of the size of genomes, coverage of knowledge, and the multitude of cellular compartments. [ 2 ]  The first genome-scale metabolic model was generated in 1995 for  Haemophilus influenzae . [ 3 ]   The first multicellular organism,  C. elegans , was reconstructed in 1998. [ 4 ]   Since then, many reconstructions have been formed. For a list of reconstructions that have been converted into a model and experimentally validated, see  http://sbrg.ucsd.edu/InSilicoOrganisms/OtherOrganisms .


Because the timescale for the development of reconstructions is so recent, most reconstructions have been built manually. However, now, there are quite a few resources that allow for the semi-automatic assembly of these reconstructions that are utilized due to the time and effort necessary for a reconstruction. An initial fast reconstruction can be developed automatically using resources like PathoLogic or ERGO in combination with encyclopedias like MetaCyc, and then manually updated by using resources like PathwayTools. These semi-automatic methods allow for a fast draft to be created while allowing the fine tune adjustments required once new experimental data is found. It is only in this manner that the field of metabolic reconstructions will keep up with the ever-increasing numbers of annotated genomes.


A reconstruction is built by compiling data from the resources above.  Database tools such as KEGG and BioCyc can be used in conjunction with each other to find all the metabolic genes in the organism of interest.  These genes will be compared to closely related organisms that have already developed reconstructions to find homologous genes and reactions. These homologous genes and reactions are carried over from the known reconstructions to form the draft reconstruction of the organism of interest. Tools such as ERGO, Pathway Tools and Model SEED can compile data into pathways to form a network of metabolic and non-metabolic pathways. These networks are then verified and refined before being made into a mathematical simulation. [ 2 ]


The predictive aspect of a metabolic reconstruction hinges on the ability to predict the biochemical reaction catalyzed by a protein using that protein's amino acid sequence as an input, and to infer the structure of a metabolic network based on the predicted set of reactions. A network of enzymes and metabolites is drafted to relate sequences and function.  When an uncharacterized protein is found in the genome, its amino acid sequence is first compared to those of previously characterized proteins to search for homology.  When a homologous protein is found, the proteins are considered to have a common ancestor and their functions are inferred as being similar.  However, the quality of a reconstruction model is dependent on its ability to accurately infer phenotype directly from sequence, so this rough estimation of protein function will not be sufficient.  A number of algorithms and bioinformatics resources have been developed for refinement of sequence homology-based assignments of protein functions:


Once proteins have been established, more information about the enzyme structure, reactions catalyzed, substrates and products, mechanisms, and more can be acquired from databases such as  KEGG ,  MetaCyc  and  NC-IUBMB .  Accurate metabolic reconstructions require additional information about the reversibility and preferred physiological direction of an enzyme-catalyzed reaction which can come from databases such as  BRENDA  or  MetaCyc  database. [ 24 ]


An initial metabolic reconstruction of a genome is typically far from perfect due to the high variability and diversity of microorganisms.  Often, metabolic pathway databases such as KEGG and MetaCyc will have "holes", meaning that there is a conversion from a substrate to a product (i.e., an enzymatic activity) for which there is no known protein in the genome that encodes the enzyme that facilitates the catalysis.  What can also happen in semi-automatically drafted reconstructions is that some pathways are falsely predicted and don't actually occur in the predicted manner. [ 24 ]   Because of this, a systematic verification is made in order to make sure no inconsistencies are present and that all the entries listed are correct and accurate. [ 1 ]   Furthermore, previous literature can be researched in order to support any information obtained from one of the many metabolic reaction and genome databases. This provides an added level of assurance for the reconstruction that the enzyme and the reaction it catalyzes do actually occur in the organism.


Enzyme promiscuity  and spontaneous chemical reactions can damage metabolites. This  metabolite damage, and its repair or pre-emption , create energy costs that need to be incorporated into models. It is likely that many genes of unknown function encode proteins that repair or pre-empt metabolite damage, but most genome-scale metabolic reconstructions only include a fraction of all genes. [ 25 ] [ 26 ]


Any new reaction not present in the databases needs to be added to the reconstruction. This is an iterative process that cycles between the experimental phase and the coding phase. As new information is found about the target organism, the model will be adjusted to predict the metabolic and phenotypical output of the cell. The presence or absence of certain reactions of the metabolism will affect the amount of  reactants /products that are present for other reactions within the particular pathway. This is because products in one reaction go on to become the reactants for another reaction, i.e. products of one reaction can combine with other proteins or compounds to form new proteins/compounds in the presence of different enzymes or  catalysts . [ 1 ]


Francke  et al.   [ 1 ]  provide an excellent example as to why the verification step of the project needs to be performed in significant detail. During a metabolic network reconstruction of  Lactobacillus plantarum , the model showed that  succinyl-CoA  was one of the reactants for a reaction that was a part of the biosynthesis of  methionine . However, an understanding of the physiology of the organism would have revealed that due to an incomplete tricarboxylic acid pathway,  Lactobacillus plantarum  does not actually produce succinyl-CoA, and the correct reactant for that part of the reaction was  acetyl-CoA .


Therefore, systematic verification of the initial reconstruction will bring to light several inconsistencies that can adversely affect the final interpretation of the reconstruction, which is to accurately comprehend the molecular mechanisms of the organism. Furthermore, the  simulation  step also ensures that all the reactions present in the reconstruction are properly balanced. To sum up, a reconstruction that is fully accurate can lead to greater insight about understanding the functioning of the organism of interest. [ 1 ]


A metabolic network can be broken down into a stoichiometric matrix where the rows represent the compounds of the reactions, while the columns of the matrix correspond to the reactions themselves.  Stoichiometry  is a quantitative relationship between  substrates  of a chemical reaction. In order to deduce what the metabolic network suggests, recent research has centered on a few approaches, such as extreme pathways, elementary mode analysis, [ 27 ]   flux balance analysis , and a number of other constraint-based modeling methods. [ 28 ] [ 29 ]


Price, Reed, and Papin, [ 30 ]  from the Palsson lab, use a method of singular value decomposition (SVD) of extreme pathways in order to understand regulation of a human  red blood cell  metabolism. Extreme pathways are convex  basis vectors  that consist of  steady state  functions of a metabolic network. [ 31 ]  For any particular metabolic network, there is always a unique set of extreme pathways available. [ 27 ]  Furthermore, Price, Reed, and Papin, [ 30 ]  define a  constraint-based approach , where through the help of  constraints  like  mass balance  and maximum  reaction rates , it is possible to develop a ‘solution space’ where all the feasible options fall within. Then, using a kinetic model approach, a single solution that falls within the extreme pathway solution space can be determined. [ 30 ]  Therefore, in their study, Price, Reed, and Papin, [ 30 ]  use both constraint and  kinetic  approaches to understand the human red blood cell metabolism. In conclusion, using extreme pathways, the regulatory mechanisms of a metabolic network can be studied in further detail.


Elementary mode analysis closely matches the approach used by extreme pathways. Similar to extreme pathways, there is always a unique set of  elementary modes  available for a particular metabolic network. [ 27 ]  These are the smallest sub-networks that allow a metabolic reconstruction network to function in steady state. [ 32 ] [ 33 ] [ 34 ]  According to Stelling (2002), [ 33 ]  elementary modes can be used to understand cellular objectives for the overall metabolic network. Furthermore, elementary mode analysis takes into account  stoichiometrics  and  thermodynamics  when evaluating whether a particular metabolic route or network is feasible and likely for a set of proteins/enzymes. [ 32 ]


In 2009, Larhlimi and Bockmayr presented a new approach called "minimal metabolic behaviors" for the analysis of metabolic networks. [ 35 ]  Like elementary modes or extreme pathways, these are uniquely determined by the network, and yield a complete description of the flux cone. However, the new description is much more compact. In contrast with elementary modes and extreme pathways, which use an inner description based on generating vectors of the flux cone, MMBs are using an outer description of the flux cone. This approach is based on sets of non-negativity constraints. These can be identified with irreversible reactions, and thus have a direct biochemical interpretation. One can characterize a metabolic network by MMBs and the reversible metabolic space.


A different technique to simulate the metabolic network is to perform  flux balance analysis . This method uses  linear programming , but in contrast to elementary mode analysis and extreme pathways, only a single solution results in the end. Linear programming is usually used to obtain the maximum potential of the objective function that you are looking at, and therefore, when using flux balance analysis, a single solution is found to the optimization problem. [ 33 ]  In a flux balance analysis approach, exchange  fluxes  are assigned to those metabolites that enter or leave the particular network only. Those metabolites that are consumed within the network are not assigned any exchange flux value. Also, the exchange fluxes along with the enzymes can have constraints ranging from a negative to positive value (ex: -10 to 10).


Furthermore, this particular approach can accurately define if the reaction stoichiometry is in line with predictions by providing fluxes for the balanced reactions. Also, flux balance analysis can highlight the most effective and efficient pathway through the network in order to achieve a particular objective function. In addition,  gene knockout  studies can be performed using flux balance analysis. The enzyme that correlates to the gene that needs to be removed is given a constraint value of 0. Then, the reaction that the particular enzyme catalyzes is completely removed from the analysis.


In order to perform a dynamic simulation with such a network it is necessary to construct an ordinary differential equation
system that describes the rates of change in each metabolite's concentration or amount. To this end, a rate law, i.e., a kinetic equation that determines the rate of reaction based on the concentrations of all reactants is required for each reaction. Software packages that include numerical integrators, such as  COPASI  or  SBMLsimulator , are then able to simulate the system dynamics given an initial condition. Often these rate laws contain kinetic parameters with uncertain values. In many cases it is desired to estimate these parameter values with respect to given time-series data of metabolite concentrations. The system is then supposed to reproduce the given data. For this purpose the distance between the given data set and the result of the simulation, i.e., the numerically or in few cases analytically obtained solution of the differential equation system is computed. The values of the parameters are then estimated to minimize this distance. [ 36 ]  One step further, it may be desired to estimate the mathematical structure of the differential equation system because the real rate laws are not known for the reactions within the system under study. To this end, the program  SBMLsqueezer  allows automatic creation of appropriate rate laws for all reactions with the network.
 [ 37 ]


Synthetic accessibility is a simple approach to network simulation whose goal is to predict which metabolic gene knockouts are lethal. The synthetic accessibility approach uses the topology of the metabolic network to calculate the sum of the minimum number of steps needed to traverse the metabolic network graph from the inputs, those metabolites available to the organism from the environment, to the outputs, metabolites needed by the organism to survive. To simulate a gene knockout, the reactions enabled by the gene are removed from the network and the synthetic accessibility metric is recalculated. An increase in the total number of steps is predicted to cause lethality. Wunderlich and Mirny showed this simple, parameter-free approach predicted knockout lethality in  E. coli  and  S. cerevisiae  as well as elementary mode analysis and flux balance analysis in a variety of media. [ 38 ]


Reconstructions and their corresponding models allow the formulation of hypotheses about the presence of certain enzymatic activities and the production of metabolites that can be experimentally tested, complementing the primarily discovery-based approach of traditional microbial biochemistry with hypothesis-driven research. [ 41 ]  The results these experiments can uncover novel pathways and metabolic activities and decipher between discrepancies in previous experimental data.  Information about the chemical reactions of metabolism and the genetic background of various metabolic properties (sequence to structure to function) can be utilized by genetic engineers to modify organisms to produce high value outputs whether those products be medically relevant like pharmaceuticals; high value chemical intermediates such as terpenoids and isoprenoids; or biotechnological outputs like biofuels, [ 42 ]  or polyhydroxybutyrates also known as bioplastics. [ 43 ]


Metabolic network reconstructions and models are used to understand how an organism or parasite functions inside of the host cell. For example, if the parasite serves to compromise the  immune system  by lysing  macrophages , then the goal of metabolic reconstruction/simulation would be to determine the metabolites that are essential to the organism's proliferation inside of macrophages. If the proliferation cycle is inhibited, then the parasite would not continue to evade the host's immune system. A reconstruction model serves as a first step to deciphering the complicated mechanisms surrounding disease. These models can also look at the minimal genes necessary for a cell to maintain virulence. The next step would be to use the predictions and postulates generated from a reconstruction model and apply it to discover novel biological functions such as  drug-engineering  and  drug delivery  techniques.


Metagenomics  is the study of  genetic  material recovered directly from  environmental  or clinical samples by a method called  sequencing . The broad field may also be referred to as  environmental genomics ,  ecogenomics ,  community genomics  or  microbiomics .


While traditional  microbiology  and microbial  genome sequencing  and  genomics  rely upon cultivated  clonal   cultures , early environmental gene sequencing cloned specific genes (often the  16S rRNA  gene) to produce a profile of diversity in a natural sample. Such work revealed that the vast majority of  microbial biodiversity  had been missed by cultivation-based methods. [ 2 ]


Because of its ability to reveal the previously hidden diversity of microscopic life, metagenomics offers a powerful way of understanding the microbial world that might revolutionize understanding of biology. [ 3 ]  As the price of DNA sequencing continues to fall, metagenomics now allows  microbial ecology  to be investigated at a much greater scale and detail than before. Recent studies use either " shotgun " or  PCR  directed sequencing to get largely unbiased samples of all genes from all the members of the sampled communities. [ 4 ]


The term "metagenomics" was first used by  Jo Handelsman ,  Robert M. Goodman , Michelle R. Rondon,  Jon Clardy , and Sean F. Brady, and first appeared in publication in 1998. [ 5 ]  The term metagenome referenced the idea that a collection of genes sequenced from the environment could be analyzed in a way analogous to the study of a single  genome . In 2005, Kevin Chen and  Lior Pachter  (researchers at the  University of California, Berkeley ) defined metagenomics as "the application of modern genomics technique without the need for isolation and lab cultivation of individual species". [ 6 ]


Conventional  sequencing  begins with a culture of identical cells as a source of  DNA . However, early metagenomic studies revealed that there are probably large groups of microorganisms in many environments that cannot be  cultured  and thus cannot be sequenced. These early studies focused on 16S  ribosomal   RNA  (rRNA) sequences which are relatively short, often  conserved  within a species, and generally different between species. Many 16S  rRNA  sequences have been found which do not belong to any known cultured  species , indicating that there are numerous non-isolated organisms. These surveys of ribosomal RNA genes taken directly from the environment revealed that  cultivation  based methods find less than 1% of the bacterial and  archaeal  species in a sample. [ 2 ]  Much of the interest in metagenomics comes from these discoveries that showed that the vast majority of microorganisms had previously gone unnoticed.


In the 1980s early  molecular work  in the field was conducted by  Norman R. Pace  and colleagues, who used  PCR  to explore the diversity of ribosomal RNA sequences. [ 7 ]  The insights gained from these breakthrough studies led Pace to propose the idea of cloning DNA directly from environmental samples as early as 1985. [ 8 ]  This led to the first report of isolating and  cloning  bulk DNA from an environmental sample, published by Pace and colleagues in 1991 [ 9 ]  while Pace was in the Department of Biology at  Indiana University . Considerable efforts ensured that these were not  PCR  false positives and supported the existence of a complex community of unexplored species. Although this methodology was limited to exploring highly conserved,  non-protein coding genes , it did support early microbial morphology-based observations that diversity was far more complex than was known by culturing methods. Soon after that in 1995, Healy reported the metagenomic isolation of functional genes from "zoolibraries" constructed from a complex culture of environmental organisms grown in the laboratory on dried  grasses . [ 10 ]  After leaving the Pace laboratory,  Edward DeLong  continued in the field and has published work that has largely laid the groundwork for environmental phylogenies based on signature 16S sequences, beginning with his group's construction of libraries from  marine  samples. [ 11 ]


In 2002,  Mya Breitbart ,  Forest Rohwer , and colleagues used environmental  shotgun sequencing  (see below) to show that 200 liters of seawater contains over 5000 different viruses. [ 12 ]  Subsequent studies showed that there are more than a thousand  viral species  in human stool and possibly a million different viruses per kilogram of  marine sediment , including many  bacteriophages . Essentially all of the viruses in these studies were new species. In 2004, Gene Tyson, Jill Banfield, and colleagues at the  University of California, Berkeley  and the  Joint Genome Institute  sequenced DNA extracted from an  acid mine drainage  system. [ 13 ]  This effort resulted in the complete, or nearly complete, genomes for a handful of bacteria and  archaea  that had previously resisted attempts to culture them. [ 14 ]


Beginning in 2003,  Craig Venter , leader of the privately funded parallel of the  Human Genome Project , has led the  Global Ocean Sampling Expedition  (GOS), circumnavigating the globe and collecting metagenomic samples throughout the journey. All of these samples were sequenced using shotgun sequencing, in hopes that new genomes (and therefore new organisms) would be identified. The pilot project, conducted in the  Sargasso Sea , found DNA from nearly 2000 different  species , including 148 types of  bacteria  never before seen. [ 15 ]  Venter thoroughly explored the  West Coast of the United States , and completed a two-year expedition in 2006 to explore the  Baltic ,  Mediterranean , and  Black  Seas. Analysis of the metagenomic data collected during this journey revealed two groups of organisms, one composed of taxa adapted to environmental conditions of 'feast or famine', and a second composed of relatively fewer but more abundantly and widely distributed taxa primarily composed of  plankton . [ 16 ]


In 2005 Stephan C. Schuster at  Penn State University  and colleagues published the first sequences of an environmental sample generated with  high-throughput sequencing , in this case massively parallel  pyrosequencing  developed by  454 Life Sciences . [ 17 ]  Another early paper in this area appeared in 2006 by Robert Edwards,  Forest Rohwer , and colleagues at  San Diego State University . [ 18 ]


Recovery of DNA sequences longer than a few thousand  base pairs  from environmental  samples  was very difficult until recent advances in  molecular biological  techniques allowed the construction of  libraries  in  bacterial artificial chromosomes  (BACs), which provided better  vectors  for  molecular cloning . [ 20 ]


Advances in  bioinformatics , refinements of DNA amplification, and the proliferation of computational power have greatly aided the analysis of DNA sequences recovered from environmental samples, allowing the adaptation of  shotgun sequencing  to metagenomic samples (known also as whole metagenome shotgun or WMGS sequencing). The approach, used to sequence many cultured microorganisms and the  human genome , randomly shears DNA, sequences many short sequences, and  reconstructs  them into a  consensus sequence . Shotgun sequencing reveals genes present in environmental samples. Historically, clone libraries were used to facilitate this sequencing. However, with advances in high throughput sequencing technologies, the cloning step is no longer necessary and greater yields of sequencing data can be obtained without this labour-intensive bottleneck step. Shotgun metagenomics provides information both about which organisms are present and what metabolic processes are possible in the community. [ 21 ]  Because the collection of DNA from an environment is largely uncontrolled, the most abundant organisms in an environmental sample are most highly represented in the resulting sequence data. To achieve the high coverage needed to fully resolve the genomes of under-represented community members, large samples, often prohibitively so, are needed. On the other hand, the random nature of shotgun sequencing ensures that many of these organisms, which would otherwise go unnoticed using traditional culturing techniques, will be represented by at least some small sequence segments. [ 13 ]


An advantage to high throughput sequencing is that this technique does not require cloning the DNA before sequencing, removing one of the main biases and bottlenecks in environmental sampling. The first metagenomic studies conducted using  high-throughput sequencing  used massively parallel  454 pyrosequencing . [ 17 ]  Three other technologies commonly applied to environmental sampling are the  Ion Torrent Personal Genome Machine , the  Illumina  MiSeq or HiSeq and the  Applied Biosystems SOLiD  system. [ 22 ]  These techniques for sequencing DNA generate shorter fragments than  Sanger sequencing ; Ion Torrent PGM System and 454 pyrosequencing typically produces ~400 bp reads, Illumina MiSeq produces 400-700bp reads (depending on whether paired end options are used), and SOLiD produce 25–75 bp reads. [ 23 ]  Historically, these read lengths were significantly shorter than the typical Sanger sequencing read length of ~750 bp, however the Illumina technology is quickly coming close to this benchmark. However, this limitation is compensated for by the much larger number of sequence reads. In 2009, pyrosequenced metagenomes generate 200–500 megabases, and Illumina platforms generate around 20–50 gigabases, but these outputs have increased by orders of magnitude in recent years. [ 24 ]


An emerging approach combines shotgun sequencing and  chromosome conformation capture  (Hi-C), which measures the proximity of any two DNA sequences within the same cell, to guide microbial genome assembly. [ 25 ]  Long read sequencing technologies, including PacBio RSII and PacBio Sequel by  Pacific Biosciences , and Nanopore MinION, GridION, PromethION by  Oxford Nanopore Technologies , is another choice to get long shotgun sequencing reads that should make ease in assembling process. [ 26 ]


The data generated by metagenomics experiments are both enormous and inherently noisy, containing fragmented data representing as many as 10,000 species. [ 1 ]  The sequencing of the cow  rumen  metagenome generated 279  gigabases , or 279 billion base pairs of nucleotide sequence data, [ 28 ]  while the human gut  microbiome  gene catalog identified 3.3 million genes assembled from 567.7 gigabases of sequence data. [ 29 ]  Collecting, curating, and extracting useful biological information from datasets of this size represent significant computational challenges for researchers. [ 21 ] [ 30 ] [ 31 ] [ 32 ]


The first step of metagenomic data analysis requires the execution of certain pre-filtering steps, including the removal of redundant, low-quality sequences and sequences of probable  eukaryotic  origin (especially in metagenomes of human origin). [ 33 ] [ 34 ]  The methods available for the removal of contaminating eukaryotic genomic DNA sequences include Eu-Detect and DeConseq. [ 35 ] [ 36 ]


DNA sequence data from genomic and metagenomic projects are essentially the same, but genomic sequence data offers higher  coverage  while metagenomic data is usually highly non-redundant. [ 31 ]  Furthermore, the increased use of second-generation sequencing technologies with short read lengths means that much of future metagenomic data will be error-prone. Taken in combination, these factors make the assembly of metagenomic sequence reads into genomes difficult and unreliable. Misassemblies are caused by the presence of  repetitive DNA sequences  that make assembly especially difficult because of the difference in the relative abundance of species present in the sample. [ 37 ]  Misassemblies can also involve the combination of sequences from more than one species into chimeric  contigs . [ 37 ]


There are several assembly programs, most of which can use information from  paired-end tags  in order to improve the accuracy of assemblies. Some programs, such as  Phrap  or  Celera  Assembler, were designed to be used to assemble single  genomes  but nevertheless produce good results when assembling metagenomic data sets. [ 1 ]  Other programs, such as  Velvet assembler , have been optimized for the shorter reads produced by second-generation sequencing through the use of  de Bruijn graphs . [ 38 ] [ 39 ]  The use of reference genomes allows researchers to improve the assembly of the most abundant microbial species, but this approach is limited by the small subset of microbial phyla for which sequenced genomes are available. [ 37 ]  After an assembly is created, an additional challenge is "metagenomic deconvolution", or determining which sequences come from which species in the sample. [ 40 ]


Gene annotations provide the "what", while measurements of  species diversity  provide the "who". [ 41 ]  In order to connect community composition and function in metagenomes, sequences must be binned.  Binning  is the process of associating a particular sequence with an organism. [ 37 ]  In similarity-based binning, methods such as  BLAST  are used to rapidly search for phylogenetic markers or otherwise similar sequences in existing public databases. This approach is implemented in  MEGAN . [ 42 ]  Another tool, PhymmBL, uses  interpolated Markov models  to assign reads. [ 1 ]   MetaPhlAn  and  AMPHORA  are methods based on unique clade-specific markers for estimating organismal relative abundances with improved computational performances. [ 43 ]  Other tools, like  mOTUs [ 44 ] [ 45 ]  and MetaPhyler, [ 46 ]  use universal marker genes to profile prokaryotic species. With the  mOTUs profiler  is possible to profile species without a reference genome, improving the estimation of microbial community diversity. [ 45 ]  Recent methods, such as  SLIMM , use read coverage landscape of individual reference genomes to minimize false-positive hits and get reliable relative abundances. [ 47 ]  In composition based binning, methods use intrinsic features of the sequence, such as oligonucleotide frequencies or  codon usage bias . [ 1 ]  Once sequences are binned, it is possible to carry out comparative analysis of diversity and richness.


After binning, assembled contigs are collected into "bins" each representing a species-like collection of organisms (see:  operational taxonomic unit ), to the best ability of the binning tool. Each bin consists of a  metagenome-assembled genome  (MAG), as all included sequences can be thought of being derived from the genome of the organism being represented. Tools based on single-copy genes such as CheckM and BUSCO can then be used to estimate the completeness percentage and contamination percentage of the MAG. [ 48 ]


Metagenomic analysis  pipelines  use two approaches in the annotation of coding regions in the assembled contigs. [ 37 ]  The first approach is to identify genes based upon  homology  with genes that are already publicly available in  sequence databases , usually by  BLAST  searches. This type of approach is implemented in the program  MEGAN 4. [ 49 ]  The second,  ab initio , uses intrinsic features of the sequence to predict coding regions based upon gene training sets from related organisms. This is the approach taken by programs such as  GeneMark [ 50 ]  and  GLIMMER . The main advantage of  ab initio  prediction is that it enables the detection of coding regions that lack homologs in the sequence databases; however, it is most accurate when there are large regions of contiguous genomic DNA available for comparison. [ 1 ]  Gene prediction is usually done after binning. [ 27 ]


The massive amount of exponentially growing sequence data is a daunting challenge that is complicated by the complexity of the  metadata  associated with metagenomic projects. Metadata includes detailed information about the three-dimensional (including depth, or height) geography and environmental features of the sample, physical data about the sample site, and the methodology of the sampling. [ 31 ]  This information is necessary both to ensure  replicability  and to enable downstream analysis. Because of its importance, metadata and collaborative data review and curation require standardized data formats located in specialized databases, such as the Genomes OnLine Database (GOLD). [ 51 ]